KTH Bachelor Thesis Report

DEGREE PROJECT IN TECHNOLOGY, FIRST CYCLE, 15 CREDITS STOCKHOLM, SWEDEN 2019

Analysis of Autonomous Buses impact on transportation between Stockholm’s universities

KTH Bachelor Thesis Report

Marc Urtasun López

KTH ROYAL INSTITUTE OF TECHNOLOGY CIVIL ENGINEERING Author

Marc Urtasun López, [email protected] Transport Science KTH Royal Institute of Technology

Place for Project

Stockholm, Sweden KTH main campus

Examiner

Albania Nissan Stockholm, Sweden KTH Royal Institute of Technology

Supervisors

Jonas Hatzenbühler, Erik Jenelius Stockholm, Sweden KTH Royal Institute of Technology Abstract

The city of Stockholm is developing fast and its population is strongly growing, new solutions for urban mobility must be found. Implementations in the public transport network are needed and the use of automated buses is a present topic for efficient and sustainable transportation. The Vetenskapsstaden area has three of the main university campus coexisting, this leads to a generation of campus- to-campus trips which creates an impact in the Stockholm’s public transport. The unique environment and singular users arise the aim to execute a potential solution to release ridership from the public transport network. This thesis analyzes and evaluates whether a new automated bus line is needed or not in the studied area. A cost model will study the proposed solutions through the rated decision variables: frequency and capacity, commercial speed and different rates of demand. The relative efficiency of the proposed implementations is studied compared with the costs of the current public transport modes used in the area. Numerical analysis and results are given for two different scenarios: implementing one bus line connecting the three campuses or three lines between campuses. The former scenario shows to be more sensitive to the studied variables and presents higher costs whereas the second-option costs have a robust response and lower overall price evaluation. For both infrastructure models, the rate of demand is crucial to evaluate the advantages of a potential solution. The established method and criteria contributes to a better understanding of the impact of autonomous buses to low-demand analytic models.

Keywords

Public Transport Network, General Costs Evaluation, Autonomous Bus, Low demand, Bachelor’s Degree Project.

ii Abstract

Stockholms stad utvecklas snabbt och befolkningen växer kraftigt, nya lösningar för rörlighet i städerna måste hittas. Implementeringar i kollektivtrafiken är nödvändiga och användningen av automatiserade bussar är ett aktuellt ämne för effektiv och hållbar transport. I Vetenskapsstaden-området samexisterar tre av universitetscampusen, vilket leder till att man kan genomföra en egen lösning för att avlasta Stockholms kollektivtrafik från campus till campus resor. Denna avhandling analyserar och utvärderar om en ny automatig serad busslinje behövs eller ej i det studerade området. En kostnadsmodell kommer att studera beslutsvariablernas känslighet: frekvens och kapacitet, kommersiell hastighet och olika efterfråganivåer. De föreslagna implementeringarnas relativa effektivitet studeras under kostnaden för de nuvarande kollektivtrafiklägena som används i området. Numerisk analys och resultat ges för två olika scenarier: en busslinje implementerad eller tre olika busslinjer. Det tidigare scenariot visar att det är mer känsligt för de studerade variablerna och ger högre kostnader medan de andra alternativskostnaderna har ett robust svar och en lägre övergripande prisutvärdering. För båda infrastrukturmodellerna är efterfrågan på avgörande betydelse för att utvärdera fördelarna med en potentiell lösning. Den etablerade metoden och kriterierna bidrar till en bättre förståelse av effekten av autonoma bussar till låga efterfrågan på analytiska modeller.

Nyckelord

Kollektivtrafik, Kostnadsbedömning, Autonoma Bussar, Låg Efterfrågan, Kandidat examensarbete.

iii Acknowledgements

This thesis is the result of a five-moth research project at Kungliga Tekniska Högskolan. With this work, I am finalizing my Bachelor Degree in Civil Engineering at Universitat Politècnica de Catalunya and my exchange year in Stockholm. My journey through the Civil engineering Bachelor Degree has been both challenging and exciting but my final thesis project was passionate and intense. But I would like to add that the help of certain persons - both at professional and personal level - was warmly and very supportive. Therefore, I would like to thank all people that somehow has made an impact on me and my degree project.

First and foremost, I definitely need to thank my daily supervisor, Jonas Hatzenbühler, he has been following up my steps on this research project and advising, guiding and supporting me on this journey. He has totally involved himself in this thesis, giving his well-founded and critic opinion during the whole process of development. Also, his adaptability, comprehension and open attitude towards any adverse situation created a perfect atmosphere of work.

