Accessibility impact of combined air transport and HSR networks
Tianyi Zhou
Delft University of Technology of University Delft
Accessibility impact of combined air transport and HSR networks
By Tianyi Zhou
in partial fulfilment of the requirements for the degree of
Master of Science
in Transport, Infrastructure, and Logistics
at the Delft University of Technology,
to be defended publicly on Thursday June 4th, 2015
Supervisor (Chair): Prof.dr. Bert Van Wee, TU Delft Thesis committee: Prof.dr. Bert Van Wee, TU Delft Dr. Jan Anne Annema, TU Delft Dr. ir. Rob van Nes, TU Delft
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
Preface This thesis presents an explorative research on the accessibility impact of combined air transport and HSR networks, it was completed in the faculty of Technology, Policy, and Management (TPM) in Delft University of Technology. The thesis was completed by TIL Master student Tianyi Zhou, supervised by the thesis committee, composed by Prof. Bert van Wee, Dr. Jan Anne Annema, and Dr. Rob van Nes, of which Prof. Bert van Wee was the chair of the committee.
I would like to take this opportunity to thank the people who have been important to me in these months. Firstly many thanks goes to my supervisors, Bert van Wee, Jan Anne Annema, and Rob van Nes, your ideas, instructions, and critical comments have helped me in conducting my research.
Tianyi Zhou
Delft, May 2015
Front page: picture of Amsterdam Airport Schiphol, source: (Thousand Wonders, 2015).
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
Contents Preface ...... I Contents ...... II List of Tables ...... V List of Figures ...... VI List of Equations ...... VII Summary...... 1 1. Introduction ...... 3 1.1. Background ...... 3 1.2. Problem definition ...... 4 1.3. Objectives ...... 4 1.4. Research question ...... 5 1.5. Methodology and research structure ...... 5 2. Literature review of accessibility measures ...... 8 2.1. Definition of accessibility...... 8 2.2. Evaluation criteria and categorization ...... 8 2.2.1. Evaluation criteria ...... 8 2.2.2. Categorization of accessibility measures ...... 9 2.3. Review of accessibility measures ...... 9 2.3.1. Infrastructure-based measures ...... 9 2.3.2. Location-based measures ...... 10 2.3.3. Person-based measures ...... 13 2.3.4. Utility-based measure ...... 14 2.4. Conclusion ...... 16 3. Conceptual framework – accessibility impact of combined air transport and HSR networks ...... 17 3.1. Introduction ...... 17 3.2. Transport and spatial scale ...... 17 3.2.1. Transport scale ...... 17 3.2.2. Spatial scale ...... 19 3.2.3. Conclusion ...... 20 3.3. Transport, land-use, and accessibility ...... 20 3.3.1. Transport and accessibility ...... 21 3.3.2. Interaction between transport and land-use systems ...... 22 3.3.3. Conclusion – transport, land-use, and accessibility ...... 25
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
3.4. Individuals ...... 25 3.5. Interaction of air transport and HSR ...... 25 3.5.1. Differences between air transport and HSR ...... 26 3.5.2. Demand-side interaction ...... 27 3.5.3. Supply-side interaction ...... 28 3.5.4. Conclusion ...... 28 3.6. Conceptual framework – synthesis ...... 29 4. Evaluation framework – variables and interaction ...... 31 4.1. Introduction ...... 31 4.2. Calculation variables ...... 31 4.2.1. Transport variables ...... 32 4.2.2. Land-use variables ...... 33 4.2.3. Individual variables ...... 34 4.2.4. Air-HSR interaction ...... 35 4.3. Evaluation variables ...... 36 4.3.1. Time-scale ...... 36 4.3.2. Usability of accessibility in economic and social evaluation ...... 36 4.4. Evaluation framework – synthesis...... 39 5. Evaluation methodology – feasible accessibility measures ...... 41 5.1. Introduction ...... 41 5.2. Theoretical basis ...... 41 5.3. Feasibility of the accessibility measures...... 42 6. Case study ...... 44 6.1. Introduction ...... 44 6.2. Studied region and assumptions in the case study ...... 45 6.2.1. Selection of the studied region ...... 45 6.2.2. Some assumptions in the case study ...... 49 6.3. Transport scenarios construction ...... 51 6.4. Accessibility measures ...... 54 6.4.1. Potential accessibility measure 1 – Logsum ...... 55 6.4.2. Potential accessibility measure 2 – Minimum ...... 56 6.4.3. Potential accessibility measure 3 – Logsum TT < 5 ...... 56 6.5. Overview of the case study ...... 57 7. Results ...... 58 7.1. Accessibilities in the four scenarios ...... 58 7.2. Empirical findings and discussion ...... 60 III
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
7.2.1. Inter-influence of accessibility of air transport and HSR ...... 60 7.2.2. Influence of competition and complementation ...... 63 7.2.3. Influence of individual variable ...... 64 7.2.4. Social equity...... 67 7.2.5. Aggregation vs. disaggregation ...... 69 7.3. Conclusion of the case study ...... 69 8. Conclusion and further research directions ...... 72 8.1. Conclusion ...... 72 8.2. Further research directions ...... 74 8.2.1. Theoretical improvement of the evaluation ...... 74 8.2.2. Interpretability and communicability ...... 74 8.2.3. Influence of the interaction of air transport and HSR ...... 75 8.2.4. Social and economic evaluation ...... 76 9. Bibliography ...... 77 10. Appendices ...... 84
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
List of Tables Table 2-1 Summary of accessibility measures ...... 16 Table 3-1 Classification of transport system levels ...... 18 Table 3-2 Classification of Chinese cities ...... 20 Table 4-1 Elements within the transport variables of accessibility evaluation ...... 32 Table 4-2 Calculation variables and evaluation variables ...... 39 Table 5-1 Capability of the accessibility measures to incorporate transport, land-use, and individual variables ...... 41 Table 5-2 Confrontation between the accessibility measures and evaluation variables ...... 43 Table 6-1 Subdivisions of Frankfurt Rhein-Main region ...... 48 Table 6-2 Assumptions in the case study ...... 51 Table 6-3 Current air transport and HSR destinations of Frankfurt Rhein-Main area ...... 52 Table 6-4 Access time, check in time, and Egress time to the airport and HSR station from the subdivisions of Frankfurt Rhein-Main area ...... 54 Table 6-5 Values of parameters of the three accessibility measures ...... 57 Table 6-6 Scenarios and accessibility measures ...... 57 Table 7-1 Normalized results of scenario 1 ...... 58 Table 7-2 Normalized results of scenario 2 ...... 59 Table 7-3 Normalized results of scenario 3 ...... 59 Table 7-4 Normalized results of scenario 4 ...... 60 Table 7-5 Comparison of weighted average and aggregate results of Frankfurt am Main, accessibility measure 1 ...... 69 Table 10-1 Synthesis of research on interaction of air transport and HSR ...... 84 Table 10-2 Population of the subdivisions of Frankfurt am Main ...... 90 Table 10-3 Access time, check in time and Egress time to the airport and HSR station from each subdivisions ...... 92 Table 10-4 Results of scenario 1 ...... 94 Table 10-5 Results of scenario 2 ...... 96 Table 10-6 Results of scenario 3 ...... 98 Table 10-7 Results of scenario 4 ...... 100
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
List of Figures Figure 1-1 Research structure ...... 7 Figure 2-1 Demonstration of PPS and PPA ...... 14 Figure 3-1 Geographical illustration of the travel levels ...... 19 Figure 3-2 Classification of settlements ...... 19 Figure 3-3 Wegener’s transport/land-use feedback circle ...... 21 Figure 3-4 Accessibility and airport ...... 21 Figure 3-5 Accessibility benefits from transport systems ...... 22 Figure 3-6 Regional interaction between land-use systems and air transport/HSR ...... 23 Figure 3-7 Interregional interaction between land-use and transport systems ...... 24 Figure 3-8 Interaction between airport and HSR station ...... 26 Figure 3-9 Main differences between air transport and HSR ...... 27 Figure 3-10 Relationships among air-HSR interaction, individual and transport components 29 Figure 3-11 Synthesis – conceptual framework ...... 30 Figure 4-1 Distance decay ...... 33 Figure 4-2 Relationship between accessibility impact and social, economic, and environmental impact ...... 39 Figure 4-3 Interrelationship amongst the evaluation variables ...... 40 Figure 6-1 Frankfurt Rhein-Main agglomeration region and Rhine-Main metropolitan region ...... 46 Figure 6-2 Locations of Frankfurt Airport and Frankfurt HSR stations ...... 47 Figure 6-3 Frankfurt subdivisions ...... 48 Figure 7-1 Logsum, scenario 1 – scenario 4 ...... 61 Figure 7-2 Minimum, scenario 1 – scenario 4 ...... 61 Figure 7-3 Logsum TT < 5, scenario 1 – scenario 4 ...... 62 Figure 7-4 Accessibility indices, scenario 3, Logsum and Minimum ...... 63 Figure 7-5 Accessibility indices, scenario 1, Logsum and Logsum TT < 5 ...... 65 Figure 7-6 Distance decay of negative exponential function using different values of β ...... 66 Figure 7-7 Comparison of the results between different 훽 values, Logsum, scenario 1 ...... 66 Figure-7-8 The lorenz curves of of Frankfurt Rhein-Main area and Frankfurt am Main, scenario 1, Logsum...... 68 Figure 10-1 The Gini index and Lorenz curve applied to the concept of sufficientarianism ... 88 Figure 10-2 Accessibility and GDP ...... 89
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
List of Equations Equation 2-1 ...... 11 Equation 2-2 ...... 12 Equation 2-3 ...... 12 Equation 2-4 ...... 12 Equation 2-5 ...... 13 Equation 2-6 ...... 13 Equation 2-7 ...... 15 Equation 2-8 ...... 15 Equation 2-9 ...... 15 Equation 2-10 ...... 15 Equation 4-1 ...... 31 Equation 4-2 ...... 32 Equation 4-3 ...... 34 Equation 4-4 ...... 34 Equation 4-5 ...... 35 Equation 6-1 ...... 55 Equation 6-2 ...... 55 Equation 6-3 ...... 55 Equation 6-4 ...... 55 Equation 6-5 ...... 56 Equation 6-6 ...... 56 Equation 6-7 ...... 56 Equation 7-1 ...... 67 Equation 7-2 ...... 67 Equation 10-1 ...... 89
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
Summary Focusing on passenger transport, accessibility indicates the opportunities transport and land- use systems enable individuals to reach various activities. As a concept used in a number of scientific fields such as transport and urban planning, accessibility plays an important role in policy making. Widely used scientifically and practically though, application of accessibility in certain field still needs exploration.
Air transport and HSR have become or are going to become the main long distance public transport modes mutually in many countries over the world. The concept of accessibility is not often applied in the planning and evaluation of air transport and HSR projects, which might be a problem, because the development of air transport and HSR projects requires large amount of investments, so that the policy makers need to be clear about the accessibility impact of these projects for their region. Furthermore, more and more regions are now served or expected to be served jointly by air transport and HSR. The complex influences of the interaction (including competition and complementation) of the two modes on each other and on the travel behaviour of individuals have drawn attention, yet these influences have not been explicitly interpreted. Therefore, this thesis presents an explorative research on the evaluation of the accessibility impact of combined air transport and HSR. The thesis is divided into a theoretical analysis and a case study.
Various types of accessibility measures have been developed for accessibility evaluation, some of which are relatively easy to apply and interpret, while some of which are more comprehensive theoretically. In the evaluation of the accessibility measures of combined air transport and HSR, it is expected that the accessibility measures should ideally combine transport variables, land-use variables, individual variables, and air/HSR interaction variables. The time-scale of the evaluation is also seen as an important variable, because it determines the collection of data and the selection of the previous mentioned variables. In a policy evaluation, the time-scale variable needs to be predefined according to the purpose of the evaluation. Moreover, it is considered important that the accessibility impact measured can be combined in a broader social and/or economic evaluation, so that the policy makers can have a comprehensive view of the impact of the projects. Therefore, the usability in social and/or economic evaluation of the accessibility measures is considered an important variable that influences the selection of feasible measures. This thesis shows that different types of accessibility measures have their own advantages and disadvantages. Out of all the accessibility measures examined in the thesis, it is considered that potential measures and utility-based measures have relatively better trade-off between ideally combining the variables, and applicability and interpretability in a broader socio-economic evaluation.
To test the applicability of these accessibility measures selected a case study for the region of Frankfurt Rhein-Main area is carried out. Due to time restriction and data availability, in the case study only potential measures are applied to evaluate the accessibility impact of (combined) air transport and HSR networks. By using the methods of minimum cost and logsum composite cost in the impedance functions, potential measures show to be capable to capture the characteristics of the competition and complementation of air transport and HSR in a mutual market. According to the results of the case study, in terms of destination accessibility to main cities in Europe, air transport provide better accessibility compared to HSR. When air transport and HSR jointly serve a region (as in this case study), better
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou accessibility is measured. The case study in the first place demonstrates the feasibility of evaluating the accessibility impact of combined air transport and HSR networks using potential measures. In practice, using more collective disaggregate and empirical data, the evaluation can provide useful information for policy makers.
In spite of the large assumptions made in the case study, some implications for policy makers can already be summarized. Firstly, the access service of air transport and HSR has an important role in the accessibility impact of the two modes. Therefore it is necessary to ensure the corresponding supportive access service in the development of air transport and HSR projects. Secondly, when the complementation of air transport and HSR is measured, the accessibility impact of the combined networks is higher than that when only the competition is included, which underpins the necessity to combine the air/HSR interaction variables in an accessibility evaluation. From the practical point of view, to improve the accessibility impact of combined air transport and HSR networks, policy makers can try to enhance the cooperation of the two modes, for example, by smoothing the transfer process between flights and high-speed trains. Thirdly, the case study also explores the social equity of the studied region in terms of accessibility. It is shown that accessibility seems a quite useful social equity indicator in the evaluation of the social impact of air transport and HSR projects.
Some implications for other regions than the studied region in the case study can also be proposed. Better integration of the development of new air transport and HSR projects can decrease the negative effects of the competition of the two modes and enhance the positive influences of the complementation, for example, by enhancing the advantages of HSR in markets with a distance less than approximately 500 km and improving the accessibility of air transport by HSR access service. For countries where air transport and HSR are rapidly being developed, for example in China, it seems important to bear this in mind in the planning of the new projects.
Compared to potential accessibility measures, utility-based accessibility measures, for example logsum measures, are more theoretically ideal, because they are expected to give more realistic results of the accessibility impact. Furthermore, logsum accessibility indices can be easily expressed in monetary units, which makes them potentially useful in economic evaluation (e.g. cost-benefit analysis) of air transport and HSR networks investments. Due to time restriction and data availability, utility-based measures are not studied in depth in this thesis. It is recommended that in future research, the feasibility of utility-based measures could also be explored using a real-world case study.
This thesis stresses the importance of combining the interaction of air transport and HSR in the accessibility evaluation. In literature, the interaction of the two modes has not been sufficiently studied, especially in terms of its influence in the accessibility impact. Further research on the interaction of air transport and HSR from supply (supply of air transport and HSR service, e.g. routes, seats, and frequency) and demand (perceptions of individuals) perspectives could be performed, so that the influence of the interaction on the accessibility impact can be more accurately measured, and more effective policies can be designed to improve the accessibility impact by optimizing the interaction of the two modes. Additionally, the evaluation of the accessibility impact can be improved in future research by including the interaction of transport and land-use systems. This interaction is not included in this thesis. All in all, this thesis establishes a theoretical basis for the evaluation of the accessibility impact of combined air transport and HSR networks, based on which further theoretical and practical studies can be performed to assist the decision making in relevant policies. 2
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
1. Introduction This thesis presents an explorative research on the accessibility impact of combined air transport and HSR networks, the research is divided to two parts: theoretical analyses and practical exploration, the outcomes of which compose the conclusion of the thesis. In the first chapter, the background, problem definition, objectives, research questions, and research structure of the thesis are described in the following sections. 1.1. Background The subject of this thesis is to explore the evaluation of the accessibility impact of combined air transport and HSR networks. Accessibility, a concept with multiple implication, has been widely used in many fields related to the functioning of societies. With the study of accessibility, its usefulness and importance have been noticed by researchers and policymakers in their research and decision making processes.
For now, accessibility has already been widely used in the planning and evaluation of transport and land-use systems, for example, many researchers focus on the accessibility to jobs (e.g. (Cervero, Rood, & Appleyard, 1995) (Levinson, 1996)). As the society develops, people are willing to cover longer distance for all kinds of activities. On world scale an overall growth of long distance passenger transport can be observed in the past decades (e.g. see (European Commission, 2012)). In this context, fast transport modes are essential to fulfil this demand. Multiple transport modes can be used for long distance transport, of which air and rail transport are the main public transport modes available.
Air transport is a rapidly growing sector in many economies. Even though the air travel market growth has slowed in recent years, it still outstrips global economy, and specially, emerging and developing markets in Asia, Latin America, and Africa contribute a majority to this growth (IATA, 2013). The growth in air transport is a consequence and also a stimulator of growth in economy (Cooper & Smith, 2002): as the development of economy, people become more wealthy and willing to make long distance travel for business and leisure; on the other hand the development of air transport stimulates the movement of people by making fast long distance travel possible.
Traditionally air transport represents the only high-speed alternative mode for long distance transport, which can be regarded as a huge advantage over conventional railway transport. However, interestingly, there is another transport mode that provides similar level of accessibility to that from air transport, which needs to be paid attention to. Recent years, with the worldwide rapid development of high-speed rail (HSR), especially in European Union and East Asia (Campos & de Rus, 2009), in some regions word wide air transport service is no longer the only mode for people to travel in a long distance within short time. Meanwhile, in some places, HSR (also conventional railway) networks also attract more passengers to hub airports, leading to a hierarchical multi-modal long distance public transport system.
With the rapid development of HSR, an efficient hierarchical multimodal public transport network can be anticipated in the future, with air transport and HSR acting as the first level, conventional railway and intercity railway (whose speed is lower than that of HSR but higher than that of conventional railway) acting as the second level, and urban public transport (subway, bus, etc.) acting as the third level of the network. Each level of the network serves
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou different types of transport demand, and also interacts with each other. For example, air transport and HSR may compete on the same route, passengers need to use urban public transport to access to and egress from an airport.
The rapid development of air transport and HSR calls for large amount of investment into the infrastructure construction, hence the approval of relevant projects needs careful investigation on the impact of air transport and HSR. Currently, research on regional impact of air transport and HSR does not sufficiently focus on accessibility impact, most research focuses on economic impact (see e.g. (York Aviation, 2004) (ATAG, 2005) (Kupfer & Lagneaux, 2009)). Meanwhile, it is very difficult for decision makers to understand how this accessibility impact contributes to the social or economic development, unlike GDP or employment, the concept of accessibility cannot be directly used in the vision of decision makers, hence it has not been recognised by decision makers as an essential indicator in the planning and evaluation of air transport and HSR projects. Therefore, there is a need to study the accessibility impact of combined air transport and HSR networks. 1.2. Problem definition To study the accessibility impact of combined air transport and HSR networks, it is necessary to firstly understand which factors (the characteristics mentioned in the last paragraph) should be included in the analysis, and to secondly understand how these factors influence the accessibility impact.
Previous literature show inadequacy in understanding the accessibility impact of combined air transport and HSR networks. By adding ‘combined’ to the concept, it means that air transport and HSR networks are studied jointly. The accessibility impact of the combined networks has not been the focus of the evaluation of the impact of the two modes, while economic impact is usually attached the most importance (see e.g. (ATAG, 2005) (York Aviation, 2004)). Some Europe-wide research on accessibility has taken the two modes into account, however, it is either that air transport and HSR are studied with other modes (road transport), or that the interaction of air transport and HSR is neglected.
To conclude, the problem of this thesis can be formulated as:
The accessibility impact of combined air transport and HSR networks has not been explicitly and sufficiently analysed. 1.3. Objectives It is expected that the research in this thesis can fill in some scientific gaps and provide some scientific and practical implications. Therefore, the objectives of this thesis are twofold, which can be discussed from scientific and practical perspectives.
Firstly, theoretically, in this thesis, the accessibility impact of combined air transport and HSR networks is for the first time explicitly studied. The thesis firstly conceptually and comprehensively explains how to understand the accessibility impact, and then variables that need to be taken into account in the evaluation process are identified. In this way, the thesis aims to strengthen the theoretical foundation of the evaluation of the accessibility impact of combined air transport and HSR networks.
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
Secondly, practically, the thesis provide insight in the application of the evaluation of the accessibility impact of combined air transport and HSR networks. This thesis firstly demonstrates the feasibility of the evaluation of the accessibility impact of combined networks, and the feasibility of the theoretical findings. Then the thesis aims at providing some empirical insights into how different variables influence the accessibility impact of combined networks, e.g. the interaction of air transport and HSR. 1.4. Research question Following the problem definition and the theoretical and practical objectives of the thesis, the research question of this thesis can be formulated as follows:
How to evaluation the accessibility impact of combined air transport and HSR networks?
This is regarded as the main research question of the thesis, it is expected that by answering the main research question, the objectives of this thesis can be achieved. In order to answer the main research question, it is split into several sub research questions, which are listed as follows:
Theoretical sub research questions:
1. In concept, what factors can influence (need to be considered in the analysis of) the accessibility impact of combined air transport and HSR networks, so that the accessibility impact can be understood comprehensively? 2. What factors need to be incorporated in the evaluation of the accessibility impact of combined air transport and HSR networks, and how? 3. What accessibility measures can be applied to evaluate the accessibility impact of combined air transport and HSR networks? Practical sub research questions: 4. How to apply the accessibility measures in the evaluation of combined air transport and HSR networks? 5. What variables influence the accessibility impact of combined air transport and HSR networks? 6. What can be learned from the evaluation of the accessibility impact of combined air transport and HSR networks? 1.5. Methodology and research structure In this section, the methods used in this thesis are presented, followed by the research structure, which is illustrated in Figure 1-1.
Theoretical analyses are used in the first half of the thesis, to conceptually explore the evaluation of the accessibility impact of combined air transport and HSR networks. In chapter 2, a literature review of accessibility measures is presented. The advantages and disadvantages of different categories of measures are analysed in the literature review, to give an overview of current methods to evaluation accessibility and their applicability.
With the characteristics of the accessibility measures discussed in chapter 2, subsequently in order to study how to evaluate the accessibility impact, firstly theoretically analyses are needed to build the conceptual foundation. In chapter 3, to clarify which matters need to be
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou taken into account in order to fully understand the accessibility impact, a conceptual framework is developed to conceptually identify the main components of the accessibility impact of combined air transport and HSR networks. Chapter 3 addresses the first sub research question.
