Performance Assessment of Road Transport Network of the Republic of Serbia in a Context of Information Scarcity

Performance assessment of road transport network of the Republic of Serbia in a context of information scarcity

Andrej Manić

Dissertation for obtaining the Degree of Master in

Complex Transport Infrastructure Systems

Jury

President: Professor Maria do Rosário Maurício Ribeiro Macário

Supervisor: Professor Filipe Manuel Mercier Vilaça Moura

Member: Professor Luis Miguel Garrido Martinez

November 2012

This thesis was completed to obtain a Master of Science Degree in Complex Transport Infrastructure Systems,

a part of the MIT Portugal Program

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Acknowledgments

I take this opportunity to express my gratitude to Professor Filipe Moura, for his help, guidance and encouragement during the creation of this thesis. Whenever I needed his advice he was there. Also, I would like to thank all my colleagues at Instituto Superior Técnico, especially Shant, Minas, Nikhil, Aivin and João for their friendship and support, for overcoming obstacles together, and for all the great moments we shared. I am truly grateful to have them in my life.

I am forever grateful to my mother, for her love and support, for believing in me, and for helping me get to where I am today. Also, I must address a special thanks to Miljana, for her endless patience and understanding.

Finally, I would like to dedicate this work to my late father, who helped me make the first steps on the road I’m on today. This is for him.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Abstract

Western Balkans is a region currently undergoing a major process of transition in light of future ascension to the European Union, and Serbia is taking its part in the process. Part of these reforms includes modernizing and reintegrating transport networks, most of which became fragmented and obsolete during the dissolution of the former Yugoslavia. This study aims to examine the road transport system at the country level of the Republic of Serbia, and for that, it will characterize current demand flows of both passenger and freight transportation within the country, while focusing on the current state of the road transport infrastructure, defining major transport routes and corridors, and identifying key weak points and issues that need to be addressed. Methodologically, a road transport model of Serbia is created using VISUM software with the support of ArcGIS package for processing geographical information. Importantly, the study will detail all methodological steps in a context of information scarcity, including network building, data gathering and model calibration. Finally, the modeling approach will enable a scenario analysis regarding future traffic growth and will be the basis for a differential comparison between the current state of transport network and the future foreseeable outcome, after the completion of the country’s official transport master plan, with a special focus on accessibility indicators. Performance analysis and differential comparison was carried out for three scenarios: 1. 2012 baseline scenario; 2. 2027 scenario with demand growth and network improvement; 3. 2027 scenario only with demand growth and no network improvement. Research concludes the existence of several weak points in the network, which, if not treated, will only aggravate the situation due to the forecasted demand growth. Also, several issues regarding accessibility need to be addressed, namely the remote regions along the borders, where situation will not improve sufficiently even with the future projects being successfully carried out, potentially generating geographical equity issues.

Keywords: Pan-European Corridors, 4 step model, accessibility indicators, transport policy, performance analysis, Serbian road network, TEN-T

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Table of contents

Acknowledgments ...... I

Abstract ...... II

Table of contents ...... III

Figure Index ...... V

Table Index ...... VII

Equation Index...... VIII

List of Acronyms ...... IX

1 Introduction ...... 1

1.1 Background and Motivation ...... 1

1.2 Research Questions ...... 4

1.3 Methodology and Dissertation Outline ...... 5

2 Literature review ...... 6

2.1 State of the art in transport modeling ...... 6

2.2 State of the practice in transport modeling ...... 12

2.3 Experience in regional traffic forecasting for Western Balkans ...... 16

3 Presentation of the case study of the Republic of Serbia ...... 19

3.1.1 General statistics and characterization of the transport system ...... 19

3.1.2 TEN-T (Trans-European Networks - Transport) ...... 25

3.1.3 Pan-European Corridors...... 26

3.1.4 SEETO (South East Europe Transport Observatory) ...... 28

3.2 Undergoing projects and planned for near future ...... 29

3.3 General Master Plan for Transport in Serbia (GMTS) project listing ...... 30

3.4 Factors for transport demand growth until 2027 ...... 38

4 National road transport model for Serbia: methodological approach ...... 41

4.1 Data collection and processing ...... 41

4.2 Transport network description ...... 44

4.3 Impedance and volume-delay functions ...... 45

III

Assessment of transport network of the Republic of Serbia in a context of information scarcity

4.4 Demand data input, calibration and testing ...... 49

4.5 Transport demand forecast until 2027 ...... 54

5 Performance analysis and differential comparison...... 56

5.1 Identification of performance problems of the transport system ...... 56

5.2 Differential comparison and discussion of results ...... 59

6 Conclusions ...... 67

6.1 Main highlights of the research ...... 67

6.2 Research limitations and leads for future work ...... 69

7 References ...... 71

8 Annexes ...... I

8.1 Transport map ...... I

8.2 District capitals ...... II

8.3 Action Plan for transport development ...... III

8.4 Road categorization...... V

8.5 GMTS summary map ...... VII

8.6 2012 data summary table ...... VIII

8.7 2027 trips forecast ...... XVI

8.8 Link saturation level comparison ...... XXIII

8.9 K factor (for number of trips) ...... XXIV

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Figure Index

Figure 1: location of Serbia in regard to other European cities ...... 2 Figure 2: Methodology outline ...... 5 Figure 3: 4-step model algorithm (Ortúzar & Luis G. Willumsen 2011) ...... 9 Figure 4: SEETO road network ...... 17 Figure 5: Location of Serbia within Europe (L) and the map of country (R) ...... 19 Figure 6: Districts of Serbia (left), and example of district of South Bačka subdivision into municipalities (right) ...... 20 Figure 7: Development of road network (Ministry of Infrastructure of the Republic of Serbia 2010) ...... 22 Figure 8: Accessibility levels 2011, according to (Laketa et al. 2011) ...... 23 Figure 9: TEN-T(Trans-European Transport Network Executive Agency 2012) ...... 25 Figure 10: Pan-European Corridors in the Balkans ...... 26 Figure 11: Core Road Network (SEETO South-East Europe Transport Observatory 2011b) .... 28 Figure 12: Infrastructural overview (L) and level of development by regions (R) (SIEPA 2012) 30 Figure 13: Belgrade Bypass ...... 30 Figure 14: Project RDA1 ...... 31 Figure 15: Project RDA2 ...... 31 Figure 16: Project RDA3 ...... 32 Figure 17: Project RDA4 ...... 32 Figure 18: Project RDA5 ...... 33 Figure 19: Project RDB6 ...... 33 Figure 20: Project RDC11 ...... 34 Figure 21: Project RDC7 ...... 34 Figure 22: Project RDC8 ...... 35 Figure 23: Project RDB9 ...... 35 Figure 24: Project RDC10 ...... 36 Figure 25: Project RDB12 ...... 36 Figure 26: Project RDC13 ...... 37 Figure 27: Overview of all major projects until 2027 ...... 37 Figure 28: correlation between population and employment per zone ...... 41 Figure 29: Number of daily trips per inhabitant as a function of GDP (Macário et al. 2012) ...... 42 Figure 30: Comparison of AADT from the model (L) and from the counts (R) ...... 50 Figure 31: Pearson correlation for the top 40 links ...... 51 Figure 32: Desire lines for main O-D pairs ...... 52 Figure 33: T-Flow Fuzzy chart (PTV vision 2011) ...... 53 Figure 34: total number of daily trips (2012-2027) ...... 55 Figure 35: Road network (L) and LOS 2012 (R) ...... 56

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Figure 36: Volume of traffic- 2012 ...... 57 Figure 37: 2 hours isochrones starting from Belgrade (L), Novi Sad (M), Niš (R); 2012 ...... 58 Figure 38: 2 hours isochrones over accessibility levels- 2012 ...... 58 Figure 39: Network comparison, 2012 (L) and 2027 (R) ...... 59 Figure 40: Comparison in accessibility from 3 main cities, 2012 (L) and 2027 (R) ...... 60 Figure 41: Link saturation comparison ...... 61 Figure 42: Link saturation comparison 2027, with and without improvements ...... 61 Figure 43: Links with higher volumes of traffic – 2027 ...... 64 Figure 44: Histogram of normalized Hansen indexes ...... 66 Figure 45: Top 5 districts with highest gains in accessibility (Hansen) ...... 66

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Table Index

Table 1: Accessibility by regions (Laketa et al. 2011) ...... 22 Table 2: SWOT analysis of Serbia’s transport sector (Ministry of Infrastructure of the Republic of Serbia 2010) ...... 24 Table 3: GDP rates – Serbia (IMF 2012) ...... 38 Table 4: Projected growth rates by vehicle types (%) (Ministry of Infrastructure of the Republic of Serbia 2010) ...... 39 Table 5: Growth rates- GDP and trips ...... 54 Table 6: link saturation comparison 2027 ...... 62 Table 7: travel time reductions ...... 63 Table 8: Hansen accessibility indexes for districts ...... 65

VII

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Equation Index

Equation 1: Daily trips per inhabitant as f (GDP) ...... 42 Equation 2: Revised trip generation formula ...... 43 Equation 3: Impedance function ...... 45 Equation 4: First rigorous Gravity model (Casey model) ...... 47 Equation 5: Doubly constrained gravity model ...... 47 Equation 6: Actual gravity model used ...... 48 Equation 7: Volume-delay function ...... 48 Equation 8: BPR Volume-delay function ...... 48 Equation 9: Hansen accessibility index ...... 65

VIII

Assessment of transport network of the Republic of Serbia in a context of information scarcity

List of Acronyms

CS Candidate States

EU European Union

FTA Free Trade Agreement

FYROM Former Yugoslav Republic of Macedonia

GC Generalized Cost

GIS Geographical Information System

GMTS General Master Plan for Transport in Serbia

NCHRP National Cooperative Highway Research Program

NHTS National Household Travel Survey

NMS New Member States

O-D Origin-Destination

SEETO South East Europe Transport Observatory

TAZ Traffic Analysis Zone

TDM Traffic Demand Model

TEN-T Trans-European Networks- Transport

VDF Volume Delay Function

1 Introduction

The present chapter introduces the topic, defines the background and motivation before presenting the research question and methodology outline.

1.1 Background and Motivation

Development and improvement of transport infrastructure in the region of Western Balkans plays a key role in regional development and also facilitates regional integration as well as integration into Trans-European Transport Network (TEN-T) and Pan-European Corridors. Western Balkans, at present, is a region undergoing a long process of transition and without a modern transportation system it will continue to lag behind its EU neighbors in economic development. One of these countries is Serbia, and it is facing significant challenges and needs to restore and expand its transport network, and while doing so, ensure integration into the main European transport corridors, and also to promote balanced regional development and good accessibility for the entire country.

Regarding transportation, both the new Member States as well as the Candidate States still have limited, not well integrated, mostly old and improperly maintained infrastructures. All of these countries, including the Balkan states, need to accelerate and try to reach Western European levels of infrastructure development, safety, environmental protection, etc., so that they can fully integrate into the European Union. The Forum of European National Highway Research Laboratories (FEHRL 2004) stated that the average motorway density in Central European countries is about 6.3 times lower than in the western EU countries and that more than 14000km of new motorways need to be built in the following years, in order to provide similar levels of network accessibility to Central and Eastern European citizens.

Republic of Serbia is no exception in this matter. It is situated in the in South-East Europe, in the center of the Balkan Peninsula, and as such, holds a very favorable position in terms of transportation, as it is a crucial connecting point between Western and Central Europe on one hand, and South-Eastern Europe and the Middle East on the other. Also, it connects Continental Europe with the Adriatic Sea as well as the Aegean and Black seas. This being said, the country also has a very sensitive geo-political and geo-strategic position, which has often lead to volatile situations in past, resulting in a lot of unused potential. With a high quality transport infrastructure, Serbia can foster closer trade links with other countries of the Western Balkans, and Europe as whole, which will in turn ensure a much easier movement of goods, people and capital, resulting in stronger ties among the countries themselves.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Figure 1: location of Serbia in regard to other European cities

It is a known fact that a region can hardly be competitive without an efficient transport network, and also that a functional transportation network can help to promote economic growth and stability. Efficient infrastructure brings closer centers of production and consumption and by doing so bolsters regional economy. As the goal and vision of Serbia is to be of sustainable economic growth and regionally well developed and balanced, good transport infrastructure is key factor in this engagement. In the past years several studies have been carried out in an effort to formulate goals, visions and ways to obtain those goals. One of the studies (Laketa et al. 2011) has defined some of these objectives well as:

 More balanced regional development and improved social cohesion;  Regional competitiveness and accessibility; and  Sustainable use of natural resources and protected and improved living environment.

These are but few goals among many, and are considered as a part of an overall country transformation plan in the pre-accession phase with the final goal of joining the European Union.

In accordance with these objectives, the main goal of this dissertation, firstly, was to critically examine the present state of road transport infrastructure network. In this sense, it was necessary to create a road transport model of Serbia for further assessments – for example, transport policy interventions. With the model in hand we can then suggest interventions in order to improve the transport network in accordance with the countrywide transportation master

Assessment of transport network of the Republic of Serbia in a context of information scarcity plan defined by the Serbian Ministry of Transport (Ministry of Infrastructure of the Republic of Serbia 2009) and create a forecast and scenario analysis for some time horizon.

At present, the transport network of Serbia is not evenly distributed in terms of accessibility (Laketa et al. 2011)The work also aims to measure those variations and to test whether the future plans pay attention to linking insufficiently accessible parts of the country, and what will be possible future benefits. Key performance indicators, such as quantity and quality of transport infrastructure, as well as travel time/distance to the regional centers play crucial role in this evaluation. As one of the goals of the Government is to promote even and balanced development of the country as a whole (Laketa et al. 2011), further development of the transport network is necessary, namely the road infrastructures, because it is the primary influence on spatial distribution of both population and production. With a well laid-out network, we would certainly witness the improvements of the country’s economic and social well-being.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

1.2 Research Questions

As it is difficult to achieve sustained economic development without efficient transport networks, the goal of this dissertation is to critically evaluate the current state of Serbia’s transport networks and give answers to the following questions:

 What is the current state of the transport network, and which are the main weak points?

 How much can we gain in terms of efficiency in transport and accessibility with a future integrated network, having completed main projects given from the country’s transport plan for the next 15 years, as opposed to the current, fragmented one?

 How can we increase the competitiveness of the country from the transport perspective, and better integrate it into the TEN-T and Pan-European Corridors?

 How will this improved transport infrastructure cope with future traffic growth?

These are the questions that define the course of the entire research, and the conclusions obtained in the end should be formulated in a manner that can give some answer to the aforementioned questions.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

1.3 Methodology and Dissertation Outline

While developing this dissertation the following general approach was followed:

Literature review

Review of conceptual aspects of traffic modelling Previous experiences in traffic forecasting for Western Balkans

Case study presentation - Serbia

Present state of transport infrastructure Undergoing and future transport projects

Model construction

Construction of road transport Base OD matrix generation (total Gravity model creation for demand Assigment of OD matrix, Calibration network and definition of TAZ trips incl. LDV and HDV) distribution (based on traffic counts) and testing

Performance analysis and scenario differential comparison

Comparison of 2012 baseline scenario with possible future states in 2027

Interpretation of results and conclusion of research

Figure 2: Methodology outline

The thesis starts with the literature review regarding transport modeling, with a brief historical note on the evolution of modeling, and present state-of-the-art in this field. With that being stated, the study then examines the present state of Serbia’s transport network, its characteristics and limitations. It then shows, in detail, steps taken in order to construct a model of the road network of Serbia. By running the model, key weak points are examined and solutions are proposed. Finally, the comparison is performed between the present, fragmented network and the desired future outcome. Evaluation of present and future road transport supply and demand is vital for long term planning and development, and good planning and realization regarding transport network is crucial as it should improve the country’s overall competitiveness.

The software tools used for this project are VISUM 12.0, a software package for traffic analysis, forecast and GIS-based data management and ArcGIS 10.0, a geographic information system (GIS) for working with maps and geographic information.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

2 Literature review

The present chapter introduces the concept of transport modeling; it’s evolution over the years, current state of the art and state of the practice. Special attention is given to regional transport modeling, in terms of geographical scale and distribution. The most commonly used model, 4- step model, is described in detail and finally, previous experiences in modeling Western Balkans is examined, as it will later be a reference for the model development.

2.1 State of the art in transport modeling

State of the art can be defined as the level of knowledge and development achieved in a certain field, by development of new techniques and procedures.

“A model is a simplified representation of a part of the real world– the system of interest– which focuses on certain elements considered important from a particular point of view” (Ortúzar & Luis G. Willumsen 2011). This is a basic definition from which we can deduce that models tend to be case and problem specific. In an attempt to categorize various model types, Ortúzar states that we can begin by differentiating physical and abstract models. Physical modes are based on design (e.g. fluid dynamics), while abstract ones differ from mental to analytical modes. From the perspective of transport modeling, analytical models are most suitable for study, particularly mathematical models; these models try to represent reality through various mathematical forms, equations and calculations, usually with the support of software packages for complex computations.

For the purpose of examining transportation related issues and for transport planning in general, specific types of models have been developed over the years; most common are travel demand models or traffic models. These are designed to evaluate transport supply and demand. At present time, we can differentiate two main types of transport problems, one where economic growth and development generates such demand that it exceeds the supply (capacity) of the transport system at hand. The other type of problem is based on situations where long periods of under-investment resulted in a poor supply system that cannot satisfy the demand. Within these travel demand models (TDMs), we can form another sub-division by the geographical area they intend to depict and forecast; these can vary from a single intersection, to a city, country or an even larger region. This dissertation focuses on modeling a wider region, and thus the focus will be on regional level modeling.

Virtually every TDM up to date features some version of a traditional 4-step model that will be described in more detail. Before going any further and explaining this type, we need to mention

Assessment of transport network of the Republic of Serbia in a context of information scarcity that there are other types of models used in transport planning. These are less common, but are still important to note:

 Economic Evaluation Models

These models represent economic models that are used to evaluate and compare the value of particular transportation improvements (Anas & Liu 2007), usually through a cost/benefit analysis, by comparing different categories in terms of gains and investment costs.

 Integrated Transportation and Land Use Models

This group of models tries to forecast how an improvement in transportation infrastructure or in transportation system in general will affect land use patterns and vice-versa. They are often integrated with traffic models, and, as such, are considered to be one of the best tools for evaluating transportation policies because they can measure accessibility and mobility, rather than just mobility. In short, land use and transportation systems are closely intertwined, and models used to support transportation planning need to be integrated with land use models to capture these effects (Waddell 2011). However, they are very complex and therefore data- intensive and cumbersome to develop, so that often they are difficult to apply, particularly for evaluating individual, small-scale projects.

A relatively new approach can be seen in application of simulation models, which model the actual behavior of individual transport user rather than groups of them, and by doing so greatly increase the detail and complexity of the model, requiring a high level of computational power and detailed data availability. It expands the array of transport modes by adding walking, cycling etc… It also adds the various effects such as local land use, parking supply and price, transport service quality. Simulation models offer a connection point between other types of models, as they are able to integrate elements from the standard traffic, economic and land use models (MOTOS 2006).

Another way to differentiate transport models is by defining in what manner they process data (Sivakumar 2007):

 Aggregate models; these were the earliest travel demand models. They were relatively simple mathematical models, such as a gravity model or an entropy model that quantified travel as a function of the size of a zone examined.

 Disaggregate models; these models use disaggregate level data on the trips made by individuals between the zones, and apply modeling methodologies such as constrained optimization and random utility maximization.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

With some of the main types of models briefly described, we can focus attention to the most widespread model of all, the classic 4-step model. Even today, this is dominant model for transportation forecasting, as it aims to recreate sequence of decisions created by individuals concerning a trip they will make, with regards to the available options, resulting in estimates of traffic loads on the transport system (Ortúzar & Luis G. Willumsen 2011).

Years of experimentation and practice in the past 50 years has led us to a general structure of a transport model that is often referred to as the classic transport model. This is the sequential 4- step model, containing the following steps:

 Trip generation  Trip distribution  Modal split  Assignment

“The four step model is the primary tool for forecasting future demand and performance of a transportation system, typically defined at a regional or sub-regional scale” (McNally 2007). The steps are chained in a sequence and the outputs of each step become inputs of the following step (Chang et al. 2002). In the trip generation step, the data collected is used to produce a number of trips that are originating from a certain zone. In essence, here each zone produces trips in some correlation to data obtained; number of households, income, car ownership, etc... It is a function of socioeconomic attributes of a zone (McNally 2007).Then, trip distribution matches origins and destination, distributing generated trips to their destination, and by doing so, produces a matrix of trips. The O-D matrices obtained through a large-scale survey such as home or roadside interviews have a tendency to be costly, labor intensive and time disruptive to planners. Therefore, the use of low cost and easily available data is particularly attractive (Tamin & L.G. Willumsen 1989). There are various ways to perform this step; the most common is the Gravity model (Ortúzar & Luis G. Willumsen 2011), of which there will be more discussion later. Modal split, as the name says, splits the trips to different travel modes, computing the proportion of trips between each OD (origin-destination) pair for a given transportation mode. Finally, assignment step loads trips onto a network and allocates them onto a route between the origins and destination, by certain transport mode. When an O-D matrix is assigned onto the network, a flow pattern will be produced. From examining this, we can identify the problems and a solution may be devised (Tamin & L.G. Willumsen 1989).