Next, I would like to thank Knut Staffan Algers for his case study idea, it was an inspiration to start this research project. He provided part of the background of this thesis, motivated me with an exciting topic and supplied this project with the needed demand survey. The given data was a save of time for me and an interesting source of information. Last but not least, Staffan has provided me with a totally sense of freedom to perform and funding help through a scholarship from Transportekonomiska Forskningsstiftelsen.

Furthermore, I want to thank Erik Jenelius for his supervision and valuable comments on the several meetings that have been carried out. His point of view and opinion through public transport networks expertise has been really enhancing.

Of course, I also want to thank to Albania Nissan (Bibbi) for her initial support. She helped me to introduce myself to the Transport Science department and gave me great advice on a lot of aspects when most needed.

iv Finally, I would like to thank my family, friends and all the people that somehow has been involved in my life these months. As an exchange student, there are a lot of new experiences and a few tough moments that are challenging, but appreciated advice and support was given to me when I asked for it. To my parents and my brother, that have experienced this thesis through my thoughts on every phone call and have supported me on every step I have made.

Marc Urtasun López, June 2019

v List of Figures

1.1 First autonomous bus in Scandinavia...... 2 1.2 Scania AB self-driven bus will be operating in Barkaby district soon. 3 1.3 Vetenskapsstaden strategic area ...... 4 2.1 Transit planning process steps defined by Ceder et al. 1986[4] . . . 6 3.1 Trips per OD over the studied time in percent [%] ...... 13 3.2 Speed profile distribution over time for a specific bus line ...... 15 4.1 Distribution of daily trips over time from campus to campus . . . . 26 4.2 Description of the connection between the university campus. . . . 28 4.3 Sketch of the first scenario of study...... 29 4.4 Sketch of the second scenario of study...... 30 5.1 Current location of KI, KTH, SU campus stop...... 32 5.2 New location of KI, KTH, SU campus stop...... 33 5.3 Load profile for the One-line scenario...... 34 5.4 Load profile for the Three-line scenario...... 35 5.5 Heat-map of general costs through vehicle capacity and speed for the 1 line scenario...... 38 5.6 Heat-map of general costs through vehicle capacity and speed for the 3 line scenario...... 39 5.7 General costs of the three studied scenarios studied over and increase of demand...... 40 5.8 Evaluation of User and Operator cost when decision variables are increased...... 42 5.9 Impact of vehicle size on the two analyzed scenarios costs...... 43 5.10 User and Operator cost of the proposed solutions over vehicle size

with the new demand on Demandf actor=2.5...... 44

vi List of Tables

4.1 Transport modes and travel times between campuses ...... 24 4.2 Campus accessing distances ...... 25 4.3 OD matrix of trips generation...... 27 5.1 Data related to the current location of the stops and the walking distances to campus...... 32 5.2 Data related to the new location of the stops and the walking distances to campus...... 33 5.3 Riding distances between campus ...... 33

vii Contents

1 Introduction 1 1.1 Background ...... 1 1.2 Scope ...... 3 1.3 Motivation, research question and objectives ...... 4

2 Literature review 6 2.1 Public transit planning process ...... 6 2.2 Headway and Vehicle capacity determination ...... 8 2.3 User and Operational objectives ...... 10

3 Methodology 13 3.1 Demand distribution per OD ...... 13 3.2 Speed Determination ...... 14 3.3 Frequency and Vehicle capacity determination ...... 16 3.4 Bus stop determination ...... 19 3.5 Computation of General Costs ...... 20

4 Case Study 24 4.1 Location and current public transport ...... 24 4.2 Demand target and definition ...... 25 4.3 Description of scenarios ...... 27 4.4 Evaluation of the Case Study ...... 30

5 Numerical Analysis and Results 32 5.1 Location of bus stops ...... 32 5.2 Load profiles ...... 33 5.3 General Costs ...... 35

6 Conclusions 45 6.1 Future Work ...... 46

References 48

viii 1 Introduction

1.1 Background

There is one concept that clearly defines the health of a city and its inhabitants, urban mobility and the shape of the uniqueness of its transportation network. Society pushes and determines the wellness of the municipality. However, the public administration and government must give tools and resources to positively achieve that state. On this sense, Stockholm as a close inner-city environment will face challenges to create an hybrid public transport network. The fact that very soon self-driven vehicles and human drivers will coexist in the same region, will make urban mobility complex.