Then in chapter 4, aiming at explaining which factors need to be considered in the evaluation of the accessibility impact and how the factors will influence it, following and according to the conceptual framework, an evaluation framework is constructed to identify the variables in the evaluation of accessibility impact of combined air transport and HSR networks. Chapter 4 addresses the second sub research question.
Then in chapter 5, the analyses in chapter 2 to chapter 4 are combined: the advantages and disadvantages of the accessibility measures elaborated in chapter 2 are reviewed according to the conceptual basis of the accessibility impact of combined air transport and HSR networks presented in chapter 3 and chapter 4. The feasibility of the accessibility measures is summarized in the end. Chapter 2 and chapter 5 address the third sub research question.
The second part of the thesis applies a case study to empirically demonstrate the feasibility of the evaluation of the accessibility impact of combined air transport and HSR networks. Furthermore, the case study also aims at deriving some empirical findings to give feedback on the theoretical analyses of the thesis. In chapter 6, the case study is introduced, including the studied region, scenarios, corresponding assumptions, and applied accessibility measures. Then in chapter 7, the results of the case study are presented and discussed. This part of the thesis addresses the last three sub research questions.
Finally in chapter 8, the outcomes of the thesis are summarized and some remarks are discussed.
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
Problem definition
Review of Research question accessibility measures
Sub-questions
Conceptual Evaluation Lead to framework framework
Derive feasible accessibility measures
Build case according Discussion and reflection to the Reality Discussion and reflection framework Abstract
Case study Construct Feasibility of Accessibility accessibility accessibility measures measures measures
Results lead to
Conclusion and discussion
Figure 1-1 Research structure
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
2. Literature review of accessibility measures In this chapter, a literature review of accessibility measures is presented, in order to examine the feasibility of accessibility measures in the evaluation of the accessibility impact of combined air transport and HSR networks. Firstly the definition of accessibility is given, then the evaluation criteria of the accessibility measures are presented. After that the measures are reviewed in different categories. Finally the measures are summarized in a conclusion. 2.1. Definition of accessibility Accessibility is a widely used concept, but there is no single standard definition of it. Accessibility has been defined as, for example, as quoted by Geurs and van Wee (Geurs & van Wee, 2004), ‘the potential of opportunities for interaction’ (Hansen, 1959), ‘the characteristics (advantage) of a location with respect to overcoming spatial impedance’ (Dalvi & Martin, 1976), ‘the freedom of individuals to decide whether or not to participate in different activities’ (Bruns, 1980), ‘the benefits provided by a transportation/land-use system’ (Ben-Akiva & Lerman, 1979), ‘an indicator for the social and economic consequences of alternative land- use transport systems’ (Geurs & Ritsema van Eck, 2001), and ‘impact of land-use and transport developments and policy plans on the functioning of the society in general’.
Synthesizing the definition given in literature, it is defined that the accessibility impact studied in this thesis is the opportunities to take long distance travel for various activities provided by transport and land-use systems. And in this thesis, the transport systems that are focused on are the combined air transport and HSR networks. 2.2. Evaluation criteria and categorization 2.2.1. Evaluation criteria Theoretically four components of accessibility can be identified, which are transport, land-use, individual, and temporal components (Geurs & van Wee, 2004). Each of the components represents one part of accessibility that should be accounted for in the evaluation. Therefore, an effective accessibility measure should be able to theoretically incorporate (parts of) the four components. For a detailed explanation of the components, refer to (Geurs & van Wee, 2004) (and see also (Geurs & Ritsema van Eck, 2001) for relevant information). Furthermore, synthesizing the criteria from other research (e.g. (Jones, 1981), (Handy & Niemeier, 1997)), Geurs and van Wee also give operationalization, which is the ease with which the measure can be used in practice, and interpretability and communicability, which is the ease with which the measure can be understood, as the extra criteria (Geurs & van Wee, 2004).
The review of the accessibility measures in this chapter follows a similar approach to that in (Geurs & van Wee, 2004), the feasibility of the accessibility measures in the evaluation of combined air transport and HSR networks is discussed in this chapter, and the summary of the feasibility will be presented in chapter 5, by combining the review in this chapter and the findings from the theoretical frameworks developed in chapter 3 and chapter 4. Therefore, based on previous work and the requirement in this thesis, the criteria of the literature review in this chapter are (1) theoretical basis, (2) data need, and (3) interpretability. The feasibility
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou of the measures in this thesis will be discussed in chapter 5. In next section, the accessibility measures are classified into four categories. 2.2.2. Categorization of accessibility measures The classification of accessibility measures can be found in several relevant research. For example, Handy and Niemeier classify accessibility measures into isochrones, gravity-based measures, and utility-based measures (Handy & Niemeier, 1997); Bradaran and Ramjerdi classify accessibility measurement approaches into travel-cost approach, gravity or opportunities approach, constraints-based approach, utility-based surplus approach, and composite approach (Bradaran & Ramjerdi, 2001); another classification can be found in (Geurs & Ritsema van Eck, 2001) and (Geurs & van Wee, 2004), they classify accessibility measures into infrastructure-based measures, location-based measures, potential-based measures, and utility-based measures. In this thesis, the classification developed by Geurs & Ritsema van Eck and Geurs & van Wee is used, so that the accessibility measures to be reviewed here are divided into four major categories:
Infrastructure-based measures This type of accessibility measures analyses the performance or service level of transport infrastructures, such as travel speed, etc. Location-based measures, This type of accessibility measures can also be referred to as activity-based measures. The measures analyse accessibility to spatially distributed activities at locations, such as jobs, etc. Person-based measures, This type of accessibility measures analyses accessibility at individual level. Utility-based measures. This type of accessibility measures analyses accessibility as the (economic) benefits that individual derive from the opportunities to access spatially distributed activities. This type of measures originally derives from economic studies.
In next section, the accessibility measures will be described respectively in the four categories, and finally be summarized based on the listed evaluation criteria. 2.3. Review of accessibility measures 2.3.1. Infrastructure-based measures Infrastructure-based measures refer to the performance level of transport infrastructure, such as the travel time of a certain section of road, or the operating speed of a certain rail line. It is a commonly used category of accessibility measure, for example, as quoted by Geurs and van Wee, in research and the policy planning of governments (Ewing, 1993) (DETR, 2000) (AVV, 2000). The accessibility indicators used are for example (Geurs & Ritsema van Eck, 2001):
Congestion probability; Travel speed; Travel-time ratio between public transport and car traffic; The number of trains delayed; And etc.
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
In comparative studies of transport infrastructures between regions, volume or scale of transport infrastructures in a region, such as the number of railway stations, can also be used as accessibility measure (Geurs & Ritsema van Eck, 2001). As for air transport and HSR, infrastructure-based accessibility indicators can be travel time, delay time or probability in peak hours, or travel speed. Infrastructure-based measures capture the transport component, and the data needed by the measures are usually easy to obtain and analyse. This category of measures is still being used in transport planning and evaluation. However, infrastructure- based measures have some problems, single utilization of this type of measures cannot sufficiently reflect the accessibility.
Infrastructure-based measures only partly grasp the transport component, and do not incorporate the land-use and individual components, which leads to the problem that infrastructure-based measures sometimes result in misleading conclusions on accessibility level. For example, Linneker and Spence show that inner London will have the lowest average accessibility of jobs and Scotland the highest in the UK, if an infrastructure-based measure (travel time costs and vehicle operation costs per kilometre) is used, whereas inner London will have the highest accessibility of jobs and Scotland the lowest, if a potential measure is used (Linneker & Spence, 1992). Similar phenomenon can also be seen in the Netherlands, when measuring the accessibility in the Randstad Area (Geurs & Ritsema van Eck, 2001).
Because of the problems and shortcomings, traditional infrastructure-based measures are not able to accurately reflect the accessibility brought from air transport and HSR. The measures are only useful when one wants to know the quality of the transport service provided by air transport and HSR. The measures do not provide information on for example what activities can be reached by air transport and HSR services, or how the quality of the services means for different individual groups. Despite the disadvantages, the indicators used in the infrastructure-based measures still compose a significant basis of accessibility, and can be taken as input of other more comprehensive measures. 2.3.2. Location-based measures Location-based measures can be divided into several types, mainly four types can be identified: distance measures, contour measures, potential measures, and measures based on balancing factors of spatial interaction models. In this section, the four types of location-based measures will be reviewed successively.
Distance measures Distance measures are the simplest location-based measures. Ingram develops relative accessibility, which can be distinguished into the relative accessibility between two points and the integral or total accessibility at a point (Ingram, 1971). Of these two types, the first one can be taken as the basic distance measures, the second one refers to contour measures, which will be discussed later in this section.
The simplest measure of relative accessibility is the distance of the straight line between two locations, whereas infrastructure-based measures between two locations can also be used, such as travel time and travel speed. When more than two locations are analysed, contour measures are introduced.
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Contour measures Contour measures, which are also called as isochronic measures (Geurs & Ritsema van Eck, 2001), have been widely used in urban or regional planning and geographical research, see e.g. (Wachs & Kumagai, 1973) (Gutiérrez & Urbano, 1996) (Bruinsma & Rietveld, 1998) (Straatemeier, 2008) (Cao, Liu, Wang, & Li, 2013). Contour measures refer to the number of opportunities that can be reached within a given travel time, distance or costs, or to the travel time, distance or costs required to reach a certain type or number of opportunities. As quoted by Geurs and Ritsema van Eck (Geurs & Ritsema van Eck, 2001), Breheny identifies three types of contour measures (Breheny, 1978):
Fixed costs: accessibility is represented by the number of opportunities accessible within a fixed cost limit; Fixed opportunities: accessibility is represented by the (average or total) time or costs required to access a fixed number of opportunities; Fixed population: accessibility is represented by the average (over the population) of the number of opportunities available within various fixed travel costs. Contour measures can also be analysed in weighted form, for example, average travel time taken over the mass (e.g. GDP) of the destinations to reach a groups of urban agglomeration (Gutiérrez, 2001).
The concept of daily accessibility is often used in accessibility evaluation, which is derived from the wishes of business travellers to travel to a location and to return in the same day (Geurs & Ritsema van Eck, 2001). Application of this measure can be found e.g. in (Speikermann & Wegener, 1996) (Gutiérrez, 2001).
There are also some shortcomings of the measures. Geurs and Ritsema van Eck point that firstly the opportunities of activities are not distinguished, so that business and leisure opportunities are valued equally. Secondly, the selection of the fixed time or distance can be quite arbitrary. Thirdly, the opportunities adjacent to the studied location and those just within the isochrones are not differentiated (Geurs & Ritsema van Eck, 2001). As a result, the contour measures are extremely sensitive to travel time/costs changes and are not useful in explaining accessibility developments in time/costs (Geurs & van Wee, 2004).
Moreover, although this type of measures include the land-use components, the transport and land-use components are incorporated separately, which means, the changes in one component do not affect the other one, while in reality, this is usually not the case. And the contour measures use fixed travel impedances which do not take into account the individuals’ preference differences. Except this, it should be noticed that contour measures are easy to interpret, and the data required are easy to obtain.
Potential measures Potential measures, or gravity-based measures as they are also called as, are widely used accessibility measures, see e.g. (Linneker & Spence, 1992) (Handy S. , 1993) (Matisziw & Grubesic, 2010) (Gutiérrez, Condeço-Melhorado, & Martín, 2010) (Cao, Liu, Wang, & Li, 2013). Some early research was made by Hansen (Hansen, 1959), and Vickerman (Vickerman R. W., 1974). The measures can be generally expressed in Equation 2-1:
퐴푖 = ∑ 푀푗푓(푐푖푗, 훽) Equation 2-1 푗∈퐿
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou where 퐴푖 represents the accessibility measured in zone i to the opportunities in zone j; L is the set of zones; 푀푗 represents the mass of the opportunities in zone j; 푓(푐푖푗, 훽) is the impedance function; 푐푖푗 represents the travel costs between i and j; and 훽 is the cost sensitivity parameter.
In practice, several distance decay functions are used as the impedance function, for example, negative exponential, power, Gaussian and logistic functions, etc. Impedance functions need to be carefully chosen, and the parameters in the functions need to be carefully estimated based on recent travel behaviour data. Lack of data on studied regions will lead to unrealistic impedance functions.
Potential accessibility measures can also be used for different transport modes and individual groups. For multi-modal situation, the fastest (or lowest-cost) mode or the composite impedance of all modes can be used (Geurs & Ritsema van Eck, 2001). Logsum composite cost can be used as a method for composite impedance, which can be expressed in Equation 2-2:
1 푐 = − ln ∑ 푒−훽푐푖푗푚 Equation 2-2 푖푗 훽 푚 where 푐푖푗푚 represents the costs between i and j by mode m, and 훽 is a constant parameter. This form of composite impedance has the same basis to the logsum measures, which will be reviewed in section 2.3.4.
Potential measures can also be weighted, for example, according to the total opportunities of all regions, by dividing Equation 2-1 by the sum of masses of all destinations (Dalvi & Martin, 1976). By identifying zones with different masses of opportunities and the utilization of cost function, potential measures combine the transport and land-use components, and include the individuals’ perceptions.
Some shortcomings can still be identified in potential measures. Firstly, the measures do not consider the capacity restriction of the opportunities and the distribution of the demand for the opportunities (Geurs & Ritsema van Eck, 2001); secondly, the measures do not consider the differences among individuals, all individuals in the same location are considered to perceive the same level of accessibility.
Measures based on balancing factors Potential measures can be adapted based on balancing factors, which are derived from singly or doubly constrained spatial interaction models, in order to incorporate competition effects (Geurs & Ritsema van Eck, 2001). Balancing factors from a doubly constrained model are mutually dependent, as shown in Equation 2-3 and Equation 2-4.
1 Equation 2-3 푎푖 = ∑푗∈퐿 푏푗퐷푗푓(푐푖푗, 훽)
1 Equation 2-4 푏푗 = ∑푖∈퐿 푎푖푂푖푓(푐푖푗, 훽) where 푎푖 and 푏푗 represent balancing factors; 푂푖 and 퐷푗 represent the volume of supply and demand; 푓(푐푖푗, 훽) represents the impedance function.
Balancing factors from a singly constrained interaction model are shown in Equation 2-5 and Equation 2-6.
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1 Equation 2-5 푎푖 = ∑푗∈퐿 퐷푗푓(푐푖푗, 훽)
1 Equation 2-6 푏푗 = ∑푖∈퐿 푂푖푓(푐푖푗, 훽) When competition exists both in origins and destinations, doubly constrained model can be used; whereas when competition only exit in origins, singly constrained model can be used. Incorporating competition effects, accessibility measures based on balancing factors can lead to more realistic factors, but the measures are relatively hard to understand due to the iterative process of the measures.
Location-based measures have been used in the evaluation of HSR in several research. For example, Gutiérrez uses weighted average travel time, potential measure, and daily accessibility measure to evaluation the accessibility of the Madrid-Barcelona-French border HSR line, showing different results among the three measures (Gutiérrez, 2001); Cao et al. use weighted average travel costs, contour measure, and potential measure as accessibility indicators of HSR in China, and compare the results with conventional rail and air transport (Cao, Liu, Wang, & Li, 2013). 2.3.3. Person-based measures Person-based measures are analysed by a space-time approach (or constraints-based approach), which tries to address the spatial and temporal constraints of individuals (Bradaran & Ramjerdi, 2001). This type of measures are founded in the space-time prism of Hägerstrand (Hägerstrand, 1970), the measures capture activity-based contextual impact, and try to incorporate inter-individual differences (e.g. gender, ethnic, etc.), using disaggregate approach (Kwan, 1998), hence providing more sensitive accessibility levels to individual variations. Relative research can be found in e.g. (Kwan, 1998) (Miller, 1999) (Kim & Kwan, 2003) (Recker, Chen, & McNally, 2001).
Space-time approach can be found in e.g. (Miller, 1999), Miller defines Potential Path Space (PPS) as the locations that can be reached by an individual based on the locations and time duration of the activities, and the projection of PPS on a two-dimensional space represents the Potential Path Area (PPA), which is the area that an individual can reach given the time budget (Miller, 1999). Figure 2-1 gives a demonstration of PPS and PPA.
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Figure 2-1 Demonstration of PPS and PPA1
Another variation in person-based accessibility is day-to-day (or week-to-week, season-to- season, etc.) variation in accessibility, which refers to variation in space-time constraints. Relevant research can be found e.g. in (Neutens, Delafontaine, Scott, & De Maeyer, 2012), which demonstrates the fluctuation of space-time accessibility over a period of one week. Together with space-time constraints, the incorporation of these factors leads to more accurate results of accessibility evaluation.
The advantage of person-based measures, allowing individuals’ characteristics to be taken into account, also leads to the main shortcoming of the measures, which is the availability of disaggregate data of individuals, categorising population into smaller groups requires a large amount of data, while larger sub group may impair the accurate of the results. Therefore, the measures are more useful in the evaluation of a relatively small region and a small groups of individuals, but this constraint can be overcome by more and more advanced geographical information technology. 2.3.4. Utility-based measure This type of measures attempt to measure the accessibility from the level of individual. Utility- based measures refer to the accessibility of one choice from a set of transport choices, which can be used to measure the benefits of different users of transport infrastructures. Utility- based measures are founded in economic theory and travel behaviour modelling. Koenig points out the essential assumptions of utility-based measures (Koenig, 1980):
For each alternative individuals are facing, they will perceive an utility, and choose the alternative with the maximum utility; The utility of each alternative of a given individual is represented by the sum of an observed component and a random component. Following these assumptions, accessibility can be measured by the denominator of the multinomial logit model, known as logsum (Neuburger, 1971) (McFadden, 1980) (Ben-Akiva & Lerman, 1985). Logsum measures interpret accessibility by calculating consumer surplus. The method is simply explained in Equation 2-7 to Equation 2-10.
1 Source: (RITA, n.d.), where axis t represents time, axes x and y compose the space. 14
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The utility that individual n obtains from alternative j is:
푈푛푗 = 푉푛푗 + 휀푛푗 Equation 2-7 where 푉푛푗 represents the observed utility individual n obtains from alternative j, and 휀푛푗 represents the unobserved or random utility.
퐶푆푛 represents the consumer surplus of person n, which is the utility in money terms the person receives, provided that utility is linear in income, 퐶푆푛 can be calculated as:
1 Equation 2-8 퐶푆푛 = 푚푎푥푗(푈푛푗∀푗) 훼푛 where 훼푛 represents the marginal utility of income and equals to 휕푈푛푗⁄휕푌푛 where 푌푛 is the income of person n. By dividing this coefficient, the utility is transformed into monetary units (Ben-Akiva & Lerman, 1985). Using a MNL model and provided the utility is linear in income, the expected 퐶푆푛 is (de Jong, Daly, Pieters, & van der Hoorn, 2007):
퐽 1 푉푛푗 Equation 2-9 퐸(퐶푆푛) = ln (∑ 푒 ) + 퐶 훼푛 푗=1 where C is an unknown constant. 퐸(퐶푆푛) represents the average consumer surplus in a group of individuals with the same representative utilities. The consumer surplus can be used as accessibility measure, known as the logsum measure. Logsum measure can be utilized to measure the accessibility change after the implementation of transport/land-use policies, when this is done, the constant C will cancel out of the equation:
퐽1 퐽0 1 1 0 푉푛푗 푉푛푗 Equation 2-10 ∆퐸(퐶푆푛) = [ln (∑ 푒 ) − ln (∑ 푒 )] 훼푛 푗=1 푗=1 A more detailed review of logsum measure is presented by de Jong et al. (de Jong, Daly, Pieters, & van der Hoorn, 2007). An application of logsum can be found in e.g. (Geurs, Zondag, de Jong, & de Bok, 2010), which uses a disaggregate logsum measure to compute changes in consumer surplus from land-use and transport developments in the Netherlands.
The disadvantages of utility-based measures firstly refer to the data need of the measures. Empirical research on travel behaviour of different individual groups is needed to construct utility functions. Some researchers have examined the usability of logsum as accessibility measure. Sweet points that the utility used in logsum measure include (1) the utility of the destination, (2) the utility that varies only with individual, representing the individual’s own characteristics, and (3) the utility involved in the individual’s choosing a destination; whereas only the third part of utility is a more appropriate measure of accessibility (Sweet, 1997). Chorus and de Jong argue that although logsum aims to measure the experienced-utility (utility of an alternative after the choice is made), it actually measures the decision-utility (utility of an alternative when a choice is made) (Chorus & de Jong, 2011).
Besides logsum measure, a second utility-based measure based on the doubly constrained interaction model can be found in (Martínez & Araya, 2000), which is pointed to result in similar measurements of economic benefits as logsum measure (Geurs & van Wee, 2004).
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Some researchers have tried to incorporate person-based measures (space-time approach) and utility-based measures. This can be done by developing an individual space-time utility function. Miller develops a space-time utility accessibility measure that incorporate the time available for activities in the utility function of logsum (Miller, 1999), Niemeier tries to incorporate individuals’ temporal and spatial constraints by including them in the utility function (Niemeier, 1997). 2.4. Conclusion The main accessibility measures have been reviewed above in previous section. In Table 2-1 (the table is adapted from (Geurs & van Wee, 2004)), the measures are summarized based on the criteria listed in previous section.
It can be seen that synthesizing the theoretical and the practical (data need and interpretability) criteria, potential measures and utility-based measures tend to have better trade-off. However, the literature review performed in this chapter is more of a general review of the accessibility measures to show the available accessibility measures and their advantages and disadvantages, the feasibility of some of the measures in this thesis is only discussed superficially, which is not enough to develop effective method to evaluate the accessibility impact of combined air transport and HSR networks.
In the next two chapters, two theoretical frameworks of the accessibility impact of combined air transport and HSR networks are developed. Based on the frameworks and the review in this chapter, the feasibility of the measures can be eventually concluded and explained.
Table 2-1 Summary of accessibility measures
Accessibility Theoretical criteria2 Data Interpreta measures need3 bility4
Transport Land-use Individual Temporal component component component component
Infrastructure- +/- - - +/- + + based Location-based - Distance +/- +/- - +/- + + measures - Contour +/- +/- - +/- + + measures - Potential + + +/- +/- + + measures - Balancing + + +/- +/- +/- - factors Person-based + + + + - - Utility-based + + +/- - +/- +/-
2 + means incorporate; +/- means partly incorporate; - means not incorporate. 3 + means relatively easy to obtain; +/- means in between; - means relatively difficult to obtain. 4 + means relatively easy to interpret; +/- means in between; - means relatively difficult to interpret. 16
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3. Conceptual framework – accessibility impact of combined air transport and HSR networks 3.1. Introduction This chapter aims at developing a framework to conceptually illustrate how combined air transport and HSR networks influence accessibility. The conceptual framework is constituted by different building blocks which respectively represent different conceptual aspects of the accessibility impact of combined air transport and HSR networks. As a preliminary step of this research, the framework is expected to generically combine all the relevant aspects, in order to guide further research.