Next, depending on the specifics, the 4-step model can be applied in different frameworks:

 Differential, when a new land use is intended, for which the additional generated traffic is estimated checking on the resulting saturation levels, which can be regarded as transport and traffic impact studies.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

 Global, when the goal is to explain the traffic loads in the system on wider level and to examine how the performance of the transport system may be improved in response to the evolution of the macro variables (population, employment, income) (MIT- PORTUGAL Course on Complex Transport Infrastructure Systems 2012)

To address these and other diversities the models need to be as flexible as possible, e.g. coping with forecast horizons from one to thirty years and wide ranges in policy and exogenous development (Fox et al. 2003). As was stated earlier, transportation forecasting can be regarded as a process of estimating the number of vehicles or people that will use a specific transportation facility in the future. By using forecasts, we can get a probable future state of the system, but we must remember that these have a certain level of accuracy and cannot be completely relied upon. Still, forecasting remains the key process in transportation planning. It can help in estimating number of trips through a road network, number of people traveling through an airport or seaport, etc. Transportation planning models are very data intensive. Forecasting begins with a thorough data collection on the current state of the transport system, and is often combined with various other data, such as census, employment, income, GDP, household data, traffic counting, etc. (Florian 2008). After a careful and systematic collection and processing, data is fed into a mode, which is then tested and calibrated. When the model is calibrated and represents the present state of the system in an acceptable manner, estimates for the future traffic can be forecasted, given the specific growth rates regarding key figures.

Zones Network Statistical data

Database

Step 1: Trip generation

Step 2: Trip distribution s n o i t a r e t I Step 3: Modal split O u t p u t s Step 4: Assignment

Evaluation

Figure 3: 4-step model algorithm (Ortúzar & Luis G. Willumsen 2011)

Assessment of transport network of the Republic of Serbia in a context of information scarcity

The fact that the 4-step model is relatively simple in terms of computational demand and concept is the main reason of its popularity and high level of utilization. Still, the model suffers from several drawbacks, which are being address in past years, by modifying and expanding the classic version of the model. Some of the downsides of the model:

 Due to the sequential nature of the model, only when the Trip Distribution step is completed and the trip matrix is created, we can proceed to the Modal Split. While trip distribution step matches origins with destinations, not knowing the travel mode has a negative impact, because in reality, the passengers are aware of available modes of transport when choosing their destination. In 4-step model this is not the case, and can lead to errors in forecast (MIT-PORTUGAL Course on Complex Transport Infrastructure Systems 2011).

 The Assignment step assigns routes to vehicles through the network from origin to destination. The problem here is that in real life scenarios, people choose a specific mode of transport based on some notion how the others choose their mode, trying to avoid congestions. Also, when we choose a mode we know the possible routes by which you can travel, and by knowing the routes (time, distance, speed...) we make a rational decision on which mode to take. And that is not the case with the model, where you first choose the mode, and then get an assigned route through which you travel.

 By creating a system of zones, there is a loss of information regarding intra-zonal trips, as they cannot be processed (Cervero 2006).

 The model does not include any possibility of representation of interdependencies between trips of one person during the day or of his trips and those of other people in the same household.

In order to tackle issues mentioned above, 4-step model is constantly being updated and improved. Some versions have added various forms of feedback loops into the process. Also, one of the key improvements was the development of tour and activity-based models, as opposed to the standard trip-based model. In trip-based model we look at a single trip/journey as a unit of analysis, and we look at a demand for trip making rather than for activities. In tour- based model we look for a tour or a round trip as a unit of analysis, and with it we can differentiate work tours and other tours, so that we can explicitly link more trips into a tour. Finally, in activity-based model we assume that travel decisions are activity based, and that knowing and understanding the travel behavior of persons is secondary to a more important understanding of activity behavior, which, in turn, induces trips (Spear 1996). In this model, travelers have space and time restrictions that limit their possibilities of activity scheduling. Activity-based models focus on what generated the activity that induced the trip (McNally &

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Rindt 2007). Unlike the trip based models, in activity approach travel decisions are driven by a set of activities that create a schedule for a person (Sivakumar 2007). Therefore, actions cannot be analyzed on an individual trip basis. The modeling of travel as tours brings the models a major step from trip modeling towards an activity-based approach to modeling transport demand (Fox et al. 2003). The collection of activities and trips performed form a person’s activity pattern, and together with the constraints, form a very complex real-life travel behavior. Also, in activity- based modeling there is an integrated schedule and tours are interdependent so that travel- scheduling decisions depend on a wider range of factors then in tour or trip-based models.

“When the model at base aims at representing the behavior of more than one individual certain degree of aggregation of the exogenous data is inevitable” (Ortúzar & Luis G. Willumsen 2011). Advances in modeling techniques, as these mentioned above, resulted in a switch from aggregate models to disaggregate ones. The primary difference between aggregate and disaggregate models is that the disaggregate models consider the effects of individual socio- demographics in regards with their travel habits (Sivakumar 2007). However, in practice, mainly because data limitations and other modeling constraints disaggregate trip-based models are often implemented in an aggregate manner, usually through aggregate zonal socio- demographic data.

To summarize, we can state that the present state-of-the art has the following highlights:

 For the trip demand: evolution towards the activity-based model;

 For the trip assignment: evolution towards simulation techniques;

 For the interfaces with other tools:

o GIS for developing and applying transportation forecasting models;

o Combination of land use and traffic models; and

o Linkage between traffic model and environmental emission models.

In the short-term, it is most likely that modifications to the 4-step will continue to dominate model evolution. “These modifications will minimally reflect fundamental advances in activity research, and may include tour and activity generation models and greater reflection of constraints on travel and activities” (McNally & Rindt 2007).

Assessment of transport network of the Republic of Serbia in a context of information scarcity

2.2 State of the practice in transport modeling

Earliest attempts to model larger geographical region date back from the 1970s, when the methods and experiences derived from urban traffic forecasting were applied to wider regions in the United States (NCHRP 2006). However, these early attempts didn’t give satisfactory results, since the models couldn’t adequately cover large areas with high enough level of detail. At the same time, within Europe, the application of complex transport models started in the UK with the Transport and Road Laboratory project RRLTAP: A System for Research on Transportation Problems and Models. Later, efforts were aimed towards two directions: first, application of the more complex disaggregate demand models in the European setting, that were first developed in the United States; and the second being work on simplifications of the conventional models, to achieve economies in the data required, computational power, or the expertise required (MOTOS 2006). With the rapid improvements in computing technology and database development, especially in the last decade, engineers now have a strong support for modeling transport on a much larger region. And with possibility comes also the interest. More and more studies are being carried out, and each generation of models is more accurate and more ambitious.

A good example of the successful transport demand model can be found in the Operational Traffic model of Copenhagen (Jovičić & Hansen 2003). This model has been developed for a long period of years, starting from 1994. It is a tour-based 4-step model that incorporates sub- models for generation, distribution and modal split step. It proved to be very reliable model, as the number and the distance forecasted trips from the generation sub-model were very close to the observed trip rates, and the difference of the forecasted traffic and the actual numbers after opening differ only 9% (comparing to the forecast). This goes to show that a good statistical database for a longer time interval is important if we aim for a high accuracy of the forecast. In other words, it proves that transport models, as with all forms of mathematical modeling, can only be as good as the data upon which they are based.

An example of transport model on the state level is a Case Study for the State of Wisconsin, US (Proussaloglou et al. 2007). The crucial data on the travel behavior was obtained by a NHTS, covering more than 17000 households. This data was used for trip generation, distribution and modal split. They stress the importance of a thorough step-by-step validation of the process, so that we can achieve a more accurate outcome. This implies that there is a need for a high level of detail in data, and show the importance of systematic approach in data collection regarding travel behavior, demographics, income, employment, household structure etc.. Again, in order to successfully create a valid model, it is necessary to provide satisfactory data to start with.

There have been many successful interstate (regional) traffic models created in the past years, but there are still many problems that need to be addressed. According to National Cooperative Highway Research Program (NCHRP 2006), several key issues remain:

Assessment of transport network of the Republic of Serbia in a context of information scarcity

 Scale of the state models;  Zoning systems that are less accurate than urban models;  Inadequate databases; and  Failure to do peak hour analysis, due to the lengths of intercity trips.

The models can be passenger only, freight only, or the combination of both. It appears that the freight demand modeling is much more complex, and often proves to be very demanding in terms of data. However, it is important to note that sometimes freight transport accounts for a very small percentage of total transport on a network, and as such, should be allocated proportional amount of time and effort to model, but it should not be ignored.

NCHRP presents different case studies in order to explain the specificities of a statewide model. One good example is the Kentucky State Model, which is concerned to be very efficient and practical; given the fact that it utilizes only secondary data, i.e. the data that has already been obtained. The network at hand is a very large one, consisting of more than 77000 links and 3600 zones for the entire state (NCHRP 2006). Zoning of the region was done so that it is compatible with other databases. It is worth noting that, in order to obtain greater accuracy, some of the zones were divided into subzones, especially in the densely populated areas, to prevent loss of data, and reduce number of intra-zonal trips. Trip generation was estimated by knowing number and average size of households, car ownership, and type of area. Trip distribution was obtained by using gravity expression, modal split was predetermined, and assignment had a feedback loop to distribution step.

Another example of regional traffic forecasting was Øresund Traffic Forecast Model (Sørensen 2001). This model was designed to assess the future impacts of a fixed link crossing the Øresund strait. This link now connects the metropolitan area of Copenhagen, Denmark with Malmö, third largest city in Sweden. It also represents an important link for longer trips, mainly from continental Europe to Scandinavian countries. It was considered to be a state of the art model at the time, as it tried to tackle a difficult task since the network included many overlapping routes and sub-routes as well as many different transport modes.

“It is a utility-based model estimated from joint Revealed Preference and Stated Preference datasets collected specifically for the study. Several sub-models (segments) were estimated for short and long trips, and for different trip purposes. In addition, both passenger and freight traffic were described by different sub-models” (Sørensen 2001).

During the development, secondary data was used to give a description of the highway and public transport networks, and the services running on the public transport network. As stated, the model contained distinct sub-models for long distance and short distance passenger trips and freight transport. The researchers concluded that the modeling of short distance trips, which was based on more extensive information, was far more precise than the corresponding

Assessment of transport network of the Republic of Serbia in a context of information scarcity modeling of long distance trips. Still, even though the model performed quite satisfactory it had a serious problem with the level of traffic, which was simply estimated too high.

Possibly the highlight of the state of practice in transport modeling is the SAMPERS Model, which is the New Swedish National Travel Demand Forecasting Tool (Algers & Beser 2004). The reason for this is due to the fact that it represents present state-of-the-art in various aspects. The main subcontractor for the modeling work was the Hague Consulting Group whose task was to create a model that could perform a more detailed analysis on what projects to include in the 10 year investment plan of Government of Sweden. The goal was to develop a user-friendly computer traffic forecasting system, which can cover all trips in Sweden, where, by the notion of ‘all trips’, the authors meant trips having at least the origin or the destination in Sweden. Effort was made to differentiate local/regional trips, which were handled with a higher resolution, and the long distance/international trips. For local and regional trips, Sweden was divided into 6000 zones, but, since that would create very big matrices and would demand a lot of time and computational power to process, the country was subdivided into 5 regions, which were able to run separately. As the financing of the project was not an issue, obtained data was very thorough and precise with a continuous travel survey performed containing 30000 interviews for the entire interview period. It contained a one-day diary including all trips, supplemented by trips over 100 km made the last month, and trips over 300 km made the second last month (Algers & Beser 2004). The model also contained an accessibility module, where a number of different accessibility measures could be analyzed in a GIS environment. Further on, it is important to note, especially because of the present dissertation focus on accessibility, that SAMPERS included three different types of accessibility measures:

 Impedance measures (generalized costs to reach certain areas);  Closeness (how many work places can be reached within a certain time); and  Model based data (passenger distance travelled).

This goes on to show the complexity of the model, and the level of detail that was applied. However, this has its price, and on a standard PC, run times for the regional models span from 4 up to 30 hours. With the advances in computer technology, current state of the art in SAMPERS as well as in other traffic forecasting models relies heavily on GIS technology that allows the results of the model to be made better visible, for the developers as well as for the policy-makers (users). These days, the networks can be very detailed and fully obtained from land use, and not just abstract representations. Also, for simulation purposes, a very high level of detail is needed and GIS tools can and are proving to be of very high value.

Before proceeding to model development, one key question needs to be answered, and that is the choice of software, as it remains an important issue. Sometimes user can already have certain software for specific tasks and the compatibility issues may influence the choice of software to be used for the model. As mentioned before, there was a joint effort in Europe to simplify models and lower the demand in required data, computer power and expertise needed,

Assessment of transport network of the Republic of Serbia in a context of information scarcity while keeping the results accurate and valid. This led to an outcome where the traditional 4-step models are still used for the majority of planning purposes. Several transportation planning software packages have been developed and are currently used for forecasting traffic. These include, among others, CUBE, EMME/2, MEPLAN, MINUTP, OMNITRANS, TP+, TRANPLAN, TRANSCAD, TRIPS and VISUM. According to MOTOS (2006), there is a big difference between the needs of EU15 countries and that of the New Member States and Candidate States, mainly due to the lack of data and expertise in the latter. For the New Member States (NMS) and Candidate States (CS) (here we include Serbia) the needs can be summarized as:

 Reliable software applications;  Ability to model trips by origin-destination (OD) matrices;  Ability to model complex transport demand;  Advanced tools for assigning traffic flows to transport network; and  User-friendliness of the models.

While the main issues in these countries are:

 The lack of reliable, adequate, and up-to-date information and data;  Low availability of high quality data;  Non-uniform structure of initial data;  Lack of qualified personnel; and  Lack of applicable professional software.

With this in mind, the chosen software package used in this thesis is VISUM 12.0: comprehensive, flexible software system for travel demand modeling and network data management. It provides a variety of assignment procedures and 4-step modeling components. VISUM 12.0 includes embedded components from which allows easier GIS integration. The program is used to build conventional 4-step models for regional and statewide planning while also serving as an analysis and data management tool. As for the GIS part, the chosen software is ArcGIS 10.0, which is used for working with maps and geographic information. It is used for creating and using maps, compiling geographic data and analyzing mapped information.

With the basic information on transport demand modeling provided, we can move on to examining previous experiences in modeling transport in Serbia and the rest of Western Balkans.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

2.3 Experience in regional traffic forecasting for Western Balkans

Transport modeling in the New EU Member States and the Candidate States is a difficult task, because the model developers are facing poor data availability and a lack of appropriate tools and instruments, as well as a lack of consistency in common transport modeling between bordering countries. Still, there is a growing interest to set up, improve and even link transport models at national or regional levels. As such Serbia is no exception.

Most of the studies to assess the situation of the regional transport network were undertaken or at least supported by the European Commission. This is for two reasons. Primarily, since Western Balkans border EU member countries and aspire to join in near future, it is of importance for all sides to take part in this integration process, and EC, by creating or backing up these studies, facilitates reform and cooperation. Secondly, the countries in the region cannot perform such studies on their own, mainly due to the lack of recourses or expertise, but also due to administrative issues concerning involvements of multiple countries. The fact is that there is not sufficient data on the region, and that the data is often incomplete or obsolete. However, it is evident that in the recent years more and more focus is given to this issues; a good proof of that is the forming of South-East Europe Transport Observatory (SEETO South- East Europe Transport Observatory 2011a), which is a link between the European Commission and the countries in the region, and also the delivery of two large scale studies, first the Transport Infrastructure Regional Study in the Balkans (TIRS) in 2002, which was later followed by Regional Balkans Infrastructure Study (REBIS) (European Commission 2003).

South East Europe Transport Observatory (SEETO) is regional transport organization formed on 11th June 2004 by the Governments of Albania, Bosnia and Herzegovina, Croatia, the Former Yugoslav Republic of Macedonia, Montenegro, Serbia, United Nations Mission in Kosovo and the European Commission. This organization is steering and promoting development of the regional transport network, and publishes multi annual plans and progress reports concerning key transport projects. SEETO created the so-called Core Regional Transport Network, which is a multi-modal transport network that connects the key points in the region, and also ensures alignment and integration with the TEN-T. This is the network according to which the model in this study was created, as it insures alignment with the studies performed and present situation ‘’on the ground’’. Observatory claims that the inclusion of its Core Network in the TEN-T guidelines is another step in the right direction towards future full integration and alignment between the region and the EU (SEETO, 2011). At the time of the creation of this dissertation the Core Network consisted of 6,554 kilometers of roads, 4,807 kilometers of rails, 4 rivers, 10 seaports, 17 airports and 8 inland waterway ports (SEETO South-East Europe Transport Observatory 2011a). The observatory monitors the progress of major projects, and suggests measures to improve the overall transport situation.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Figure 4: SEETO road network

The Transport Infrastructure Regional Study (TIRS) in the Balkans has been facilitated by the Stability Pact for South-East Europe. The French consulting firm, Louis Berger S.A., carried out the work from March 2001 to January 2002. According to TIRS, the main objectives of this study were “to identify major international and regional routes in the region, define a coherent medium term network to be used as a framework for planning, programming and coordinating infrastructure investments, and finally, to define short-term priority projects suitable for international financing” (Louis Berger S.A. 2002b). At the time when this study was carried out, the region was still settling down from the conflicts in the ‘90s, and the conclusions obtained are at present somewhat obsolete, given that a decade has passed, and a lot of things have changed. Still, it is worth nothing that the analysis concluded that transport demand by road should continue to increase regularly in the medium term, which it did, supported by the economic recovery in the region; however traffic congestion is expected to become a constraint in the medium term. Concerning the other modes of transport, the conclusions are still valid, mainly the fact that the facilities of other transport modes are underutilized, and that there is room to improve, given the fact that most of the airports and ports currently have less traffic than before the dissolution of the Yugoslavia. In these modes, modernization and rehabilitation should be the way to go. Regarding intermodal transport, the study found that this form of transport is still limited in the region, while the existing facilities are largely under-utilized. Finally, the study concludes that the aim should be to facilitate traffic flows on regional level in order to promote trade and the movement of people, which should greatly contribute to regional development.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

The TIRS study was only the first phase of a project that was undertaken over a period of several years. The second phase was started again with the support of the European Commission. In this phase, a new study entitled REBIS-Transport (Regional Balkan Infrastructure Study-Transport) was completed year after TIRS (Louis Berger S.A. 2002b).

The Regional Balkans Infrastructure Project-Transport (REBIS) was started with the purpose of assisting Balkan countries in the developing of coordinated strategies for transport development. Again, the focus was on the Core Road Network, with an additional study to identify projects that could be viable for international financing. Since this project involved many different stakeholders, the organization itself was designed in such fashion to bring the region more together; the offices were set up in the capitals: Belgrade, Zagreb, Sarajevo, Skopje, and Tirana. As with the previous studies the core network included the main corridors and routes between the five capitals of the region and the cities of Banja Luka, Podgorica and Priština. The network also links the aforementioned cities with the neighboring countries, in accordance to the TEN-T and Pan-European networks. Sea ports in the Adriatic were included, as were the ports on river Danube.

In order to create a valid starting point for the assessment of future projects, a traffic forecasting model was created, and projections made for up to the year 2025. In a moderate growth scenario, “road traffic will increase by 200-300%; rail traffic will grow at a much slower pace: 40- 60%; growth in inland waterway transport is estimated to 160-215% and air traffic at 315-830%. By the end of the period, vehicle ownership and trip rates will have reached the levels which are currently found in many West European countries” (European Commission 2003).

What is also worth noting is the fact that the key input parameters in the mentioned forecast model were present and assumed growth rates of GDP and population. And according to study, modal split between road and rail traffic will continue to change in favor of road traffic, “increasing from 87%- 92% to 92%-94% over the next 25 years; for freight the increase of road traffic will be from 79%-95% to 88%-98%” (European Commission 2003).