It is interesting to analyze how technology is pushing society and the capacity of human beings to adapt. Nowadays, transportation is turning from sufficient to sustainable and efficient. In 2010, Stockholm was named the ”European Green capital” by the European Commission 1 based on different parameters but also because of its low transport emissions and the non-use of fossil fuels.

Several projects have been launched in Stockholm as a starting point to introduce Autonomous Vehicles in the current public transport networks. In January 2018, the district of Kista (Stockholm) was launching the first smart self-driven bus of the Scandinavian countries.”Bus company Nobina was behind the venture, in cooperation with tech experts at Ericsson, SJ, KTH, Klövern, Urban ICT Arena and Stockholm City, funded by among others” recorded Vinnova via Drive Sweden2.

This was a milestone in the swedish transportation. Later on, Nobina implemented autonomous buses in Barkaby. In February 2019, Scania AB announced that two self-driven vehicles will start to operate in Barkarby, which has been described as the world’s most modern city traffic system said by Scania AB 3. 1http://ec.europa.eu/environment/europeangreencapital/winning-cities/2010-stockholm/ 2https://www.thelocal.se/ 3https://www.scania.com/group/en/nobina-and-scania-pioneer-full-length-autonomous- buses-in-sweden

1 Figure 1.1: First autonomous bus in Scandinavia.

Further studies are being conducted, the Integrated Transport Research Lab (ITRL4) division of KTH university believes about autonomous buses operating at the different KTH campuses. This is supported by solving the last mile problem. The problematic of elevated access times at university campuses has already some solutions. Several companies are operating in Stockholm with different last mile transport products. An example is the so called ”electric scooter”, up to four different firms are in Stockholm’s market, e.g. VOI5 or Lime6. Also, not only the electric scooter is trying to solve this concern. Bikes are a common mode of transport in Stockholm and so does the urban strategy model support. ”The proportion of all journeys at peak hours performed by bicycle must be not less than 15 per cent by 2030” mentioned in Stockholm’s Traffic Guidelines. The number of trips produced by bikes in campuses (students) is 28% of total trips but it goes up to 70% when done by public transport. This rate is due to passengers travelling in public transport will take high amounts of time walking to their destiny. University campuses are distinct communities in which people

4https://campi.kth.se/en/nyheter/forsta-steget-mot-sjalvkorande-campusbuss-1.812191 5https://www.voiscooters.com/ 6https://www.li.me/

2 Figure 1.2: Scania AB self-driven bus will be operating in Barkaby district soon. from different backgrounds, lifestyles and attitudes meet. That makes them ideal places to perform sustainable policies and transform transportation patterns [1]. Together with an expected increase of student demand, this ecosystems will face challenging management difficulties. Hence, commuting in this hubs must become an easy and smooth expression of what we understand today as accessing trips.

In addition, generation of trips is part of the demand forecasting. It is such a complex step that involves the sense of individual and population choice behavior. Future generation and attraction of passengers can be described by the present demand trips together with future characteristic parameters. It is important to be aware of future socioeconomic characteristics of the area, spatial distribution of it, modal shares used and highlight the prospective transport network.

1.2 Scope

The scope of this project is directly related to the named Vetenskapsstaden area. It is a strategic region where three campus from the main universities of Stockholm coexist. The fact that they are located roughly close makes this area special and potentially interesting for an unique public transport network. Besides SU, KTH and KI (universities located in Vetenskapsstaden area), there is a strategic point that interacts with the campuses: the Albano campus.

Research investigated the peculiarity of that area, this work is oriented to support

3 the existing studies and give another point of view and approach to the already studied case.

Figure 1.3: Vetenskapsstaden strategic area

Public transportation is already serving on these campus connections, but through different modes of transport and transfers between them. Additionally, the existence of several educational joined programs between institutions and the high reputation of the universities will prompt an increase of students. The sum up of ideas pose a dilemma. Will the current transportation system on the Vetenskapsstaden area be resistant to the upcoming challenges?

1.3 Motivation, research question and objectives

The stimulation of this degree project is lead by an aim to get deep knowledge on planning and designing new public transport networks. The validity of a prospective bus lines is studied in comparison with the current public transport network on the Vetenskapsstaden area. Is it feasible to convey campus to campus demand in one strategic bus infrastructure? Moreover, autonomous buses create a really contrasted impact with the conventional operating buses. Which urban situations allow autonomous bus systems play a role over conventional bus networks?

Given this motivation, the objective of this thesis is the development of an

4 analytic model in order to tactically analyze frequencies and vehicle capacities that captures the demand based on the optimization of Public Transport costs. This leads to the following research question:

How do bus line frequency and vehicle capacity affect user and operational objectives in a low-demand scenario?