In this chapter, from section 3.2 to 3.5, the building blocks of the framework are presented respectively. Finally in section 3.6 the building blocks are synthesized into the conceptual framework. 3.2. Transport and spatial scale Accessibility is provided by transport and land-use systems, hence to study the accessibility impact of combined air transport and HSR networks, it is necessary to firstly clarify the transport and spatial scale of the study. Over the development of human society, many factors, including the interaction between transport demand and supply, and technology, settlement, and decision processes, have naturally led to a hierarchical structure of multimodal transport network, in which different network levels can be distinguished functionally (van Nes, 2002). In this section, the transport and spatial scales of the study are clarified according to this hierarchical structure. 3.2.1. Transport scale Transport system is a hierarchical system, each level of which is suited for specific trip types, especially with respect to trip length, while also provides access to higher level systems, and each level has its own characteristics regarding access density, system density, and system speed, so that different transport modes can serve proper levels, mainly based on their speed (van Nes, 2002). In this thesis, four levels of transport system are classified, as shown in Table 3-1.
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Table 3-1 Classification of transport system levels
System level Function Trip length5 Transport mode6 Local Local transport system provides < 5 km Bus, tram, subway, transport service within the car, bicycle districts in the studied region. Regional Regional transport system 5 – 30 km Bus, tram, subway, provides transport service within car, conventional the studied region, usually rail between different districts; and transport service between the studied region and its adjacent areas (could be a same-level or lower-level region). Interregional Interregional transport system 30 – 2000 km Air transport, HSR, provides long transport service conventional rail, between the studied region and bus, car other regions on the same or lower level compared to the studied region, which are not adjacent to the studied region. Global Global transport system provides > 2000 km Air transport, HSR extremely long transport service, usually between different continents, e.g. from Amsterdam to New York.
The available transport modes for each network level are identified according to the speed. As shown in Table 3-1, long distance transport refers to interregional and global transport, which is the main level served by air transport and HSR. It should be borne in mind that the distances listed in Table 3-1 are just for indicating the differences between different levels. In reality, the distance classification depends on the place where the study takes place. The four transport system levels are illustrated in Figure 3-1.
5 The trip length of each level of transport system may differ in different circumstances, the distances given in the table are just for indicating the differences among different levels. 6 The transport modes listed for each level do not suit for all distance, for example, sometimes bus is no longer available. 18
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Global regions Local travel Adjacent Regional travel regions
Interregional travel Studied Region Global travel
Remote Regions
Figure 3-1 Geographical illustration of the travel levels 3.2.2. Spatial scale Different levels of transport systems functionally serve different levels of regions. In this section hierarchical levels in spatial pattern are clarified. Some research on theoretical spatial hierarchical classification can be found in literatures, for example, the classification presented by de Jong, as quoted by van Nes, as shown in Figure 3-2, classifies settlements into five levels, namely metropolis, agglomeration, city, town, and village. Assuming a perfect circle shape of each level, the radius of each level of settlement is listed in the figure (van Nes, 2002).
Metropolis Agglomeration City Town Village Radius 30 km 10 km 3 km 1 km 0.3 km Figure 3-2 Classification of settlements
Besides spatial size, population is another important standard of the classification of settlements, for example, China just updated the classification of Chinese city size, classifying the cities into five types based on their population volume, as shown in Table 3-2 (State Council of the People's Republic of China, 2014).
In the clarification of transport scale, air transport and HSR are identified to serve long distance transport, referring to interregional and global transport. As stated, different levels of transport systems functionally serve different levels of regions. Therefore it is necessary to define the level of region that is served by air transport and HSR. Based on the classifications introduced in this section, the region served by air transport and HSR is defined as a city or a group of cities which has a radius of more than 10 km or has a population of more than 1 million, which means this thesis focuses on agglomerations or metropolises. It should be noticed that within the region, hierarchical levels of settlements still exist, for example, an
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou agglomeration is composed by several cities, and each city is composed by several districts. This is useful in the classification of transport systems.
Table 3-2 Classification of Chinese cities
Classification of city Urban population (million) Small city < 0.5 Medium city 0.5 – 1 Big city 1 – 5 Huge city 5 – 10 Mega-city > 10
Although the studied region has been defined as an agglomeration or metropolis with a radius of more than 10 km or has a population of more than 1 million, in practice, sometimes it is still not clear how to define the boundary of the studied region. Different methods are used in different research. In an ex-ante analysis (analysing the accessibility impact from a potential airport on a given region, aiming at justifying whether or not to build an airport or HSR station and deciding the location of them), the region can be predefined, based on administrative boundary; while in an ex-post analysis (analysing the accessibility impact form an existing airport or HSR station on their impact region), the studied region can be decided by an area around the stations, which can be accessed within a fixed time by the fastest available regional transport mode (e.g. Poelman defines 90 minutes driving time by car as the fixed time (Poelman, 2013)).
In this thesis, the boundary of the region served by air transport and HSR is defined by administrative boundary of the studied region. In the case study chapter this will be discussed in detail. 3.2.3. Conclusion Following the classification of transport system, the scope of this thesis can be concluded. The studied region in this thesis is agglomeration or metropolis with a radius of more than 10 km or with a population of more than 1 million. This thesis focuses on long distance transport provided by air transport and HSR, which refers to the interregional and global multimodal transport service. 3.3. Transport, land-use, and accessibility Accessibility is obtained via the air transport and HSR services provided for the users, however, land-use system is an inevitable component to be taken into account. Land-use system represents the distribution of activities and the users, or in other words, the destinations and the origins of individuals’ travel. Therefore in this section the relationship among transport, land-use, and accessibility is discussed.
Generally, transport and land-use systems are interacted to constitute the distribution of people and their activities and movement in the society. Wegener gives a circle to illustrate the interaction between land-use and transport system (Wegener, 2004), as shown in Figure 3-3.
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Mode choice Route Destination choice choice Link loads Trip decision
Travel times/ Car distances/costs ownership Transport Accessibility Activities Land-use Attractiveness Moves
Location Location decisions of decisions of investors users Construction
Figure 3-3 Wegener’s transport/land-use feedback circle7
This circle reflects the basic relationship between transport and land-use systems, and that how they interact with each other, it can be seen that accessibility acts as a bridge between transport and land-use systems. When it comes to air transport and HSR, basic principle will not change but distinctive characteristics do exist. In the following sections, firstly transport systems (air transport and HSR) and accessibility are discussed, then the following section introduces the interaction between transport and land-use systems. Finally the interaction among the three components is concluded. 3.3.1. Transport and accessibility Air transport and HSR provide opportunities for individuals to travel a long distance to and from various activities. Considering the region served by air transport and HSR, two kinds of travel can be observed: outward travel which refers to the travel out of the region made by individuals living in it, and inward travel which refers to the travel into the region made by individuals living outside it. The two types of travel refer to two types of accessibility, as illustrated in Figure 3-4.
Active accessibility: Benefit from the opportunities of outward travel
Accessibility
Passive accessibility: Benefit from the opportunities of inward travel
Figure 3-4 Accessibility and airport
7 Source: (Wegener, 2004) 21
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Both active and passive accessibilities are derived from the (better) opportunities of people to move. Individuals either benefit from better service (e.g. faster, more comfortable) or new service: air transport and HSR are much faster than conventional transport mode, especially for quite long distance, and also give users opportunities to travel to destinations which cannot be accessed without them (especially air transport).
Unlike that as illustrated in Figure 3-3, a single trip via air transport or HSR is a multi-modal trip. Individuals need to make at least three mode choice decisions: access mode choice, main mode choice (air transport or HSR), and egress mode choice, which means that the accessibility impact of air transport and HSR do not only depend on the characteristics of the two modes, but also those of their access and egress modes. Therefore just by linking a region to air transport and HSR networks does not adequately mean good accessibility, good access and egress service is essential, too.
Active accessibility and passive accessibility are not always equally important for each region. It is reasonable to imagine that for a tourist attraction, local residents and organizations (shops, companies, etc.) value passive accessibility (tourists travel from other regions to spend money in the region) more than active accessibility. The differences of active accessibility and passive accessibility will be further discussed in the evaluation framework.
Besides active and passive accessibility, which can also be considered as benefits for users, air transport and HSR transport also provide benefits for non-users. Individuals with uncertain demand for a certain transport facility are willing to pay to preserve the availability of the facility, for the reasons that they regard the provision of the facility as benefits for others, which can be referred to as altruistic values, or they prefer to have an alternative even though they may not use it normally, which can be referred to as option values (Geurs, Haaijer, & van Wee, 2006). Besides, environmental benefits can also be partly included in these category of values, this issue will be discussed in next chapter.
To conclude, accessibility impact from air transport and HSR can be briefly illustrated in Figure 3-5.
•Active accessibility User benefits •Passive accessibility
Non-user •Option values benefits •Altruistic values
Figure 3-5 Accessibility benefits from transport systems 3.3.2. Interaction between transport and land-use systems In this section, the interaction between transport and land-use systems is discussed. As stated in Figure 3-3, land-use systems are inevitable in the generating of accessibility. Land-use can also be referred to as spatial development, Geurs and van Wee conclude that land-use system mainly consists ‘(a) the distribution of the supply of land and buildings in space, i.e. locations for specific land-use functions (e.g. nature areas, houses, offices, schools, shops) and their characteristics, such as density, diversity and design; (b) the distribution of the demand for human activities, e.g. living, working, shopping, education or leisure locations, and (c) the confrontation between land-use demand and supply, resulting in the spatial structures expressed by people’s interaction patterns (Geurs & van Wee, 2004). The spatial structures
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou determined by the interaction between demand and supply requires transport systems in spatial interaction.
The interaction shown in Figure 3-3 has long been noticed and attempted to implement in operational model (see e.g. (Lowry, 1964) (Boyce, Chon, Lee, Lin, & LeBlanc, 1983)), see (Wegener, Overview of land-use transport models, 2004) for an extensive overview. It can be seen that transport and land-use systems interact by accessibility and activities. Simply speaking, the interaction between transport and land-use systems can be expressed as: land- use system determines the demand for transport system, and by providing accessibility, transport system can change the land-use system. In this thesis, the interaction between land- use system and air transport/HSR systems follows similar feedback circle. However, since the scope of this research is on regional level, examining relevant literatures, the interaction concerning the subject of this thesis can be reflected from two perspectives: regional and interregional.
Regional interaction On the one hand, firstly, according to Figure 3-3, because of the need for activities which is determined by land-use system, air transport and HSR are developed to connect a certain region; then, within the catchment region of an air or HSR hub (as a point of the whole transport networks), the location of the hub (which is usually a multimodal station) generates impact on the land-use pattern of the region (Kamga, 2015), which, for example, including fostering new urbanizing concentrations around the hub to promote economic activities (Urena, Menerault, & Carmendia, 2009) (Chatman & Noland, 2014), and reorganizing the urban transport systems of the region (Carmendia, Ribalaygua, & Urena, 2012). The impact on land-use pattern and transport systems following this regional transport/land-use interaction can both interact with accessibility, as shown in Figure 3-6.
Development of air Opportunities to make transport and HSR long distance travel
Need Land-use systems
Urbanizing Infrastructure influence concentrations, housing construction - stations areas, etc.
Reorganize, e.g. new road and public transport lines
Influence Urban transport systems
Change
Access and egress service Accessibility
Figure 3-6 Regional interaction between land-use systems and air transport/HSR
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Firstly, around air and HSR hubs, new urbanizing concentrations will develop, for example, because of the logistics and accessibility advantages, companies and industries will prefer to be located near air and HSR hubs, and people will prefer to live near HSR hub (not close to airports because of the noise pollution). The changes of the distribution of peoples’ activities and living will in return influence the transport system and accessibility. For one thing, the distribution represents the distribution of the origins and destinations of the air transport and HSR, and will influence the provision of urban transport system (access and egress service); for the other, the changes of the distribution represent the distribution of individuals, which will directly influence the accessibility, since in this thesis, accessibility is perceived as a benefit for individuals, this topic will be discussed in more detail in the next chapter.
Secondly, the development of air transport and HSR will reorganize the urban transport systems of the region. Public transport and roads will be added and constructed to connect the hubs to urban areas, which improve the access and egress service of the hubs, consequently influence the distribution accessibility in space. And the distribution of the accessibility in space will co-determine the decisions in land-use choices (locating houses and firms, etc.), which, as stated in previous paragraphs, in return influence the transport system and accessibility.
Interregional (national) interaction On the other hand, on the higher level (interregional or in other words, national), new urban regions will develop and concentrate around the points of the long distance transport networks, which will for example boost the migration of industries and population (e.g. see the regional impact of HSR in France in (Bonnafous, 1986)).
Long distance travel demand
Air transport and HSR development
Accessibility impacts
Region function and attraction changes
Figure 3-7 Interregional interaction between land-use and transport systems
As shown in Figure 3-7, long distance travel demand of people fosters the development of air transport and HSR networks, which generates accessibility impact on the region along the route in the networks. These impact can initiate land-use changes on interregional level. Firms and industries will move to regions with higher accessibility (with other factors remaining
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou unchanged), and some people also prefer to live in these regions, increasing the population of them. These land-use changes will lead to function and attraction changes of the regions, and consequently redistribute the long distance travel demand of people. 3.3.3. Conclusion – transport, land-use, and accessibility In conclusion, land-use component is inevitable in accessibility research, which is reflected by the interaction between transport and land-use systems. When it comes to the subject in this thesis, the transport/land-use interaction can be divided into regional and interregional level interaction.
Accessibility acts as a bridge in each feedback circle of transport/land-use interaction, interacting with both systems. Therefore in the evaluation of accessibility, land-use component and the interaction between transport and land-use systems need to be taken into account. Besides being considered from the perspective of space, transport/land-use changes can also be considered from the perspective of time, Wegener and Fürst identify four rates of changes, from very slow to immediate (Wegener & Fürst, 2004). Therefore, the time- scale of the research will also influence the results, this will be discussed in more detail in the next chapter. 3.4. Individuals Accessibility is discussed from the perspective of individuals, and any other social groups such as companies, institutes, etc. This means that the attention is paid to the accessibility perceived by an individual or a group of individuals, instead of to that of a certain location. Hence, the characteristics of (groups of) individuals or companies also form an important component of the accessibility – individual component.
Individual component reflects the needs (depending on age, income, gender, etc.), abilities (availability of travel modes, etc.), and opportunities (depending on e.g. travel budget) of (groups of) individuals (Geurs & van Wee, 2004). These characteristics determine the demand of individuals and how they perceive the accessibility impact of transport systems. For example, an airport far away from the city centre with poor public feeding service is more accessible to individuals with car than those without one. Individuals perceive different accessibility impact from the same transport/land-use systems, because of the differences of their characteristics, and these characteristics may strongly influence the total aggregate accessibility results. Therefore, individual component is an inevitable part of the accessibility impact in this thesis. 3.5. Interaction of air transport and HSR For now, the aspects that have been described are the same as those that are incorporated in accessibility analysis in literature. Compared to literature, this thesis adds an important aspect that has not been addressed explicitly in accessibility analysis before, which is multiple modes interaction.
In this section, the interaction of air transport and HSR is discussed and its importance in the accessibility impact of combined air transport and HSR networks is described. The interaction of air transport and HSR can be divided into competition and complementation, as shown in Figure 3-8. The interaction can be observed from demand and supply perspectives, which can be respectively linked to individual and transport component of the accessibility impact. In 25
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou this section, firstly the differences between the operation of air transport and HSR are presented, then the interaction is discussed from demand and supply perspectives respectively, finally the interaction of air transport and HSR is concluded.
Current users Airport Airport Competition in the same Users transferred from HSR markets HSR station HSR station
Competition with other modes
Competition between airport and HSR station Integration of airport and HSR station
Figure 3-8 Interaction between airport and HSR station
In the Appendices, a full review of research on interaction of air transport and HSR is presented in Table 10-1. 3.5.1. Differences between air transport and HSR Air transport and HSR operate in different way, making the comparison between the two modes from a supply-oriented perspective a delicate matter (Dobruszkes, 2011). Three main differences can be observed from air transport and HSR operation: (1) service mode; (2) route length and operational speed; (3) service characteristics; and (4) location of access nodes.
Firstly, air transport directly serves a pair of regions, while HSR serves a series of regions. This difference means that HSR is more geographically efficient than air transport, since it can link more regions than air transport over the same distance. In this case, HSR station is a good supplement to long distance travel market of a region.
Secondly, air transport can provide longer direct connection than HSR, but the distance that two modes can cover (partly) overlaps with each other. Commonly, the operational speed (cruise speed) of air transport is 800 km/h – 900 km/h, and the operational speed of HSR is usually between 200 km/h – 300 km/h. This difference leads to both competition and integration potential.
Thirdly, the price of air transport and HSR differs with each other. Traditionally, the price of air transport is commonly higher than that of HSR, however due to the pricing strategy of airlines and the development of low-cost airlines, this might be reversed in some cases. Besides the price, other characteristics, such as comfort, frequency, and reliability, might also be different between the two modes.
Finally, airports, as the access nodes of air transport, are usually located far from the city centre; while HSR stations, as the access nodes of HSR, are usually located more closely to the
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou city centre. Exceptions sometimes exist for HSR stations when HSR stations are integrated with airports.
200 – 12000 km, 800 – 900 km/h
Air transport A B
30 – 2000 km, 200 – 300 km/h
HSR A B C D
Figure 3-9 Main differences between air transport and HSR8
The differences are concluded and illustrated in Figure 3-9.To conclude, the differences of air transport and HSR lead to both competition and complementation potential of the two modes. In next two sections, the competition and complementation are discussed respectively. 3.5.2. Demand-side interaction It should be noticed that air transport and HSR are not the only competitors in long distance travel, conventional railway and road transport can also act as competitors, depending on the characteristics of passengers and routes, while in this thesis, only the competition of air transport and HSR is included.
The demand-side interaction of air transport and HSR reflects the passengers’ travelling behaviour. From demand perspective, literature on interaction of air transport and HSR mainly addresses the interaction by the method of passenger mode choice modelling using information from empirical research. Campos and de Rus show that the demand of HSR in Europe has grown a lot over the past two decades (Campos & de Rus, 2009). The competition between the two modes has been confirmed by many researchers, of which the essential impact factor is the travel time: the competitive effect quickly decreases when the in-vehicle travel time exceeds 2 – 3 h (Park & Ha, 2006), Besides travel time, travel costs, access and egress time, frequency, reliability, security, and comfort are also used as variables and (partly) proved to be relevant (see e.g. (Wardman, Bristow, Toner, & Tweddle, 2002) (Gonzáles- Savignat, 2004) (Steer Davies Gleave, 2006) (Román, Espino, & Martín, 2007) (Martín, Román, García-Palomares, & Gutiérrez, 2014)). Complementation of air transport and HSR is also addressed in some research, and schedule coordination of the two modes is found to be crucial (Román & Martín, 2014). In the research on the demand of general rail service to and from airport, ticket fare and service quality is considered to be important factors that influence the passenger demand (Lythgoe & Wardman, 2002).
It can be concluded from literature that the main factors that influence the demand-side interaction of air transport and HSR are derived from the characteristics of transport service and individuals: travel time, price, comfort, safety, etc. and demographic information.
8 The location of airport does not mean that the airport has to be located at the edge of the studied region, it just aims at showing that airports are normally located farther from the centre of the region than HSR stations. 27
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
However, literature does not address all the demand-side interaction of air transport and HSR. Option values of the two modes have not been studied before. The users of air transport may be well willing to pay for the existence of HSR service, even if they do not use it, and vice versa, which can also be regarded as a benefit from the interaction of the two modes. To conclude, the demand-side interaction results in the changes of individuals’ need for air transport and HSR, consequently influences the accessibility. 3.5.3. Supply-side interaction The supply-side interaction reflects the perceptions of airlines and rail operators towards the interaction of air transport and HSR. From supply perspective, researchers mainly study the influence of HSR service on the seats and frequencies provided by airlines (see e.g. (Jiménez & Betancor, 2012) (Dobruszkes, Dehon, & Givoni, 2014) (Albalate, Bel, & Fageda, 2015)). It is confirmed that HSR service does have impact on the seats and frequencies of air service, besides, other characteristics such as ticket fare can also be influenced (Steer Davies Gleave, 2006). These studies usually apply the methods of ex-post regression models using empirical data or expert interview. Travel time of HSR is also found to be an essential impact factor in the competition (see e.g. (Dobruszkes, 2011) (Dobruszkes, Dehon, & Givoni, 2014)). Meanwhile, some of the research confirm the potential complementation between the two modes, especially in hub airports (Albalate, Bel, & Fageda, 2015).
The impact factors of supply-side interaction are mainly derived from the characteristics of air transport and HSR services. From supply point of view, the interaction of air transport and HSR results in the changes of the supply of the two modes, reflected by the changes in seats, frequencies, price, etc. As an important component of accessibility, the changes of transport component can influence the accessibility impact. Meanwhile, as discussed in last section, characteristics of transport service are impact factors in the demand-side interaction, thus the changes resulted from the supply-side interaction will influence the demand-side interaction. 3.5.4. Conclusion In conclusion, competition of air transport and HSR can be observed, mainly in short and medium-haul markets, the most important factors that influence the competition of the two modes are travel time (including access and egress time), travel costs and frequency. At the same time, HSR can also act as feeding service for airports to expand the catchment area. Although some short-haul flights might lose many passengers to HSR, HSR improves the air service in hub airports by reducing terminal time and access time, consequently reducing the travel time of air transport.
From demand and supply perspectives, the interaction of air transport and HSR respectively influences the individuals’ needs and characteristics of air transport and HSR services. The relationships among the interaction, individual component, and transport component of the accessibility impact are illustrated in Figure 3-10.
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Air/HSR interaction
Demand-side Supply-side interaction interaction
Individual component Transport component
Figure 3-10 Relationships among air-HSR interaction, individual and transport components
The changes of individuals’ needs and transport service also depend on the time-scale of observation. The reaction of individuals and air transport and HSR operators to the impact of the interaction of air transport and HSR sometimes will occur in short time, and sometimes the reaction is in long term. This matter will also be discussed in more detail in the next chapter. 3.6. Conceptual framework – synthesis In conclusion, the accessibility studied in this thesis is defined as the opportunities to take long distance travel for various activities provided by transport and land-use systems. The transport system that is focused on is the combined air transport and HSR networks, which provide long distance transport service. The region level served by this level of transport service is normally a city or a group of cities (agglomeration or metropolis) which has a radius of more than 10 km or has a population of more than 1 million.