Consequently, the most dominant and important transport mode is and will remain, by far, the road transport, and that ability to provide adequate supply for its growth will be crucial, although alternative modes must improve likewise towards a multi-modal transport system and avoid the traditional approach of ‘predict-and-provide’. Also, it leaves space for applying intermodal and multi-modal solutions, where appropriate, in order to ease the pressure of the road transport. With this in mind, we can now go deeper in the specifications of the case study of Serbia’s transportation system, focusing on the road transport for the aforementioned reasons.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

3 Presentation of the case study of the Republic of Serbia

This chapter gives a basic description of the current transport system of Serbia, beginning with a small historical note, a SWOT analysis, a description of undergoing and planned projects and ending with a description of factors that can influence demand growth for the next period.

3.1.1 General statistics and characterization of the transport system

As has been said in the previous chapters, Serbia is a landlocked country located at the crossroads of Central and South-East Europe, holding a central position in the Balkan Peninsula. Serbia borders Hungary to the North; Romania and Bulgaria to the East; Former Yugoslav Republic of Macedonia to the South; and Croatia, Bosnia and Herzegovina, and Montenegro to the West. Furthermore, it borders Albania through Kosovo, whose status as part of Serbia is disputed, and as such, will not be a subject of examination in this thesis.

Figure 5: Location of Serbia within Europe (L) and the map of country (R)

Assessment of transport network of the Republic of Serbia in a context of information scarcity

 Population:

Serbia covers an area of 88,361 km2 and has a population of more than 7.1 million (excluding Kosovo). The capital and the largest city is Belgrade, with the population of 1.639.121 in its metro area (Statistical Office of the Republic of Serbia 2012). Other important cities are Novi Sad, in the north, with a population of 335.701, Niš in the southeast with 257.867, and Kragujevac to the south with 177.468 people living in its metro area.

 Administrative divisions:

Serbia is divided into 150 municipalities and 24 cities, which are the basic units of local self- government. The city may and may not be divided into city municipalities. Municipalities are the basic entities of local self-government in Serbia. Each municipality has an assembly, a municipal president, public service property and a budget. Municipalities usually have more than 10.000 inhabitants. For administrative purposes municipalities are gathered into districts, with a total of 29 districts, created by joining the 150 aforementioned municipalities (for the map of district capitals see Annex).

Figure 6: Districts of Serbia (left), and example of district of South Bačka subdivision into municipalities (right)

Assessment of transport network of the Republic of Serbia in a context of information scarcity

 Economy:

Regarding economy, Serbia is a middle-income country with potential for significant economic development. Most economic activities are focused on services (about 65% of GDP), industry (24%), and agriculture (11%) (Tošić & Jovanović 2009). In the late 1980s, while starting a shift from a planned economy to a market economy, Serbia had a relatively healthy economic position, but it was seriously impacted by economic sanctions from 1992–1995, as well as excessive damage to infrastructure and industry during the 1999 NATO bombing. Nevertheless, after year 2000 the country went through an economic liberalization process, and experienced fast economic growth. GDP per capital (nominal) went from $1,160 in 2000 to $6,539 in 2012 (IMF 2012). Serbia signed Free Trade Agreement (FTA) with the EU and Russia, which enables it to exports all products originating from Serbia without customs and other fees. EU countries were the largest export partners (54.2%) and the largest import partners (52.9%) of Serbia in 2009 (Statistical Office of the Republic of Serbia 2012).

 Transport:

Regarding transport, Serbia holds a very good position due to the proximity to EU, the rest of South-East Europe, and in extent to the Middle East. It borders the EU at the Hungarian, Bulgarian, and Romanian state lines. With the total road length of about 38.000 km (Government of the Republic of Serbia 2007), the road network in the Republic of Serbia is well- developed, although its quality is reduced due to insufficient investments and inadequate maintenance in the period 1990-2000. According to the Public Enterprise "Roads of Serbia" (Government of the Republic of Serbia 2009) road network of Serbia in the total length of 40.845 km consists of (see Annex for road categorization):

 5.525 km of state roads of category I;  11.540 km of state roads of category II; and  23.780 km of local roads.

On the network there are:

 498 km of toll motorways and  136 km of toll semi-motorways

According to (SEETO South-East Europe Transport Observatory 2011b), by far, the dominant mode of transport is road, with the traffic flow on the network ranging from less than 2,000 veh/day, to more than 100,000 veh/day. Also, 63% of the Core Road Network had traffic with more than 5000 veh/day in 2010, and a very small (< 10%) of network segments with very low traffic flows, less than 2000 veh/day. This supports the SEETO statement of the importance of the network in the regional, but also in the European context. Also, in is worth noting, that around 12% of the network presents volumes bigger than 15,000 veh/day. Still, the fact is that

Assessment of transport network of the Republic of Serbia in a context of information scarcity underdeveloped connections between the major cities represent a barrier for fast and convenient travel within the region.

Furthermore, if we compare the population density (inhabitants per km2) with the road network length (km), we can conclude that, at present, the network is sufficiently developed.

Figure 7: Development of road network (Ministry of Infrastructure of the Republic of Serbia 2010)

But the accessibility varies greatly between regions, which is a factor that must be addressed in the near future; the present accessibility levels can be observed in the following table and figure:

Table 1: Accessibility by regions (Laketa et al. 2011)

Far above the Above average Average Below average Far below average average North Bačka North Banat Zlatibor South Bačka West Bačka Central Banat Moravica Mačva South Banat Kolubara Rasina Raška Bor Srem Šumadija Nišava Pirot Zaječar City of Belgrade Braničevo Pčinja Podunavlje Toplica

Pomoravlje Jablanica

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Figure 8: Accessibility levels 2011, according to (Laketa et al. 2011)

In 2010, the first major step towards development of modern transport system occurred, in the creation of General Master Plan for Transport in Serbia (GMTS) until 2027. The goal of GMTS is:

‘’..to contribute to expanded, improved and safer transport networks, which will attract new investments to the poorer regions, improve the quality of regional life, promote trade and contribute to the improvement of relations with neighboring countries’’ (Ministry of Infrastructure of the Republic of Serbia 2009).

The plan is envisaged to be economical and technically feasible, reasonable and practical, so that it can be gradually applied up to 2027. The idea is also to harmonize Serbia’s own transport network with the TEN-T and Pan-European Corridors. The fact that a number of EU member states, such as Italy, Greece and Slovenia enjoy close geographical but also historical and cultural ties goes in favor to the country’s reconstruction efforts. This plan will be a basis for all future projects that will be financed from the EU pre-accession and accession funds as well as from any other sources of financing.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

With GMTS provided, a SWOT analysis on Serbia’s transport sector can be performed. It is intended to help in specifying the overall objectives and identifying the internal and external factors that are favorable and unfavorable to achieve these objectives:

Table 2: SWOT analysis of Serbia’s transport sector (Ministry of Infrastructure of the Republic of Serbia 2010)

 Geostrategic position Strengths  Level of network development  Infrastructural recourses S  Defined goals and policies  Availability of local knowledge and expertise  Serbia’s political position Weaknesses  Main corridors not completed  Turbulent period in past decades (wars, sanctions, NATO bombing) W  Insufficiently developed transport institutions  Lack of stable financing  Pressure on one mode of transport (road)  Region’s interest in Serbia’s development Opportunities  Possibility to influence the development of European networks and align it to Serbia’s plans  Shorter and easier transit compared to present TEN-T network corridors through Bulgaria and Romania O  Multimodal transport development, with focus on inland waterway transport by Danube and Sava  Good touristic opportunities and development  Existence of alternative routes through Romania and Threats Bulgaria (TEN-T)  Non-complementary strategies of neighboring countries  Numerous border crossings T  Manifestation of partial and individual interests  Corruption, weak legal system  Insufficient financing of transport sector

In general, the goals of such analysis are to help decision maker use the strengths, detect and capture opportunities, improve weak spots, and to try to elude the threats. Also, the decision maker should strive to attain competitive advantages by joining strengths with opportunities, and to try to convert weaknesses and/or threats into strengths and opportunities. With that in mind,

Assessment of transport network of the Republic of Serbia in a context of information scarcity and in order to examine the situation in a wider aspect, we must focus on the three crucial network systems that shape Serbia’s own transport sector.

3.1.2 TEN-T (Trans-European Networks - Transport)

The idea of Trans-European Networks was first implemented in the 1980s with the creation of a Single European Market, and since then, in order to promote free flow of goods, people and services, there is a need for an efficient transport infrastructure which can link different regions and national networks in to a larger network designed to serve the entire continent of Europe. This network is also a significant factor for economic growth and development and for creation of employment for the whole of Europe, as it is a planned set of road, rail, air and water transport networks designed to serve the entire continent.

“The TEN-T envisages coordinated improvements to primary roads, railways, inland waterways, airports, seaports, inland ports and traffic management systems, so as to provide integrated and intermodal long-distance high-speed routes for the movement of people and freight throughout Europe” (European Commission 2011).

Figure 9: TEN-T(Trans-European Transport Network Executive Agency 2012)

With Serbia’s orientation towards EU, it is of high importance to take part in TEN-T, for it can ensure economic, social and territorial cohesion and to improve accessibility throughout the

Assessment of transport network of the Republic of Serbia in a context of information scarcity country and the wider region. Furthermore, it is an essential process in order to harmonize and create complementary networks with those of the surrounding countries. Presently, the main concern is that Serbia is not actively engaged in the process, since it is not a EU Member State. The connecting entity that ensures harmonization of the internal transport plan and that of the European Commission is the aforementioned South East Europe Transport Observatory. In this case, SEETO’s Core Network ensures full connectivity with the TEN-T.

3.1.3 Pan-European Corridors

The ten Pan-European transport corridors were defined at the second Pan-European transport Conference in Crete, March 1994. They are designated as routes in Central and Eastern Europe that required major investment in the next decades. Pan-European transport corridors, as with the TEN-T, are multimodal corridors, which implies the combination and linking of several forms of transport modes. These corridors are distinct from the Trans-European Transport Network, which is a European Union project. Nowadays, there are proposals to combine the two systems, mainly due to the fact that majority of the involved countries became members of the EU.

Figure 10: Pan-European Corridors in the Balkans

Regarding Serbia, Corridor X forms the backbone of the country’s transport, as it connects Austria, Hungary, Slovenia, Croatia, on one side, with Bulgaria, Macedonia, Greece and Turkey on the other. The two branches of the Corridor (one from Croatia, another from Hungary) meet

Assessment of transport network of the Republic of Serbia in a context of information scarcity at Belgrade, run together south-east until the city of Niš, and then branch out, one towards Greece, and other towards Bulgaria. Furthermore, Corridor X is connected with Corridors IV, V, VII and VII. This Corridor, with its basic direction from Salzburg to Thessaloniki, connects eight states; of the total length (2.360 km), through Republic of Serbia flows 874 km (37% of corridor); and of the total international land transport of Serbia, more than 77% of goods are transported through routes on Corridor X: 66,8% in export, 73,2% in import and 90,1% in transit in 2008 (Laketa et al. 2011).

Another very important corridor, Corridor VII, is the river Danube that is the second longest river in Europe and represents the main inland waterway transport corridor linking Europe through the Rhine, the Main and the Rhine-Main-Danube canal. It connects the North Sea with the Black Sea crossing the countries of Germany, Austria, Slovakia, Hungary, Croatia, Serbia, Romania, Bulgaria, Moldova and the Ukraine. The watercourse through the Republic of Serbia has the length of almost 600 km.

Recently, a new corridor was proposed, Corridor XI, which stretches diagonally through Serbia, from North-East, to South-West. It is intended to connect Romania, through Serbia and Montenegro, to Italy. It is known that this will pass through Belgrade and will incorporate the Belgrade (Serbia) - Bar (Montenegro) highway. It is often referred to as the Belgrade- South Adriatic, since it will also incorporate Montenegrin port of Bar, linking Corridor IV in Romania with Bari in Italy. This route holds high significance for Serbia, but also for the region, because with it Balkans gain transversal transport route “North - South”, with the connection to the Adriatic.

Apparently, a good strategy is needed in order to capture the full potential of the Corridors, otherwise, the transport operations will move to neighboring countries, if these provide better services.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

3.1.4 SEETO (South East Europe Transport Observatory)

Since the Western Balkan states are not yet full EU members, the SEETO transport observatory serves as a link between individual countries as well as between the region and the EU. As such, and in accordance with both EU and the Western Balkan representatives, the Core Road Network was formed, which will ensure connectivity and harmonization between the region and both TEN-T and the Pan-European Corridors (figure 11):

Figure 11: Core Road Network (SEETO South-East Europe Transport Observatory 2011b)

The SEETO Comprehensive Road Network is “a one-layer network that provides high level connectivity to all parts of the SEE region and with TEN-T, encouraging long distance international traffic” (SEETO 2011). The Core Road Network has a total of 6.554 km, with 2.987 km of Corridors and 3.567 km of Routes. Out of that, the biggest part of the network is located in Serbia and Croatia and accounts for 49.3% of total length.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

3.2 Undergoing projects and planned for near future

Now that we have briefly examined the main characteristics of Serbia’s transport system, we can continue on to describing what lies ahead. According to official statistics (Statistical Office of the Republic of Serbia 2012), the contribution of Transport, Storage and Communications (TSC) sector to Serbia’s GDP is substantial, especially because it has been rising steadily from 2004.This sector is representing around In 10%-12% of country’s GDP it the past years, and has been employing more than 100.000 people. In order to be able to fully understand the situation, it is important to point out key projects of Serbia’s transport sector for the following years. These goals have been stated in GMTS, and further summarized by (Tošić & Jovanović 2009):

1. Integrating Serbian transport network into the TEN-T.

2. Efficient use of comparative advantages of each transport mode.

3. Rising the quality of transport services, by increasing efficiency, better organization of transport operations from economics, safety and environmental perspective.

4. Increasing the level of safety and security of the transport system.

5. Strengthening and gradual liberalization of transport market.

6. Reducing adverse environmental impacts of transport.

7. Establishing stable financing of transport system’s development.

Furthermore, it is a fact that the rehabilitation and modernization of transport infrastructure is of high priority not just for Serbia, but also for the international organizations, which are providing aid, loans and transition assistance.

It is apparent, by looking at figure 12, that the most developed regions are located centrally and toward West and North, while the less developed ones are to the South and East. With this in mind, the GMTS is intended to balance out these economic disproportions and help to even out strong differences between regions. The cities of Belgrade, Novi Sad, Kragujevac and Niš are intended to be regional centers, through which inhabitants will be provided with links to other parts of the county.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Figure 12: Infrastructural overview (L) and level of development by regions (R) (SIEPA 2012)

Next, we can proceed to identify key projects created by GMTS, which are currently underway or are planned for near future (Ministry of Infrastructure of the Republic of Serbia 2010).

3.3 General Master Plan for Transport in Serbia (GMTS) project listing

1. Belgrade Bypass:

A much needed bypass will soon join two branches of Corridor X, from the north (E-70) and from the west (E- 75), and create a transit route around the capital towards Niš, while also providing several other connections to routes to the West, South-West and South, in direction of cities Obrenovac, Valjevo, Čačak etc.. Also it will provide new crossing over Danube and Sava. This bypass will significantly reduce the pressure on Figure 13: Belgrade Bypass Belgrade’s bridges since they currently represent significant bottlenecks. Total length of the bypass is 75 km; projected speed is 120 km/h with a full motorway profile. Estimated cost is €221 million.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

2. RDA1:

Section of E-75 (Horgoš – Novi Sad). This road is a part of Corridor X, from the city of Novi Sad to the Hungarian border. Currently works are approaching the end in an effort to upgrade it from half-motorway profile to a full motorway, (2×2) by creating a parallel highway next to the present one, and separating two traffic flows. Total length is 108 km and the cost is estimated at €132 million.

Figure 14: Project RDA1

3. RDA2:

This branch of E-75 (Kelebija- Subotica south) is a part of Corridor X and it is intended to provide connection to a second border crossing towards Hungary, effectively creating a bypass around the city of Subotica and easing the pressure on the Horgoš border crossing. It will be a full motorway profile, with the total length being 22,3 km and the cost estimated at €120 million.

Figure 15: Project RDA2

Assessment of transport network of the Republic of Serbia in a context of information scarcity

4. RDA3:

This section of E-75 (Grabovnica – FYROM) also belongs to the Corridor X, more precisely its southward branch towards FYROM, and further to Greece. This link connects Central Europe, through Belgrade and Niš, with Skopje, Thessaloniki and Athens. It is projected as a full motorway profile, with the total length of 98,1 km and the cost estimated at €605 million. Works are currently underway.

Figure 16: Project RDA3

5. RDA4:

Section of E-80 (Niš – Dimitrovgrad) belongs to the Corridor X. it is corridor’s eastward branch towards Bulgaria and Turkey. This is the key link for both Serbia and the continent, as it provides the shortest land route between continental Europe and Middle East. Total length of this branch is 83,4 km, together with 90 bridges (11,5 km total) and 13 tunnels (8,2 km total). As the terrain is rather difficult the estimated cost is set at €650 million. Works are currently underway.

Figure 17: Project RDA4

Assessment of transport network of the Republic of Serbia in a context of information scarcity

6. RDA5:

The main road M1.11 (Kragujevac – Batočina) is projected as full motorway profile, since this link connects Kragujevac with the Corridor X. It is of high importance because the city is a regional industrial center, with significant foreign investments, notably the FIAT automotive factory and assembly line; which is currently one of the main industrial projects in Serbia. The link will provide good accessibility to the city and the industrial zone. Total length is 25,1 km and the cost stands at €75 million.

Figure 18: Project RDA5

7. RDB6:

This section of E-763 (Beograd – Požega) is intended to be a link of high importance, since it will connect Belgrade to South Adriatic. It will be the fastest link from Corridor X to the coast, it is important from economic, strategic, and industrial standpoint. The port of Bar, Montenegro is the closest seaport from Belgrade, and traditionally serves as an exit to the sea trade routes. This section length is 111 km and the cost is estimated at €850 million. Works are currently underway.

Figure 19: Project RDB6

Assessment of transport network of the Republic of Serbia in a context of information scarcity

8. RDC11:

Following the previous project, from Belgrade to Požega, this section of E-763 (Požega – Boljare) is the following link to the border with Montenegro. It is a part of SEETO Core Road Network, with the length of 110 km of full motorway profile (2×2) and the estimated costs at €2 billion. This is financially the most demanding project, and feasibility study is underway, and no construction work has yet been initiated.

Figure 20: Project RDC11

9. RDC7:

This section of E-70 (Belgrade- Pančevo- Vršac) is envisaged as a motorway, which will be a part of new Corridor XI, linking Romania, through Serbia and Montenegro, to the Adriatic, and on to Italy. The length will be 91,5 km and the cost €570 million.

Figure 21: Project RDC7

Assessment of transport network of the Republic of Serbia in a context of information scarcity

10. RDC8:

Section of E-761 (Pojate – Preljina) holds a high significance for Serbia, even though it is of secondary priority in European networks. This is because the link connects central parts of the county with two important routes, Corridor X to the East, and Belgrade - South Adriatic to the West. Length is 109,6 km and the cost estimated to €413 million. E-761 is important as it is a part of regional network that connects Turkey, Bulgaria, Serbia, Bosnia & Herzegovina and Croatia.

Figure 22: Project RDC8

11. RDB9:

This Section of E-761 (Požega – Užice - Kotroman) is intended to be a motorway link that branches of from Belgrade – South Adriatic motorway, and serves as a connecting point to Bosnia & Herzegovina. This link can help connect regional airports at Sarajevo (BiH), Niš (Serbia) and Sofia (Bulgaria). Total length is 60 km of full motorway profile and the cost is estimated at €480 million.

Figure 23: Project RDB9

Assessment of transport network of the Republic of Serbia in a context of information scarcity

12. RDC10:

To the east, one of the main projects alongside Corridor X, that was mentioned already, is the section of E-761 (Bulgarian border - Zaječar - Paraćin) will connect eastern regions of Serbia with E-75/ Corridor X; with the total length of 95km of full motorway profile and estimated cost at €670 million.

Figure 24: Project RDC10

13. RDB12:

Regional link (Novi Sad – Ruma - Šabac - Loznica) is intended to connect Novi Sad through western Serbia with Bosnia and further on to the Adriatic. High levels of traffic are forecasted on the section Šabac – Ruma and here is planned full motorway profile, while the rest will be a regional highway (1×1) Total length is 120km, and the cost is €200 million.

Figure 25: Project RDB12

Assessment of transport network of the Republic of Serbia in a context of information scarcity

14. RDC13:

According to GMTS, this link (Hungarian border - Kikinda - Zrenjanin - Pančevo - Kovin) will serve as a regional highway (1×1), to provide better accessibility to the northeastern parts of county and to serve as a link to the Corridor XI (from Romania to South Adriatic). Total length is 204,2 km, and the cost estimated to €220 million. Technical studies are currently underway.