The second objective of the thesis is to test and describe the sensitivity of user and operational objectives in terms of vehicle speed on the case study. Finally, the author’s motivation about the ”last mile” problem conducts to study an efficient and optimal solution to reduce walking distances to bus stops in a hub (e.g university) and in the practical case study.

The structure of the report is designed for a fully understanding of the research, methodology, results and discussion; finishing with the final conclusion of the project. In Chapter 2, the literature review and a detailed view of the case study is presented. This is followed by the methodology implemented in chapter 3. Next, the case study will be explained an over viewed and after, the application of methodology in detail and results are given in chapter 5. Finally a final conclusion will be given in the last chapter.

5 2 Literature review

In this chapter, previous work has been reviewed in order to get a solid basis for an optimal solution of the research question and application of the work. Review of articles, books and other research work has been done for the different methods applied in the project.

2.1 Public transit planning process

For the scope of this thesis, the public transit planning process is partially applied. In [9] different approaches of the mentioned process are analyzed and reviewed. As the literature assesses [4], the public transit planning process is usually divided in a sequence of five steps: (1) the design of routes, (2) the setting of frequencies, (3) the timetabling, (4) the vehicle scheduling and (5) the crew scheduling and fostering. This review addresses the first three, and thus fundamental elements of the public transit planning process. Also called strategic (step 1) and tactical (steps 2 and 3) planning, respectively [9]. The framework of the project involves the tactical planning as steps 2 and 3 mentioned. It is based on the public demand using the planned network, the area of work and its characteristics, vehicles (buses in this case) and drivers if needed. The following table exposes the different steps described in the transit planning process [4].

Figure 2.1: Transit planning process steps defined by Ceder et al. 1986[4]

The Figure2.1 is based on a multi-objective model, in the project scope the

6 Cost Structure will have as decision variables the service frequency and vehicle capacity. Each variable having its relative constraints. Even if the general transit planning is shifted in different parts and steps it remains to be a complex problem, from a computational view. It must be said that the first approach to this problem was close to a hundred years ago [13]. Even though new technology and optimization methods have been developed, it remains high-computational difficult and time costly.

2.1.1 Transit network

Routing is set in this step of the process, understanding a route as multiple sequence of bus stops. Furthermore, the demand of the planned network will play a role and its fundamental. It is given by the origin and the destiny of each passenger. Demand is mostly provided by OD matrices that would store the amount of flow going from one point (origin) to another(destiny). One constrain is that obviously the bus could never provide the desired service to all passengers due to its route. So, OD matrices should consider the strategic areas as transfer zones and bus stops [9]. Nevertheless, the importance of the OD matrices defining the demand of society should be linked with a entire community survey, reaching not only the public transport user but also those who are not.

It is important to highlight that the purpose and motivation of this thesis together with the data and OD matrices provided define most of this section and part of the routing problem. In addition, this step considers demand satisfaction and operator objectives. On the one hand, demand satisfaction reflects users contentment. So, it is important to fulfill or partially fulfill users demand which is the purpose of the public transport planning. On the other hand, operators have its own intent. The amount and the length of the routes will lead to higher cost to the operator and too many sources.

2.1.2 Service frequency

This section is oriented to determine the frequency of the studied lines through the steps and for each period of time studied. Understanding frequency as the

7 number of vehicles that the line demands in a determined period. Moreover, an element that also plays a role here is the inverse of the headway. ”It corresponds to the time elapsing between consecutive line run departures”[9]. However, as a constraint, frequency must always be in the range of the imposed frequency by the transport agency. In this project a low boundary for frequency is taken.

The frequency of service is literally dependent on the route of the bus line, so the network is one of the major inputs. This is due to the desire of matching different bus lines to increase the user service and its contentment.

First of all, network routes should be defined for the matter case. Additionally, demand must be given (OD matrices) in order to distribute uniformly trips over the period. The definition of demand is given by type of period (peak or off- peak) and the week elasticity of demand (must consider that weekdays have different rate of demand than weekends). Regarding the operator restrictions again, frequency can also be restricted by the bus fleet. The number of vehicles demanded can differ from what the operator will accept or can offer.

2.2 Headway and Vehicle capacity determination

This section is providing an understanding on how to compute headway and vehicle capacity in public transport networks. The development of this problem has a start point in the early 80’s, where several models where developed to determine headway and vehicles capacity. Later on, from the first exact optimization models, public transport planning jumped to sophisticated heuristic optimization methods.