Four important components are identified in the conceptual framework:
Transport component Transport component reflects the characteristics of the transport systems (including air and HSR networks, and their feeding urban transport networks), the characteristics express the disutility for individuals to use the systems for their travel purpose. Land-use component Land-use component reflects the land-use system or pattern. Transport/land-use interaction results in transport/land-use changes and consequently influence the accessibility impact. Individual (and firm) component Individual component reflects the characteristics of individuals, which influence the level of access of individuals to transport and land-use systems, or in other words, individuals’ perceptions (preferences) of transport and land-use systems. Air-HSR interaction component Transport and individuals behaviour changes resulting from the interaction of air transport and HSR can influence the joint accessibility impact of the two modes, the interaction can be divided in to competition and complementation, and be addressed from demand and supply perspectives.
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Compared to the general components of accessibility introduced in the last chapter, the temporal component is not included in the framework, the reason is that the temporal component is combined into the other components in this framework. Furthermore, air-HSR interaction is identified as a new component of the accessibility, the reason is that although it belongs to the context of transport component, the interaction does also have influences individual component, and it is the main point that makes the research in this thesis distinctive compared to other accessibility analyses. In fact, how to understand and measure the influence of the interaction is the main challenge in the accessibility impact analysis of combined air transport and HSR networks.
The conceptual framework is illustrated in Figure 3-11. In the next chapter, an evaluation framework will be presented to illustrate the variables in the evaluation of accessibility impact. The derivation of the variables in the evaluation framework directly follows the analyses in this chapter, and the variables will reflect the influence of the aspects addressed in this chapter.
Needs and attributes of individuals and social groups
Air/HSR interaction
Demand-side interaction
Air transport and HSR Land-use system
Supply-side interaction
Accessibility Active accessibility: Passive accessibility: benefits opportunities for individuals in from individuals brought from the region other regions
Figure 3-11 Synthesis – conceptual framework
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4. Evaluation framework – variables and interaction 4.1. Introduction Last chapter presents a framework to conceptually illustrate how combined air transport and HSR influence accessibility. Four components have been identified: transport, individual, land- use, and air-HSR interaction. Following the components, the variables in the evaluation of accessibility can be derived, composing an evaluation framework of the accessibility impact of combined air transport and HSR.
The variables are divided into two categories: calculation variables and evaluation variables. Calculation variables are the variables that jointly determine the absolute value of accessibility, including transport, land-use, individual, and air-HSR interaction variables. Evaluation variables are the variables that can influence the method or the results of the evaluation of the accessibility, including the variables of time-scale and usability in social and economic evaluation.
In the following sections, section 4.2 presents calculation variables; section 4.2.3 presents the evaluation variables; and section 4.2.2 synthesizes the variables and concludes the evaluation framework. 4.2. Calculation variables Calculation variables include transport, land-use, individual, and air-HSR interaction variables. In the calculation of the accessibility, these four variables should be (partly) incorporated, as shown in Equation 4-1.
Equation 4-1 퐴 = 퐹 (퐷 , 푐 (푐 )) 푖푗푘푝 푗푘푝 푖푗푘푝푚∈푀 푖푗푘푝푚 where 퐴푖푗푘푝 represents the accessibility of individual (group) k for purpose p from origin i to destination j; 퐷푗 represents the quality of destination j perceived by individual (group) k for purpose p; 푐 (푐 ) represents the transport service level perceived by individual 푖푗푘푝푚∈푀 푖푗푘푝푚 (group) k for purpose p resulted from the interaction of air transport and HSR; M is the transport service set which includes air transport and HSR; 푐푖푗푘푝푚 represents the service level of air transport or HSR perceived by individual (group) k for purpose p.
This equation indicates that generally, accessibility is a function of the quality of the destinations, transport service level, the characteristics of individuals (groups), and the influence of air-HSR interaction. In the evaluation of accessibility, the evaluation approaches need to be able to include at least one of the variables. In the following sections, the four variables are explained respectively.
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4.2.1. Transport variables Transport variables are derived from the transport component as discussed in section 3.3 and shown in Figure 3-11. Transport variables represent the service level of the transport infrastructure, which can be expressed in a generalised cost function, for example:
푐푖푗 = 훼 ∗ 푡푖푗 + 훽 ∗ 푐푠푖푗 + 훾 ∗ 푒푖푗 Equation 4-2 where 푡푖푗 represents the travel time between origin i and destination j, 푐푠푖푗 represents the travel cost (price) between origin i and destination j, and 푒푖푗 represents the service level or ‘effort’ of the transport service. 훼, 훽, 훾 are weighting coefficients to reflect the importance of each element and to transfer the elements into the same unit. For example, in practice, travel price and effort can also be converted into travel time equivalence.
The characteristics of air transport and HSR services represent the disutility of the individuals who use the service to cover the distance of their travel. From Equation 4-3 and section 3.3 it is known that the characteristics (generalized costs) include the travel time (in-vehicle time, waiting time, access time, egress time, etc.), costs, and effort (safety, comfort, reliability, etc.). As public transport modes, the elements of air transport and HSR in these three categories are different from that of private transport modes, such as car. The difference is shown in Table 4-1.
Table 4-1 Elements within the transport variables of accessibility evaluation9
Car Air transport /HSR Time Walking to/from parking Access/egress time places Waiting time at terminal In vehicle time and station (Congestion time) (Delay time) Finding parking places In vehicle time Transfer time
Costs Fixed costs (for purchasing Ticket price the car) (Insurance costs) Fuel costs Maintenance costs Parking fee Road pricing Effort Level of (dis)comfort Level of (dis)comfort Reliability Reliability Accident risk Accident risk Physical availability Physical availability Convenience (e.g. transfer times)
9 Adapted based on (Geurs & Ritsema van Eck, 2001) 32
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
The combination of the characteristics lead to a generalized transport cost of individuals, which influence their decision of whether making the trip or not, and turns into part of the disutility of the trip they make.
The influence of the characteristics of transport service can be reflected by impedance function, or distance decay: the trip likelihood declines with the increasing of the disutility of the transport service between the origin and the destination. Figure 4-1 shows some examples of the distance decay illustrated by impedance functions.
Figure 4-1 Distance decay10
Better characteristics (or higher service level) lead to higher level of accessibility. In the evaluation of accessibility, the accessibility measures should be able to reflect this trend, which means accessibility should be a monotone decreasing function of the generalised costs of the transport systems, keeping other factors constant. 4.2.2. Land-use variables Land-use variables are derived from the land-use component as shown in Figure 3-11. The variables reflect the land-use system, which consists of the amount, quality, and spatial distribution of opportunities supplied, and the distribution of the demand for these opportunities (where individuals live), according to the analysis in section 3.3.
The quality of the destinations determines the accessibility perceived by individuals. For example, a businessman will reasonably value the accessibility to Amsterdam more than equal one to Delft. Since this research focuses on disaggregate level, the distribution of the demand in origin i will influence the aggregate results of accessibility. In an disaggregate evaluation, the distribution of the demand for the opportunities can influence the aggregate results of the evaluation.
To conclude, in the evaluation of accessibility, land-use variables should be taken into account in the accessibility measures, according to the description in this section.
10 Source: (Geurs & Ritsema van Eck, 2001) 33
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
4.2.3. Individual variables In the evaluation of accessibility from air transport and HSR, individuals’ characteristics need to be taken into account, according to section 3.4 and Figure 3-11. As stated in section 3.4, individual component describes the needs (depending on age, income, educational level, etc.), abilities (depending on individuals’ availability of travel modes, etc.), and opportunities (depending on individuals’ travel budget, etc.) of individuals (Geurs & van Wee, 2004).
These characteristics influence individuals’ levels of access to transport modes and the quality of the opportunities, determining the demand of individuals and how they perceive the accessibility impact of transport systems, usually, individuals with different characteristics may differently perceive the accessibility impact: individuals of different income level travel in different trip rates, and individuals travel for business and leisure purpose are willing to pay different level of price for the same amount of distance. Therefore, it is necessary to include individuals’ characteristics in the evaluation of accessibility.
Although accessibility is studied on disaggregate level, it is not possible to investigate the characteristics of each individual, hence individuals are normally aggregated into groups, in each of which the individuals are considered to have the same characteristics.
Individuals are usually categorized in terms of their own characteristics and their trip purposes. The characteristics can include for example the following ones:
Age Income Gender Educational level
As for trip purposes, mainly two types are normally identified:
Business Leisure
It should be noticed that other trip purposes can also be identified, such as visiting friends and families. Depending on the purpose of certain evaluation, these purposes can be either separately addressed or combined into other types, for example, visiting friends and families can be combined into leisure purpose.
Equation 4-3 and Equation 4-4 describe how the individual variables influence transport and land-use variables.
Equation 4-3 푐푖푗푘푝 = 퐹푘푝(푐푖푗, 훼푘푝)
Equation 4-4 퐷푗푘푝 = 퐹푘푝(퐷푗, 훽푘푝) where 푐푖푗푘푝 represents transport service level perceived by individual group k for purpose p from origin i to destination j; 푐푖푗 represents the transport service level from origin i to destination j; 퐷푗 represents the quality of destination j. 훼푘푝 and 훽푘푝 represent parameters that express the influence of individual variables on transport and land-use variables. Accessibility is regarded as a benefit perceived by individuals from the transport and land-use systems. It is supposed that each individual group k traveling from i to j for purpose p will have different feelings of the same disutility 푐푖푗, and differently value the quality of destination j
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
(퐷푗). In the evaluation of accessibility, individual variables should be combined to reflect this trend. 4.2.4. Air-HSR interaction As illustrated in the conceptual framework in chapter 3, the interaction of air transport and HSR is one important component of accessibility. In the evaluation of the accessibility, as a variable, the interaction can influence individuals’ level of access and usage of the transport modes, as shown in Equation 4-5.
Equation 4-5 푐푖푗푘푝 = 퐹푚∈푀(푐푖푗푘푝푚) where 푐푖푗푘푝 represents transport service level perceived by individual group k for purpose p from origin i to destination j; 푐푖푗푘푝푚 represents the transport service level of transport mode m (air transport or HSR) perceived by individual group k for purpose p from origin i to destination j.
Equation 4-5 indicates that the interaction of air transport and HSR influences the transport service level and the way individuals perceive it. From the perspective of demand, for example, after the construction of a new HSR station in an airport, some individuals may choose HSR for certain routes instead of air service; while some other individuals may value the airport more because of the better access/egress service (by HSR), or the more options they can choose when making a long distance travel.
From the perspective of supply, for example, the frequency of air transport service on a route may decrease or increase because of the introduction of HSR service on the same route, depending on the view of the operators (to avoid loss or to increase competiveness).
The conversion of the preference of individuals to the modes can result from many factors. As for competition, for example, an individual may choose HSR over air transport because of the shorter access time to the HSR station (in the case that the in-vehicle time is comparable), while another individual may choose air transport over HSR because of his/her preference to shop in the airport.
The complementation of air transport and HSR indicates that the multi-modal transport service can be regarded as an added value, however, it is not clear how it benefits the users, or in other words, how individuals perceive the multi-modal transport service. For example, an individual may value the HSR service provided in an airport, either because that he/she can use HSR to access to and egress from the airport, or because that he/she is willing to have an alternative to air transport even though he/she will rarely use it.
The impact factors that influence the choice of individuals are quite diverse in practice. In order to clearly understand what factors impact the choice of individuals and the extent of the impact, empirical analysis on individuals’ choice behaviour needs to be performed. Moreover, after identifying the factors, the findings of the empirical analysis should be connected to accessibility analysis by certain way. For example, the factors that influence the preference of individuals, such as comfort or reliability, can be transferred to travel time using a travel time equivalence method, so that the costs of the mode can be generalized into a single unit.
The empirical analysis of the factors and the methods to connect the result to accessibility indicators is not in the scope of this thesis, instead, it is one essential successor of the research 35
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou in this thesis. Without sufficient analyses on individuals’ choice behaviour, it is difficulte to accurately incorporate the interaction of air transport and HSR in the study of their accessibility impact.
One simple but theoretical correct approach to combine the interaction of air transport and HSR is to use a method of composite cost, meaning that the costs of the modes in the same route are combined into a composite cost by certain way, so that the competition or complementation of the modes can be reflected to certain extent. This can be seen in chapter 2, and will be discussed in more detail in chapter 6. 4.3. Evaluation variables The evaluation variables are the variables that can influence the method or the results of the evaluation of the accessibility, including the variables of time-scale and usability in social and economic evaluation. 4.3.1. Time-scale Time-scale of the evaluation is relevant when the accessibility impacts in different time are compared. For example, policy makers might want to measure the accessibility impact of an airport after 10 years of its completion, it can be imagined that the impact will be different from that after 5 or 25 years of its completion. This is because that transport and land-use systems are changing over time, therefore the transport and land-use variables used in the evaluation of the accessibility impact in different time are also different.
Due to the different rates of transport and land-use changes, different types of changes are considered in evaluations with different time-scale. Four types of rates of changes can be identified (adapted from (Wegener & Fürst, 2004)):
Very slow change: transport and land-use infrastructure. Large infrastructure projects require long time to build, and once in place, are rarely abandoned. Slow changes: workplaces and housing. Buildings have a life-span of up to half or even one hundred years and take several years from planning to completion. Workplaces (non-residential buildings) and housing buildings exist much longer than the firms or households that occupy them. Fast change: employment, population, transport supply. Jobs are created or occupied, which affects the employment and its distribution; similarly, households are created, grow or decline, which determines the distribution of population. Air transport and HSR operators adjust their service over time, which influences the transport service level.
In the evaluation of the accessibility impact of combined air transport and HSR networks, the three types of transport and land-use changes can be incorporated according to the time-scale of the evaluation. 4.3.2. Usability of accessibility in economic and social evaluation Transport appraisals usually fall in three aspects: economic, ecological, and social (Geurs, Boon, & van Wee, 2009) thus accessibility can also be used as social or economic indicator (Geurs & van Wee, 2004). At the same time, along with the accessibility brought by air transport and HSR, they also generate external impact on individuals, which are mainly
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou environmental impact. Accessibility can be used as social and economic indicators, and environmental impact also deserves attention:
Social indicator means that accessibility can be used to reflect the availability of social and economic opportunities for individuals, i.e. the access to activities and the potential for social interaction, as well as social equity impact (Geurs & van Wee, 2004). Economic indicator means that accessibility impact can be linked to economic impact from air transport and HSR, which are usually divided into (1) direct economic impact, the economic costs and benefits directly related to a project, and (2) indirect economic impact, the economic impact not directly related to the project but resulting from the direct impact (Geurs & van Wee, 2004). Environmental impact refers to the impact on individuals’ living environment resulting from air transport and HSR infrastructure. In this section, the usability of accessibility in social and economic is introduced. Environmental evaluation can be combined in either social or economic evaluation. Usability in social evaluation Social impact of air transport and HSR is generated along with accessibility impact. Social impact can be divided into two parts: interaction opportunities and social equity. As for interaction opportunities, firstly, with air transport and HSR, individuals can travel to locations which they cannot travel to without the two modes; secondly, better accessibility provides individuals with better opportunities to access living resources such as jobs, foods, leisure, and visiting each other, consequently generating positive social impact. As for social equity, accessibility impact can either improve or damage it: on the one hand, new air and HSR service provided for under-developed and remote regions can enhance social equity; on the other hand, investment of air transport and HSR is always attracted to developed and major regions, which enlarges social inequity. For example Spiekermann and Wegener point that the trans- European rail network us actually enlarging the differences in accessibility between central and peripheral regions in Europe, instead of narrowing them as planned (Speikermann & Wegener, 1996), similar research can be seen e.g. in (Vickerman, Spiekermann, & Wegener, 1999).
Accessibility to opportunities itself can be regarded as social impact for individuals, while social equity becomes quite important in the project appraisal. However, this dimension of accessibility has not been sufficiently captured in traditional evaluation frameworks: decision makers usually prefer cost-benefit analysis (CBA) or multi-criteria analysis (MCA) as evaluation method for transport decision making (Hayashi & Morisugi, 2000), while social aspects are not equally paid attention on, especially for equity aspect (or in other words, distribution effects).
Social equity is reflected by the distribution of accessibility among different social groups and areas. Two methods to identify social equity are Gini index (van Wee & Geurs, 2011) or normalised scores (Thomopoulos & Grant-Muller, 2013).
If taking into account social equity, accessibility evaluation should take one more step to include social equity measurement. In practice, the measurement of social equity is not as simple as stated in the last paragraph. Equal distribution of accessibility is usually not pursued by policy makers: the equity of the distribution of accessibility should be weighed by the development level or other factors of each area (or person). Therefore, standards need to be built in the evaluation of the social-equity dimension of accessibility. 37
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
Usability in economic evaluation As for economic impact, some researchers have examined the relation between transport investment and the promotion of economy (Banister & Berechman, 2001) (Lakshmanan, 2011), production function approach is used as an economic benefit measure. Dividing the economic impact into direct and indirect impact, the accessibility impact can be linked to direct economic impact: air and HSR service provided for individuals can generate travel-cost savings (cost, time, etc.) for individuals, improving individuals’ welfare. Besides, better accessibility also attracts business and tourism activities and investment, consequently improve the productivity and GDP of the region, which can be linked to indirect economic impact.
There has been many research on the economic impact of air transport (Robertson, 1995) (Hakfoort, Poot, & Rietveld, 2001) (Cooper & Smith, 2002) (Oxford Economic Forecasting, 2006) and HSR (Gutiérrez, 2001) (The World Bank, 2014), most of them are with respect to GDP created and monetary value added. Four types of impacts can be identified in these research:
Direct impact: employment and other outputs directly related to the operation of an airport; Indirect impact: employment and other outputs related to activities down in the supply chain of the operation of an airport; Induced impact: employment and other outputs generated by the spending of the direct and indirect employees; Catalytic impact: impact related to the wider role of the airport on regional development. However, accessibility is mentioned in some research, but not in sufficiently detail (accessibility can be linked to catalytic impact to some extent (Halpern & Brathen, 2011)).
When linking to economic evaluation, feasible accessibility measures need to be chosen to measure accessibility impact. In the next chapter, this matter will be discussed in more detail.
Environmental impact from air transport and HSR Environmental impact from air transport and HSR is not negligible, both the construction of air transport and HSR infrastructure and the operation of the service produce environmental impact, which can be divided into positive and negative impact:
Positive impact, such as energy saving resulted from the transition in mode split after the development of air transport and HSR, especially HSR. This part of impact is not often examined, but are still mentioned in some research, e.g. (ATAG, 2005) (The World Bank, 2014). Negative impact, including air and water pollution, greenhouse gas emissions, land take, and noise, etc. This part of impact are usually taken as the external impact in the evaluation of air transport and HSR development, see e.g. for air transport, (Franssen, Staatsen, & Lebret, 2002) (Franssen, van Wiechen, Nagelkerke, & Lebret, 2004) for HSR (van Wee, van den Brink, & Nijland, 2003) (Campos & de Rus, 2009) (The World Bank, 2014). From the perspective of individuals’ welfare, environmental impact can also be taken as social- economic impact, since environmental issues are closely related to the living quality and health of individuals, and negative environmental impact can be monetised and used in CBA
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou of transport/land-use appraisal. Therefore, environmental impact can be jointly measured in social evaluation and/or economic evaluation.
Figure 4-2 presents the relationship between accessibility impact and social, economic, and environmental impact: in the evaluation, environmental impact can be either measured as social impact or economic impact.
Social impact Environmental impact Economic impact
Figure 4-2 Relationship between accessibility impact and social, economic, and environmental impact 4.4. Evaluation framework – synthesis In this chapter, an evaluation framework is presented. In the framework, four calculation variables and three evaluation variables are identified, as shown in Table 4-2.
Table 4-2 Calculation variables and evaluation variables
Calculation variables Transport Individual Land-use Air-HSR interaction Evaluation variables Time scale Usability in social evaluation Usability in economic evaluation
Equation 4-1 summarizes the relationships among the calculation variables. It indicates that accessibility is a function of the quality of the destinations, transport service level, the characteristics of individuals (groups), and the influence of air-HSR interaction. In the evaluation of accessibility, the evaluation approaches need to be able to include at least one of the variables.
Time-scale of the evaluation of the accessibility impact affects the transport and land-use changes that will be incorporated in the evaluation, which influence the data and the approach used in the evaluation. Utilization of accessibility evaluation in social and economic evaluation indicates that (1) the distribution of accessibility over individual groups/areas can be used as an indicator of social equity in social evaluation and (2) accessibility can be regarded as benefit for individuals and be used in macro- or micro- economic evaluation.
It should be born in mind that these variables do not separately influence the results of the evaluation, instead, they are also interrelated, as shown in Figure 4-3.
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
Time-scale
Individual variable
Multimodal interaction
Transport variable Land-use variable
Usability in social- economic evaluation
Figure 4-3 Interrelationship amongst the evaluation variables
Transport and land-use variables need to be jointly combined in the evaluation of accessibility, while the interaction between multiple modes have influence on both variables. These three variables are then all influenced by individual variable, since the extent to which they influence the evaluation differs among different individual groups. And after all, time-scale variable acts an important role in the selection and application of all the variables, for example, the transport changes in short term and long term are different. Usability in social and economic evaluation should be considered in the selection and construction of the accessibility indicators in practice.
In the next chapter, accessibility measures are reviewed to identify feasible ones in this thesis. The criteria of the literature review are derived based on the evaluation framework presented in this chapter.
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5. Evaluation methodology – feasible accessibility measures 5.1. Introduction As analysed in last chapter, transport, land-use, individual, and air-HSR interaction variables are identified as the calculation variables, and time-scale and usability in social and economic evaluation are identified as the evaluation variables in the evaluation of the accessibility impact. In this chapter, the capabilities of the accessibility measures to incorporate the variables are examined, in order to examine the feasibility of the accessibility measures in the evaluation of the accessibility impact of combined air transport and HSR networks.
This chapter aims to identify the feasibility of the accessibility measures in the evaluation of the accessibility impact of combined air transport and HSR networks. In section 5.2, firstly the capability of the accessibility measures to incorporate the evaluation variables is discussed; then in section 5.3, the feasibility of the accessibility measures is summarized. 5.2. Theoretical basis Chapter 2 performs a literature review of accessibility measures, four types of accessibility measures are identified, including infrastructure-based, location-based, person-based, and utility-based measures, of which location-based measures have four sub-categories: distance, contour, potential, and balancing measures. In this chapter, the accessibility measures reviewed in chapter 2 are confronted with the evaluation variables identified in the evaluation framework in chapter 4, using a similar approach in Table 2-1.