Figure 26: Project RDC13

Finally, joining all projects, we can examine the planned future state of the transport network:

Figure 27: Overview of all major projects until 2027

Assessment of transport network of the Republic of Serbia in a context of information scarcity

The aforementioned projects are official, according to GMTS (Ministry of Infrastructure of the Republic of Serbia 2010), and will serve as a key point in the creation of the model in this master thesis. All of these projects will be incorporated in the model as scenario analysis is being performed. This subchapter covered the future supply regarding Serbia’s transport system, while the next subchapter will examine possible future demand, which represent another important part of the problem studied here.

3.4 Factors for transport demand growth until 2027

In Serbia, as in most other cases, the projected economic growth rates can provide adequate basis for aggregate transport demand forecast. In the period between 2000 and 2009, according to the official statistical data, GDP growth varied between 2,4% and 9,3%, with the average growth of around 5%. With the onset of global economic crisis, Serbia’s economy felt the first impact in 2009, when GDP fell 3%. Nowadays, we can see evidence of stagnation and that of a slow economic recovery, with the forecast for 2013 of 3% growth. Judging by the data provided, transport sector gave significant contribution in terms of GDP growth (Government of the Republic of Serbia 2007).

Table 3: GDP rates – Serbia (IMF 2012)

During the 1990s, Serbia was exposed to wars and economic sanctions. We can feel the consequences of that period even today, as the economy is still not at level prior to dissolution of Yugoslavia. For example, by the end of 2005, the transport demand in the Republic of Serbia was still considerably lower than in the beginning of 1990’s, as it peaked at the level that is between 30% and 40% of the demand in the 1990 (Government of the Republic of Serbia 2007). Still, significant progress has been made since 2000 with the new government creating the starting point for economic and social reforms as well as for the increased foreign aid and investments. One of the key factors in strong development in the past years was expanding the private sector, and its share in the economy. Still, in 2012, average monthly net salary with tax, medical care and retirement subtracted stood at US$ 479 or € 358, while the GPD per capita (PPP) was $10.810 and the GDP per capita (nominal) $5.816, which is still far below EU

Assessment of transport network of the Republic of Serbia in a context of information scarcity average (IMF 2012). Furthermore, evidence that the economy is still fragile, especially during global recession, can be seen in high inflation, and the rise of public debt, which, fortunately, still stands around 50% of Serbia’s GDP.

Table 4: Projected growth rates by vehicle types (%) (Ministry of Infrastructure of the Republic of Serbia 2010)

Vehicle type Period Light Medium Heavy Articulated Passenger Bus truck truck truck truck 2006-2010 4,30 3,00 2,80 3,50 3,80 4,20 2011-2025 5,00 5,00 5,00 5,00 5,00 5,00 2026- 4,00 4,00 4,00 4,00 4,00 4,00

Nevertheless, as far as transportation sector is concerned, the outlook is positive, with good growth rates. Still, this might create problems in the longer run if adequate transport infrastructure supply is not provided. According to TIRS (Louis Berger S.A. 2002a) congestion is expected to become an issue in the medium term, but only in a few sections of the highway network:

 On Corridor X between Belgrade and Hungary;  On Corridor X between Leskovac and FYROM;  On the E 763 road between Belgrade and Čačak; and  Near the largest cities.

As in Serbia, the forecast for the entire region of Western Balkans is showing strong increase in road transportation in the forthcoming years. This leads to conclusion that sooner or later, the major effort will focus on the improvement of existing segments and on the development of new segments of the road network in the region. The increase of transport demand can be due to industrial development, intensified trade and removal of administrative and other obstacles that will facilitate better transport flows, especially on the Pan-European Corridors VII and X (Government of the Republic of Serbia 2007). If we look at the previous experiences of the New Member States, rapid development prior to joining EU is expected. However, growth of 5% per year is unlikely to be maintained on the long run, as it will usually settle to a more stable figure. On the other hand, GDP growth can be lowered if the country faces certain negative developments, possibly political in nature, along the way to becoming full member of EU. Some of the possible negative factors include debt repayment, decrease in population, unemployment, volatile fuel prices, etc.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Again, it is worth mentioning the summary of the TIRS and REBIS studies, for the period until 2025, which stated that “road traffic will increase by 200-300%”; and that the modal share of road traffic will increase “from 87%- 92% to 92%-94% over the next 25 year, and for the freight the increase of road traffic will be from 79%-95% to 88%-98%”.

In the Government’s strategy for the transport system development (including railway, road, inland waterway, air and intermodality), the following key conclusions were pointed out, regarding future growth in transport sector (Government of the Republic of Serbia 2007):

1.” Transport activities and investments in transport system in the Republic of Serbia will follow the GDP growth, with corresponding elasticity;

2. Modal split will depend on many factors, above all on the economic development of the country, domestic and regional transport policy, capacities and offered services;

3. Forecasts for one mode of transport must consider capacities and services, which will be offered by other modes competing for the same transport work;

4. Regardless of the results of the forecasts, the container transport growth will be fast, considering international trends;

5. It is assumed that in the Republic of Serbia long-term transport forecasts will be very uncertain, even with the use of the best methods;

6. In order to estimate whether a project will return the investment, risk analysis methods will have to be used;

7. Present modal split for freight, which is estimated at about 80-85% for road transport, 10-15% for railway, and about 7% for IWT, will probably continue for some time, at least in the next mid- term period; and

8. The existing capacities of transport networks in the Republic of Serbia, with necessary modernization and local improvements will be sufficient in the next ten-year period.”

These and other facts, forecasts and statements in the chapter go to show that, even though the future is highly uncertain, we can have some confidence regarding road transport, as it will be, by far, the dominant mode, and the one that we should keep our focus on. With this in mind, and with the required data obtained from various official sources, although facing some scarcity of information, we can proceed into the construction of a road transport model for Serbia, and to examine on our own behalf, what are possible future scenarios regarding Serbia’s transport sector.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

4 National road transport model for Serbia: methodological approach

This chapter forms the backbone of the research, as all the steps taken in order to create the model, are described in high detail. Firstly, the process of data collection and trip generation is explained, followed by the network development. Next, impedance and volume-delay functions used are described, at the trip distribution step performed by creating and using a Gravity model. Then, demand data is inputted in the model, tested and calibrated, based on existing traffic counts. Finally, transport demand forecast is carried out.

4.1 Data collection and processing

Statistics can be defined as the regular recording of general data usually gathered and published by official statistics office. Statistical Office of the Republic of Serbia was consulted in order to collect basic socioeconomic statistical data. As we mentioned before, Serbia is divided into districts that are the further subdivided into municipalities. Therefore, for every municipality in Serbia (excluding Kosovo) the following data was obtained:

 Total population and active population  Number of households and number of employed persons

Further indicators were derived from these, e.g. average household size. It was interesting to see how the population and employment numbers correlate between each other The conclusion we can take from this is that employment opportunities increase non-linearly with the population concentration, or that more densely populated territories definitely concentrate more job opportunities and therefore become important trip generators and attractors (Figure 28):

160.000 140.000 y = 2E-06x2 + 0,258x + 880,9

120.000 R² = 0,7849 100.000 80.000 60.000

Employment 40.000 20.000 - - 50.000 100.000 150.000 200.000 250.000 Population

Figure 28: correlation between population and employment per zone

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Next, in order to develop trip generation of the step of the 4-step model, we had to correlate GDP per capita with the number of daily trips per inhabitant. Based on the Technical Report on Kazakhstan (Macário et al. 2012), we used the following trip generation logistic function calibrated for a benchmarking analysis of 71 European cities based on the database from the UITP (Union Internationale des Transport Publics). This mobility variable was estimated through the GDP per capita projections.

Equation 1: Daily trips per inhabitant as f (GDP)

4,0

Prague - 3,70 Melbourne - 3,70 Graz - 3,70 Geneva - 3,70 Clermont Ferrand - 3,60 Lille - 3,60 3,5 Lyon - 3,40 Stuttgart - 3,30 Bern - 3,30 Bologna - 3,20 Hamburg -Zurich3,20Oslo - 3,20 - 3,20Munich - 3,20 Nantes - 3,10 Helsinki - 3,10 Berlin - 3,10 3,0 Copenhagen - 3,00 Santiago - 3,00 GlasgowMarseille - 3,00 - 3,00 Amsterdam - 2,90 Chicago - 2,90 Budapest - 2,80 Singapore - 2,90 ManchesterBrussels - 2,80- 2,80 Paris - 2,80 Moscow - 2,70 Rotterdam - 2,70Stockholm - 2,80 Madrid - 2,70 ViennaLondon - 2,70 - 2,70 Dubai - 2,60 Hong Kong - 2,60 2,5 Ciudad de MéxicoNewcastle - 2,50 - 2,50 Ghent - 2,50 Astana - 2,4 Guadalajara - 2,30 Warsaw - 2,30 Rome - 2,20 Aktobe - 2,1 Almaty - 2,2

Daily tripsinhabitantperDaily 2,0 Valencia - 2,10 Krakow - 2,00 Lima - 1,90 Buenos Aires - 2,00 Río de SãoJaneiro Paulo - 1,90 - 1,90 Bilbao - 2,00 Seville - 1,80 Barcelona - 1,80 Turin - 1,80 Curitiba - 1,80 Belo HorizonteAthens - 1,60 Porto- 1,60 AlegreLisbon - 1,60 - 1,60 1,5 Montevideo - 1,50

Bogotá - 1,30

1,0 0 10.000 20.000 30.000 40.000 50.000 60.000 70.000 GDP per capita (2010 US$) Cities Logistic Predicted Cities KAZ Figure 29: Number of daily trips per inhabitant as a function of GDP (Macário et al. 2012)

As noticeable in the previous figure, we can conclude that the dispersion is significant, and that even the cities with the same GDP per capita can produce different number of daily trips. This became apparent in our case as well, by overestimating actual number of trips. Nevertheless, this approach still gave reasonably good results and due to its relative simplicity (only one variable needed) proved to be the best choice for this thesis. Unfortunately, the only GDP figures available from IMF and other sources were the ones regarding Serbia as a whole, and not divided by municipalities. In order to tackle this issue, we have obtained average salaries for each municipality, sorted from the highest to the lowest, and created an ‘’earning factor’’- θ. The zone with the highest average salary (New Belgrade) became the reference level with θ = 1, and all other zones had lower factors (0,9; 0,87; 0,64, etc.) translating the ratio between each of

Assessment of transport network of the Republic of Serbia in a context of information scarcity these zones and the highest income zone. By doing so, the accuracy of the model increased, as the lowest income zone (Bela Palanka- in South-East of the county) had a factor of only 0,27:

Equation 2: Revised trip generation formula

Since we are examining all trips in one day and not peak hour analysis, we have generated the trips in one direction, i.e. single-constrained Origin-Destination matrix (hereon, OD matrix). Hence, the matrix is symmetrical, representing (or assuming) that all trips that leave a zone at the beginning of the day, come back by the end. With the average household size in Serbia standing at 3,02 persons the average number of trips per day per household was calculated at 4,75. This resulted in overall number of trips per day for the entire country to be 11.857.140.

These are total trips by all modes available to any zone. Here lies the largest problem of the model: how to find out what percentage of these trips are actual inter-zonal car trips, which are not loaded to the modeled network? The solution for this issue will be stated in the subchapter regarding calibration (refer to section 4.4).

Regarding zoning system, when creating a model, the region that is examined is divided into zones (discretization of space), thus creating our TAZs (traffic analysis zones). Originally, the region was to be divided into 29 districts,. However, we realized that this level of detail would be insufficient, so division was made according to municipalities. Another detail went in favor of choosing municipalities over districts, and that is the fact that the statistical data was aligned with the GIS data for the zoning system. In other words, the GIS shapes obtained were exact match to the municipalities from the Statistics Office, which greatly contributed to model accuracy and performance. By using data provided, the first outcome of the trip generation step was obtained, which will further calibrated later on.

Finally, we added external trips to the OD matrix, i.e. trips coming from foreign countries and heading to foreign countries”. As no sufficient data was available in order to model these trips, and also due to the fact that this could be a model for itself, requiring much more time and effort, the following approximation was carried out. From the official traffic counts, we were able to acquire the number of trips crossing borders (from the counts on the links just prior to border crossing). With those counts, and assuming that they are all transit trips, we have enlarged our trip matrix to encompass the surrounding foreign countries. This way the model is more realistic, depicting not only internal traffic, but also external transit trips.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

4.2 Transport network description

Networks form the basis of every transport-modeling project. The next step in the process was to create the transport network to which the generated traffic will be assigned. Traditionally, transport networks have been modeled in a relatively coarse manner. Still, if possible, when building road networks, modelers should make use of existing sources, such as national road databases in GIS. Generally, when a network is formed, it is usually consisted of links and nodes, where links are representing the streets, boulevards, highways, railroad tracks, waterways and similar form of connection between nodes; while the nodes can represent a simple junction or an entire city, depending on a geographical level of analysis that is being performed. In this model, the construction of the network proved to be very time consuming, as the only GIS data available online was of poor quality, hence it was decided to create the network manually, using GIS data as background. Before starting the mapping process, TIRS study (Louis Berger S.A. 2002a) was consulted to help in categorizing the road network. According to TIRS, there were 6 types of roads in the network:

1. Sub-standard 2 lane: 5-6m pavement on a 8-9m platform; 2. Standard 2 lane: 7m pavement on 11m platform reflecting EU norms; 3. Standard 2 lane + (crawler lane): As standard above plus 3rd lane of 3.5m; 4. Half Motorway profile (2x 3.75m lane carriageway with shoulders); 5. Expressway: 4 lane divided carriageway (4x3.5m lane carriageway with shoulders) plus partial grade separation for junctions); and 6. 2×2 lane Motorway (TEM recommended profile and grade separation junctions).

For the purpose of this project, we have adopted a relatively similar categorization, with some simplifications and adjustments. This way, some categories were merged together, since the level of detail of TIRS study was greater, as was the availability of data needed. The following road categories feature in our model:

1. Local route, which represents a 1x1 lane road with the capacity of 7000 veh/day and the speed of 50km/h.

2. Secondary/Regional route, which represents a 1x1 lane road with the capacity of 7000 veh/day and the speed of 60km/h.

3. Primary/Trunk route, which represents a 1x1 lane road with the capacity of 12000 veh/day and the speed of 80km/h (core network routes).

4. Half-Motorway (adopted category 4), which represents a 1x1 lane road with the capacity of 14000 veh/day and the speed of 100km/h.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

5. Expressway (adopted category 5), a 4 lane divided carriageway, with the capacity of 55000 veh/day and the speed of 80 km/h (mostly roads leading to the capital)

6. Urban Arterial, same as Expressway, but for inner-city traffic, with the speed restriction at 60km/h.

7. Motorway (adopted category 6), which represents 2×2 lane road with the capacity of 55000 veh/day and the speed 120km/h, with tolling system (highest ranking category in the model).

One of the biggest challenges during the creation of the model was determining link capacities. Considering the guidelines from Highway Capacity Manual (HCM), estimating capacity levels relies on detailed description of a set of factors beyond our reach for this work, such as shoulder width, gradient, existence of crawler lines etc.. Furthermore,, the roads included here are often not within a certain Western European or US standard and hence the HCM would not guarantee good fitting, necessarily. In fact, capacities obtained from HCM were too high, which could overly relax our model. Therefore, we have decided to adopt similar values as those from the TIRS study, because these were calculated especially for the study region and reflect better the real situation. The low capacities could be explained not necessarily by the geometry of the road, but rather with the poor condition of roads, at least the ones when the TIRS study was carried out.

4.3 Impedance and volume-delay functions

In order to perform the distribution of the generated trips (2nd of the 4-step model), we needed to create an impedance matrix. We adopted the following impedance function:

Equation 3: Impedance function

Where:

 - is the time travelled between O-D pair;  - distance travelled upon assignment to the network; and  - is a binary variable with value 1 if tolls exist on that link, 0 otherwise;

Assessment of transport network of the Republic of Serbia in a context of information scarcity

The coefficients were created so as to reflect:

 Operational cost per kilometer, with the fuel consumption of 7l/100 km;  Value of time (VOT), with the average cost of 20 cents/min; and

The reason that the toll charges for motorways were set to a lower values than real ones, was meant to show the difference in road quality between motorway and all other link types, which give preference to motorways. Otherwise, too high number of vehicles would switch to other links than it actually is. Alternatively, we could have reduced capacity to simulate higher volume- to-capacity ratios, but on fairly fragile grounds (no better information was available than the one explain in the previous section). This reduction in toll values can also be interpreted as an increase of willingness-to-pay for better driving conditions in motorways when compared to alternative highways. Finally and instead of reducing the toll tariffs, we could have set an additional cost component to impedance function to account for poor driving conditions that would represent the increased friction perceived by users. Algebraically, this would imply bringing back the toll tariffs to the original values and shift those values to the new component of the impedance function. This might originate interesting research to account for increased friction perceived by users in the impedance function due to poor state of roads. One way to account for this would be to estimate the delays induced by such poor conditions. We would call it road “State-Delay Functions” – an analogy to “Volume-Delay Functions”.

With the network prepared and the impedance function created, first skim matrices of impedances and distances between OD pairs were created in VISUM. This gave us the matrix of generalized costs between each pair of zones; however, the diagonal was still missing. The impedance matrix diagonal represents generalized cost for intra-zonal trips, for each zone. Naturally, it could not be computed at this stage since it was still unknown what the distance for trips within the zone is (in the distance matrix it was represented by 0). To achieve this, the following procedure was followed:

 For each O-D pair, except the diagonal, impedance value was divided by respective distance. This gave us the generalized cost per kilometer for each O-D pair.  Average value was computed, this represents the average generalized cost per

kilometer (cij/km), for the entire matrix/network.

 With the average cij /km calculated, the next step was to calculate the average length of intra-zonal trips.  No information is available regarding the average trip length of each municipality. As a proxy, it was decided to adopt the area-equivalent circle radius of each municipality. As

such, we multiplied the average trip length by the network average cij/km, thus obtaining the diagonal of the impedance matrix.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

With the full distance and impedance matrices calculated, we could go further and proceed with trip distribution. This step was performed using the common Gravity model. Originally, the Gravity model computes the number of trips between origin i and destination j as a function of the masses of the origin and destination balanced the distance between them (dij) using a scaling factor k. This way the model represents the relationships between each O-D pair. It is an analogy with the Newton’s Law of gravity, which states that every point mass in the universe attracts every other point mass with a force that is directly proportional to the product of their masses and inversely proportional to the square of the distance between them. In this case, the bigger the zone (in terms of population and employment) the more travel we can expect between them and, inversely, the further apart zones are, the less travel there will be between them. Here, the generator is the population of the zone, while the attractor is the number of employments in a particular zone; and the amount of ‘’pull’’ between each O-D pair is represented through number of trips. In the simplest form, we can represent it as following:

Equation 4: First rigorous Gravity model (Casey model) where:

 and are the populations of the zones of origin and destination;

 is the distance between i and j (a simplistic representation of the deterrence costs between zones); and

 α is a proportionality factor (with ).

The impedance that represents the decrease in number of trips as the distance or generalized cost increase is not necessarily linear. In fact, there are three popular versions of impedance function (Ortúzar and Willumsen, 2011):

- Negative exponential function:

- Power function:

- Combined function (aka as the Tanner function): where represents generalized travel cost and and n are parameters for calibration. Here, we used the negative exponential function. Much research has been devoted to the definition of different formulations of the Gravity model to better represent the trip distribution between zones, always trying to keep the Gravitational logic. A generally accepted formulation of the Gravity model for a doubly constrained model is (Ortúzar and Willumsen, 2011):

Equation 5: Doubly constrained gravity model

Assessment of transport network of the Republic of Serbia in a context of information scarcity

where, and

The actual formula used for this origin-constrained model (all day trips considered justify our assumption of symmetrical OD matrix – refer to section 4.1) is:

Equation 6: Actual gravity model used

where β is a calibration parameter that represents the average traveler perception of generalized costs (the higher β is, the bigger is the resistance to travelling. Conversely, the lower β is, the smaller is the resistance to travelling). Limiting the usefulness of the gravity model is its aggregate nature. It is very successful in explaining the choice of a large number of individuals, but the choice of a single person can vary significantly from the predicted value.

Another key element in road traffic assignment is the Volume Delay Function (VDF), which shows the effects of link saturation on trip times. There are several formulas used for describing volume delay, ranging from simple linear to far more complex functions. In general, the formula describes the connection between free flow traffic on a link, and a flow on a saturated one, as the following, based on the fundamental road traffic engineering equation (flow=density x speed) :

Equation 7: Volume-delay function

Where:

 -free flow time;  - volume on a link; and  - capacity of that link.