The very first to tackle the headway problem from analytic was Newell, G. [11]. Its work studied the difference of time between two consecutive departures with passengers waiting time as a objective. His approach takes frequency and number of passengers per vehicle as a square root proportion of the demand arrival rate. Both cases with and without capacity as a constraint are reviewed. Later in time, Salzborn, F.J. [14] introduced the vehicle scheduling on the optimization of frequency. In this case, Salzborn considers that buses can be used for more than one trip, stating the idea of trip changing. The aim of its work is to determine

8 the minimum fleet size for a given route and a known passenger rate of arriving. Assumptions are done by fixing the maximum load of buses in the peak periods. Moreover, frequencies are set to minimize the waiting time of passengers in bus stops.

Ceder, A. [3] presented different methods that allow the solution of an efficient frequency-setting. It studies two main ideas: (1) to set the frequency of the bus line in order to minimize fleet size and provide an adequate service; (2) ”to construct an evaluation tool that will efficiently allocate the cost of gathering appropriate passenger-load data at route level”[3]. It must be mentioned that passenger- load information is needed and studied by periods and between stops. Ceder, A. [3] established four different methods, dividing them in two Max. load (point check) methods and two Load profile (ride check) methods. A criterion based the measure of the load profile density, understanding this concept as the passengers per km. The criterion determines either point check or ride check methods should be used. Thus, different types of data should be collected. Furth and Wilson [7] set headways by the restriction of service standards of crowding (service level) and policy headways. This constraints are also used by Ceder, A. [3] for its studies. More precisely, policy headways is set to be a lower-bound value based on productivity and costs measures.

The main reports and studies for frequency optimization have been showed. The following research review is dedicated to get deeper sense on how to compute optimal vehicle size of a public transport service. It must be said that as vehicle capacity and headway are directly implicated between each other on its calculation, the calculation of a vehicle size will end with an associated headway. The problematic explicitly stated on the following reports are always the same, there is a trade-off between the user costs and the operator of the system costs. On the one hand, user will be directly affected from headway determination through waiting time and affected from vehicle size by its occupancy (discomfort feeling on overcrowded trips). On the other hand, operators will try to get high revenues with the less costs possible. Low headway is leading to higher frequencies and so, big bus fleets.

Walter, A. [16] created a basic and simple model to calculate optimum size

9 of vehicles. As his formulation implies, ”the optimal size which minimizes passenger’s waiting costs and wage vehicle costs is indirectly proportional to the total number of passengers” [Walter1982]. That turned to a model where the increase of passengers implied low vehicle size and headways. In 1988, Oldfield and Bly [12] developed a more sophisticated model to approach the problem. Developing the idea that the use of smaller buses will offer a better level of service (high frequencies) for the user, but the cost of each seat will be greater. An analytic model is defined to express the optimal vehicle size in terms of factors as operating costs, level of demand and demand elasticity. Later on, a different approach and study was realized by Shih and Mahmassani [15] where the author’s model used an iterative process to achieve optimal frequencies and vehicles sizes. As a starting point, the model sets frequencies to each route and the OD matrix is defined. Following, capacities are computed per line by the maximum on-boar passenger load per line and period. Then, an updated frequency is calculated from the assumptions of vehicle size and a stated preliminary maximum load factor. That procedure is iterated, thus the frequency will converge to a final value. It is an unlikely development because it is not aiming to minimize the general costs but rather optimize the vehicle size for each line. A completely new research was made with the purpose of studying the viability of having discerned vehicle sizes in terms of the value of time. Gronau, R. [8] explored the assumption that high values of time demands will be operated by low vehicle size whereas low values of time demand will be served by bigger and longer buses. The results showed that ”the longer the route and the more dispersed the distribution of the population, the greater becomes the tendency to use two types of vehicles rather than one” [8].

2.3 User and Operational objectives

This section is dedicated to review a cost model according to the scope of the thesis. The understand of a model based on user and operator cost is basic to accomplish the goals and research question of this report. Moreover, over the years technology has developed slightly as mentioned in the introduction (1). The fact that autonomous vehicles will play a role in today’s and future’s frame has

10 arisen new approaches on several cost structure problems.

The importance of automated vehicles in transportation and it is such a fact that new technology will potentially improve the present statement of mobility, it has already been supported by Fagnant and Koeckelman. [6]. Automated vehicles have been studied in many ways as a private competitor of the public transport or as a complement of the current public transport network. However there is no such a literature review focused to develop bus transit itself. A case study in Stockhom, Sweden was done by Bergqvist and Astrand [2] investigating the use of mini autonomous buses. The model aim was to study the viability and analyze if autonomous buses could replace high-frequency lines in the city of Stockholm. Positive results showed how the implementation in the current public transport network was dropping down operational costs and minimized.