The capability of the measures to incorporate transport, land-use, and individual variables can be reflected by the review in chapter, and can be derived from Table 2-1, as shown in Table 5-1.
Table 5-1 Capability of the accessibility measures to incorporate transport, land-use, and individual variables
Accessibility measures Transport variables Land-use variables Individual variables Infrastructure-based +/- - - Location-based - Distance measures +/- +/- - - Contour measures +/- +/- - - Potential measures + + +/- - Balancing factors + + +/- Person-based + + + Utility-based + + +/-
In the next several sections, the capability of the accessibility measures to incorporate air-HSR interaction, time-scale, and usability in social and economic evaluation are discussed.
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Air-HSR interaction The interaction of air transport and HSR can be addressed from demand and supply perspectives, as stated in previous chapters. As stated in previous chapters, in the impedance function, when multiple modes are considered jointly, transport costs of the modes can be combined using certain methods, such as minimum cost and logsum composite cost. Therefore, potential measures and balancing factors are potentially capable to incorporate the interaction variable. Utility-based measures perceive accessibility as the outcome of a set of choices, so that this type of measures are the most theoretically capable to incorporate the interaction of the two modes.
Time-scale Time-scale variable can be incorporated in all the accessibility measures, by using a scenario approach. The problems of incorporating time-scale variable in application can be that for an ex-ante evaluation how to estimate the conditions in future, and for an ex-post evaluation how to collect accurate and useful historical data.
Social and economic usability The shortcomings of infrastructure-based measures also limit their usability as input for social and economic evaluation. Because of the exclusion of land-use and individual components, the measures cannot be used in social evaluation. As for economic evaluation, using travel costs as input of the rule-of-half measure of consumer surplus has been pointed out to be incorrect if land-use pattern is to change as a result of the transport plan (Geurs & van Wee, 2004).
Contour measures are extremely sensitive to travel time/costs changes and are not useful in explaining accessibility developments in time/costs, thus the social and economic usability of the measures are relatively low.
Potential measures, balancing factors, person-based measures, and utility-based measures are all potentially useful in social evaluation, such as equity evaluation, which has been demonstrated in some literature (Fürst, et al., 1999). But not all these measures are useful in economic evaluation. Some researchers apply potential accessibility index as input of the production function of GDP (Fürst, et al., 1999), which is feasible but lacks strong theoretical basis. Utility-based measures have the advantage in this issue, the measures can be linked to micro-economic theory, measuring the accessibility in monetary terms. The measures have advantages over traditional rule-of-a-half evaluation of consumer surplus change, since they incorporate land-use components. The measures have basis in travel behaviour theory, hence having potential in reflecting the interaction of air transport and HSR. Furthermore, utility- based measures may indicate a larger improvement of accessibility at regions with low accessibility levels than at regions with high levels (Geurs & van Wee, 2004), which is relevant in social evaluation of transport and land-use development. 5.3. Feasibility of the accessibility measures The confrontation between the accessibility measures and evaluation variables is shown in Table 5-2, see footnote for the explanations of the indices in the table.
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Table 5-2 Confrontation between the accessibility measures and evaluation variables11
T12 L I A TS SU EU Infrastructure-based +/- - - - + - +/- Location-based - Distance measures +/- +/- - - + - - - Contour measures +/- +/- - - + - - - Potential measures + + +/- +/- + + +/- - Balancing factors + + +/- +/- + + +/- Person-based + + + - + + - Utility-based + + +/- + + + +
As shown in Table 5-2, because of the incapability to incorporate the interaction of air transport and HSR, infrastructure-based, distance, contour, and person-based measures are considered to be less feasible in the evaluation of the accessibility impact of combined air transport and HSR networks. Of the other three types of measures, measures using balancing factors are less feasible because of the difficulty to interpret it. Potential measures and utility- based measures are more feasible than other measures, of which utility-based measures are more theoretically ideal, while potential measures are more practically applicable.
It should be borne in mind that this thesis does not reject the application of the less feasible accessibility measures in the evaluation of the accessibility impact of combined air transport and HSR networks. The measures are still potentially useful when one only wants to know part(s) of the accessibility impact.
11 In the table, T, L, I, A, TS, SU, and EU respectively represent transport, land-use, individual, air/HSR interaction, time-scale, social usability, and economic usability. 12 The meanings of +, +/-, and - are the same as those in Table 2-1. 43
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
6. Case study 6.1. Introduction The second part of the thesis, the case study, describes the application of accessibility measures in the Frankfurt Rhein-Main area.
In chapter 2, a literature review is performed on the accessibility measures, four types of accessibility measures are identified.
In chapter 3, in the conceptual framework, transport component, land-use component, individual component, and interaction of air transport and HSR are identified to together add to accessibility impact of combined air transport and HSR networks.
In chapter 4, in the evaluation framework, transport variable, individual variable, land-use variable, multiple modes interaction variable, time-scale variable, and the usability of accessibility in social and economic evaluation are identified to be the variables in the evaluation of the accessibility impact.
In chapter 5, based on the review in chapter 2 and the frameworks in chapter 3 and 4, potential measures and utility-based measures are identified as the most feasible accessibility measures to evaluate the accessibility impact of combined air transport and HSR networks.
In this chapter, due to the data availability and time restriction, the case study is not able to combine all the contents incorporated in the conceptual and evaluation frameworks. The objectives of the case study are as follows:
To demonstrate the feasibility of the conceptual and evaluation frameworks, in other words, to demonstrate that it is doable to evaluate the accessibility impact of combined air transport and HSR networks. To demonstrate how the interaction and different perspectives of the interaction of air transport and HSR influence the accessibility impact of combined air transport and HSR networks. To demonstrate the influence of individual variables in the accessibility impact of combined air transport and HSR networks. To briefly illustrate the usability of accessibility in social evaluation. To derive some empirical experiences in the application of accessibility measures.
This chapter presents the construction of the case study and accessibility measures, including three parts of work: selecting studied region and making assumptions of the case study, constructing accessibility measures, and constructing evaluation scenarios. The results of the case study are presented in the next chapter.
Frankfurt Rhein-Main area is selected as the studied region in the case study, and four transport scenarios are constructed. Accessibility measures are selected based on the criteria presented in the last chapter, and applied in the case study. Only active accessibility is measured in the case study. And the case study does not take the perception of companies into account.
In the case study, the accessibility measures are applied to two modes (air transport and HSR). People usually make long distance travel for business and leisure purpose, and population can
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou to some extent represent the business attraction of a city. Because of data and time restriction, only some of the variables identified in the evaluation framework can be included in the case study, in the following sections, the assumptions will be given in detail.
Four transport scenarios are constructed in the case study: (1) Frankfurt Rhein-Main area is only connected to air transport network by an international airport; (2) Frankfurt Rhein-Main area is only connected to HSR network by HSR station; (3) Frankfurt Rhein-Main area is connected to air transport and HSR networks by an international airport and a HSR station in the city centre; and (4) Frankfurt Rhein-Main area is connected to air transport and HSR networks by an international airport and a HSR station in the airport. The construction of the transport scenarios is based on the main distinction of this research compared to others: the incorporation of the interaction of air transport and HSR.
In the following parts of this chapter, section 6.2 describes the selection of the studied region and the assumptions made in the case study. Section 6.3 describes the four evaluation scenarios. Section 6.4 presents the construction of the accessibility measures. Section 6.5 gives an overview of the case study and the application of the accessibility measures. 6.2. Studied region and assumptions in the case study In this section, the selected studied region is described, and some assumptions in the case study are introduced.
In section 6.2.1, the criterion of the selection of the studied region are presented, and the studied region is described. In section 6.2.2, some assumptions in the case study are explained. 6.2.1. Selection of the studied region In the case study, Frankfurt Rhein-Main area is selected as the studied region, the selection is based on the following criterion:
Spatial scale The selected region is expected to accord to the definition given in chapter 3, that is, the region should be able to be regarded as an agglomeration or metropolis, with a radius of more than 10 km or has a population of more than 1 million. Transport infrastructure: The selected region is expected to own an international airport and/or a HSR station, so that transport scenarios can be abstracted from the existing transport pattern of the selected region. Data availability: Needed data should be relatively easy to access. This is because of the time restriction of a Master thesis.
Synthesizing the standard listed above, Frankfurt Rhein-Main area becomes an ideal option for the case study. The reason is that Frankfurt Rhein-Main area is an agglomeration area in Germany, owing an international airport and two HSR stations, and the relevant data are relatively easy to obtain from various sources.
Frankfurt Rhein-Main agglomeration area owns an area of approximate 2500 km2, and a population of 2.2 million, as part of the Frankfurt Rhine-Main Metropolitan Region, it is an
45
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou agglomeration-level urban area in Germany (Regional Authority FrankfurtRheinMain, 2013). Frankfurt, as the central and largest city of this region, is the air and rail transport hub of Germany and Europe. Frankfurt Airport provides national and international air transport service for this region, Frankfurt (M) Hbf and Frankfurt (M) Flughafen Fernbf provide high speed rail transport service for this region. Meanwhile, relevant transport data are quite accessible on the website of Germany Railway, Eurostat, Frankfurt airport, etc. Therefore, Frankfurt Rhein-Main agglomeration region is selected as the studied region in this case study.
Figure 6-1 shows Frankfurt Rhein-Main agglomeration area (area in purple) and Rhine-Main metropolitan region (area in green).
Figure 6-1 Frankfurt Rhein-Main agglomeration region and Rhine-Main metropolitan region13
Transport infrastructure in the studied region Frankfurt (M) Hbf and Frankfurt (M) Flughafen Fernbf together provide HSR transport service for the region, connecting other main regions in Germany and most of the important cities in Europe. The locations of the airport and HSR stations are shown in Figure 6-2. Frankfurt (M)
13 Source: (Regional Authority FrankfurtRheinMain, 2013)
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Hbf is located in the city centre, Frankfurt (M) Flughafen Fernbf is located next to the airport, in the southwest of the city, thus HSR and air transport are integrated at this location.
Figure 6-2 Locations of Frankfurt Airport and Frankfurt HSR stations14
Land-use pattern in the studied region The Frankfurt Rhein-Main area can be divided into eight subdivision, the subdivisions and their population are shown in Table 6-1.
14 Source: made by the author using the original figure from (Google Inc., n.d.) 47
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
Table 6-1 Subdivisions of Frankfurt Rhein-Main region15
Subdivision Population Groß-Gerau 24,076 Hochtaunus 229,167 Main-Kinzig 404,995 Main-Taunus 228,021 Offenbach 119,203 Wetterau 298,429 Rheingau-Taunus 181,190 Frankfurt am Main 669,628 Total 2,154,709
As the core of the agglomeration, Frankfurt am Main owns the largest population, the city can be further divided into 44 subdivisions. As shown in Figure 6-3.
Figure 6-3 Frankfurt subdivisions16
15 Source: (European Commission, 2015) 16 Source: (TUBS, 2010) 48
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The population of each subdivisions is shown in Table 10-2 in the appendices. The identification of the subdivisions aims to enable the accessibility evaluation to take into account the influence of the access time to air transport and HSR services from the different locations in the studied region. 6.2.2. Some assumptions in the case study In this section, some assumptions made in the case study are explained.
Type of accessibility measured In the case study, destination accessibility is measured, meaning that the accessibility to different destinations connected by combined air transport and HSR networks are measured. The attribute of each destination is regarded as the attraction to individuals, such as population, GDP, etc.
The case study only measure the accessibility impact of combined air transport and HSR, thus any other long distance transport modes, such as conventional train and car, are not taken into account.
Destinations selection Frankfurt Rhein-Main area is connected by air transport and HSR services to a large amount of European and oversea destinations. Frankfurt Airport provides air transport service to all the main cities in Europe and many other cities in other continents, while the two HSR stations in Frankfurt provide opportunities for individuals in the area to travel to most European countries by HSR.
In the case study, in case of lack of data, it is not possible to include all the destinations of the air transport and HSR services, and there is no need to do so. Some assumptions can be made to simplify the data collection and calculation process.
Amongst all the destinations connected by air transport and HSR networks, only the destinations in Europe and with a population higher than half a million are selected as the destinations. The selection of the destinations is due to three reasons. Firstly, the numbers of the destinations need to be limited to a certain level so that the data collection (of the transport costs of air transport and HSR, etc.) is a doable task. Secondly, the population can to some extent be assumed to reflect the attraction of of opportunities provided by the destinations. It is fair to assumed that the destinations with higher population also owns more commertial and entertainmental opportunities. Thirdly, it is assumed that only opportunities to large cities are valued by individuals, so that a half a million population floor is defined to filter the destinations.
Table 6-3 gives the destinations with a population higher than half a million of Frankfurt Airport and Frankfurt HSR (the two HSR stations are considered jointly here).
As for air transport, only direct flight is considered to be a connection, while as for HSR, connections with on maximum one change is considered to be an effective connection, and at least one leg of the whole connection should be operated by HSR. If multiple alternatives are available on these routes, the one with less travel time is chosen.
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Transport costs As stated in chapter 4, transport costs include travel time (including access and egress time), price, frequency, and comfort, etc. However, in this case study, not all these types of costs can be taken into account. For one reason, the time limitation and data availability do not allow the inclusion of all the costs, for the other, it is difficult to effectively combine these types of costs into a generalized cost.
Amongst all the costs, the travel time and price are the most easily available and interpretable ones, however, price is difficult to be incorporated. The reason is that due to the pricing strategy of the airlines and HSR operators, the ticket prices of the flights and trains fluctuate from day to day, it is difficult to fairly determine which price to use in the case study. Besides, the standard ticket prices of the flights and trains are not easily accessible on the website of the airlines and HSR operators.
Therefore, in the case study, the transport costs are reflected by travel time, including access time, in-vehicle time, egress time, and check in time.
Transport and land-use changes Due to the interaction of air transport and HSR, the supply of air transport and HSR and the land-use system in the studied region will differ among different scenarios. Compared to that in the situation where air transport and HSR are interacted, the air transport service in the situation where only air transport is available will provide more short-haul routes (e.g. the flight Frankfurt – Cologne was cancelled due to the HSR connection between the two cities), and the ticket price might be higher. The land-use system also varies in different scenarios, depend on the time-scale of the evaluation (e.g. due to the construction of a new HSR station, the area around the station will attract more residents). According to chapter 2, to comprehensively understand the accessibility impact of air transport and/or HSR networks, these changes need to be taken into account, so that in the evaluation of the accessibility impact, the accessibility measures need to be able to reflect the expected influence of the transport and land-use changes.
However, since the case study does not aim at comprehensive results, the transport and land- use changes can be neglected. This is also due to the availability of relevant materials and time restriction of the thesis. Therefore, for the sake of simplicity, it is assumed that in each scenario of the case study, the air transport and HSR connections are in accordance to those listed in Table 6-3.
Disaggregate level As shown in the conceptual and evaluation framework, disaggregate evaluation is essential to reflect the influence of the differences of individuals on the accessibility impact. However in the case study, due to the lack of data, the disaggregate evaluation is not performed.
In the case study, it is assumed that the individuals in each subdivisions of the studied region have the same characteristics, in other words, it is assumed the results of the case study will reflect the average accessibility of each subdivision. The case study does not aim at measuring the accessibility of different individual groups for different trip purposes, thus such level of disaggregation is sufficient in the case study, it is already able to reflect (1) the influence of access time on the accessibility, and (2) the distribution of accessibility among the subdivisions.
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In conclusion, the assumptions concerning the construction of the case study are made from four perspectives, which are destination selection, transport costs, transport and land-use changes, and disaggregate level. The assumptions are concluded in Table 6-2.
Table 6-2 Assumptions in the case study
Perspective Assumption Type of accessibility measured Destination accessibility Destinations selection Only European destinations with a population no less than half a million connected by air transport (direct flight) and/or HSR (maximum one transfer is allowed) are included Transport costs Only incorporate travel time, including in- vehicle time, access time, egress time, and check in time Transport and land-use changes In each scenario, the transport and land-use pattern are the same as current situation, which is according to the data collected from various data sources Disaggregate level The individuals living in each subdivision are regarded as an entirety
6.3. Transport scenarios construction This section describes the transport scenarios constructed in the case study. Three scenarios are constructed:
Scenario 1: Frankfurt Rhein-Main area is served by Frankfurt Airport, and the HSR service is removed. Scenario 2: Frankfurt Rhein-Main area is served by Frankfurt am Main Hbf (HSR station), the air transport service and the HSR station in the airport are removed. Scenario 3: Frankfurt Rhein-Main area is served by both Frankfurt Airport and Frankfurt am Main Hbf, but the HSR station in the airport is removed. Scenario 4: Frankfurt Rhein-Main area is served by both Frankfurt Airport and a HSR station in the airport, the HSR station in the city centre is removed.
By comparing the accessibility of the three scenarios, the case study can illustrate how HSR service adds to the accessibility impact of air transport and vice versa, and in scenario 3, using different accessibility measures, the influence of the interaction of the two modes can be explored.
As stated in previous section, destination accessibility is measured in the case study, and the population of each destination represents the opportunities of each destination. Destinations with a population no less than half a million are taken into account. The population and connection condition of each destination is shown in Table 6-3, the source of the data are from relevant website.
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Table 6-3 Current air transport and HSR destinations of Frankfurt Rhein-Main area
Destination Population17 Air_Des18 Time HSR_Des20 Time (hour)19 (hour)21 Istanbul 9,897,599 y 2.92 n n/a London (greater city) 8,256,400 y 1.75 y 6.50 Berlin 3,501,872 y 1.17 y 4.00 Ankara 3,401,573 y 3.25 n n/a Madrid 3,233,527 y 2.58 n n/a Barcelona (greater city) 3,202,571 y 2.00 y 11.50 Milano (greater city) 3,105,489 y 1.17 y 8.00 Napoli (greater city) 3,103,234 y 1.92 n n/a Athina (greater city) 2,989,023 y 2.75 n n/a Greater Manchester 2,693,800 y 1.75 n n/a Roma 2,638,842 y 1.83 y 16.50 Izmir 2,386,759 y 3.00 n n/a Paris 2,249,977 y 1.25 y 3.83 Bucuresti 1,883,425 y 2.25 y 26.73 Lisboa (greater city) 1,860,256 y 3.00 n n/a Hamburg 1,798,836 y 1.08 y 3.62 Budapest 1,727,495 y 1.50 y 10.50 Warszawa 1,715,517 y 1.58 n 10.00 Barcelona 1,620,943 y 2.00 n n/a Wien 1,598,626 y 1.33 y 6.83 Stockholm (greater city) 1,579,896 y 2.08 n n/a München 1,378,176 y 0.92 y 3.18 Lyon 1,307,101 y 1.25 y 6.95 Dublin (greater city) 1,261,332 y 2.08 n n/a Praha 1,246,780 y 1.00 n n/a Sofia 1,208,097 y 2.17 n n/a Brussel 1,159,448 y 0.92 y 3.10 Lille 1,113,813 n n/a y 5.05 Birmingham 1,079,900 y 1.58 n n/a Helsinki (greater city) 1,059,631 y 2.42 n n/a Marseille 1,042,873 y 1.58 y 8.38
17 Source: (European Commission, 2015) 18 Source: (Frankfurt Airport, 2015), y means the destination is directly connected, while n means not. 19 Source: (Frankfurt Airport, 2015) 20 Source: (DB, 2015), the meaning of y and n is the same as that of air destination. 21 Source: (DB, 2015) 52
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Amsterdam (greater city) 1,021,754 y 1.17 y 4.00 Köln 1,017,155 n n/a y 1.08 Rotterdam (greater city) 977,584 n n/a y 4.50 Porto (greater city) 975,300 y 2.67 n n/a Torino 872,091 y 1.25 n n/a Valencia 797,028 y 2.25 n n/a Bilbao (greater city) 785,793 y 2.08 n n/a Kraków 758,334 y 1.50 y 12.70 Cyprus 730,400 y 3.75 n n/a Bordeaux 720,028 n n/a y 8.55 Toulouse 714,318 y 1.75 y 11.00 Sevilla 702,355 y 2.92 n n/a Zaragoza 679,624 y 2.33 n n/a Palermo 654,987 y 2.33 n n/a Riga 649,853 y 2.08 n n/a Wroclaw 631,188 y 1.25 n n/a Stuttgart 613,392 y 0.67 y 1.30 Oslo 613,285 y 2.00 n n/a Zürich (greater city) 598,986 y 0.92 y 4.17 Glasgow 594,100 n n/a n n/a Nantes 593,983 n n/a n n/a Düsseldorf 592,393 y 0.83 y 1.87 Dortmund 580,956 n n/a y 2.63 Essen 573,468 n n/a y 2.30 Málaga 567,433 y 2.92 n n/a Poznan 550,742 y 1.33 y 7.20 København 549,050 y 1.42 y 9.25 Bremen 548,319 y 0.92 y 3.67 Vilnius 533,279 y 2.08 n n/a Leipzig 531,809 y 0.92 y 3.50 Dresden 529,781 y 1.00 y 5.33 Hannover 525,875 y 0.83 y 2.30 Nice 522,008 y 1.50 y 10.95 Göteborg 520,374 y 1.67 n n/a Nürnberg 510,602 y 0.67 y 2.00 Antwerpen 507,368 n n/a y 3.87
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As assumed in previous section, the travel time to each destinations are regarded as the proxy of transport cost, including access time, check in time, in vehicle time, and egress time. The in vehicle time by air transport and HSR to each destination is shown in Table 6-3.
The access time, check in time, and egress time of each subdivision in Frankfurt Rhein-Main area to the airport and HSR station are shown in Table 6-4. The access time to airport and HSR is estimated using Google Map (Google Inc., n.d.), the access mode is set as public transport, the check in time in airport is assumed to be one hour, and it is assumed that there is no check in time in HSR station, which is the case in some European countries such as the Netherlands and Germany.
The egress time is the estimated egress time of the airports/HSR stations in the destinations. Here for sake of simplicity, the egress time of air transport is set as half an hour, and that of HSR be zero.
The whole table including the access time, check in time, and egress time of all subdivisions in the studied region is shown in Table 10-3 in the appendices.