From the mathematical perspective, the function must be continuous, strictly increasing and non-negative (Jastrzebski 2000), but there exists another perspective, which is far more difficult to characterize: the behavioral perspective. For the purpose of this thesis, the following VDF was adopted from the Bureau of Public Roads (BPR), included in VISUM’s libraries, and it represents the standard, and commonly used option:

Equation 8: BPR Volume-delay function

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Where

 - travel time on loaded network

 - travel time on empty network (free flow)



Based on the previous procedure, we obtained an initial matrix of trips, but we needed to divide it into two: one for private cars and another for freight. As we didn’t have additional information on industrial activity and freight itself, we made an assumption stating that of all trips 95% is private and 5% freight; with 1 unit of freight vehicle accounting for 3 personal vehicles, the ratio adopted from (Transport Research Board 2000) These two matrices will later be used in the model. With the initial distribution matrices obtained, we could go forward to calibration and testing. The assignment procedure carried out was the Equilibrium assignment. This procedure distributes the demand according to Wardrop's First Principle, which states that "Every road user selects his route in such a way, that the impedance on all alternative routes is the same, and that switching to a different route would increase personal travel time (user optimum)."

4.4 Demand data input, calibration and testing

After trip distribution, modal split and assignment follow in the 4-step model. As mentioned earlier, one of the main problems we have encountered while creating the model is the fact that we didn’t know what is the actual percentage of the trip matrix that actually loads the road network links. In other words, what percentage of the total trips is non-road and, therefore, would be assigned to the railways or air modes. The fact is that the majority of trips made were once in close vicinity of the place of origin. Therefore it came as no surprise that the diagonal of the matrix (intra-zonal trips) was holding high values. Still, when first running the model, the load links for inter-zonal trips was far above link capacities. As we have adopted capacity values from the TIRS study and assumed that this was solid ground, the only solution was to calibrate the number of trips, based on official traffic counts from the ‘’Putevi Srbije’’ (Roads of Serbia) Government Agency. After comparing our results with these traffic counts, we concluded that:

 The overall network pattern of link load estimates matches the overall network pattern of the traffic counts; and  The link loads are too high, due to the fact that we still haven’t removed those trips that are not ensured by road vehicles.

In other words, the model was performing satisfactory, but we had a scaling problem. This could be solved by dividing the entire matrix by a uniform factor, i.e. scaling down overall link loads,

Assessment of transport network of the Republic of Serbia in a context of information scarcity but not changing the network trip pattern. We concluded that 15% matrix load match the observed counts in the manner that is the most acceptable of all. The fact the only 15% of total trips is actually loaded on to the road network need explanation; firstly, part is due to road modal share; another is due to the occupancy rates of cars; and finally, to overestimates of trip generation (the dispersion between the 71 cities used to calibrate is very high). This is due to the function used that is apparently not applicable in this case, mainly because of the lower standard of living in the region, comparing to the Western Europe. These are additional problems that need to be resolved in future research. This comparison can be graphically represented on the following figure, where on the left we have model results and on the right the official counts (using the same color coding):

Figure 30: Comparison of AADT from the model (L) and from the counts (R)

The assignment procedure in VISUM was the Linear User Cost Equilibrium (LUCE). “The procedure seeks at every node a deterministic equilibrium for the local route choice of users directed toward a same destination among the links of its forward star. The cost function associated to each one of these travel alternatives expresses the average impedance to reach the destination by continuing the trip with that link, linearized at the current flow pattern.” (G Gentile et al. 2009). This way, LUCE achieves a very high convergence speed that compares favorably to the other methods, while it assigns the demand flow of each O-D pair on several paths at once.

Furthermore, and in order to confirm that we have found a desired order of magnitude, we have performed correlation analysis on some of the main links, mostly motorways and primary roads. This was done by extracting the volumes on those links and manually inputting the respective counts. Pearson correlation (that assumes linearity in the relationship between two sets of

Assessment of transport network of the Republic of Serbia in a context of information scarcity values) was calculated. The value obtained was 0,74, which is satisfactory, given that we are examining a network on a national level (values above 0,6 or 60% can be regarded as good correlation):

VOLVEHPRT(AP) COUNTS VOLVEHPRT(AP) 1 COUNTS 0,743543852 1

Figure 31: Pearson correlation for the top 40 links

As calibration can give quite different matrix from the starting on, it is important that this step is carried out carefully as the accuracy of the model is dependent on good calibration. Once the calibration is complete, both regarding the impedance function and the matrix itself, this ‘’setup’’ will be used for forecasting, as the future trip matrix will be sensitive to the transport network modifications and to the future levels of trip making from and to each zone. The gravity model is considered to be a more robust distribution model, as it depicts accurately trips levels between zones and is sensitive to the network. Still, sometimes it proves to be difficult to calibrate and also does not contain sensitivity on the level of individual travel. In this case, characterization of the network is very important to achieve desired accuracy and level of detail. With this in mind, the data for each link included its length, its travel speed and the capacity in passenger car equivalent units (PCU) per hour.

Another way to show the pattern of trips is by using desire lines. These lines represent graphically the number of trips between O-D pairs. Graphically, the higher the number is, the wider will the corresponding bar be (see Figure 32: Desire lines for main O-D pairs). It is a good visual aid to estimate roughly the pattern of movements. However, it is used for a limited set of O-D pairs, i.e. the ones with the highest values, to prevent cluttering the image.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Figure 32: Desire lines for main O-D pairs

The figure shows that the main activities are centered along the Corridor X, particularly in and around Novi Sad, Belgrade, Kragujevac and Niš, which was an expected outcome. With the model calibrated and running, we can proceed to the actual forecasting and analysis. It’s worth noting that desire line for the city of Niš (South-East) is unrealistically big, due to the fact that this city is not subdivided in smaller zones, as are other major cities. Because of this all trips are collected in a single zone, producing bigger desire line than it actually is.

Traffic assignment procedure is crucial step in model development as it shows the outcome of the model operations, by providing insights into the characteristics of the network, enabling traffic forecasts and providing initial steps for the calculation of derived impacts.

Before proceeding to actual forecasting, one more procedure was carried out in order to examine the starting data and how it represents actual traffic counts. The T-Flow Fuzzy is a matrix correction procedure included in VISUM and used to correct demand matrices, so that the estimated volumes on certain links match the real volumes (counts). This procedure can be useful when a matrix generated from the transport network model is to be calibrated or when a matrix generated from incomplete or unreliable data needs improvement. T-Flow Fuzzy procedure can help in solving issues like these as the update only affects the demand matrix. This correction was done only on some of the main links, and resulted in only slight changes, as the matrix was already calibrated through the procedure described above.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Network model Count Data

Assignment T-Flow Fuzzy

Demand Matrix

Figure 33: T-Flow Fuzzy chart (PTV vision 2011)

At this point, we can proceed to actual traffic forecasting for 2027.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

4.5 Transport demand forecast until 2027

In order to better understand and analyze the transport network and the demand forecast, 3 different scenarios will be examined:

 Present state (2012);  Future state without network improvements (2027); and  Future state with network improvements (2027).

As mentioned earlier, by using observed counts we were able to calibrate the generation of trips and to draw near to the observed network load. Now, we needed to estimate growth rate of GDP, which is crucial element in forecasting future demand. In other words, we used growth factor method. Methods like these assume that the profile of future trips will remain largely the same as the present, with one exception: the volume of trips will increase or decrease according to growth in the O-D zone, i.e. the generators and attractors. It is important to note that in situations where significant land-use change is expected, this method would not give satisfactory result. Fortunately, this was not the case with this model. The success a growth factor method is largely dependent on how precisely we can estimate growth rates. Therefore, we wanted to examine three possible scenarios with three different growth rates: firstly, the optimistic scenario with the average GDP growth rate of 5%, which was the case for the past 10 years; secondly, a neutral scenario with a more modest 3% growth rate; and finally, the pessimistic growth rate near stagnation, i.e. 1%.

We considered one additional factor for trip forecasting. As was mentioned earlier, the earning factor added to the trips generation function, calculated the difference of earnings between the wealthiest zone and all the other zones. Because one of the main goals of the Government is to balance out differences in development between regions, the lower limit of θ was set to 0,7. In other words, this simulates that in 15 years, difference in earning between any two regions in Serbia will not be more than 30%. With that in mind, next figure show total number of trips generated in 2027, under the 3 mentioned scenarios:

Table 5: Growth rates- GDP and trips

Total number of Growth GDP Total number of GDP 2012 daily trips 2012 rate 2027 daily trips 2027 optimistic 5% 12,06 12.841.797 neutral 5,8 11.857.140 3% 9,04 12.420.468 pessimistic 1% 6,73 12.083.217

What becomes apparent, according to the formula used, is that the differences in total trip numbers will not vary greatly between scenarios, and will not grow significantly comparing to

Assessment of transport network of the Republic of Serbia in a context of information scarcity

2012, less than 10%. If we look at the following figure, we can examine growth patterns through the years:

Total number of daily trips 13.000.000 12.800.000

12.600.000

12.400.000 optimistic trips 12.200.000 neutral 12.000.000 pessimistic 11.800.000 2010 2015 2020 2025 2030 year

Figure 34: total number of daily trips (2012-2027)

After analyzing the 3 scenarios and concluding the difference between them is not sufficient for separate analysis. We have decided to adopt the neutral scenario (3%) as our future forecast, and based on that, and the planned projects from GMTS, we can proceed to performance analysis and differential comparison of the state in 2012 and future states in 2027, with and without network improvements.

Furthermore, in order to better evaluate the forecasted growth, the k factors ware calculated for every zone in the network (see Annex). The factors obtained are result of the GDP growth rate forecast and of our arbitrary earning factor difference reduction (0,7≤θ≤1). The minimum increase in number of trips, comparing to 2012, is 4%, while the maximum is 8%. Implicitly, we assume that those municipalities where the k growth factor are higher, the economic wealth will increase more the other and thus meaning that more trips are generated per GDPpc.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

5 Performance analysis and differential comparison

In this chapter, the results obtained from the model are examined, in an effort to identify key performance issues. Also, comparison between present state (2012) and future outcomes (2027) is carried out. Finally, the results are discussed and possible improvements identified.

5.1 Identification of performance problems of the transport system

When the assignment was performed for the 2012 scenario, several issues were identified. Overall, network capacity does not seem to be an issue. The following figure shows the Levels of Service (LOS) for the network, according to HCM adopted level categorization:

Figure 35: Road network (L) and LOS 2012 (R)

Most of the links show that the LOS is satisfactory, while the main problems identified are:

 Road leading to the main cities; especially Belgrade, Novi Sad, Niš, Kragujevac and Požarevac  Bridges of Belgrade, especially the Pančevo bridge over Danube  The primary road M-22.1 that connects Belgrade and Novi Sad

Assessment of transport network of the Republic of Serbia in a context of information scarcity

 Approaches to Belgrade from the South-West (‘’Ibarska magistrala’’), East (from Smederevo) and North (through Zemun)  Section of primary road M-21 connecting the cities of Ruma and Šabac  Section of primary road E-761 (Pojate- Preljina) connecting Čačak, Kraljevo, Trstenik and Kruševac  Majority of Corridor X, especially the section from Novi Sad to Niš

These issues match to the ones carried out in previous studies, and are in line with the earlier statements that the road geometry and designed capacity is not the issue, but rather the poor condition that some roads are in. Also, it goes in line with the preceding facts, that the amount of traffic still has not reached even 50% of 1990 level.

Figure 36: Volume of traffic- 2012

Regarding actual volumes of traffic, the pattern is mostly the same. This however, could also have another meaning. The low volumes and good levels of service in some remote regions may indicate that some regions have low accessibility, and are difficult to reach. Therefore, good LOS in remote regions should not be seen necessarily as a good performance, but rather as a probable sign that the region is not well connected with the rest of the country, hence presenting low traffic volumes. This is a very important issue that needs to be identified and addressed. One way to examine accessibility between regions is by creating isochrones, i.e. lines connecting places at an equal journey time from the same starting location. On the

Assessment of transport network of the Republic of Serbia in a context of information scarcity following figure, 2 hours isochrones were charted on a previously loaded network, starting from the 3 largest cities: Belgrade, Novi Sad and Niš. In the graph, the darkest the links are more than 2 hours away from those 3 cities.

Figure 37: 2 hours isochrones starting from Belgrade (L), Novi Sad (M), Niš (R); 2012

And if we place these isochrones together over the accessibility levels from Figure 8: Accessibility levels 2011, according to (Laketa et al. 2011), we can see that these isochrones cannot reach the majority of the regions with low accessibility. In fact, we can verify that the black lines (i.e., links more than 2 hours distant from those 3 cities) are overlapping the most remote regions previously identifies.

Figure 38: 2 hours isochrones over accessibility levels- 2012

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Therefore, we can conclude that, at present, the regional equity in terms of accessibility is not satisfactory, especially in regions located along or close to the borders. The regions that suffer the most from accessibility issues are the extreme West, South-West and East of the county. At the same time, capacity is not a critical issue, except on the mentioned links. Next, we will perform a differential comparison between the present state that was described and two future scenarios, one with the forecasted increase in demand and network improvements, and other without network improvements.

5.2 Differential comparison and discussion of results

In the previous chapter we have described how the network was created, and what are the future projects on it. These projects are now implemented in the network, resulting in upgrading some existing links and creation of new one, as described in section 3.2. This way we have covered the ‘’supply side’’ of the future model. And by updating trip matrices using forecasts and growth rates, the ‘’demand side’’ was also updated. The difference in road network can be seen on the following figure:

Figure 39: Network comparison, 2012 (L) and 2027 (R)

With the network improved, and new demand inserted into the model, the comparison in terms of accessibility was carried out. Again, 2 hours isochrones were created starting from Belgrade, Novi Sad and Niš, in order to see the difference in isochrones reach on the loaded network. The comparison can be seen on the following figure:

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Figure 40: Comparison in accessibility from 3 main cities, 2012 (L) and 2027 (R)

From this comparison we can conclude that the accessibility problems have been mitigated with partial success, at least from this perspective. The districts in the West and South (green circles) will witness significant improvements due to the motorway that will pass through the region, along with the upgrading of the primary roads. Still, very little improvement is visible in the East (orange circle), due to the fact that none of the proposed projects pass through this region. This issue should be investigated further.

Certainly there are other ways to measure the change, but they require data that is currently out of reach. However, several other indicators were calculated, and highlighted an important clue in assessing the change. The following graph illustrates the shift in link saturation, by LOS, between the present state and the future states, with and without improvements (see annex for more details):

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Figure 41: Link saturation comparison

Again, the absolute values here are less important, especially because of the difficulty to determine precise link capacity values. What should be more carefully examined is the difference or shift between scenarios, and the increase of the number of links with high saturation. The next figure shows that without improvements the number of links with high saturation will almost double in next 15 years:

Figure 42: Link saturation comparison 2027, with and without improvements

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Also in the table form:

Table 6: link saturation comparison 2027

only main roads only main roads links 3090 links 2952 2027 with improvements 2027 without improvements LOS A ≤33% 2036 66% LOS A ≤33% 1573 53% LOS B ≤55% 482 16% LOS B ≤55% 520 18% LOS C ≤77% 273 9% LOS C ≤77% 367 12% LOS D ≤92% 88 3% LOS D ≤92% 145 5% LOS E ≤100% 36 1% LOS E ≤100% 54 2% LOS F >100% 175 6% LOS F >100% 292 10%

Furthermore, increase in efficiency regarding travel times between zones is clearly visible. Overall, there are positive results over the entire network and slightly in favor of the regions with accessibility rated Below Average and Far Below Average. What is interesting to note, is that the largest gains have been seen in the Below Average category, and not it Far Below Average, which is in line with the previous conclusion from isochrones test, that the most remote regions, especially Bor in the East, still do not show satisfactory results. These results can be seen in the Table 7: travel time reductions.

In order to calculate these changes we obtained travel times for each district from all other districts, after the assignment procedure. These were travel times between zones on a loaded network ( ), in 2012. We calculated average travel times for base scenario in 2012, and then for the scenario with network improvements in 2027. The results have been collected and gains in travel times have been shown as percent-increase of the old travel time and have been color coded. Also, for comparison, the direct distance travel times, for a speed of 120 km/h have been calculated.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Table 7: travel time reductions

Direct distance travel Gains District time (@120 km/h) [hr] 2012 tcur (hr) 2027 tcur [hr] (2012-2027) Belgrade 0,96 1,83 1,67 8% Bor 1,39 3,27 2,99 9% Braničevo 1,07 2,41 2,32 4% Central Banat 1,34 2,63 2,45 7% Jablanica 1,64 3,05 2,91 5% Kolubara 1,02 2,22 1,87 15% Mačva 1,20 2,57 2,28 11% Moravica 1,06 2,45 1,98 19% Nišava 1,37 2,38 2,23 7% North Bačka 1,69 2,74 2,58 6% North Banat 1,65 3,24 3,00 7% Pčinja 1,96 3,51 3,07 12% Pirot 1,76 3,03 2,66 12% Podunavlje 0,95 1,83 1,72 6% Pomoravlje 1,03 2,05 1,95 5% Rasina 1,14 2,36 2,02 14% Raška 1,26 2,96 2,46 17% South Bačka 1,34 2,45 2,29 6% South Banat 1,11 2,28 2,08 9% Srem 1,17 2,16 2,00 8% Šumadija 0,95 1,90 1,76 7% Toplica 1,36 2,70 2,55 6% West Bačka 1,67 3,08 2,94 5% Zaječar 1,34 2,73 2,42 11% Zlatibor 1,25 2,99 2,53 16% Grand Total 1,27 2,59 2,35 10%

Overall, the gain for the entire network is around 10% in travel time reduction, comparing to the base travel times in 2012. Next, if we look on the forecasted volumes of traffic in 2027, on the improved network, we can evaluate usage rate of the new links. As such, we can identify which projects will present higher benefits to the overall situation. By removing the color coding for the links with low forecasted volumes, and by keeping the ones with higher volumes of Annual Average Daily Traffic (AADT), we can examine the situation further by looking at the following figure:

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Figure 43: Links with higher volumes of traffic – 2027

Overall, the network improvements show good utilization rates, and contribute to shortening travel times and increasing accessibility. Naturally, some of the links had shown more satisfactory volumes of traffic, through which they can justify the investments. Of the 13 projects listed in GMTS, the model shows high volumes (i.e., volumes >5000veh/day) on the following links:

 Belgrade bypass  Motorway Novi Sad-Hungarian border (project RDA1)  Motorway section on Corridor X; Grabovnica-Macedonian border (project RDA3)  Motorway section on Corridor X; Niš-Dimitrovgrad (project RDA4)  Motorway connecting Kragujevac with Corridor X (project RDA5)  Motorway Belgrade- South Adriatic; section Belgrade-Požega (project RDB5)  E-761 motorway on the section Pojate – Preljina (project RDC8) and Paraćin- Bulgarian border (project RDC10)  Section of primary road Ruma-Šabac (project RDB12)

Furthermore, and in order to better understand shifts in accessibility, the Hansen (1959) measure was calculated. Here, the idea is to estimate accessibility of a zone by calculating the opportunities available (in this case employment) in all other zones, weighted by a function of the difficulty of reaching those zones. This index shows the relation between the attractiveness of destinations (in this case employment numbers), and the reduction of this attractiveness due to the difficulty of travelling to them:

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Equation 9: Hansen accessibility index

Where:

- The opportunities at zone j for a given purpose (employment)

- Function to represent the deterrent effect of the travel cost

These indicators were calculated for 2012 and for 2027, for each of 161 zones. Then, these zones were aggregated into districts and the average values are presented on the following table:

Table 8: Hansen accessibility indexes for districts

Districts Hansen Accessibility Index 2012 Hansen Accessibility Index 2027 Shift Belgrade 0,319 0,323 1,41% Bor 0,041 0,044 6,94% Braničevo 0,116 0,118 1,53% Central Banat 0,134 0,138 2,67% Jablanica 0,053 0,055 3,70% Kolubara 0,162 0,177 8,95% Mačva 0,123 0,135 9,60% Moravica 0,107 0,121 12,77% Nišava 0,092 0,095 3,04% North Bačka 0,109 0,111 1,59% North Banat 0,081 0,086 7,02% Pčinja 0,032 0,037 14,24% Pirot 0,049 0,055 12,18% Podunavlje 0,195 0,199 1,79% Pomoravlje 0,130 0,134 2,99% Rasina 0,098 0,105 6,91% Raška 0,064 0,073 13,19% South Bačka 0,173 0,176 1,25% South Banat 0,179 0,186 3,76% Srem 0,220 0,224 1,61% Šumadija 0,167 0,171 2,47% Toplica 0,071 0,074 3,32% West Bačka 0,095 0,096 0,93% Zaječar 0,065 0,070 8,17% Zlatibor 0,067 0,076 14,00% Grand Total 0,136 0,141 3,94%

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Also, these values have been normalized (0-100) and a histogram was created, by using intervals of 10. This way, we can see the shift in accessibility for the 161 zones in the study area, from 2012 to 2027, on the following figure:

20 2012 15 2027

10 number ofmunicipalities number 5

0 10 20 30 40 50 60 70 80 90 100 intervals

Figure 44: Histogram of normalized Hansen indexes

The top five districts with the largest increase in accessibility levels according to Hansen index can be seen on the following figure:

Figure 45: Top 5 districts with highest gains in accessibility (Hansen)

Assessment of transport network of the Republic of Serbia in a context of information scarcity

6 Conclusions

The final chapter consists of the main highlights of the research, a detailed explanation of research limitation that were encountered, and how they impacted the model. With that in mind, the leads for future work are presented, reinforcing the idea that this dissertation was just the first step in a large and complex effort to produce a detailed and accurate road traffic model for an entire country.