Lam [10] went one step further when he developed a simulation platform to determine optimal fleet sizes, vehicle capacity and frequencies. The platform was supported by using dynamic OD matrices, including bus scheduling, cooperative maneuvering and management of road intersection priority [17]. Zhang et al. in 2019 compared different bus services formulating a cost model with different levels of automation, differing from semi-automation and full-automation. Their study was surprisingly the first model to address the semi-autonomous vehicles implementation, considering it such a tool to identify different scenarios where this technology could be implemented concluding favorable against conventional buses. The sensitivity of parameters describing the level of automation is part of the report [17]. It is an analytical cost model focused to minimize general costs, which include: labor costs (describing the participation of the driver in the riding time), capital costs (level of automation will affect the complexity of the bus, the automation of a bus will increase its capital cost), and user costs (waiting costs and riding costs). The vehicle size (capacity of the bus) is also analyzed as a decision variable that will lead to frequency and bus service to optimization, so a trade- off between service characteristics (frequency and fleet size) affecting user and vehicle capacity will occur due to its relation with the different costs.

The paper of Zhang et al. [17] describes two modes of automation but, for the interest of this thesis only the full-automated is reviewed. The full-automated

11 buses are considered a level 5 of automation that consists a complete independent bus regarding drivers. As a characteristic implications, the capital cost will be much higher than the conventional buses case, but as mentioned before, the labor costs will be explicitly reduced.

12 3 Methodology

This part of the report is determined to explain the methodology used for this project and the different models used to pursue the objectives of the thesis and solve the research question. The methods and theoretical methodology are within the literature review summarized in section 2.

3.1 Demand distribution per OD

Further on, the demand problem will be explained through the Case study. Meanwhile. it have to be said that the total amount of trips distributed in time per day is given and the OD matrix describing the trips generated per OD also, but the trip description of each OD over time is not carried out. Nevertheless, the method to do it so will be following the total trips distribution over time of the whole demand, that leads to a weighted (over the total amount of trips) profile over time.

Trips per OD over studied time in percent [%] 0.14

0.12

0.1

0.08

0.06 % day trips

0.04

0.02

0 7 8 9 10 11 12 13 14 15 16 17 18 19 Day periods [h]

Figure 3.1: Trips per OD over the studied time in percent [%]

13 Figure 3.1 shows the trips profile over the studied periods that will follow the different OD trips distribution. By knowing the total trips generated over time (as it is known), the profile distribution of trips over time per OD can be computed as it follows:

Dkij = Pk · DT OT ALij (1)

Equation (1) describes the computation of the demand per OD per hour (i defining the origin and j the destination) and over time (k for the different periods, e.g. k=1 for 7 a.m. to 8 a.m.). DT OT AL can be taken from Table 4.3. Pk is obtained from Figure 3.1. Once all the OD trips distribution are computed separately, the on-board passengers between stops can also be described. Depending on the definition of the scenario and the bus line route, the in-vehicle demand travelling each period of time will be different.

The definition of the load profile between stops is generated thanks to equation (1). This will be useful for the calculation of the frequency of service in the next steps.

3.2 Speed Determination

This section will explain how commercial speed is computed, the aim is to be able to control the commercial speed of the vehicle depending on the period and operating line. As the equation (2) explains, the commercial speed of the vehicle is computed in terms of the hourly demand. This will lead that during the peak- hours, the vehicle will operate at a higher speed in order to reduce the total user cost as much as possible. It is an application from the automated vehicles and it is designed to make the system more efficient.

max(P · D ) − (P · D ) k kij k kij (1/5) Vkij = Vmax · (1 − log[ + 1]) (2) max(Pk · Dkij)

The equation 2 is computed to be shaped in terms of Vmax which gives the threshold value of desired speed. Speed is depending on the k period and also

14 the OD route. The following plot shows and example on how the speed profile looks for a Vmax = 20km/h.

The following figure 3.2 is describing the commercial speed through the studied hours. As explained, Vmax is restricting commercial speed and so, the given values will be given lowering the threshold.

Figure 3.2: Speed profile distribution over time for a specific bus line

In addition, once the speeds are computed by the expression (2), the value is rounded to be a multiple of 0.5. This makes the approach more realistic.