Table 6-4 Access time, check in time, and Egress time to the airport and HSR station from the subdivisions of Frankfurt Rhein-Main area
Access time (h) Check in Egress time (h) time in Airport Hbf HSR in airport airport Airport Hbf Groß-Gerau 0.550 0.350 0.550 1.000 0.500 0.000 Hochtaunus 0.880 0.533 0.880 1.000 0.500 0.000 Main-Kinzig 1.380 1.000 1.380 1.000 0.500 0.000 Main-Taunus 1.000 0.717 1.000 1.000 0.500 0.000 Offenbach 0.500 0.300 0.500 1.000 0.500 0.000 Wetterau 1.783 1.383 1.783 1.000 0.500 0.000 Rheingau-Taunus 0.700 1.000 0.700 1.000 0.500 0.000 Frankfurt am Main 0.167 0.000 0.167 1.000 0.500 0.000
The destinations and travel time in each scenario can be calculated according to the data shown in Table 6-3, Table 6-4, and Table 10-3, to determine the transport and land-use variables in each scenario. 6.4. Accessibility measures In this section, three accessibility measures are constructed: potential measure using logsum composite cost (named as Logsum), potential measure using fastest composite costs (named as Minimum), and potential measure combined with daily accessibility (named as Logsum TT < 5). In section 6.4.1 - 6.4.3, the three measures and the selection of the value of parameters will be introduced.
According to the aims and the assumptions presented in previous sections, and considering the data availability, potential measures are applied in the case study. The data need of utility- based measures cannot be fulfilled by the available data in this thesis.
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In order to accomplish the objectives of the case study, potential measures with different focuses need to be identified. In general, the potential measures calculate the sum of destination opportunities in terms of population in all the destinations identified in previous sections weighted by an impedance function of travel time. In the following sections, the accessibility measures and the parameters used are presented. 6.4.1. Potential accessibility measure 1 – Logsum The potential measures assume that the attraction of a destination increases with size and declines with travel costs. The potential accessibility of subdivision i is the sum of destination population 퐷푗 in all connected destinations j weighted by an impedance function of composite costs 푐푖푗 between subdivision i and destination j:
퐴푖 = ∑ 퐷푗퐹(푐푖푗) Equation 6-1 푗 Negative exponential function is selected as the impedance function of the potential measure:
− α 푐푖푗 Equation 6-2 퐹(푐푖푗) = 푒
Although there is no strong evidence to support the effectiveness of negative exponential function in reflecting the distance decay of long distance transport, the case study does not aim to calculate the accurate destination accessibility level of the region. Therefore, a negative exponential function that reflects the trend of distance decay is sufficient in the case study.
In order to reflect the combined effect of air transport and HSR, the costs of the two modes need to be aggregated. There are essentially two ways of aggregating accessibility indicators across modes: logsum composite and fastest mode (Wegener & Bokemann, 1998).
In this accessibility measure, the logsum method is used:
1 푐 = − ln ∑ 푒−훽푐푖푗푚 Equation 6-3 푖푗 훽 푚∈{퐴푖푟 푡푟푎푛푠푝표푟푡,퐻푆푅} The logsum composite cost is lower than the cost of each mode, thus it is assumed that the logsum composite cost is able to reflect the complementation of air transport and HSR, indicating that the individuals value the opportunities that there are two modes available for one route.
The generalized cost from subdivision i to destination j by mode m is calculated by the weighted sum of access time, check in time, in vehicle time, and egress time:
푐푖푗푚 = 훼푚푎푐푐푒푠푠푡푖푚푒푖푗푚 + 휇푚퐶ℎ푒푐ℎ푖푛푡푖푚푒푚 + 훾푚푖푛푣푒ℎ푖푐푙푒푡푖푚푒푖푗푚 Equation 6-4 + 훿푚푒푔푟푒푠푠푡푖푚푒푖푗푚 The weight of each component of the total cost reflect sensitivity of each component, the higher the weight of one component is, the higher disutility an individual will addresses to it.
In order to derive the aggregation of accessibilities of the subdivisions to the accessibility of the studied region, a way of average value can be performed. The accessibility of region r 퐴푟 is the average of the accessibilities of the subdivision i belonging to region r (where 푅푟 is the set of subdivisions of region r), weighted by their population 푃표푝푢푙푎푡푖표푛푖 , where 푃표푝푢푙푎푡푖표푛푟 is the regional population:
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1 Equation 6-5 퐴푟 = ∑ 퐴푖푃표푝푢푙푎푡푖표푛푖 푃표푝푢푙푎푡푖표푛푟 푖∈푅푟
6.4.2. Potential accessibility measure 2 – Minimum As for accessibility measure 2, the way to aggregate the costs of multiple modes is to use the fastest mode:
Equation 6-6 푐푖푗 = min (푐푖푗푚) 푚∈{퐴푖푟 푡푟푎푛푠푝표푟푡,퐻푆푅} By taking into account the fastest mode when both air transport and HSR are available on one route, this accessibility measure only consider the fastest one, indicating that only the mode with the lowest transport cost is valued by the individuals. By this way, the competition of the air transport and HSR is measured.
Except the way to aggregate the costs of multiple modes, the other parts of this accessibility measure are the same as that of Logsum.
The different ways to incorporation of the costs of multiple modes of Logsum and accessibility measure 2 aim to compare the different influences of competition and complementation of air transport and HSR. 6.4.3. Potential accessibility measure 3 – Logsum TT < 5 Daily accessibility counts the number of opportunities which can be reached within a day. Combined with potential measure, accessibility measure 3 is:
∑ Equation 6-7 퐴푖 = 푗∈(푡푖푗<푇) 퐷푗퐹(푐푖푗); where 푇 is set as 5 hours, meaning that the when the total travel time exceeds 5 hours, the destinations are excluded.
This measure stressing the preference of the individuals for daily return from the destinations. By comparing the result of this measure to that of Logsum, the influence of the different attributes of individuals on the accessibility impact of combined air transport and HSR networks.
Except the combination of daily accessibility, the other parts of this accessibility measure are the same as that of Logsum.
The values of the parameters of the three accessibility measures are shown in Table 6-5.
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Table 6-5 Values of parameters of the three accessibility measures
Parameter Value22 α 0.00723 훽 0.0124 25 훼푚 1.2
휇푚 1
훾푚 1
훿푚 1 푇 5
6.5. Overview of the case study In this chapter, the studied region, assumptions, scenarios, and accessibility measures of the case study are described.
Frankfurt Rhein-Main area is selected as the studied region in the case study, and three accessibility measures are applied in four transport scenarios, which are briefly concluded in Table 6-6. The three measures are all in the pattern of potential measure, due to the time and data restrictions to apply more disaggregate measures.
Table 6-6 Scenarios and accessibility measures
Available Airport HSR station located HSR station located infrastructure26 in city centre in the airport Scenario 1 Scenario 2 Scenario 3 Scenario 4
The accessibility of the studied region is calculated by aggregating the accessibilities of each subdivisions of the regions using weighted average method indicated in Equation 6-5.
The case study is built in Matlab R2014a. In the next chapter, the results of the case study are presented and the implications of the results are discussed.
22 The source and assumptions are the same as that in Logsum 23 Source: (Fürst, et al., 1999) 24 Source: (Fürst, et al., 1999), the value is adapted from 0.03 in literature to 0.01 in the case study, to stress the effect of the complementation of the two modes. 25 The weight of access time is set as 1.2, while that of others are set of 1, indicating the assumption that the individuals perceive access time as more annoying than others. 26 The locations of the infrastructures are the same as those of the current infrastructures. 57
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7. Results In this chapter, the results of the case study are presented and discussed. The data of the case study are processed in Excel and imported into Matlab. The computation is performed in Matlab, and the results are processed in Matlab and Excel.
In section 7.1, the normalized accessibility indices in four scenarios are listed in several tables with explanations of the results.
In section 7.2, the theoretical and empirical findings from the results are discussed, according to the objectives of the case study identified in the last chapter.
In section 7.3, the results and findings of the case study are concluded, and implications of the case study and the feedback from the case study on the theoretical outcomes of the thesis are discussed. 7.1. Accessibilities in the four scenarios This section presents the results of the case study. The three accessibility measures are applied in the four scenarios, deriving the accessibility of each subdivision and the whole studied region. The accessibility of Frankfurt am Main is measured by calculating the weighted average of the accessibility of each subdivision of it.
To make the results of the case study more readable, the results are normalized by taking the weighted average accessibility of Frankfurt Rhein-Main are in scenario 1 measured by Logsum as 100, the accessibilities of other subdivisions are normalized accordingly.
The normalized results of the four scenarios using the three accessibility measures are listed in Table 7-1 to Table 7-4. The results of the subdivisions of Frankfurt am Main are shown in Table 10-4 to Table 10-7 in the appendices.
Scenario 1 – Airport only The results of measures Logsum and Minimum are identical because that in this scenario only airport serves the region but the differences between the measures are the way they measure the interaction of air transport and HSR.
Table 7-1 Normalized results of scenario 1
Subdivision Logsum & Minimum Logsum TT < 5 Groß-Gerau 122 116 Hochtaunus 103 87 Main-Kinzig 80 61 Main-Taunus 97 79 Offenbach 125 119 Wetterau 66 30 Rheingau-Taunus 113 99 Frankfurt am Main 118 107 Weighted average of Frankfurt Rhein- Main area 100 83
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Scenario 2 – HSR only The results of measures Logsum and Minimum are identical because that in this scenario only HSR station serves the region but the differences between the measures are the way they measure the interaction of air transport and HSR.
Table 7-2 Normalized results of scenario 2
Subdivision Logsum & Minimum Logsum TT < 5 Groß-Gerau 37 30 Hochtaunus 34 27 Main-Kinzig 27 17 Main-Taunus 31 24 Offenbach 38 31 Wetterau 22 10 Rheingau-Taunus 27 17 Frankfurt am Main 37 30 Weighted average of Frankfurt Rhein- Main area 31 23
Scenario 3 – Airport & HSR station in city centre Table 7-3 Normalized results of scenario 3
Subdivision Logsum Minimum Logsum TT < 5 Groß-Gerau 146 132 137 Hochtaunus 126 113 106 Main-Kinzig 98 88 73 Main-Taunus 117 106 96 Offenbach 150 136 140 Wetterau 80 72 38 Rheingau-Taunus 130 120 110 Frankfurt am Main 142 128 128 Weighted average of Frankfurt Rhein-Main area 120 109 99
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Scenario 4 – Airport and HSR station integrated Table 7-4 Normalized results of scenario 4
Subdivision Logsum Minimum Logsum TT < 5 Groß-Gerau 143 131 126 Hochtaunus 121 111 95 Main-Kinzig 94 86 67 Main-Taunus 114 104 87 Offenbach 147 134 130 Wetterau 77 70 34 Rheingau-Taunus 133 121 106 Frankfurt am Main 139 127 118 Weighted average of Frankfurt Rhein-Main area 117 107 91
As shown in the tables, different subdivisions of the studied region have different accessibilities, and the accessibility in the four scenarios also differ from each other. The different aspects respectively stressed by the three accessibility measures also have different influence on the accessibility of the subdivisions.
In the next section, the results will be analysed and the findings are discussed. 7.2. Empirical findings and discussion In this section, the empirical findings of the case study are described and discussed, from the perspectives of the objectives of the case study. Section 7.2.1 describes the influence of HSR on accessibility impact of air transport and vice versa; section 7.2.2 describes the influence of competition and complementation of air transport and HSR on the accessibility impact; section 7.2.3 describes the influence of individual variable on the accessibility impact; section 7.2.4 gives a preliminary discussion of the usability of accessibility in the evaluation of social equity; and section 7.2.5 discusses the differences between aggregate and disaggregate measurement of the accessibility impact. 7.2.1. Inter-influence of accessibility of air transport and HSR The diversities of the accessibilities of the subdivisions in the four scenarios respectively measured by the three accessibility measures are shown in Figure 7-1 – Figure 7-3.
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Groß-Gerau Hochtaunus Main-Kinzig Main-Taunus Offenbach Wetterau Rheingau-Taunus Frankfurt am Main Frankfurt Rhein-Main
Figure 7-1 Logsum, scenario 1 – scenario 4
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Groß-Gerau Hochtaunus Main-Kinzig Main-Taunus Offenbach Wetterau Rheingau-Taunus Frankfurt am Main Frankfurt Rhein-Main
Figure 7-2 Minimum, scenario 1 – scenario 4
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Groß-Gerau Hochtaunus Main-Kinzig Main-Taunus Offenbach Wetterau Rheingau-Taunus Frankfurt am Main Frankfurt Rhein-Main
Figure 7-3 Logsum TT < 5, scenario 1 – scenario 4
It can be seen that the accessibility in the four scenarios measured by the three accessibility measures have the same trend of variety. Comparing the accessibility indices in scenario 1 and scenario 2, it can be seen that the accessibility impact of air transport is higher than that of HSR, referring to the geographical and demographical attributes of the studied region and the destinations, the main reasons are that (1) air transport provides faster transport service to most of the destinations considered in the case study; (2) the case study only takes into account the destinations with a population higher than 0.5 million, hence many destinations to which HSR can provide a faster transport service from the studied region are excluded; and (3) Frankfurt Rhein-Main area is a highly developed agglomeration with sound urban road and public transport systems, hence the access time to the airport is comparable to that to the HSR station.
The second reason is related to the practical issue of the case study. The limitation of 0.5 million population and the limitation of transfer times of the connections (no transfer for flights and on maximum one transfer for high speed train) are arbitrary determinations, the two limitations exclude certain amount of air transport and HSR destinations which are potentially valuable for individuals.
Comparing the accessibility indices in scenario 3 to scenario 1 and scenario 2, it can be seen that the combined air transport and HSR networks improve the accessibility of the studied region with only air transport or HSR service. The improvement is not gained by simply adding up the separate accessibility impact of air transport and HSR, because of the interaction between the two modes.
The difference between scenario 3 and scenario 4 is the location of the HSR station, in scenario 3, the HSR station is located in the city centre while in scenario 4 it is integrated in the airport.
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Comparing the results in scenario 3 and scenario 4, locating the HSR station in the city centre is better than locating it in the airport. This is due to that the HSR station in the airport needs more access time than that in the city centre. The approach to measure the integration of air transport and HSR in the case study is quite simple and general, hence the result here cannot draw a conclusion on the comparison of the HSR station in the city centre and that in the airport. This will be further discussed in the next section.
In conclusion, the combined air transport and HSR networks can improve the accessibility impact on the studied region of a single air transport or HSR network. The integration of the airport and HSR station has a different effect compared to separately locating them, but the quantitative explanation of the difference is beyond the scope of the case study. 7.2.2. Influence of competition and complementation Figure 7-4 illustrates the accessibility indices in scenario 3 measured by Logsum and Minimum.
Figure 7-4 Accessibility indices, scenario 3, Logsum and Minimum
As introduced in section 6.4, when both air transport and HSR services are available on one connection, Logsum uses a logsum composite cost method to combine the transport costs of the two modes, while Minimum only incorporate the mode with lower transport cost. In other words, Minimum only measures the competition of air transport and HST, while Logsum also considers the complementation of the two modes. The logsum composite cost is lower than either air transport cost or HSR cost, indicating that due to the complementation of the two modes, the transport costs perceived by individuals are lower than that when only one mode is available. The result illustrated in Figure 7-4 demonstrates that when both competition and complementation of air transport and HSR are taken into account, the accessibility impact is higher than that when only competition of the two modes is measured. The implication is that if the complementation of air transport and HSR can be enhanced by certain measures, such
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As stated in chapter 2 and chapter 4, the competition of the two modes can be analysed from demand and supply perspectives. From the demand perspective, in the case study, Minimum assumes that individuals will choose the alternative with lower disutility (fastest mode) over the one with higher, however in reality, travel time is not the only variable that influence the choice behaviour, and lower disutility does not one hundred percent lead to the choice of individuals. From the supply perspective, the supply of air transport and HSR (e.g. seats and frequencies) will change due to the competition, while this is not taken into account in the case study (the transport and land-use components are constant).
As for the complementation of the two modes, it can be reflected by for example the possibility to access to the airport by HSR, or the availability of one mode when the other is temporarily out of operation due to various reasons (e.g. strike). While in the case study, the effects of complementation are simply reflected by assuming a lower disutility when two modes are both available. In order to realistically measure the complementation of air transport and HSR, further research should be performed on the forms of complementation and empirically how individuals perceive it.
In spite of the limitations due to the assumptions made in the case study, the case study still demonstrates the effects of the competition and complementation of air transport and HSR, and compares the differences. 7.2.3. Influence of individual variable Figure 7-5 illustrates the accessibility indices in scenario 1 measured by Logsum and Logsum TT < 5.
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Figure 7-5 Accessibility indices, scenario 1, Logsum and Logsum TT < 5
The difference between Logsum and Logsum TT < 5 is that the latter one only incorporates the destinations to which the travel time is lower than 5 hour from the origins. In this way, the two measures are able to reflect the accessibility impact perceived by two groups of individuals with different attributes: the latter group has higher time sensitivity thus they only value the destinations that allowing a daily return.
Figure 7-5 illustrates the difference between the two groups of individuals. It can be seen that different groups of individuals do perceive different accessibilities from the same transport networks. In the evaluation of accessibility, it is important to clarify of which individual group the accessibility is measured, in order to derive reliable results.
Some other ways can also be used to distinguish different individual groups. For example, applying different parameters in the impedance function can reflect the sensitivity of travel time for different groups of individuals. As shown in Figure 7-6, the red curve is the graph of the negative exponential function used in Logsum, in the function, the value of the parameter 훽 indicates the sensitivity of travel time, and is set as 0.007. When a group of individuals with higher travel time sensitivity (e.g. business travellers against leisure travellers) is measured, the parameter can be set as a higher value, for example, 0.014, as graphed by the blue curve. It can be seen that the blue curve has a larger decreasing rate than the red one does, indicating that these individuals have a higher time sensitivity.
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Figure 7-6 Distance decay of negative exponential function using different values of β
Apply the 훽 value of 0.014 in Logsum, and measure the accessibility of scenario 1, the comparison of the results with the original ones is shown in Figure 7-7.
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Groß-Gerau Hochtaunus Main-Kinzig Main-Taunus Offenbach Wetterau Rheingau-Taunus Frankfurt am Main Frankfurt Rhein-Main
Figure 7-7 Comparison of the results between different 훽 values, Logsum, scenario 1
From Figure 7-7 it can be seen that the difference between the accessibilities measured by accessibility measures using different parameter values can be quite large. Therefore, in the
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Overall, the distinction between individual groups affects the definition of the aggregate travel costs (what elements to be incorporated), the sensitivity for these costs as well as the attraction variables ( 퐷푗 which reflects the quality of the attraction). The methods to distinguish individual groups, such as the selection of parameter values in the accessibility measures, should be carefully determined, ideally based on empirical research of individuals’ behavior. 7.2.4. Social equity In chapter 4, usability of accessibility in social and economic evaluation of transport and land- use has been described. This section discuss the usability of accessibility in revealing social equity as part of the social evaluation. The Lorenz curve is used in this section. In this section the Lorenz curve is a graph showing the cumulative distribution function of the accessibility, which can reveal the distribution inequality in accessibility. The Lorenz curve is graphed by drawing the scatterplot with smooth lines of the data calculated in Equation 7-1 and Equation 7-2.
Equation 7-1 푋푖 = 푃푖/ ∑ 푃푖 푖 Equation 7-2 푌푖 = (퐴푖 ∗ 푃푖)/ ∑(퐴푖 ∗ 푃푖) 푖 where 퐴푖 represents the accessibility of area i and 푃푖 represents the population of area i. Figure-7-8 shows the Lorenz curves of Franfurt Rhein-Main area and Frankfurt am Main, and the equal distribution Lorenz curve.
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Figure-7-8 The lorenz curves of of Frankfurt Rhein-Main area and Frankfurt am Main, scenario 1, Logsum
Compare the Lorenz curves of Franfurt Rhein-Main area and Frankfurt am Main to the equal distribution Lorenz curve, it can be seen the accessibilities are relatively equally distributed in both areas, and Frankfurt am Main have more equally distributed accessibility than Frankfurt Rhein-Main area does. The reasons are that Frankfurt Rhein-Main area (including Frankfurt am Main) owns a highly developed urban public transport system, improving the cohesion of the whole area. And as the central urban area of Frankfurt Rhein-Main area, Frankfurt am Main is more geographical cohesive, and owns a more intensive public transport system than Frankfurt Rhein-Main area.
This is only a preliminary try to link the accessibility evaluation to the social evaluation. As further steps, for example, the changes of distribution of accessibility over a period of time can be analysed or estimated, to evaluate the development trend of social equity, or the distribution of accessibility can be compared to the distribution of GDP or other social and economic indicators to explore the relationship between different aspects of social development.
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7.2.5. Aggregation vs. disaggregation The accessibility of a region can be measured in both aggregate and disaggregate way, of which disaggregate way of measuring leads to more realistic result.
Table 7-5 Comparison of weighted average and aggregate results of Frankfurt am Main, accessibility measure 1
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Weighted 118 37 142 139 average result Aggregate result 148 44 205 200
Table 7-5 gives the comparison of the accessibilities of Frankfurt am Main respectively measured by taking the weighted average of the accessibilities of the subdivisions and taking the region as a homogeneous area.
It can be seen that the accessibility measured by the latter way is higher than that measured by the first one. The reason is that when the whole area is taken into account as an entirety, the origins of all the individuals are abstractly assumed to be the centre of the area, in this case the access time to the airport and the HSR station of many individuals who live relatively far from the centre of the area decreases significantly, due to the highly developed public transport system in the centre. Therefore, in the case study, the aggregate measurement will overestimate the accessibility impact, and fail to reflect the distribution of the accessibility over the whole region (the quality of the access transport service in each part of the region). 7.3. Conclusion of the case study The case study does not consider a scenario, in which the studied region is served by an airport, a HSR station in city centre, and a HSR station integrated in the airport, due to time restriction. But the accessibility of such scenario is quite predicable according to existing results. The accessibilities of the subdivisions in such a case will be slightly higher than those in the third and fourth scenarios, since the two HSR stations make HSR service more accessible to the whole region than only one does. However since the service of the two stations is considered to be identical, major improvement cannot be expected. Moreover, although the method of logsum composite cost capture the phenomenon of the complementation of the two modes, in this case study, it does not make a difference between the HSR stations in the city centre and in the airport. In other words, although the HSR station in the airport can improve the complementation of air transport and HSR, the improvement cannot be explicitly measured in the case study.
This chapter presents the results of the case study, and the findings from the results. The findings can be concluded as follows.
Air transport provides better destination accessibility to big cities for individuals in Frankfurt Rhein-Main area than HSR does. Adding air transport to HSR improves the accessibility of the area and it is also the case vice versa. In the case of adding HSR to existing air transport, differently located HSR stations lead to different added accessibility, indicating the importance of access service of HSR, and this is also the case for air transport.