6.1 Main highlights of the research

Recalling the main objective of the dissertation, it was intended to critically examine the present state of the Serbian road transport infrastructure network and analyze the possible outcomes from new projects programmed for the near future. Here, we have built a detailed and accurate model of the road network and simulated satisfyingly a very complex reality in a context of information scarcity. In fact, we managed to capture and recreate some of the crucial aspects of the present situation and to give valuable information and contribution to identify some existing problems and foresee those that will probably remain even after the projects are installed, eventually.

The results and values obtained with the present research should not be considered in absolute terms, but more as general indicators of present and future traffic patterns. The issues with scaling, capacities and actual number of trips on the network also suggest that these conclusions should be regarded in relative or comparative terms; e.g. how does present state compare to the future one, or how improved network will be able to satisfy future demand, comparing to the network with no improvements. Still, the model shows and confirms statements from previous studies, regarding possible bottlenecks, and issues with accessibility. Also, it acknowledges the importance of Corridor X, which serves as a backbone to whole transport system of the country.

As was mentioned in the previous studies, and confirmed in this one, the characteristics of the transport network can be summarized as:

 A network with a valuable strategic position, and a lot of unused capacity and potential for economic fostering;  Comparing to the population, the network has adequate road density;  Capacities of the network are generally sufficient, except on the links mentioned; and  Accessibility is a weak point, especially in regions along the borders.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

Regarding trip forecasts, the formula used led us to conclude that the overall number of trips will not rise more than 1/5 of the present volumes. Still, without improvements, those links that are already saturated will experience additional buildup of negative impacts. Also it became apparent that different GDP growth rates (1%, 3% and 5%) did not evidence sufficient differences and therefore the neutral (middle) scenario was adopted for further comparative analysis.

The majority of the planned projects have a good level of future demand supporting them. It is apparent that, in order to catch up with EU norms and standards, a thorough modernization of the network is needed, especially in the case of new motorways. Without good connections between regions, as well as between Serbia and the neighboring countries, many districts are under threat to be excluded from the main traffic flows. This would only aggravate the present condition, where the difference (in terms of earnings and standard) between certain regions is very high, up to 4 times. The projects will improve average travel time through network by 15%, with certain region experiencing much higher benefits. Furthermore, no region would suffer from these undertakings. In that sense, big majority of projects are justified.

Regarding accessibility, the results show that the future project will help the overall situation, but there will still be certain regions that will show signs of low accessibility. The improvement in average travel time to a certain district from all others is about 10%, with the individual improvements ranging from 4% to 19%. Further improvements are necessary, in order to bridge the gap between regions, in terms of development, income and movement of goods and people.

Transport network exhibits vulnerable points, mostly on bridges in the capital. This however, is likely to be successfully addressed with the future completion of bypass motorway.

Utilization of motorways on corridor X is and will remain very high, stressing the need to quickly complete the missing links and integrate it into European networks. This will further facilitate trade and generate economic activities. Future Corridor XI, stretching from Romanian to Montenegrin border, will be of high value as it will intersect Corridor X near Belgrade, which could lead to a creation of a transport hub and attract investment. For other links, careful examination of future demand is needed when considering upgrade; sometimes it will be sufficient to modernize a present road, and maintain it well, as opposed to investing into a higher category road.

On the side note, we would like to stress the importance of good accessibility and balanced social equity throughout the county, especially on the bordering regions. This is even more important in Serbia, due to the fact that there is a number of ethnic minorities living at the bordering regions that need to be better integrated in the society, increasing social welfare and the overall quality of life for all the inhabitants.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

6.2 Research limitations and leads for future work

Research limitations, and way to deal with them, are probably of equal importance as the results presented. Thorough the modeling process, we have encountered many obstacles, which need to be acknowledged. We have aimed to create a starting point in road transport model of Serbia, which can later be upgraded and expanded. Here, we will list various limitations encountered and ways in which these issues were overcome.

 Data; Model is extremely dependent on data availability. Ultimately, no matter how sophisticated the model is, if the data is missing, incomplete or corrupted, the end results will be poor. We have faced to types of data problem: statistical and geographical. The statistical problem related to lack of data regarding trips and land use. If the land use information was available and clear, distribution would be more accurate. In this case, population and employment were used as a proxy to determine generators and attractors of trips. The geographical problem, however, was more demanding. Fortunately good GIS data was obtained for the zoning system, but the only road network available was of very poor quality. The only way, at the time, when this dissertation was created, to solve this problem was to create a network manually, following the maps obtained from ArcGIS. This was a tedious process, but it ensured that the entire network had the same accuracy and level of detail. If a GIS data bundle would be available (like zones, links and nodes) this would significantly increase the accuracy of the entire model, at least for future work.

 Exact impedance and volume delay functions; from the previous studies, no information was given in the formulas used in the model. Usually, these types of functions are calibrated to accurately depict a certain region, and they differ from country to country. We have adopted standard ones, and with some trial and error adjusted them slightly to give more satisfactory results.

 Network design problems; there were many assumptions and approximations that were necessary, mainly due to lack of data or sheer dimension of the issues. Firstly, and it was mentioned previously, the issues with capacity. Actual capacity can differ greatly from a starting default value (e.g. 2000 veh/h/lane) due to gradients, existence of crawler line geometry of the road, signalization, etc.. These can have considerable impact, and needs to be included if the model is to be upgraded. Special consideration should go to type of terrain the road goes through, and this should influence capacities. Furthermore, as Serbia has two distinct geographical areas; North, which is completely flat and South, which is mostly hilly and mountainous terrain, the differences in network performance between regions would be even more distinct.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

 Transit (external) trips ; Modeling transit trips can be quite different from modeling inner trips. So different, that it may require a separate sub-model to describe these types of movements. In this thesis, due to various restrictions, a broad simplification had to be applied. The only data available were the counts at the border crossings, which gave us a general idea how the flows work and what are the volumes. In the future scenarios, these volumes were increased using growth rates for all trips.

 Freight; as with transit trips, freight traffic requires a separate sub-model in order to be realistic and accurate. As very little data was available, and also due to the fact that a relatively small percentage of all traffic is freight, general simplification was applied; a 5% of all trips was considered to be freight, and a unit of freight was set to be equal to 3 personal car units. If land use data was available, greater accuracy could have been achieved.

With the conclusions and research limitations stated, it is necessary to provide some leads for future work. As the model at hand needs additional refinement and improvement, we will list possible directions in which the future research should go:

 Acquiring of a more accurate GIS data for road network, preferably from and official source;

 Reassess network capacities using additional data (geometry, gradient, crawler lines, etc.)

 Creation of a sub-model for external transit trips, with separate growth rate functions more

adapted to international economic relationship trends between different bordering countries;

 Creation of freight sub-model, using accurate land use data regarding industry; and

 Considering performing a strategic environmental impact assessment, focusing on major

aspects, e.g. air emissions, biodiversity, protected areas, among a few others.

Again, these suggestions are to be considered if the model will be revised in any future research. In that way, more accurate forecasts could be obtained as they are critical for performing future cost-benefit analyses, environmental impact assessments, and social impact assessments that usually create starting point for decisions should new infrastructure be built or not.

Assessment of transport network of the Republic of Serbia in a context of information scarcity

7 References

Algers, S. & Beser, M., 2004. SAMPERS - The New Swedish National Travel Demand Forecasting Tool. , pp.1–18.

Anas, A. & Liu, Y., 2007. a Regional Economy, Land Use, and Transportation Model: Formulation, Algorithm Design, and Testing. Journal of Regional Science, 47(3), pp.415– 455. Available at: http://doi.wiley.com/10.1111/j.1467-9787.2007.00515.x.

Cervero, R., 2006. Alternative Approaches to Modeling the Travel-Demand Impacts of Smart Growth. Journal of the American Planning Association, 72(3), pp.285–295. Available at: http://www.tandfonline.com/doi/abs/10.1080/01944360608976751.

Chang, K., Khatib, Z. & Ou, Y., 2002. Effects of zoning structure and network detail on traffic demand modeling. Environment and Planning B: Planning and Design, 29(1), pp.37–52. Available at: http://www.envplan.com/abstract.cgi?id=b2742 [Accessed September 6, 2012].

European Commission, 2011. Mid-term evaluation of the TEN-T Programme ( 2007-2013 ). , (March 2011).

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FEHRL, 2004. Road Transport in Europe 2025.

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Assessment of transport network of the Republic of Serbia

8 Annexes

8.1 Transport map

Assessment of transport network of the Republic of Serbia

8.2 District capitals

Assessment of transport network of the Republic of Serbia

8.3 Action Plan for transport development

III

Assessment of transport network of the Republic of Serbia

8.4 Road categorization

Excerpt from DECREE ON CRITERIA FOR STATE ROAD CATEGORIZATION

(„Official Gazette of the Republic of Serbia“, No. 37/2009)

CRITERIA FOR CATEGORIZATION OF CATEGORY I STATE ROADS

Article 3

Roads are categorized as category I state roads if they correspond to one of the primary or secondary criteria.

3) Primary Criteria

Article 4

Roads are categorized as category I state roads if:

1) they belong to the international E- roads networks in the territory of the Republic of Serbia in accordance with the European Agreement on Main International Traffic Arteries;

2) they are connected by traffic with roads in territories of the neighboring countries corresponding to the category I state roads;

3) they connect state, macro-regional and/or the most important regional centers of traffic gravitation.

2. Secondary Criteria

Article 5

Roads are categorized as category I state roads if:

1) average annual daily traffic (AAGT) in 2007 is more than 5,000 vehicles per day;

2) they provide a degree of serving to the territory and population along the category I state road;

3) they provide a degree of directness of interconnection of state, macro-regional and/or most important centers of traffic gravitation.

Assessment of transport network of the Republic of Serbia

III CRITERIA FOR CATEGORIZATION OF CATEGORY II STATE ROADS

Article 6

Roads are categorized as category II state roads if they correspond to one of the primary or secondary criteria.

3) Primary Criteria

Article 7

Roads are categorized as category II state roads if:

1) they interconnect regional centers of traffic gravitation or the most important centers of traffic gravitation;

2) they connect regional centers of traffic gravitation or the most important district centres of traffic gravitation with a category I state road network.

3) they are connected by traffic with roads in the territories of the neighboring countries corresponding to the category II state roads;

4) they run in parallel with category I state road network for which provision of the highest level of access control is requested (highways and similar).

2. Secondary Criteria

Article 8

Roads are categorized as category II state roads if:

1) average annual daily traffic (AAGT) in 2007 is more than 1,500 vehicles per day;

2) they provide a degree of serving to territory and population along the category II state road;

3) they provide a degree of directness of interconnection of regional centers of traffic gravitation and/or the most important centers of traffic gravitation or with network of category I state roads

Assessment of transport network of the Republic of Serbia

8.5 GMTS summary map

VII

Assessment of transport network of the Republic of Serbia

8.6 2012 data summary table

Bor 1 Bor 55,817 38,767 13,010 23% 860 17 269 19,516 2.86 42737 0.62 4.52 88264

Bor 2 Kladovo 23,613 15,260 4,110 17% 651 14 234 8,256 2.86 44364 0.65 4.53 37422

Bor 3 Majdanpek 23,703 16,273 6,189 26% 972 18 286 8,288 2.86 39382 0.58 4.50 37312

Bor 4 Negotin 43,418 26,221 7,264 17% 1,116 19 306 15,181 2.86 38733 0.57 4.50 68287

Braničevo 5 Golubac 9,913 5,975 1,193 12% 400 11 183 3,137 3.16 38959 0.57 4.97 15596

Braničevo 6 Kučevo 18,808 11,296 2,919 16% 717 15 245 5,952 3.16 39310 0.57 4.97 29604

Braničevo 7 Malo Crniće 13,853 8,064 1,082 8% 270 9 150 4,384 3.16 32412 0.47 4.93 21601 Petrovac na Braničevo 8 Mlavi 34,511 20,535 6,465 19% 656 14 235 10,921 3.16 35245 0.52 4.95 54022

Braničevo 9 Požarevac 74,902 49,084 23,554 31% 494 13 204 23,703 3.16 50234 0.73 5.05 119643 Veliko Braničevo 10 Gradište 20,659 12,664 2,757 13% 377 11 178 6,538 3.16 35394 0.52 4.95 32345

Braničevo 11 Žabari 13,034 7,142 1,073 8% 265 9 149 4,125 3.16 38550 0.56 4.97 20495

Braničevo 12 Žagubica 14,823 8,746 1,627 11% 760 16 253 4,691 3.16 37566 0.55 4.96 23277

Belgrade 13 Barajevo 24,641 16,492 4,414 18% 223 8 137 8,896 2.77 48685 0.71 4.42 39278

Belgrade 14 Čukarica 168,508 118,440 38,945 23% 171 7 120 60,833 2.77 47924 0.70 4.41 268331

Belgrade 15 Grocka 75,466 53,396 12,717 17% 272 9 151 27,244 2.77 49480 0.72 4.42 120423

Belgrade 16 Lazarevac 58,511 39,643 22,338 38% 385 11 180 21,123 2.77 60833 0.89 4.49 94790

Belgrade 17 Mladenovac 52,490 35,186 11,734 22% 350 11 171 18,949 2.77 34563 0.51 4.33 82090 Novi Belgrade 18 Beograd 217,773 153,311 88,064 40% 36 3 55 78,618 2.77 68388 1.00 4.53 356334

Belgrade 19 Obrenovac 70,975 48,397 14,339 20% 424 12 189 25,623 2.77 54043 0.79 4.45 113949 VIII

Assessment of transport network of the Republic of Serbia

Belgrade 20 Palilula 155,902 107,660 61,635 40% 463 12 197 56,282 2.77 57316 0.84 4.47 251391

Belgrade 21 Rakovica 99,000 69,983 19,886 20% 30 3 50 35,740 2.77 38457 0.56 4.35 155647

Šumadija 22 Rača 12,959 7,939 1,864 14% 220 8 136 4,291 3.02 26531 0.39 4.67 20046

Šumadija 23 Topola 25,292 15,873 3,491 14% 354 11 172 8,375 3.02 33446 0.49 4.72 39494

Toplica 24 Blace 13,759 8,299 1,975 14% 308 10 161 4,586 3 23722 0.35 4.62 21201

Toplica 25 Kuršumlija 21,608 13,824 3,552 16% 1,016 18 292 7,203 3 26206 0.38 4.64 33410

Toplica 26 Prokuplje 48,501 31,050 9,743 20% 771 16 254 16,167 3 33578 0.49 4.69 75750

Toplica 27 Žitorađa 18,207 11,218 1,952 11% 213 8 134 6,069 3 37924 0.55 4.71 28604

Zaječar 28 Boljevac 15,849 9,577 2,060 13% 809 16 261 5,446 2.91 36660 0.54 4.56 24857

Zaječar 29 Knjaževac 37,172 22,597 7,234 19% 1,145 19 310 12,774 2.91 25503 0.37 4.50 57419

Zaječar 30 Sokobanja 18,571 11,610 4,138 22% 527 13 210 6,382 2.91 42183 0.62 4.60 29345

Zaječar 31 Zaječar 65,969 43,217 15,686 24% 1,011 18 291 22,670 2.91 37356 0.55 4.57 103562

West Bačka 32 Apatin 32,813 22,230 6,106 19% 355 11 173 11,473 2.86 58885 0.86 4.62 53021

Belgrade 33 Savski Venac 42,505 28,665 82,946 195% 12 2 31 15,345 2.77 55250 0.81 4.45 68351

Belgrade 34 Sopot 20,390 13,056 6,854 34% 273 9 151 7,361 2.77 39734 0.58 4.36 32113

Belgrade 35 Stari Grad 55,543 37,666 76,801 138% 8 2 26 20,052 2.77 57403 0.84 4.47 89573

Belgrade 36 Surčin 38,695 27,022 5,740 15% 275 9 152 13,969 2.77 66883 0.98 4.52 63190

Belgrade 37 Voždovac 151,768 103,599 41,349 27% 152 7 113 54,790 2.77 48042 0.70 4.41 241713

Belgrade 38 Vračar 58,386 38,371 38,585 66% 5 1 20 21,078 2.77 58920 0.86 4.48 94348

Belgrade 39 Zemun 152,950 107,550 51,686 34% 157 7 115 55,217 2.77 54785 0.80 4.45 245801

Belgrade 40 Zvezdara 132,621 92,570 39,703 30% 30 3 51 47,878 2.77 48946 0.72 4.42 211474

Assessment of transport network of the Republic of Serbia

Jablanica 41 Bojnik 13,118 7,758 1,719 13% 261 9 148 3,987 3.29 21385 0.31 5.05 20149

Jablanica 42 Crna Trava 2,563 1,429 560 22% 323 10 165 779 3.29 39985 0.58 5.18 4038

Jablanica 43 Lebane 24,918 15,987 2,558 10% 322 10 164 7,574 3.29 28375 0.41 5.10 38642

Jablanica 44 Leskovac 156,252 104,241 26,914 17% 1,028 18 294 47,493 3.29 36090 0.53 5.16 244872

Jablanica 45 Medveđa 10,760 6,572 1,130 11% 553 13 215 3,271 3.29 33257 0.49 5.14 16798

Jablanica 46 Vlasotince 33,312 21,777 5,223 16% 311 10 162 10,125 3.29 27208 0.40 5.09 51577

South Bačka 47 Bač 16,268 10,760 3,579 22% 366 11 175 5,728 2.84 30943 0.45 4.42 25317 Bačka South Bačka 48 Palanka 60,966 41,690 13,920 23% 591 14 223 21,467 2.84 44934 0.66 4.50 96693 Bački South Bačka 49 Petrovac 14,681 9,885 2,922 20% 163 7 117 5,169 2.84 34643 0.51 4.44 22962

South Bačka 50 Bečej 40,987 27,700 9,661 24% 503 13 205 14,432 2.84 32101 0.47 4.43 63885

South Bačka 51 Beočin 16,086 10,997 3,336 21% 177 8 122 5,664 2.84 62263 0.91 4.61 26109

South Bačka 52 Novi Sad 299,294 210,903 148,585 50% 728 15 247 105,385 2.84 53310 0.78 4.56 480041

South Bačka 53 Srbobran 17,855 11,851 2,829 16% 301 10 159 6,287 2.84 38092 0.56 4.46 28058 Sremski South Bačka 54 Karlovci 8,839 6,125 790 9% 55 4 68 3,112 2.84 45406 0.66 4.51 14028

South Bačka 55 Temerin 28,275 19,719 6,831 24% 175 7 121 9,956 2.84 38503 0.56 4.47 44457

South Bačka 56 Titel 17,050 11,377 1,712 10% 254 9 146 6,004 2.84 36607 0.54 4.45 26739

South Bačka 57 Vrbas 45,852 31,264 15,142 33% 388 11 181 16,145 2.84 42819 0.63 4.49 72515

South Bačka 58 Žabalj 27,513 18,370 4,144 15% 402 11 184 9,688 2.84 41819 0.61 4.49 43453

South Banat 59 Alibunar 22,954 14,673 3,129 14% 623 14 229 7,861 2.92 29611 0.43 4.54 35656

South Banat 60 Bela Crkva 20,367 13,636 2,982 15% 382 11 179 6,975 2.92 33473 0.49 4.56 31805

South Banat 61 Kovačica 27,890 18,403 3,471 12% 422 12 188 9,551 2.92 35419 0.52 4.57 43668

Assessment of transport network of the Republic of Serbia

South Banat 62 Kovin 36,802 24,440 6,363 17% 736 15 249 12,603 2.92 40020 0.59 4.60 57983

South Banat 63 Opovo 11,016 7,320 608 6% 211 8 133 3,773 2.92 38974 0.57 4.59 17331

South Banat 64 Pančevo 127,162 88,821 39,016 31% 799 16 259 43,549 2.92 53403 0.78 4.68 203982