15 3.3 Frequency and Vehicle capacity determination

The author has already stated that frequency and capacity are the decision variables and will critically affect the model. The method used for the design frequencies of service and the vehicle capacity is reviewed in the section (2) and developed by Ceder, A.[3]. It is a suitable model due to the information of demand that it is actually surveyed. The so called, load profile will be applied. In order to proceed with the method, a few points should be commented first. The constrain of the policy headway (directly related the frequency) is fixed as H = 20min or f = 3veh/h. Moreover, it is usually asked that headway should fit in an hour in order to not make the next hour dependent on the previous demand, concept called clock-wise headway. Additionally, vehicle capacity is ranged between two design values, 6 passenger/vehicle and 16 passenger/vehicle, this is due to several companies7 advice.

The industry that has been designing and producing the called ”mini-bus” advice the use of those values. Also, Zhang et al. [17] reflect that a design vehicle capacity of 15 pass/veh could be the optimal value.

The two methods developed by Ceder, A. [3] will be explained as well as the criterion for its choice. The use of this methodology leads to the need of a deep understanding of the method.

Maximum load methods (point check): Method (1) is based on the peak- load factor, which is the ratio between the average maximum passengers observed on board per period over the desires occupancy of the bus (capacity times desired occupancy factor). The basic objective is to ensure adequate space to accommodate the maximum number of on-board passengers. The measure of the on-board passengers is once of the keys of the method, it is usually carried out by a checker that stands in the bus stop, recording data of passengers alighting. So, the first method of the called point check methods selects the maximum value between the peak-load factor (already explained) and the minimum required frequency (inverse of headway policy). The second point-check method (2) is evaluated based on the maximum load in each period, differing on this with method (1).

7https://navya.tech/en/autonom-shuttle/, https://easymile.com/ and https://localmotors.com/meet-olli/

16 The designed frequency is also selected as the maximum value between the new computed peak-factor value and the minimum frequency required by policy. It is convenient to highlight that method (1) is cost saving in data collecting against method (2). Method (1) is cheaper due to the static checker gathering data at the same bus stop during the whole work-day whereas in method (2) the checker is moving every period to take the maximum load on each period.

Load profile methods (ride check): ride check methods determines frequency by approaching the problematic from a passenger-km scope rather than on a max load measure (methods (1) and method (2)). Consequently, Ceder, A. [3] formulate the method (3) as the maximum value between three parameters. Firstly, the demand weighted parameter consists of the ratio between: the sum of the on-board passengers times the travelled distance through the line per period, against the desired capacity times the total length of the route. The second parameter is the average maximum passengers on-board for each period against capacity. Finally, the restrictive parameter of the minimum frequency or maximum headway allowed by transport policy. This method allows the planner to ensure that all passengers could board in the bus in average. Most important, this method (3) allows the planner to actively act with the results of it by handling situations of demand change without increasing the bus fleet, re-balancing of the bus fleet (e.g. maintenance of vehicles, emergencies or unexpected demand situations), or even for those buses who are driven by humans and expected an unusual situation. However, on the other side of the pros, there are unpleasant situations for the users due to the method (3) frequency design, for example when travellers spend long trips in an overcrowded bus where occupancy is greater with than the desired occupancy. Hence, to control and reduce this discomfort method (4) is introduced as a hybrid practice between method (2) and method (3). This last method (4) presents a parameter that controls the ”allowable portion of route length per period in which the on-board demand can exceed desired occupancy”[3]. Furthermore, the extremes of the unpleasant control parameter (from 0 to 1) describes the boundaries of method (4).

Although ride check methods are resource saving on the planning process, it will end up on user costs. The following explanation sets a criterion to either

17 chose maximum load methods or load profile methods. The following criterion aims to give tools and arguments to the transit agency for an optimum choice of method. The idea behind this procedure is to distinguish the studied case and analyze if it is either a flat demand profile (these cases ask for method (1) or (2) as a efficient solution) or peak and irregular demand profiles (method (3) or (4)). The determining property is the load profile density. The density is described by Ceder, A. [3] as ”the observed measure of total passenger-km, divided by the product of the length of the route and its maximum load”. As expected, relatively high values of this load profile density result from a flat load profile whereas small values of load profile density explain irregular load profiles among the route stops. A mathematical model to explain and explore the load profile density can be developed, the log-normal model is proposed by Ceder, A. [3]. Ultimately, once the load profile density is computed, decision can be taken on which method can be used. For densities below 0.5, ride check methods. Moreover, there is no clear or obvious decision for density values between 0.5 and 0.85. Finally, densities over 0.85 should use point check methods [3].