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The joint accessibility impact of air transport and HSR does not equal to the sum of the impacts of the two modes respectively, due to the interaction of the two modes. When respectively focusing on the competition and complementation of air transport and HSR, the influence of the interaction are different, hence in order to obtain realistic result, both parts of the interaction need to be effectively taken into account. Individuals’ differences have significant impact on the accessibility. Individuals with different characteristics and travelling for various purposes perceive different accessibility levels from the same transport service. Accessibility can be potentially linked to social equity appraisal, according to a preliminary attempt in the case study. It turns out that Frankfurt Rhein-Main has a quite equal distribution of accessibility. In the evaluation of accessibility, higher disaggregate level leads to more accurate measurement of accessibility.
It can be concluded from the findings that it is feasible to evaluate the accessibility impact of combined air transport and HSR networks. The evaluation is able to provide some potentially useful implications for decision makers, for example: the locations of the air transport and HSR influence the level of accessibility, and the distribution of accessibility helps to determine whether there is a need to improve the access transport networks in certain areas. However, due to the assumptions in the case study, some important aspects are not covered in the case study:
In the case study, destination accessibility is measured, and only the ones that have a population higher than 0.5 million are included. It can be seen from Table 6-3 that these destinations are distributed all over Europe, and many are far away (over 500 kilometres) from the studied region. Due to this limitation, the accessibility impact of HSR is probably be underestimated, since HSR is mainly competitive to air transport on the route under 500 kilometres (Dobruszkes, Dehon, & Givoni, 2014). In real life evaluation, the destinations should be carefully selected, in order to reflect the real perception of individuals. Travel time is used as the proxy of travel cost, and other important factors are neglected, such as price, frequency, and comfort, etc. The exclusion of price is a main defect of the case study. However, it is not clear how the results will change if price is included, because the price of air transport and HSR is quite dynamic due to the price management of the operators, and the comparison of the price of the two modes is difficult to predict. The exclusion of frequency and comfort is mainly due to the lack of effective method to generalize them. The interaction of air transport and HSR is addressed by methods from literature (fastest mode and logsum composite cost). However, the competition of the two modes is not simply that the one with higher cost is entirely substituted by the one with lower cost (the method of fastest mode), and whether logsum composite cost can effectively reflect the influence of the interaction needs further verification. Further research is needed to explore the effects. Individual variable is only superficially addressed in the case study. Firstly, individual groups are not categorized, all the individuals are assumed to own the same characteristics. Secondly, travel purpose is not differentiated. Thirdly, the values of the parameters in the accessibility measures are derived from literature, instead of relevant empirical study, thus the accessibility measured in the case study cannot realistically reflect the need of individuals. By comparing the results of the first and 70
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the third accessibility measures, the influence of the need of different individual groups can be demonstrated to some extent, but it is far from enough to obtain realistic results. The disaggregate level of the case study is not sufficient to reflect the accessibility of the studied region. The studied region is divided into subdivisions based on administrative division, the individuals in each subdivisions are aggregated in the centres of the subdivisions. This is another main defect of the case study. Thanks to the GIS technology, now it is possible to disaggregate the studied region to small segmentations for more accurate results. The transport and land-use systems are assumed to be constant in all the scenarios in the case study, in fact, the case study measures the accessibility of the studied region of a fixed year instead of over years. However, in order to understand the impact of e.g. a new HSR station, it is important to know the accessibility impact over certain years. In reality, transport and land-use changes resulted from interaction of transport and land-use systems and interaction of air transport and HSR can affect the accessibility, thus further work can be done to measure the changes. For example, the locations of airports and HSR stations might change the residential pattern. The accessibility impact distribution is used to reflect the social equity in the case study, which aims at showing how the accessibility impact can be used in social evaluation. Without reliable references and deeper analysis, the results of the case study cannot sufficiently indicate the equity level of the studied region. As for the economic evaluation, the case study does not attempt to examine the usability of accessibility in it. The usability of the accessibility impact in social and economic evaluation needs further study.
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8. Conclusion and further research directions 8.1. Conclusion Accessibility is a widely used concept in the planning and evaluation of transport and land-use projects. However, studies on the joint impact of air transport and HSH networks in terms of accessibility are rarely seen in literature, in spite of its value in the planning and evaluation of air transport and HSR projects. In this context, this thesis performs an explorative research on how to evaluate the accessibility impact of combined air transport and HSR networks.
Theoretically, the thesis develops an evaluation framework, following which feasible accessibility measures can be selected to evaluate the accessibility impact of combined air transport and HSR networks in different conditions. Compared to extant studies on accessibility, the thesis stresses the necessity of incorporating the interaction of multiple modes (in this case, air transport and HSR) in the evaluation. From the supply perspective, the interaction of air transport and HSR has impact on the changes of provided air and HSR service, influencing the transport variables in the evaluation. From the demand perspective, the interaction influences the users’ perceptions of the transport service, which has an influence on the individual variables. Furthermore, the interaction can be divided into competition and complementation, of which the competition leads to changes of transport service and the users’ perceptions of the service (e.g. the introduction of HSR on certain routes might devalue air transport for individuals), while the complementation can enhance the transport service perceived by the users (e.g. integration, option value, etc.). When evaluating the accessibility impact of combined air transport and HSR networks, the effect of air-HSR interaction should be incorporated.
Besides air-HSR interaction, the variables of transport, land-use, individual, time-scale and the usability in social and economic evaluation are also important variables that need to be incorporated in the evaluation of the accessibility impact of combined air transport and HSR networks. The transport, land-use, individual, and air-HSR interaction variables are regarded as calculation variables, which determine the absolute value of the accessibility, while time- scale and usability in social and economic evaluation are regarded as evaluation variables, which influence the approach and data collection of the evaluation.
Various types of accessibility measures have been developed for accessibility evaluation, each type of measures has its advantages and disadvantages, and applicability in different conditions. Synthesizing the characteristics and the requirements of the accessibility evaluation, potential accessibility measures and utility-based accessibility measures have relatively better trade-off between ideally combining the variables, and applicability and interpretability. Nevertheless, other types of accessibility measures have applicability in evaluating certain aspects of the accessibility impact of the combined air transport and HSR networks. For example, when focusing on how the transport component of accessibility impact, one can use infrastructure-based measure to evaluate the changes of air transport and HSR services in different scenarios. A summary of the feasibility of the accessibility measures can be seen in Table 5-2.
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In the case study of the thesis, practically, the feasibility of potential measures in the evaluation of the accessibility impact of combined air transport and HSR networks is demonstrated. It is shown that these measures are relatively easy to interpret and can be applied rather easily, due to their simpler form and lower data need than more complicated utility-based measures. Moreover, potential measures are able to combine transport, land- use, individual, and air-HSR interaction, which enables them to capture a relatively full picture of the accessibility impact.
By using the methods of minimum cost and logsum composite cost in the impedance functions, potential measures capture the characteristics of the competition and complementation of air transport and HSR in a mutual market. Some of the findings of the case study are quite straightforward, in terms of destination accessibility to main cities in Europe, air transport is more advantageous than HSR for the region of Frankfurt Rhein-Main area, and combined air transport and HSR indeed improve the accessibility of the region. As for the air-HSR interaction, when the complementation is measured, the accessibility impact is higher than that when only the competition is measured. Meanwhile, the case study demonstrates the usability of the distribution of the accessibility over the region in social equity evaluation by the method of Lorenz curve, which shows that the city of Frankfurt am Main has more equally distributed accessibility than Frankfurt Rhein-Main area does, indicating the importance of access service in air transport and HSR services.
Some practical implications can be learned from the case study. Firstly, it is necessary to ensure the corresponding supportive access service in the development of air transport and HSR projects. Secondly, to improve the accessibility impact of combined air transport and HSR networks, policy makers can try to enhance the cooperation of the two modes, for example, by smoothing the transfer process between flights and high-speed trains in an integrated air- HSR terminal. For the countries where new air transport and HSR projects are rapidly being or will be developed, for example China, negative effects of the competition of air transport and HSR can be decreased and positive influences of the complementation can be enhanced already in the planning stage of the projects to improve the accessibility impact.
The theoretical framework developed in the thesis is generally practical. However, the evaluation process applied in the case study cannot be easily transferred to another region. For example, in practical, because of the different spatial scale, and transport development strategy, the studied regions of air transport and HSR can be quite different in different place. For instance, although located near the city centre of Shanghai, Shanghai Hongqiao Airport serves a significant proportion of national passengers from Jiangsu Province, who access the airport by car, HSR and long distance bus. Thus studied region in this case should not be defined by the administrative boundary. Also, the perceptions of individuals can vary over different countries, hence the values of the parameters in the accessibility measures need careful determination.
Some issues are not addressed in this thesis. Compared to potential accessibility measures, utility-based accessibility measures, for example logsum measures, are more theoretically ideal. Furthermore, logsum accessibility indices can be easily expressed in monetary unit, which makes them potentially useful in economic evaluation (e.g. cost-benefit analysis) of air transport and HSR networks. Due to time restriction and data availability, utility-based measures are not applied in this thesis, in future research, the feasibility of utility-based measures can be explored in case study. In the case study, the scenario with multiple airports or multiple HSR stations are not included, however in reality, multiple airports and HSR 73
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou stations can be observed in many metropolitan regions. The accessibility impact can obviously be improved in this case, but whether it justifies the construction of multiple airports and HSR stations can still be unclear. Some studies regard multiple airports as one single entirety in the evaluation, which is doable, but further study is needed to examine the differences when they are separately considered.
In the next section, some further research directions of this topic are discussed, to guide future research on this topic. In spite of the theoretical and practical limitations of the thesis, it is a promising start of the evaluation of the accessibility impact of combined air transport and HSR networks. 8.2. Further research directions This section discusses the further research directions under this topic. Mainly four directions are concluded. 8.2.1. Theoretical improvement of the evaluation The practice of evaluating accessibility impact of combined air transport and HSR networks can be much improved by theoretically applying more advanced measures that can be computed with state-of the practice data. An initial step towards improved accessibility evaluations is to apply more advanced potential accessibility measures with more disaggregate and comprehensive data.
Furthermore, there is a need for more research on the application of utility-based accessibility measures in the evaluation, especially for the evaluations that economic costs and benefits are needed to be measured. Compared to potential accessibility measures, the applications of utility-based accessibility measures in practical evaluations seem to be surprisingly little, despite their theoretical advantages. Logsum measures, for example, as one type of utility- based measures, can be applied in evaluations of policy changes of air transport and HSR projects. 8.2.2. Interpretability and communicability Literature shows a trend towards more complex and disaggregated accessibility measures, partly in response to the recognition that the aggregate measures lack many important details, and consequently cannot reflect realistic situations. The results of the case study also demonstrate this to some extent. However, two matters still need attention. Firstly, feasible accessibility measures need to be developed for the topic of this thesis. Secondly, increased complexity increases the effort for calculations and the difficulty of interpretation and communication between researchers and decision makers. For effective evaluations of air transport and HSR related policies, there is clearly a need for accessibility measures that are relatively easy to interpret for both researchers and decision makers, and which can be applied with state-of-the practice data.
The interpretations of more complex accessibility measures can firstly be improved by comparing the accessibility indices across place, time or both place and time, rather than focusing on absolute levels of accessibility. Secondly, the interpretation can be much improved by measuring the separate influence of different components of accessibility. For example, sometimes evaluation of the influence of travel time changes can still be effective in policy making process. Thirdly, the accessibility indices can be attempted to be transferred to
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou monetary value, to make it more interpretable and communicable in the evaluations, for example, by using utility-based accessibility measures. 8.2.3. Influence of the interaction of air transport and HSR This thesis stresses the importance of combining the interaction of air transport and HSR in the accessibility evaluation. However, in literature, the interaction of the two modes has not been sufficiently studied, especially in terms of its influence in the accessibility impact. Further research on the interaction of air transport and HSR from supply (supply of air transport and HSR services, e.g. routes, seats, and frequency) and demand (perceptions of individuals) perspectives can be performed, so that the influence of the interaction on the accessibility impact can be more accurately measured, and more effective policies can be made to improve the accessibility impact by optimizing the interaction of the two modes.
Influence of the perception of individuals and companies (demand side) of the interaction of air transport and HSR on the accessibility impact From demand perspective, literature on air-HSR interaction mainly address the interaction by the method of passenger mode choice modelling using information from empirical research. Although there have already been some research on the demand-side interaction of air transport and HSR (as shown in the last paragraph), and the choice behaviour and preference of passengers have been explored in some routes and regions, the influence of such interaction has not been explicitly explored.
Further research can be performed on how users and non-users in a region perceive the (potential) air transport and HSR service in a mutual market. The findings from the research on individuals’ preference and behaviour can be combined with accessibility measures in the evaluation of the accessibility impact. For example, using state-of-choice survey and discrete choice models, the impact variables of the travellers’ choice of air transport and HSR can be identified, which can be used to improve the construction of generalized costs in accessibility measures. Furthermore, research on the perception of companies (or employers) towards air transport and HSR, and their interaction can also be a direction. Such research can help policy makers to understand the changes of company attraction of the region between different air transport and HSR policies.
Influence of the perception of airlines and rail operators (supply side) of the interaction of air transport and HSR on the accessibility impact From supply perspective of the interaction of air transport and HSR, the perception of airlines and rail operators are focused. Their decisions have a direct and significant impact on the supply of transport service. However, the research do not make a further step, to study the influence of the interaction on the accessibility impact of combined air transport and HSR networks. Although in literature the changes supply of air service have been linked to the introduction of HSR and some of its attributes, there has been no research on how to estimate the changes, and subsequently how these changes influence the accessibility impact. Further research is needed in this direction. There are multiple databases that provide relevant data on the supply of air transport and HSR (e.g. UIC, OAG, etc.), methods such as regression modelling can be performed in such research.
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8.2.4. Social and economic evaluation As stated in chapter 4, accessibility can be potentially used in social and economic evaluation of air transport and HSR projects and policies. The development of air transport and HSR needs large amount of investment in infrastructure construction, thus the social and economic costs and benefits need carefully examination. Although the case study presents a simple example of the application of accessibility distribution in social equity evaluation, and monetary accessibility index can be used in economic evaluation, there are still nearly no applications in the context of air transport and HSR. Further research could focus on the interpretability of the accessibility impact of combined air transport and HSR in terms of social and economic evaluation (e.g. CBA, MCA, etc.).
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10. Appendices The appendices contains the tables of the review of literature on interaction of air transport and HSR, the population of the subdivisions of Frankfurt am Main, the access time, check in time, and egress time of these subdivisions, and the calculation results of the four scenarios.
Synthesis of research on interaction of air transport and HSR Table 10-1 Synthesis of research on interaction of air transport and HSR
Demand Supply Competition Complementation Method Findings
(Albalate, Bel, & X X X Ex-post Impact of HSR on air service frequencies and seats, Fageda, 2015) Empirical distinct between routes with and without a hub airport as an end point and examine the influence of the Regression location of the HSR station, HSR as feeding service in hub airports (Román & Martín, X X Discrete choice Obtain a range of willingness-to-pay values for service 2014) models quality attributes, finding some important results that can be used to infer policy conclusions about the real attractiveness of the Air–HSR integrated alternative. Find that schedule coordination which reduces connecting time will be crucial. (Martín, Román, X X Ex-ante Stressing the significant of access time in the degree of García-Palomares, & Mode choice competitiveness of air-HSR (mode choice), and access Gutiérrez, 2014) model time can affect the market share estimated with
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sampled travellers (Dobruszkes, Dehon, X X X Ex-post Shorter HSR travel times involve less air services, with & Givoni, 2014) Empirical similar impact on both airline seats and flights. This impact quickly drops between 2.0- and 2.5-h HSR travel Regression time. The impact of HSR frequencies is much more limited. Hubbing strategies led by the airlines may lead to the issue of airports being serviced by HSR. (Harvey, Thorpe, X Questionnaire Public attitudes to HSR, six factors are identified: travel Caygill, & Namdeo, security, improvement to road and air, prestige of HSR, 2014) comfort, negative of HSR, usefulness of travel time (Clewlow, Sussman, & X X Ex-post Although improvements in rail travel times have Balakrishnan, 2014) Empirical, resulted in reductions in short-haul air travel, variations econometric in city and airport characteristics significantly influence analysis the substitution between air and rail. This paper also finds that HSR substitution has resulted in a modest Regression reduction in system-wide air travel demand, whereas the expansion of low-cost carriers has led to a significant increase in total European air traffic. (Yang & Zhang, 2012) X X Examine the effect of competition. Airfare decreases in rail speed if the marginal cost of HSR with respect to rail speed is not too large. Whether rail fare increases in rail speed depends on the marginal cost of HSR and the weight of welfare. (Jiménez & Betancor, X X X Ex-post HSR reduces the number of air transport operations by 2012) Regression 17%, and air market share declined in Spain. (Behrens & Pels, X X Ex-post Travel and frequency are the main determinants of 2012) travel behaviour.
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Multinomial and mixed logit choice model (Dobruszkes, 2011) X X Compare the For a given city-pair, the decline in the number of flights overall depends on various conditions, including length of the dynamics in the HST journey and the strategies adopted by the airlines. supply of air Some carriers reduce their supply in terms of the compared to number of seats but increase the number of flights in HSR order to compete more effectively with the HSTs. (Román & Martín, X X Ex-ante choice Stress the important role that access time to terminals 2011) modelling using may play in terms of modal competition between rail RP/SP database and plane for interurban travel passengers (Adler, Pels, & Nash, X X Game theory Justifying the HSR projects in spite of its vast fixed costs. 2010) Provide insight for a cost-benefit analysis. (Román, Espino, & X X Ex-ante choice Cast some doubts on the competition effect of HSR to Martín, 2010) modelling using air service, especially when the air market has already RP/SP database been characterized by a high frequency. (Mao, 2010) X X Ex-post analysis Ticket fare is an important impact factor of the on comparison passenger choice between air service and HSR on of passenger Beijing-Shanghai route. throughput, ticket fares, travel time (Clever & Hansen, X X Access to/egress from HSR affects intermodal 2008) competition. (Martín & Nombela, X X Gravity model Impacts of HSR vary by spatial location of routes, while 2007) to estimate HSR passengers are attracted from air transport. traffic flow,
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Mode choice model to estimate mode split (Román, Espino, & X X Disaggregated Obtain different willingness-to-pay measures for Martín, 2007) mode choice improving service quality. In general, values for travel models using time savings are higher for mandatory trips and data provided increases as the level of comfort falls. Also a high by mixed willingness-to-pay estimate for reductions in delay time, revealed and being higher in the case of high-speed train than for air stated transport. preferences database (Park & Ha, 2006) X X Stated The operation of HSR in a short-haul route would reduce preference the preferences of passengers to use air service. survey (Steer Davies Gleave, X X X Case study, Reductions in air service fares due to introduction of 2006) Operator HSR. Competition between HSR and air transportation is interview less straightforward where air transportation is operated by low-cost carriers. (Gonzáles-Savignat, X X Ex-ante choice Price levels and travel time is determinant factors in the 2004) modelling competition of the two modes. (Wardman, Bristow, X X X Demand Rail is potentially competitive in short haul long distance Toner, & Tweddle, modelling transport market 2002)
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Gini index combined with Lorenz curve as a way to evaluate accessibility distribution The Gini index is a statistical measure of inequality, the Lorenz curve is then used to show the cumulative distribution of accessibility over the cumulative population ordered by increasing share of accessibility (Lucas, van Wee, & Maat, 2015): the Gini index is the area between the line of equal distribution and the Lorenz curve, divided by the triangle covering the x-axe, the y-axe and the line of equal distribution. The value will move from 0 (all accessibility is allocated to one person or area) to 1 (accessibility is equally distributed), as shown in Figure 10-1. For more details, see (Lucas, van Wee, & Maat, 2015).
100 %
e lin
n f o i o t
u e
b r ri y a t t s i l i h i
d s
l b i a Gini-index e u s v s q i t
E e a c l c u Ans a m u C
Lorenz curve As Xs B
Cumulative share of people 100 % from low to high level of accessibility Figure 10-1 The Gini index and Lorenz curve applied to the concept of sufficientarianism27
27 Sufficientarianism assumes that everybody should be well off up to a certain minimum threshold, which is ‘sufficient’ for fulfilling their basic needs and to guarantee their continued wellbeing (Lucas, van Wee, & Maat, 2015). 88
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Accessibility and economic evaluation In (Fürst, et al., 1999), accessibility is used as a variable in the production function, so that the accessibility change is linked to the growth of GDP. The framework can be shown in Figure 10-2.
Regional accessibility Regional accessibility in year n in year n+1
Regional GDP in year n
Figure 10-2 Accessibility and GDP28
This relationship can be expressed by Equation 10-1:
Equation 10-1 퐺푖(푡) = 푓(퐶푖(푡), 퐿푖(푡), 퐴푖(푡), 푅푖(푡)) where 퐺푖(푡) is the regional GDP of region i in year t; 퐶푖(푡), 퐿푖(푡), 퐴푖(푡), 푅푖(푡) are respectively the collective industry endowment, the labour factor, accessibility, and the residual factors of region i in year t, keeping the other variables constant, regional GDP is a function of accessibility.
Another method is to use accessibility as consumer surplus, so that accessibility can be used in commonly used appraisal method such as CBA. The logsum accessibility measure can be linked to micro-economic theory, allowing for the calculation of consumer surplus, providing another direction to link accessibility to economic evaluation (Geurs & van Wee, 2004), this will be introduced in more detail in the next chapter.