South Banat 65 Plandište 13,377 8,610 2,249 17% 421 12 188 4,581 2.92 23143 0.34 4.50 20596

South Banat 66 Vršac 54,369 36,794 14,893 27% 809 16 261 18,620 2.92 52894 0.77 4.68 87155

Kolubara 67 Lajkovac 17,062 11,091 3,050 18% 191 8 127 5,613 3.04 57733 0.84 4.90 27528

Kolubara 68 Ljig 14,629 8,901 2,648 18% 288 10 155 4,812 3.04 35061 0.51 4.76 22894

Kolubara 69 Mionica 16,513 10,199 1,943 12% 331 10 167 5,432 3.04 32957 0.48 4.74 25768

Kolubara 70 Osečina 15,135 9,688 1,653 11% 320 10 164 4,979 3.04 33143 0.48 4.75 23624

Kolubara 71 Ub 32,104 20,214 3,988 12% 467 12 198 10,561 3.04 37195 0.54 4.77 50388

Kolubara 72 Valjevo 96,761 65,512 33,267 34% 926 17 279 31,829 3.04 36467 0.53 4.77 151717

Mačva 73 Bogatić 32,990 21,081 3,364 10% 352 11 172 10,540 3.13 36402 0.53 4.91 51722

Mačva 74 Koceljeva 15,636 9,952 1,965 13% 257 9 147 4,996 3.13 40137 0.59 4.93 24639

Mačva 75 Krupanj 20,192 13,527 2,104 10% 335 10 168 6,451 3.13 35546 0.52 4.90 31621

Mačva 76 Ljubovija 17,052 11,347 3,074 18% 279 9 153 5,448 3.13 39454 0.58 4.93 26845

Mačva 77 Loznica 86,413 58,838 16,115 19% 608 14 226 27,608 3.13 32437 0.47 4.88 134752

Mačva 78 Mali Zvornik 14,076 9,541 2,592 18% 144 7 110 4,497 3.13 38970 0.57 4.92 22146

Mačva 79 Šabac 122,893 83,505 28,989 24% 778 16 256 39,263 3.13 41915 0.61 4.94 194118

Mačva 80 Vladimirci 20,373 12,624 1,953 10% 340 10 169 6,509 3.13 33927 0.50 4.89 31834

Moravica 81 Čačak 117,072 79,364 28,174 24% 632 14 230 38,766 3.02 38916 0.57 4.75 184175 Gornji Moravica 82 Milanovac 47,641 31,720 10,952 23% 833 16 264 15,775 3.02 37266 0.54 4.74 74780

Assessment of transport network of the Republic of Serbia

Moravica 83 Ivanjica 35,445 23,726 8,936 25% 1,082 19 301 11,737 3.02 29775 0.44 4.69 55072

Moravica 84 Lučani 24,614 15,716 4,233 17% 505 13 206 8,150 3.02 35618 0.52 4.73 38549

Nišava 85 Aleksinac 57,749 36,612 8,780 15% 710 15 244 19,444 2.97 37440 0.55 4.66 90668

Nišava 86 Doljevac 19,561 12,589 1,628 8% 124 6 102 6,586 2.97 36733 0.54 4.66 30682

Nišava 87 Gadžin Han 10,464 5,261 3,314 32% 341 10 169 3,523 2.97 26036 0.38 4.59 16175

Nišava 88 Merošina 14,812 9,014 1,706 12% 202 8 130 4,987 2.97 34163 0.50 4.64 23152

Nišava 89 Niš 250,518 173,151 78,167 31% 578 14 220 84,349 2.97 39306 0.57 4.67 394317

Nišava 90 Ražanj 11,369 6,613 941 8% 300 10 159 3,828 2.97 32885 0.48 4.63 17740

Nišava 91 Svrljig 17,284 10,111 3,594 21% 501 13 205 5,820 2.97 20376 0.30 4.56 26511

Pčinja 92 Bosilegrad 9,931 6,095 1,490 15% 522 13 209 2,797 3.55 31546 0.46 5.53 15468

Pčinja 93 Bujanovac 43,302 26,328 7,442 17% 469 12 198 12,198 3.55 35407 0.52 5.56 67798

Pčinja 94 Preševo 34,904 20,523 3,702 11% 292 10 157 9,832 3.55 33691 0.49 5.55 54522

Pčinja 95 Surdulica 22,190 14,617 5,318 24% 561 13 217 6,251 3.55 36560 0.53 5.57 34797

Pčinja 96 Trgovište 6,372 3,915 1,061 17% 417 12 187 1,795 3.55 31258 0.46 5.53 9920

Pčinja 97 Vladičin Han 23,703 15,813 4,639 20% 367 11 176 6,677 3.55 20666 0.30 5.45 36371

Pčinja 98 Vranje 87,288 59,278 20,555 24% 874 17 271 24,588 3.55 35298 0.52 5.56 136647

Pirot 99 Babušnica 15,734 9,013 2,425 15% 553 13 215 4,432 3.55 28170 0.41 5.50 24393

Pirot 100 Bela Palanka 14,381 8,634 2,544 18% 516 13 208 5,268 2.73 18335 0.27 4.18 21996

Pirot 101 Dimitrovgrad 11,748 7,551 1,962 17% 439 12 192 4,303 2.73 40044 0.59 4.30 18510

Pirot 102 Pirot 63,791 42,220 19,507 31% 1,152 19 311 23,367 2.73 34785 0.51 4.27 99793

Podunavlje 103 Smederevo 109,809 74,558 27,347 25% 495 13 204 34,208 3.21 52301 0.76 5.14 175887

XII

Assessment of transport network of the Republic of Serbia

Average trips per Population AREA Radius Intrazonal Number of household Average Earning household by District Zone # Municipality Total Population (15-64) Employment ratio (km^2) (km) impedance households size wage factor GDP TOTAL from zone Smederevska Podunavlje 104 Palanka 56,011 36,945 10,922 19% 431 12 190 17,449 3.21 40076 0.59 5.06 88254

Podunavlje 105 Velika Plana 44,470 28,649 7,421 17% 341 10 169 13,854 3.21 39592 0.58 5.05 70023

Pomoravlje 106 Ćuprija 33,567 21,726 8,341 25% 277 9 152 10,970 3.06 36471 0.53 4.80 52632

Pomoravlje 107 Despotovac 25,611 15,501 4,488 18% 613 14 227 8,370 3.06 42533 0.62 4.84 40488

Pomoravlje 108 Jagodina 70,894 37,901 30,395 43% 477 12 200 23,168 3.06 37091 0.54 4.80 111253

Pomoravlje 109 Paraćin 58,301 30,672 12,119 21% 547 13 214 19,053 3.06 39938 0.58 4.82 91845

Pomoravlje 110 Rekovac 13,551 4,698 1,392 10% 372 11 177 4,428 3.06 33119 0.48 4.78 21151

Pomoravlje 111 Svilajnac 25,511 10,692 4,478 18% 333 10 167 8,337 3.06 39557 0.58 4.82 40168 Aleksandrov Rasina 112 ac 29,389 18,789 4,887 17% 385 11 180 9,043 3.25 31120 0.46 5.06 45747

Rasina 113 Brus 18,764 12,103 2,648 14% 624 14 229 5,774 3.25 29987 0.44 5.05 29163

Rasina 114 Ćićevac 10,755 6,833 1,960 18% 126 6 103 3,309 3.25 30186 0.44 5.05 16720

Rasina 115 Kruševac 131,368 88,704 29,209 22% 858 17 268 40,421 3.25 39886 0.58 5.12 206936

Rasina 116 Trstenik 49,043 32,181 10,632 22% 445 12 193 15,090 3.25 28922 0.42 5.04 76111

Rasina 117 Varvarin 20,122 12,253 2,094 10% 236 9 141 6,191 3.25 37964 0.56 5.11 31615

Raška 118 Kraljevo 121,707 81,109 34,571 28% 1,490 22 354 35,587 3.42 39430 0.58 5.38 191600

Raška 119 Novi Pazar 85,996 56,410 19,400 23% 721 15 246 25,145 3.42 35066 0.51 5.35 134582

Raška 120 Raška 26,981 17,958 6,274 23% 677 15 238 7,889 3.42 35926 0.53 5.36 42274

Raška 121 Tutin 30,054 19,201 3,027 10% 755 16 252 8,788 3.42 42799 0.63 5.41 47529 Vrnjačka Raška 122 Banja 26,492 17,313 9,060 34% 246 9 144 7,746 3.42 33725 0.49 5.34 41384

North Bačka 123 Bačka Topola 38,245 25,619 7,969 21% 611 14 226 14,324 2.67 35516 0.52 4.18 59889

North Bačka 124 Mali Iđoš 13,494 8,923 1,959 15% 188 8 126 5,054 2.67 38180 0.56 4.20 21207

XIII

Assessment of transport network of the Republic of Serbia

North Bačka 125 Subotica 148,401 102,020 45,496 31% 890 17 273 55,581 2.67 43752 0.64 4.23 234991

North Banat 126 Ada 18,994 12,818 4,370 23% 240 9 142 7,061 2.69 39127 0.57 4.23 29889

North Banat 127 Čoka 13,832 9,087 1,629 12% 342 10 169 5,142 2.69 37979 0.56 4.23 21733

North Banat 128 Kanjiža 27,510 18,361 5,145 19% 384 11 180 10,227 2.69 49046 0.72 4.29 43873

North Banat 129 Kikinda 67,002 46,044 16,583 25% 800 16 259 24,908 2.69 39929 0.58 4.24 105550 Novi North Banat 130 Kneževac 12,975 8,620 2,419 19% 279 9 153 4,823 2.69 39642 0.58 4.24 20432

North Banat 131 Senta 25,568 17,164 6,156 24% 304 10 160 9,505 2.69 48565 0.71 4.29 40749 Central Banat 132 Novi Bečej 26,924 18,060 4,287 16% 637 14 231 9,581 2.81 35711 0.52 4.40 42172 Central Banat 133 Nova Crnja 12,705 8,029 1,326 10% 273 9 151 4,521 2.81 37884 0.55 4.41 19959 Central Banat 134 Sečanj 16,377 10,476 2,472 15% 568 13 218 5,828 2.81 36337 0.53 4.41 25674 Central Banat 135 Žitište 20,399 13,055 3,425 17% 558 13 216 7,259 2.81 32610 0.48 4.38 31817 Central Banat 136 Zrenjanin 132,051 91,104 31,050 24% 1,377 21 340 46,993 2.81 46426 0.68 4.47 209855

Srem 137 Inđija 49,609 33,818 9,793 20% 391 11 181 16,427 3.02 42619 0.62 4.77 78435

Srem 138 Irig 12,329 8,090 1,537 12% 254 9 146 4,082 3.02 40720 0.60 4.76 19443

Srem 139 Pećinci 21,506 14,446 3,727 17% 499 13 205 7,121 3.02 57379 0.84 4.87 34681

Srem 140 Ruma 60,006 40,602 11,619 19% 608 14 226 19,870 3.02 39305 0.57 4.75 94450

Srem 141 Šid 38,973 26,183 7,074 18% 664 15 236 12,905 3.02 40610 0.59 4.76 61452 Sremska Srem 142 Mitrovica 85,902 58,566 16,004 19% 759 16 252 28,444 3.02 45647 0.67 4.79 136372

Srem 143 Stara Pazova 67,576 46,879 13,544 20% 329 10 166 22,376 3.02 44450 0.65 4.79 107106

Šumadija 144 Aranđelovac 48,129 32,574 12,412 26% 364 11 175 15,937 3.02 33127 0.48 4.71 75122

Šumadija 145 Batočina 12,220 7,967 1,483 12% 133 7 106 4,046 3.02 32088 0.47 4.71 19047

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Assessment of transport network of the Republic of Serbia

Šumadija 146 Knić 16,148 9,640 1,641 10% 422 12 188 5,347 3.02 35332 0.52 4.73 25280

Šumadija 147 Kragujevac 175,802 123,338 49,044 28% 845 16 266 58,213 3.02 42556 0.62 4.77 277931

Šumadija 148 Lapovo 8,228 5,343 1,851 22% 48 4 63 2,725 3.02 36530 0.53 4.74 12902

West Bačka 149 Kula 48,353 32,787 9,288 19% 492 13 203 16,907 2.86 37425 0.55 4.49 75914

West Bačka 150 Odžaci 35,582 23,802 6,689 19% 420 12 188 12,441 2.86 36871 0.54 4.49 55822

West Bačka 151 Sombor 97,263 65,910 23,575 24% 1,208 20 318 34,008 2.86 42044 0.61 4.52 153660

Zlatibor 152 Arilje 19,784 13,196 6,109 31% 351 11 172 6,221 3.18 31854 0.47 4.95 30826

Zlatibor 153 Bajina Bašta 29,151 19,583 4,847 17% 647 14 233 9,167 3.18 44692 0.65 5.04 46219

Zlatibor 154 Čajetina 15,628 10,005 4,003 26% 642 14 232 4,914 3.18 35070 0.51 4.98 24458

Zlatibor 155 Kosjerić 14,001 9,009 2,644 19% 364 11 175 4,403 3.18 63777 0.93 5.17 22771

Zlatibor 156 Nova Varoš 19,982 13,590 3,491 17% 587 14 222 6,284 3.18 38946 0.57 5.00 31436

Zlatibor 157 Požega 32,293 21,113 6,459 20% 435 12 191 10,155 3.18 36695 0.54 4.99 50650

Zlatibor 158 Priboj 30,377 21,369 6,533 22% 565 13 218 9,553 3.18 28473 0.42 4.93 47114

Zlatibor 159 Prijepolje 41,188 27,959 6,927 17% 863 17 269 12,952 3.18 30545 0.45 4.95 64063

Zlatibor 160 Sjenica 27,970 17,938 3,243 12% 1,094 19 303 8,796 3.18 35095 0.51 4.98 43774

Zlatibor 161 Užice 83,022 57,874 25,064 30% 616 14 227 26,108 3.18 44239 0.65 5.04 131550

7,498,001 5,008,611 2,002,373 22% 484 12 192 2,485,542 3.02 4.75 11,857,140

Assessment of transport network of the Republic of Serbia

8.7 2027 trips forecast

Average Earning trips per trips per trips per total trips District Zone # Municipality wage factor household total trips optimistic household total trips neutral household pessimistic

Bor 1 Bor 42737 0,70 4,87 95085 4,72 92079 4,60 89793

Bor 2 Kladovo 44364 0,70 4,87 40225 4,72 38953 4,60 37986

Bor 3 Majdanpek 39382 0,70 4,87 40379 4,72 39102 4,60 38131

Bor 4 Negotin 38733 0,70 4,87 73963 4,72 71625 4,60 69847

Braničevo 5 Golubac 38959 0,70 5,38 16887 5,21 16353 5,08 15947

Braničevo 6 Kučevo 39310 0,70 5,38 32040 5,21 31027 5,08 30256

Braničevo 7 Malo Crniće 32412 0,70 5,38 23599 5,21 22853 5,08 22285

Braničevo 8 Petrovac na Mlavi 35245 0,70 5,38 58790 5,21 56932 5,08 55518

Braničevo 9 Požarevac 50234 0,73 5,42 128394 5,24 124157 5,10 120935

Braničevo 10 Veliko Gradište 35394 0,70 5,38 35193 5,21 34080 5,08 33234

Braničevo 11 Žabari 38550 0,70 5,38 22204 5,21 21502 5,08 20968

Braničevo 12 Žagubica 37566 0,70 5,38 25251 5,21 24453 5,08 23846

Belgrade 13 Barajevo 48685 0,71 4,73 42067 4,58 40717 4,46 39690

Belgrade 14 Čukarica 47924 0,70 4,72 287096 4,57 278011 4,46 271101

Belgrade 15 Grocka 49480 0,72 4,74 129105 4,58 124901 4,47 121704

Belgrade 16 Lazarevac 60833 0,89 4,88 103099 4,69 99076 4,55 96016

Belgrade 17 Mladenovac 34563 0,70 4,72 89418 4,57 86591 4,46 84441

Belgrade 18 Novi Beograd 68388 1,00 4,98 391176 4,76 374309 4,60 361477

Belgrade 19 Obrenovac 54043 0,79 4,80 122883 4,63 118557 4,50 115268

Belgrade 20 Palilula 57316 0,84 4,84 272227 4,66 262138 4,52 254466

Belgrade 21 Rakovica 38457 0,70 4,72 168648 4,57 163317 4,46 159262

Šumadija 22 Rača 26531 0,70 5,14 22076 4,98 21378 4,86 20847

Šumadija 23 Topola 33446 0,70 5,14 43085 4,98 41723 4,86 40687

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Assessment of transport network of the Republic of Serbia

Average Earning trips per trips per trips per total trips District Zone # Municipality wage factor household total trips optimistic household total trips neutral household pessimistic

Toplica 24 Blace 23722 0,70 5,11 23439 4,95 22698 4,83 22134

Toplica 25 Kuršumlija 26206 0,70 5,11 36810 4,95 35646 4,83 34761

Toplica 26 Prokuplje 33578 0,70 5,11 82622 4,95 80010 4,83 78024

Toplica 27 Žitorađa 37924 0,70 5,11 31016 4,95 30035 4,83 29290

Zaječar 28 Boljevac 36660 0,70 4,96 26999 4,80 26146 4,68 25496

Zaječar 29 Knjaževac 25503 0,70 4,96 63323 4,80 61321 4,68 59799

Zaječar 30 Sokobanja 42183 0,70 4,96 31636 4,80 30636 4,68 29875

Zaječar 31 Zaječar 37356 0,70 4,96 112379 4,80 108827 4,68 106125

West Bačka 32 Apatin 58885 0,86 5,01 57529 4,82 55346 4,68 53686

Belgrade 33 Savski Venac 55250 0,81 4,81 73823 4,64 71173 4,51 69159

Belgrade 34 Sopot 39734 0,70 4,72 34735 4,57 33637 4,46 32801

Belgrade 35 Stari Grad 57403 0,84 4,84 97008 4,66 93408 4,52 90670

Belgrade 36 Surčin 66883 0,98 4,96 69242 4,75 66312 4,59 64083

Belgrade 37 Voždovac 48042 0,70 4,72 258656 4,57 250453 4,46 244214

Belgrade 38 Vračar 58920 0,86 4,86 102374 4,67 98488 4,53 95532

Belgrade 39 Zemun 54785 0,80 4,81 265323 4,63 255871 4,50 248683

Belgrade 40 Zvezdara 48946 0,72 4,73 226564 4,58 219258 4,46 213702

Jablanica 41 Bojnik 21385 0,70 5,60 22347 5,43 21640 5,29 21103

Jablanica 42 Crna Trava 39985 0,70 5,60 4366 5,43 4228 5,29 4123

Jablanica 43 Lebane 28375 0,70 5,60 42448 5,43 41106 5,29 40086

Jablanica 44 Leskovac 36090 0,70 5,60 266178 5,43 257763 5,29 251363

Jablanica 45 Medveđa 33257 0,70 5,60 18330 5,43 17750 5,29 17310

Jablanica 46 Vlasotince 27208 0,70 5,60 56748 5,43 54954 5,29 53589

South Bačka 47 Bač 30943 0,70 4,84 27713 4,69 26837 4,57 26170

South Bačka 48 Bačka Palanka 44934 0,70 4,84 103857 4,69 100573 4,57 98076

South Bačka 49 Bački Petrovac 34643 0,70 4,84 25009 4,69 24219 4,57 23617

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Assessment of transport network of the Republic of Serbia

Average Earning trips per trips per trips per total trips District Zone # Municipality wage factor household total trips optimistic household total trips neutral household pessimistic