28 Adapted from (Wegener & Bokemann, 1998) 89
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Settings, data, and original results of the case study Table 10-2 Population of the subdivisions of Frankfurt am Main
Subdivision Population Altstadt 3,473 Bahnhofsviertel 2,159 Bankenviertel 2,125 Bergen-Enkheim 17,808 Berkersheim 3,384 Bockenheim 33,067 Bonames 6,311 Bornheim 26,332 Dornbusch 18,413 Eckenheim 14,287 Eschersheim 14,693 Fechenheim 15,962 Flughafen 218 Frankfurter Berg 6,863 Gallus 25,843 Ginnheim 16,450 Griesheim 22,229 Gutleutviertel 5,738 Harheim 4,039 Hausen 7,133 Heddernheim 16,232 Hochst 13,723 Innenstadt 6,550 Kalbach-Riedberg 7,232 Nied 17,641 Nieder-Erlenbach 4,577 Nieder-Eschbach 11,572 Niederrad 22,667 Niederursel 16,169 Nordend 54,432 Oberrad 12,662 Ostend 26,547 Praunheim 15,730 Preungesheim 12,853 Riederwald 4,824 Rodelheim 17,504 Sachsenhausen 55,785 Schwanheim 20,127 Seckbach 10,079
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Sindlingen 8,940 Sossenheim 15,664 Unterliederbach 14,127 Westend 25,550 Zeilsheim 11,914
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Table 10-3 Access time, check in time and Egress time to the airport and HSR station from each subdivisions
Access time (h) Check in time Egress time (h) Airport Hbf HSR in airport in airport Airport Hbf Groß-Gerau 0.550 0.350 0.550 1.000 0.500 0.000 Hochtaunus 0.880 0.533 0.880 1.000 0.500 0.000 Main-Kinzig 1.380 1.000 1.380 1.000 0.500 0.000 Main-Taunus 1.000 0.717 1.000 1.000 0.500 0.000 Offenbach 0.500 0.300 0.500 1.000 0.500 0.000 Wetterau 1.783 1.383 1.783 1.000 0.500 0.000 Rheingau-Taunus 0.700 1.000 0.700 1.000 0.500 0.000 Frankfurt am Main 0.167 0.000 0.167 1.000 0.500 0.000 Altstadt 0.350 0.167 0.350 1.000 0.500 0.000 Bahnhofsviertel 0.267 0.100 0.267 1.000 0.500 0.000 Bankenviertel 0.267 0.117 0.267 1.000 0.500 0.000 Bergen-Enkheim 0.833 0.467 0.833 1.000 0.500 0.000 Berkersheim 0.667 0.400 0.667 1.000 0.500 0.000 Bockenheim 0.533 0.233 0.533 1.000 0.500 0.000 Bonames 0.967 0.700 0.967 1.000 0.500 0.000 Bornheim 1.133 0.683 1.133 1.000 0.500 0.000 Dornbusch 0.567 0.300 0.567 1.000 0.500 0.000 Eckenheim 0.583 0.317 0.583 1.000 0.500 0.000 Eschersheim 0.567 0.300 0.567 1.000 0.500 0.000 Fechenheim 1.000 0.667 1.000 1.000 0.500 0.000 Flughafen 0.000 0.467 0.000 1.000 0.500 0.000 Frankfurter Berg 0.667 0.400 0.667 1.000 0.500 0.000 Gallus 0.367 0.100 0.367 1.000 0.500 0.000 Ginnheim 0.350 0.333 0.350 1.000 0.500 0.000 Griesheim 0.450 0.133 0.450 1.000 0.500 0.000 Gutleutviertel 0.367 0.100 0.367 1.000 0.500 0.000 Harheim 0.833 0.650 0.833 1.000 0.500 0.000 Hausen 0.733 0.550 0.733 1.000 0.500 0.000 Heddernheim 0.717 0.450 0.717 1.000 0.500 0.000 Hochst 0.533 0.217 0.533 1.000 0.500 0.000 Innenstadt 0.283 0.100 0.283 1.000 0.500 0.000 Kalbach-Riedberg 0.650 0.383 0.650 1.000 0.500 0.000 Nied 0.600 0.283 0.600 1.000 0.500 0.000 Nieder-Erlenbach 0.883 0.383 0.883 1.000 0.500 0.000 Nieder-Eschbach 0.667 0.400 0.667 1.000 0.500 0.000 Niederrad 0.333 0.350 0.333 1.000 0.500 0.000 Niederursel 0.617 0.350 0.617 1.000 0.500 0.000 Nordend 0.633 0.433 0.633 1.000 0.500 0.000 Oberrad 0.717 0.533 0.717 1.000 0.500 0.000
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Ostend 0.383 0.200 0.383 1.000 0.500 0.000 Praunheim 0.617 0.350 0.617 1.000 0.500 0.000 Preungesheim 0.617 0.350 0.617 1.000 0.500 0.000 Riederwald 0.500 0.317 0.500 1.000 0.500 0.000 Rodelheim 0.733 0.383 0.733 1.000 0.500 0.000 Sachsenhausen 0.500 0.300 0.500 1.000 0.500 0.000 Schwanheim 0.950 0.633 0.950 1.000 0.500 0.000 Seckbach 0.733 0.550 0.733 1.000 0.500 0.000 Sindlingen 0.850 0.450 0.850 1.000 0.500 0.000 Sossenheim 1.117 0.683 1.117 1.000 0.500 0.000 Unterliederbach 0.733 0.417 0.733 1.000 0.500 0.000 Westend 0.417 0.217 0.417 1.000 0.500 0.000 Zeilsheim 0.850 0.533 0.850 1.000 0.500 0.000
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Table 10-4 Results of scenario 1
Logsum Logsum_N Minimum Logsum Subdivisions Logsum Minimum TT < 5 o _No TT < 5_No Groß-Gerau 1.72E+07 1.72E+07 1.63E+07 122 122 116 Hochtaunus 1.46E+07 1.46E+07 1.23E+07 103 103 87 Main-Kinzig 1.13E+07 1.13E+07 8.61E+06 80 80 61 Main-Taunus 1.37E+07 1.37E+07 1.12E+07 97 97 79 Offenbach 1.77E+07 1.77E+07 1.67E+07 125 125 119 Wetterau 9.25E+06 9.25E+06 4.30E+06 66 66 30 Rheingau-Taunus 1.60E+07 1.60E+07 1.39E+07 113 113 99 Frankfurt am Main 1.67E+07 1.67E+07 1.51E+07 118 118 107 Frankfurt Rhein- Main (aggregate) 1.41E+07 1.41E+07 1.17E+07 100 100 83 Subdivisions of Frankfurt am Main Altstadt 1.90E+07 1.90E+07 1.86E+07 135 135 132 Bahnhofsviertel 1.99E+07 1.99E+07 1.94E+07 141 141 137 Bankenviertel 1.99E+07 1.99E+07 1.94E+07 141 141 137 Bergen-Enkheim 1.49E+07 1.49E+07 1.26E+07 106 106 89 Berkersheim 1.62E+07 1.62E+07 1.41E+07 115 115 100 Bockenheim 1.74E+07 1.74E+07 1.65E+07 123 123 117 Bonames 1.40E+07 1.40E+07 1.14E+07 99 99 81 Bornheim 1.28E+07 1.28E+07 1.04E+07 91 91 73 Dornbusch 1.71E+07 1.71E+07 1.62E+07 121 121 115 Eckenheim 1.69E+07 1.69E+07 1.48E+07 120 120 105 Eschersheim 1.71E+07 1.71E+07 1.62E+07 121 121 115 Fechenheim 1.37E+07 1.37E+07 1.12E+07 97 97 79 Flughafen 2.27E+07 2.27E+07 2.26E+07 161 161 160 Frankfurter Berg 1.62E+07 1.62E+07 1.41E+07 115 115 100 Gallus 1.89E+07 1.89E+07 1.84E+07 134 134 131 Ginnheim 1.90E+07 1.90E+07 1.86E+07 135 135 132 Griesheim 1.81E+07 1.81E+07 1.77E+07 128 128 125 Gutleutviertel 1.89E+07 1.89E+07 1.84E+07 134 134 131 Harheim 1.49E+07 1.49E+07 1.26E+07 106 106 89 Hausen 1.57E+07 1.57E+07 1.37E+07 111 111 97 Heddernheim 1.58E+07 1.58E+07 1.38E+07 112 112 98 Hochst 1.74E+07 1.74E+07 1.65E+07 123 123 117 Innenstadt 1.97E+07 1.97E+07 1.92E+07 140 140 136 Kalbach-Riedberg 1.64E+07 1.64E+07 1.43E+07 116 116 101 Nied 1.68E+07 1.68E+07 1.46E+07 119 119 104 Nieder-Erlenbach 1.46E+07 1.46E+07 1.23E+07 103 103 87 Nieder-Eschbach 1.62E+07 1.62E+07 1.41E+07 115 115 100 Niederrad 1.92E+07 1.92E+07 1.88E+07 136 136 133
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Niederursel 1.67E+07 1.67E+07 1.45E+07 118 118 103 Nordend 1.65E+07 1.65E+07 1.44E+07 117 117 102 Oberrad 1.58E+07 1.58E+07 1.38E+07 112 112 98 Ostend 1.87E+07 1.87E+07 1.83E+07 133 133 130 Praunheim 1.67E+07 1.67E+07 1.45E+07 118 118 103 Preungesheim 1.67E+07 1.67E+07 1.45E+07 118 118 103 Riederwald 1.77E+07 1.77E+07 1.67E+07 125 125 119 Rodelheim 1.57E+07 1.57E+07 1.37E+07 111 111 97 Sachsenhausen 1.77E+07 1.77E+07 1.67E+07 125 125 119 Schwanheim 1.41E+07 1.41E+07 1.15E+07 100 100 81 Seckbach 1.57E+07 1.57E+07 1.37E+07 111 111 97 Sindlingen 1.48E+07 1.48E+07 1.25E+07 105 105 88 Sossenheim 1.29E+07 1.29E+07 1.04E+07 92 92 74 Unterliederbach 1.57E+07 1.57E+07 1.37E+07 111 111 97 Westend 1.84E+07 1.84E+07 1.80E+07 131 131 127 Zeilsheim 1.48E+07 1.48E+07 1.25E+07 105 105 88
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Table 10-5 Results of scenario 2
Minimu Logsum Logsum_N Minimum_ Logsum TT Subdivisions Logsum m TT < 5 o No < 5_No Groß-Gerau 5.21E+06 5.21E+06 4.24E+06 37 37 30 Hochtaunus 4.75E+06 4.75E+06 3.75E+06 34 34 27 Main-Kinzig 3.75E+06 3.75E+06 2.39E+06 27 27 17 Main-Taunus 4.33E+06 4.33E+06 3.42E+06 31 31 24 Offenbach 5.34E+06 5.34E+06 4.35E+06 38 38 31 Wetterau 3.09E+06 3.09E+06 1.45E+06 22 22 10 Rheingau-Taunus 3.75E+06 3.75E+06 2.39E+06 27 27 17 Frankfurt am Main 5.20E+06 5.20E+06 4.21E+06 37 37 30 Frankfurt Rhein- Main 4.38E+06 4.38E+06 3.21E+06 31 31 23 Subdivisions of Frankfurt am Main Altstadt 5.71E+06 5.71E+06 4.65E+06 40 40 33 Bahnhofsviertel 5.91E+06 5.91E+06 4.81E+06 42 42 34 Bankenviertel 5.86E+06 5.86E+06 4.77E+06 42 42 34 Bergen-Enkheim 4.91E+06 4.91E+06 4.00E+06 35 35 28 Berkersheim 5.08E+06 5.08E+06 4.14E+06 36 36 29 Bockenheim 5.52E+06 5.52E+06 4.50E+06 39 39 32 Bonames 4.36E+06 4.36E+06 3.45E+06 31 31 24 Bornheim 4.40E+06 4.40E+06 3.48E+06 31 31 25 Dornbusch 5.34E+06 5.34E+06 4.35E+06 38 38 31 Eckenheim 5.29E+06 5.29E+06 4.31E+06 38 38 31 Eschersheim 5.34E+06 5.34E+06 4.35E+06 38 38 31 Fechenheim 4.44E+06 4.44E+06 3.51E+06 31 31 25 Flughafen 4.91E+06 4.91E+06 4.00E+06 35 35 28 Frankfurter Berg 5.08E+06 5.08E+06 4.14E+06 36 36 29 Gallus 5.91E+06 5.91E+06 4.81E+06 42 42 34 Ginnheim 6.21E+06 6.21E+06 5.06E+06 44 44 36 Griesheim 5.81E+06 5.81E+06 4.73E+06 41 41 34 Gutleutviertel 5.91E+06 5.91E+06 4.81E+06 42 42 34 Harheim 4.48E+06 4.48E+06 3.54E+06 32 32 25 Hausen 4.71E+06 4.71E+06 3.72E+06 33 33 26 Heddernheim 4.95E+06 4.95E+06 4.03E+06 35 35 29 Hochst 5.57E+06 5.57E+06 4.54E+06 39 39 32 Innenstadt 5.91E+06 5.91E+06 4.81E+06 42 42 34 Kalbach-Riedberg 5.12E+06 5.12E+06 4.17E+06 36 36 30 Nied 5.38E+06 5.38E+06 4.39E+06 38 38 31 Nieder-Erlenbach 5.12E+06 5.12E+06 4.17E+06 36 36 30 Nieder-Eschbach 5.08E+06 5.08E+06 4.14E+06 36 36 29
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Niederrad 5.21E+06 5.21E+06 4.24E+06 37 37 30 Niederursel 5.21E+06 5.21E+06 4.24E+06 37 37 30 Nordend 4.99E+06 4.99E+06 4.07E+06 35 35 29 Oberrad 4.75E+06 4.75E+06 3.75E+06 34 34 27 Ostend 5.61E+06 5.61E+06 4.57E+06 40 40 32 Praunheim 5.21E+06 5.21E+06 4.24E+06 37 37 30 Preungesheim 5.21E+06 5.21E+06 4.24E+06 37 37 30 Riederwald 5.29E+06 5.29E+06 4.31E+06 38 38 31 Rodelheim 5.12E+06 5.12E+06 4.17E+06 36 36 30 Sachsenhausen 5.34E+06 5.34E+06 4.35E+06 38 38 31 Schwanheim 4.51E+06 4.51E+06 3.57E+06 32 32 25 Seckbach 4.71E+06 4.71E+06 3.72E+06 33 33 26 Sindlingen 4.95E+06 4.95E+06 4.03E+06 35 35 29 Sossenheim 4.40E+06 4.40E+06 3.48E+06 31 31 25 Unterliederbach 5.03E+06 5.03E+06 4.10E+06 36 36 29 Westend 5.57E+06 5.57E+06 4.54E+06 39 39 32 Zeilsheim 4.75E+06 4.75E+06 3.75E+06 34 34 27
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Table 10-6 Results of scenario 3
Minimu Logsum Logsum_N Minimum_ Logsum TT Subdivisions Logsum m TT < 5 o No < 5_No Groß-Gerau 2.06E+07 1.87E+07 1.93E+07 146 132 137 Hochtaunus 1.77E+07 1.59E+07 1.49E+07 126 113 106 Main-Kinzig 1.38E+07 1.24E+07 1.04E+07 98 88 73 Main-Taunus 1.66E+07 1.49E+07 1.36E+07 117 106 96 Offenbach 2.11E+07 1.91E+07 1.98E+07 150 136 140 Wetterau 1.13E+07 1.01E+07 5.43E+06 80 72 38 Rheingau-Taunus 1.83E+07 1.69E+07 1.56E+07 130 120 110 Frankfurt am Main 2.01E+07 1.81E+07 1.81E+07 142 128 128 Frankfurt Rhein- Main 1.70E+07 1.53E+07 1.40E+07 120 109 99 Subdivisions of Frankfurt am Main Altstadt 2.27E+07 2.06E+07 2.18E+07 161 146 155 Bahnhofsviertel 2.37E+07 2.15E+07 2.27E+07 168 152 161 Bankenviertel 2.36E+07 2.15E+07 2.27E+07 168 152 161 Bergen-Enkheim 1.82E+07 1.63E+07 1.54E+07 129 116 109 Berkersheim 1.96E+07 1.77E+07 1.70E+07 139 125 121 Bockenheim 2.10E+07 1.89E+07 1.96E+07 149 134 139 Bonames 1.68E+07 1.52E+07 1.38E+07 119 108 98 Bornheim 1.58E+07 1.41E+07 1.28E+07 112 100 91 Dornbusch 2.06E+07 1.86E+07 1.92E+07 146 132 136 Eckenheim 2.04E+07 1.84E+07 1.78E+07 145 131 126 Eschersheim 2.06E+07 1.86E+07 1.92E+07 146 132 136 Fechenheim 1.66E+07 1.50E+07 1.36E+07 118 106 97 Flughafen 2.57E+07 2.39E+07 2.52E+07 182 169 179 Frankfurter Berg 1.96E+07 1.77E+07 1.70E+07 139 125 121 Gallus 2.27E+07 2.05E+07 2.18E+07 161 146 155 Ginnheim 2.31E+07 2.08E+07 2.22E+07 164 148 157 Griesheim 2.19E+07 1.98E+07 2.10E+07 155 140 149 Gutleutviertel 2.27E+07 2.05E+07 2.18E+07 161 146 155 Harheim 1.78E+07 1.62E+07 1.50E+07 126 115 106 Hausen 1.88E+07 1.70E+07 1.62E+07 133 120 115 Heddernheim 1.91E+07 1.72E+07 1.66E+07 135 122 118 Hochst 2.10E+07 1.89E+07 1.97E+07 149 134 139 Innenstadt 2.35E+07 2.13E+07 2.26E+07 167 151 160 Kalbach-Riedberg 1.97E+07 1.78E+07 1.72E+07 140 126 122 Nied 2.03E+07 1.83E+07 1.77E+07 144 130 126 Nieder-Erlenbach 1.80E+07 1.61E+07 1.52E+07 127 114 108 Nieder-Eschbach 1.96E+07 1.77E+07 1.70E+07 139 125 121
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Niederrad 2.25E+07 2.06E+07 2.16E+07 160 146 153 Niederursel 2.01E+07 1.81E+07 1.75E+07 142 128 124 Nordend 1.98E+07 1.79E+07 1.72E+07 140 127 122 Oberrad 1.89E+07 1.71E+07 1.64E+07 134 121 116 Ostend 2.24E+07 2.03E+07 2.15E+07 159 144 152 Praunheim 2.01E+07 1.81E+07 1.75E+07 142 128 124 Preungesheim 2.01E+07 1.81E+07 1.75E+07 142 128 124 Riederwald 2.11E+07 1.91E+07 1.97E+07 150 136 140 Rodelheim 1.91E+07 1.72E+07 1.66E+07 135 122 118 Sachsenhausen 2.11E+07 1.91E+07 1.98E+07 150 136 140 Schwanheim 1.70E+07 1.54E+07 1.40E+07 121 109 99 Seckbach 1.88E+07 1.70E+07 1.62E+07 133 120 115 Sindlingen 1.81E+07 1.62E+07 1.53E+07 128 115 109 Sossenheim 1.59E+07 1.42E+07 1.29E+07 112 101 91 Unterliederbach 1.90E+07 1.71E+07 1.66E+07 135 121 117 Westend 2.20E+07 2.00E+07 2.11E+07 156 141 150 Zeilsheim 1.79E+07 1.62E+07 1.51E+07 127 114 107
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Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
Table 10-7 Results of scenario 4
Minimu Logsum Logsum_N Minimum_ Logsum TT Subdivisions Logsum m TT < 5 o No < 5_No Groß-Gerau 2.02E+07 1.85E+07 1.78E+07 143 131 126 Hochtaunus 1.71E+07 1.56E+07 1.35E+07 121 111 95 Main-Kinzig 1.33E+07 1.21E+07 9.40E+06 94 86 67 Main-Taunus 1.61E+07 1.47E+07 1.22E+07 114 104 87 Offenbach 2.07E+07 1.89E+07 1.83E+07 147 134 130 Wetterau 1.09E+07 9.91E+06 4.83E+06 77 70 34 Rheingau-Taunus 1.87E+07 1.71E+07 1.50E+07 133 121 106 Frankfurt am Main 1.96E+07 1.79E+07 1.66E+07 139 127 118 Frankfurt Rhein- Main 1.66E+07 1.51E+07 1.28E+07 117 107 91 Subdivisions of Frankfurt am Main Altstadt 2.24E+07 2.04E+07 2.03E+07 158 145 144 Bahnhofsviertel 2.33E+07 2.13E+07 2.11E+07 165 151 150 Bankenviertel 2.33E+07 2.13E+07 2.11E+07 165 151 150 Bergen-Enkheim 1.75E+07 1.60E+07 1.39E+07 124 113 98 Berkersheim 1.91E+07 1.74E+07 1.56E+07 135 123 110 Bockenheim 2.04E+07 1.86E+07 1.80E+07 144 132 127 Bonames 1.64E+07 1.50E+07 1.24E+07 116 106 88 Bornheim 1.51E+07 1.38E+07 1.14E+07 107 98 81 Dornbusch 2.00E+07 1.83E+07 1.77E+07 142 130 125 Eckenheim 1.99E+07 1.82E+07 1.62E+07 141 129 115 Eschersheim 2.00E+07 1.83E+07 1.77E+07 142 130 125 Fechenheim 1.61E+07 1.47E+07 1.23E+07 114 104 87 Flughafen 2.67E+07 2.44E+07 2.46E+07 189 173 175 Frankfurter Berg 1.91E+07 1.74E+07 1.56E+07 135 123 110 Gallus 2.22E+07 2.02E+07 2.01E+07 157 144 142 Ginnheim 2.24E+07 2.04E+07 2.03E+07 158 145 144 Griesheim 2.13E+07 1.94E+07 1.93E+07 151 138 137 Gutleutviertel 2.22E+07 2.02E+07 2.01E+07 157 144 142 Harheim 1.75E+07 1.60E+07 1.38E+07 124 113 98 Hausen 1.84E+07 1.68E+07 1.50E+07 131 119 106 Heddernheim 1.86E+07 1.70E+07 1.52E+07 132 120 108 Hochst 2.04E+07 1.86E+07 1.80E+07 144 132 127 Innenstadt 2.31E+07 2.11E+07 2.10E+07 164 150 149 Kalbach-Riedberg 1.92E+07 1.76E+07 1.57E+07 136 124 111 Nied 1.97E+07 1.80E+07 1.61E+07 140 128 114 Nieder-Erlenbach 1.71E+07 1.56E+07 1.35E+07 121 111 96 Nieder-Eschbach 1.91E+07 1.74E+07 1.56E+07 135 123 110
100
Accessibility impact of combined air transport and HSR networks | Tianyi Zhou
Niederrad 2.25E+07 2.06E+07 2.04E+07 160 146 145 Niederursel 1.95E+07 1.78E+07 1.60E+07 139 127 113 Nordend 1.94E+07 1.77E+07 1.58E+07 137 125 112 Oberrad 1.86E+07 1.70E+07 1.51E+07 132 120 107 Ostend 2.20E+07 2.01E+07 1.99E+07 156 142 141 Praunheim 1.95E+07 1.78E+07 1.60E+07 139 127 113 Preungesheim 1.95E+07 1.78E+07 1.60E+07 139 127 113 Riederwald 2.07E+07 1.89E+07 1.83E+07 147 134 130 Rodelheim 1.84E+07 1.68E+07 1.51E+07 131 119 107 Sachsenhausen 2.07E+07 1.89E+07 1.83E+07 147 134 130 Schwanheim 1.65E+07 1.51E+07 1.26E+07 117 107 90 Seckbach 1.84E+07 1.68E+07 1.50E+07 131 119 106 Sindlingen 1.74E+07 1.59E+07 1.38E+07 123 112 98 Sossenheim 1.52E+07 1.39E+07 1.15E+07 108 98 81 Unterliederbach 1.84E+07 1.68E+07 1.51E+07 131 119 107 Westend 2.16E+07 1.97E+07 1.96E+07 153 140 139 Zeilsheim 1.74E+07 1.59E+07 1.37E+07 123 112 97
101