South Bačka 50 Bečej 32101 0,70 4,84 69822 4,69 67615 4,57 65936

South Bačka 51 Beočin 62263 0,91 5,02 28448 4,82 27316 4,67 26454

South Bačka 52 Novi Sad 53310 0,78 4,91 517193 4,74 499205 4,61 485525

South Bačka 53 Srbobran 38092 0,70 4,84 30416 4,69 29455 4,57 28723

South Bačka 54 Sremski Karlovci 45406 0,70 4,84 15057 4,69 14581 4,57 14219

South Bačka 55 Temerin 38503 0,70 4,84 48167 4,69 46644 4,57 45486

South Bačka 56 Titel 36607 0,70 4,84 29045 4,69 28127 4,57 27428

South Bačka 57 Vrbas 42819 0,70 4,84 78110 4,69 75640 4,57 73762

South Bačka 58 Žabalj 41819 0,70 4,84 46869 4,69 45387 4,57 44260

South Banat 59 Alibunar 29611 0,70 4,97 39103 4,82 37866 4,70 36926

South Banat 60 Bela Crkva 33473 0,70 4,97 34696 4,82 33599 4,70 32764

South Banat 61 Kovačica 35419 0,70 4,97 47511 4,82 46009 4,70 44867

South Banat 62 Kovin 40020 0,70 4,97 62693 4,82 60711 4,70 59204

South Banat 63 Opovo 38974 0,70 4,97 18766 4,82 18173 4,70 17721

South Banat 64 Pančevo 53403 0,78 5,05 219795 4,87 212139 4,74 206316

South Banat 65 Plandište 23143 0,70 4,97 22788 4,82 22068 4,70 21520

South Banat 66 Vršac 52894 0,77 5,04 93850 4,87 90608 4,73 88143

Kolubara 67 Lajkovac 57733 0,84 5,31 29825 5,12 28713 4,97 27867

Kolubara 68 Ljig 35061 0,70 5,18 24921 5,01 24133 4,89 23534

Kolubara 69 Mionica 32957 0,70 5,18 28130 5,01 27241 4,89 26565

Kolubara 70 Osečina 33143 0,70 5,18 25783 5,01 24968 4,89 24348

Kolubara 71 Ub 37195 0,70 5,18 54690 5,01 52961 4,89 51646

Kolubara 72 Valjevo 36467 0,70 5,18 164834 5,01 159623 4,89 155660

Mačva 73 Bogatić 36402 0,70 5,33 56199 5,16 54422 5,04 53071

Mačva 74 Koceljeva 40137 0,70 5,33 26636 5,16 25794 5,04 25154

Mačva 75 Krupanj 35546 0,70 5,33 34397 5,16 33310 5,04 32483

XVIII

Assessment of transport network of the Republic of Serbia

Average Earning trips per trips per trips per total trips District Zone # Municipality wage factor household total trips optimistic household total trips neutral household pessimistic

Mačva 76 Ljubovija 39454 0,70 5,33 29048 5,16 28130 5,04 27432

Mačva 77 Loznica 32437 0,70 5,33 147206 5,16 142552 5,04 139013

Mačva 78 Mali Zvornik 38970 0,70 5,33 23979 5,16 23221 5,04 22644

Mačva 79 Šabac 41915 0,70 5,33 209351 5,16 202732 5,04 197698

Mačva 80 Vladimirci 33927 0,70 5,33 34706 5,16 33609 5,04 32774

Moravica 81 Čačak 38916 0,70 5,14 199434 4,98 193129 4,86 188334

Moravica 82 Gornji Milanovac 37266 0,70 5,14 81157 4,98 78592 4,86 76640

Moravica 83 Ivanjica 29775 0,70 5,14 60381 4,98 58472 4,86 57021

Moravica 84 Lučani 35618 0,70 5,14 41930 4,98 40605 4,86 39597

Nišava 85 Aleksinac 37440 0,70 5,06 98377 4,90 95266 4,78 92901

Nišava 86 Doljevac 36733 0,70 5,06 33323 4,90 32269 4,78 31468

Nišava 87 Gadžin Han 26036 0,70 5,06 17826 4,90 17262 4,78 16833

Nišava 88 Merošina 34163 0,70 5,06 25233 4,90 24435 4,78 23828

Nišava 89 Niš 39306 0,70 5,06 426762 4,90 413270 4,78 403009

Nišava 90 Ražanj 32885 0,70 5,06 19367 4,90 18755 4,78 18289

Nišava 91 Svrljig 20376 0,70 5,06 29444 4,90 28513 4,78 27805

Pčinja 92 Bosilegrad 31546 0,70 6,05 16918 5,86 16383 5,71 15976

Pčinja 93 Bujanovac 35407 0,70 6,05 73766 5,86 71434 5,71 69660

Pčinja 94 Preševo 33691 0,70 6,05 59460 5,86 57580 5,71 56150

Pčinja 95 Surdulica 36560 0,70 6,05 37801 5,86 36606 5,71 35697

Pčinja 96 Trgovište 31258 0,70 6,05 10855 5,86 10512 5,71 10251

Pčinja 97 Vladičin Han 20666 0,70 6,05 40379 5,86 39102 5,71 38131

Pčinja 98 Vranje 35298 0,70 6,05 148697 5,86 143996 5,71 140421

Pirot 99 Babušnica 28170 0,70 6,05 26803 5,86 25956 5,71 25311

Pirot 100 Bela Palanka 18335 0,70 4,65 24498 4,50 23724 4,39 23135

Pirot 101 Dimitrovgrad 40044 0,70 4,65 20013 4,50 19380 4,39 18899

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Assessment of transport network of the Republic of Serbia

Average Earning trips per trips per trips per total trips District Zone # Municipality wage factor household total trips optimistic household total trips neutral household pessimistic

Pirot 102 Pirot 34785 0,70 4,65 108669 4,50 105234 4,39 102621

Podunavlje 103 Smederevo 52301 0,76 5,53 189255 5,34 182782 5,20 177860

Podunavlje 104 Smederevska Palanka 40076 0,70 5,47 95416 5,30 92399 5,16 90105

Podunavlje 105 Velika Plana 39592 0,70 5,47 75755 5,30 73361 5,16 71539

Pomoravlje 106 Ćuprija 36471 0,70 5,21 57182 5,05 55374 4,92 53999

Pomoravlje 107 Despotovac 42533 0,70 5,21 43629 5,05 42250 4,92 41201

Pomoravlje 108 Jagodina 37091 0,70 5,21 120769 5,05 116951 4,92 114047

Pomoravlje 109 Paraćin 39938 0,70 5,21 99317 5,05 96177 4,92 93789

Pomoravlje 110 Rekovac 33119 0,70 5,21 23084 5,05 22355 4,92 21800

Pomoravlje 111 Svilajnac 39557 0,70 5,21 43458 5,05 42085 4,92 41040

Rasina 112 Aleksandrovac 31120 0,70 5,54 50065 5,36 48482 5,23 47278

Rasina 113 Brus 29987 0,70 5,54 31965 5,36 30954 5,23 30186

Rasina 114 Ćićevac 30186 0,70 5,54 18321 5,36 17742 5,23 17302

Rasina 115 Kruševac 39886 0,70 5,54 223788 5,36 216713 5,23 211332

Rasina 116 Trstenik 28922 0,70 5,54 83546 5,36 80904 5,23 78896

Rasina 117 Varvarin 37964 0,70 5,54 34278 5,36 33195 5,23 32370

Raška 118 Kraljevo 39430 0,70 5,83 207330 5,64 200776 5,50 195790

Raška 119 Novi Pazar 35066 0,70 5,83 146496 5,64 141864 5,50 138342

Raška 120 Raška 35926 0,70 5,83 45963 5,64 44510 5,50 43404

Raška 121 Tutin 42799 0,70 5,83 51198 5,64 49579 5,50 48348

Raška 122 Vrnjačka Banja 33725 0,70 5,83 45130 5,64 43703 5,50 42618

North Bačka 123 Bačka Topola 35516 0,70 4,55 65151 4,40 63091 4,30 61525

North Bačka 124 Mali Iđoš 38180 0,70 4,55 22987 4,40 22261 4,30 21708

North Bačka 125 Subotica 43752 0,70 4,55 252804 4,40 244812 4,30 238733

North Banat 126 Ada 39127 0,70 4,58 32357 4,44 31334 4,33 30556

North Banat 127 Čoka 37979 0,70 4,58 23563 4,44 22818 4,33 22252

Assessment of transport network of the Republic of Serbia

Average Earning trips per trips per trips per total trips District Zone # Municipality wage factor household total trips optimistic household total trips neutral household pessimistic

North Banat 128 Kanjiža 49046 0,72 4,60 47009 4,45 45491 4,34 44336

North Banat 129 Kikinda 39929 0,70 4,58 114139 4,44 110531 4,33 107786

North Banat 130 Novi Kneževac 39642 0,70 4,58 22103 4,44 21404 4,33 20873

North Banat 131 Senta 48565 0,71 4,59 43635 4,44 42238 4,33 41175

Central Banat 132 Novi Bečej 35711 0,70 4,79 45866 4,64 44416 4,52 43313

Central Banat 133 Nova Crnja 37884 0,70 4,79 21643 4,64 20959 4,52 20439

Central Banat 134 Sečanj 36337 0,70 4,79 27899 4,64 27017 4,52 26346

Central Banat 135 Žitište 32610 0,70 4,79 34750 4,64 33651 4,52 32816

Central Banat 136 Zrenjanin 46426 0,70 4,79 224951 4,64 217840 4,52 212431

Srem 137 Inđija 42619 0,70 5,14 84510 4,98 81838 4,86 79806

Srem 138 Irig 40720 0,70 5,14 21003 4,98 20339 4,86 19834

Srem 139 Pećinci 57379 0,84 5,27 37559 5,08 36165 4,93 35106

Srem 140 Ruma 39305 0,70 5,14 102221 4,98 98990 4,86 96532

Srem 141 Šid 40610 0,70 5,14 66391 4,98 64292 4,86 62696

Srem 142 Sremska Mitrovica 45647 0,70 5,14 146336 4,98 141709 4,86 138191

Srem 143 Stara Pazova 44450 0,70 5,14 115117 4,98 111478 4,86 108710

Šumadija 144 Aranđelovac 33127 0,70 5,14 81989 4,98 79397 4,86 77425

Šumadija 145 Batočina 32088 0,70 5,14 20817 4,98 20159 4,86 19658

Šumadija 146 Knić 35332 0,70 5,14 27508 4,98 26639 4,86 25977

Šumadija 147 Kragujevac 42556 0,70 5,14 299482 4,98 290014 4,86 282813

Šumadija 148 Lapovo 36530 0,70 5,14 14017 4,98 13573 4,86 13236

West Bačka 149 Kula 37425 0,70 4,87 82370 4,72 79766 4,60 77786

West Bačka 150 Odžaci 36871 0,70 4,87 60615 4,72 58698 4,60 57241

West Bačka 151 Sombor 42044 0,70 4,87 165689 4,72 160451 4,60 156467

Zlatibor 152 Arilje 31854 0,70 5,42 33702 5,25 32637 5,12 31827

Zlatibor 153 Bajina Bašta 44692 0,70 5,42 49659 5,25 48089 5,12 46895

XXI

Assessment of transport network of the Republic of Serbia

Average Earning trips per trips per trips per total trips District Zone # Municipality wage factor household total trips optimistic household total trips neutral household pessimistic

Zlatibor 154 Čajetina 35070 0,70 5,42 26623 5,25 25781 5,12 25141

Zlatibor 155 Kosjerić 63777 0,93 5,65 24857 5,42 23847 5,24 23079

Zlatibor 156 Nova Varoš 38946 0,70 5,42 34040 5,25 32964 5,12 32145

Zlatibor 157 Požega 36695 0,70 5,42 55012 5,25 53273 5,12 51950

Zlatibor 158 Priboj 28473 0,70 5,42 51748 5,25 50112 5,12 48868

Zlatibor 159 Prijepolje 30545 0,70 5,42 70165 5,25 67946 5,12 66259

Zlatibor 160 Sjenica 35095 0,70 5,42 47647 5,25 46141 5,12 44995

Zlatibor 161 Užice 44239 0,70 5,42 141430 5,25 136958 5,12 133558

5,15 12.841.797 4,99 12.420.468 4,86 12.083.217

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Assessment of transport network of the Republic of Serbia

8.8 Link saturation level comparison

links 4498 2027 with improvements LOS A ≤33% 3336 74% LOS B ≤55% 543 12% LOS C ≤77% 301 7% LOS D ≤92% 101 2% LOS E ≤100% 36 1% LOS F >100% 181 4%

links 4352 2027 without improvement LOS A ≤33% 2841 65% LOS B ≤55% 596 14% LOS C ≤77% 405 9% LOS D ≤92% 153 4% LOS E ≤100% 58 1% LOS F >100% 299 7%

links 4352 2012 LOS A ≤33% 3055 70% LOS B ≤55% 602 14% LOS C ≤77% 310 7% LOS D ≤92% 110 3% LOS E ≤100% 55 1% LOS F >100% 220 5%

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Assessment of transport network of the Republic of Serbia

8.9 K factor (for number of trips)

trips trips % Zone # Municipality name 2012 2027 k factor growth

1 Bor 88264 92079 1,043 4% 2 Kladovo 37422 38953 1,041 4% 3 Majdanpek 37312 39102 1,048 5% 4 Negotin 68287 71625 1,049 5% 5 Golubac 15596 16353 1,049 5% 6 Kučevo 29604 31027 1,048 5% 7 Malo Crniće 21601 22853 1,058 6% 8 Petrovac na Mlavi 54022 56932 1,054 5% 9 Požarevac 119643 124157 1,038 4% 10 Veliko Gradište 32345 34080 1,054 5% 11 Žabari 20495 21502 1,049 5% 12 Žagubica 23277 24453 1,051 5% 13 Barajevo 39278 40717 1,037 4% 14 Čukarica 268331 278011 1,036 4% 15 Grocka 120423 124901 1,037 4% 16 Lazarevac 94790 99076 1,045 5% 17 Mladenovac 82090 86591 1,055 5% 18 Novi Beograd 356334 374309 1,050 5% 19 Obrenovac 113949 118557 1,040 4% 20 Palilula 251391 262138 1,043 4% 21 Rakovica 155647 163317 1,049 5% 22 Rača 20046 21378 1,066 7% 23 Topola 39494 41723 1,056 6% 24 Blace 21201 22698 1,071 7% 25 Kuršumlija 33410 35646 1,067 7% 26 Prokuplje 75750 80010 1,056 6% 27 Žitorađa 28604 30035 1,050 5% 28 Boljevac 24857 26146 1,052 5% 29 Knjaževac 57419 61321 1,068 7% 30 Sokobanja 29345 30636 1,044 4% 31 Zaječar 103562 108827 1,051 5% 32 Apatin 53021 55346 1,044 4% 33 Savski Venac 68351 71173 1,041 4% 34 Sopot 32113 33637 1,047 5% 35 Stari Grad 89573 93408 1,043 4% 36 Surčin 63190 66312 1,049 5% 37 Voždovac 241713 250453 1,036 4% 38 Vračar 94348 98488 1,044 4% 39 Zemun 245801 255871 1,041 4%

XXIV

Assessment of transport network of the Republic of Serbia

trips trips % Zone # Municipality name 2012 2027 k factor growth 40 Zvezdara 211474 219258 1,037 4% 41 Bojnik 20149 21640 1,074 7% 42 Crna Trava 4038 4228 1,047 5% 43 Lebane 38642 41106 1,064 6% 44 Leskovac 244872 257763 1,053 5% 45 Medveđa 16798 17750 1,057 6% 46 Vlasotince 51577 54954 1,065 7% 47 Bač 25317 26837 1,060 6% 48 Bačka Palanka 96693 100573 1,040 4% 49 Bački Petrovac 22962 24219 1,055 5% 50 Bečej 63885 67615 1,058 6% 51 Beočin 26109 27316 1,046 5% 52 Novi Sad 480041 499205 1,040 4% 53 Srbobran 28058 29455 1,050 5% 54 Sremski Karlovci 14028 14581 1,039 4% 55 Temerin 44457 46644 1,049 5% 56 Titel 26739 28127 1,052 5% 57 Vrbas 72515 75640 1,043 4% 58 Žabalj 43453 45387 1,045 4% 59 Alibunar 35656 37866 1,062 6% 60 Bela Crkva 31805 33599 1,056 6% 61 Kovačica 43668 46009 1,054 5% 62 Kovin 57983 60711 1,047 5% 63 Opovo 17331 18173 1,049 5% 64 Pančevo 203982 212139 1,040 4% 65 Plandište 20596 22068 1,071 7% 66 Vršac 87155 90608 1,040 4% 67 Lajkovac 27528 28713 1,043 4% 68 Ljig 22894 24133 1,054 5% 69 Mionica 25768 27241 1,057 6% 70 Osečina 23624 24968 1,057 6% 71 Ub 50388 52961 1,051 5% 72 Valjevo 151717 159623 1,052 5% 73 Bogatić 51722 54422 1,052 5% 74 Koceljeva 24639 25794 1,047 5% 75 Krupanj 31621 33310 1,053 5% 76 Ljubovija 26845 28130 1,048 5% 77 Loznica 134752 142552 1,058 6% 78 Mali Zvornik 22146 23221 1,049 5% 79 Šabac 194118 202732 1,044 4% 80 Vladimirci 31834 33609 1,056 6% 81 Čačak 184175 193129 1,049 5% 82 Gornji Milanovac 74780 78592 1,051 5%

XXV

Assessment of transport network of the Republic of Serbia

trips trips % Zone # Municipality name 2012 2027 k factor growth 83 Ivanjica 55072 58472 1,062 6% 84 Lučani 38549 40605 1,053 5% 85 Aleksinac 90668 95266 1,051 5% 86 Doljevac 30682 32269 1,052 5% 87 Gadžin Han 16175 17262 1,067 7% 88 Merošina 23152 24435 1,055 6% 89 Niš 394317 413270 1,048 5% 90 Ražanj 17740 18755 1,057 6% 91 Svrljig 26511 28513 1,076 8% 92 Bosilegrad 15468 16383 1,059 6% 93 Bujanovac 67798 71434 1,054 5% 94 Preševo 54522 57580 1,056 6% 95 Surdulica 34797 36606 1,052 5% 96 Trgovište 9920 10512 1,060 6% 97 Vladičin Han 36371 39102 1,075 8% 98 Vranje 136647 143996 1,054 5% 99 Babušnica 24393 25956 1,064 6% 100 Bela Palanka 21996 23724 1,079 8% 101 Dimitrovgrad 18510 19380 1,047 5% 102 Pirot 99793 105234 1,055 5% 103 Smederevo 175887 182782 1,039 4% Smederevska 104 Palanka 88254 92399 1,047 5% 105 Velika Plana 70023 73361 1,048 5% 106 Ćuprija 52632 55374 1,052 5% 107 Despotovac 40488 42250 1,044 4% 108 Jagodina 111253 116951 1,051 5% 109 Paraćin 91845 96177 1,047 5% 110 Rekovac 21151 22355 1,057 6% 111 Svilajnac 40168 42085 1,048 5% 112 Aleksandrovac 45747 48482 1,060 6% 113 Brus 29163 30954 1,061 6% 114 Ćićevac 16720 17742 1,061 6% 115 Kruševac 206936 216713 1,047 5% 116 Trstenik 76111 80904 1,063 6% 117 Varvarin 31615 33195 1,050 5% 118 Kraljevo 191600 200776 1,048 5% 119 Novi Pazar 134582 141864 1,054 5% 120 Raška 42274 44510 1,053 5% 121 Tutin 47529 49579 1,043 4% 122 Vrnjačka Banja 41384 43703 1,056 6% 123 Bačka Topola 59889 63091 1,053 5% 124 Mali Iđoš 21207 22261 1,050 5%

XXVI

Assessment of transport network of the Republic of Serbia

trips trips % Zone # Municipality name 2012 2027 k factor growth 125 Subotica 234991 244812 1,042 4% 126 Ada 29889 31334 1,048 5% 127 Čoka 21733 22818 1,050 5% 128 Kanjiža 43873 45491 1,037 4% 129 Kikinda 105550 110531 1,047 5% 130 Novi Kneževac 20432 21404 1,048 5% 131 Senta 40749 42238 1,037 4% 132 Novi Bečej 42172 44416 1,053 5% 133 Nova Crnja 19959 20959 1,050 5% 134 Sečanj 25674 27017 1,052 5% 135 Žitište 31817 33651 1,058 6% 136 Zrenjanin 209855 217840 1,038 4% 137 Inđija 78435 81838 1,043 4% 138 Irig 19443 20339 1,046 5% 139 Pećinci 34681 36165 1,043 4% 140 Ruma 94450 98990 1,048 5% 141 Šid 61452 64292 1,046 5% 142 Sremska Mitrovica 136372 141709 1,039 4% 143 Stara Pazova 107106 111478 1,041 4% 144 Aranđelovac 75122 79397 1,057 6% 145 Batočina 19047 20159 1,058 6% 146 Knić 25280 26639 1,054 5% 147 Kragujevac 277931 290014 1,043 4% 148 Lapovo 12902 13573 1,052 5% 149 Kula 75914 79766 1,051 5% 150 Odžaci 55822 58698 1,052 5% 151 Sombor 153660 160451 1,044 4% 152 Arilje 30826 32637 1,059 6% 153 Bajina Bašta 46219 48089 1,040 4% 154 Čajetina 24458 25781 1,054 5% 155 Kosjerić 22771 23847 1,047 5% 156 Nova Varoš 31436 32964 1,049 5% 157 Požega 50650 53273 1,052 5% 158 Priboj 47114 50112 1,064 6% 159 Prijepolje 64063 67946 1,061 6% 160 Sjenica 43774 46141 1,054 5% 161 Užice 131550 136958 1,041 4% max 8% min 4%

XXVII