Following Ceder, A. [3] on his model to compute frequencies and the observed demand between campuses, the method to use will be the Maximum load method or point check. This choice is due to the definition of the demand load profile. It ends up being a flat profile through the different OD’s and resulting a close-to-unit density (Ceder, A advises to use point check methods on this cases).

The method follows on firstly, determining the load profile (section 3.2.1). This will lead to the determination of either method (1) or method (2) of the point check methods. It must be said that if the difference between the two methods is not significant, method (1) is taken for its convenience when gathering the data. Method (1) is based on the belief of offering an adequate service to the maximum number of on-board passengers along the entire route over a given period k (hours).It follows the next equations:

Pmk Fk = and k ∈ periods (3) d0

18 Pmdk F1k = max{ ,Fmk} and k ∈ periods (4) d0

Equation 3 represents the peak-load factor concept, and d0 the desired occupancy of the vehicle. The method (1) is designed to give service to the route that caries the most on-board passengers over the k periods.

The terms Pmdk and Pmd are used for the (average) observed load at the daily max load point at time k and the total load observed at this point. Fmk is the minimum required frequency (reciprocal of policy headway)

On the other hand, method (2) tries to focus on the maximum load observed in each time period.

Pmk F2k = max{ ,Fmk} and k ∈ periods (5) d0

It can be observed from equations (4) and (5) that method (1) plays on the cheap side of the design, and method (2) is designing frequencies on the safe side in order to cover the maximum on-board passengers rate.

3.4 Bus stop determination

An advantage of the autonomous vehicles, in this case the mini-bus automated vehicle, is that can access further in campuses without creating a big impact. The infrastructure needed is less than the conventional buses. For this reason, the use of automated buses drop down the accessing time of passengers, the vehicle can arrive closer to the origin of passengers and closer to the destination of these.

This problem will be solved by reducing the mean walking distances of passengers. This is proceed by minimizing the standard deviation of the taken samples from each campus. The assumption made is that the demand of each bus stop (campus) is uniformly distributed through the campus.

The standard deviation is the following one:

19 v u u 1 ∑N t · − ¯ 2 ∈ σ = − (Xl X) and l = 1, ..., N origin/destination (6) N 1 l=1

Equation (6) shows a group of observed samples (Xl distances to bus stop) and the mean value of the samples (X¯). The last term is the one to minimize for each bus stop. Once, the method is done, the new walking distance (Lwalking) will be the minimized mean value of walking distances.

3.5 Computation of General Costs

This section is lead to explain the needed expressions for calculating the different costs involved in the GC (Generalized Costs).

The objective function is the following one:

GC = UserCosts + OperatorCosts (7)

Several parameters should be explained, the next table explain the associated parameters to the equations (8)(10)(9).

20 Parameters VOT (€/h) Value of time

Waccess weight of access time

Wwaiting weight of waiting time

Wriding weight of riding time

Wtransfer weight of transfer time AT (h) Access time WT (h) Waiting time RT (h) Riding time T ransferT (h) Transfer time

Ccapital(€) Capital costs

Clabor(€) Labor costs β Level of automation of a vehicle η Level of automation of the driving

ccptl(€/veh) Unit capital cost

bcptl(€/seats − veh) Unit capital* cost

clabor(€/veh) Unit labor cost

ccptl(€/veh) Unit labor* cost

d0(pass) Desirable occupancy per vehicle C(pass) Vehicle capacity H(h) Designed headway

Vc(km/h) Commercial speed W (km/h) Walking speed

Lriding(km) Route length

Laccessing(km) Accessing length

Ltransfer(km) Transfer length The table presented above shows the meaning of the studied parameters. Important to highlight that the called * parameters are dependent of the number of seats in the vehicle (representing the size of the vehicle). Also, VOT represents the value of time when the bus is riding and on the users different phases of the trip. The value of time and the weights of the different parts of the trip

(Waccess,Wwaiting and Wriding) are taken from The National guidelines for transport

21 system management in Australia [5]. The aim of the model is to minimize equation 7, to do so, the variables of frequency and vehicle capacity will be studied and define them as the decision variables. UserCosts is associated to the costs linked to user and equation(8) describes the costs of the operator during the service. The model is developed based on the studies reviewed in the section (2.3) and the article of Zhang et al. [17].

The equations below describe the analytic expressions for the computation of the the bus line costs [17]. ∑ ∑ ∑ · 13 n n · UserCosts = VOT [ k=1 i=1 j=1(Waccess AT +

+ Wwaiting · WT + Wriding · RT + Wtransfer · T ransferT )]

OperatorCosts = Ccapital + Clabor (8)

Lriding C = ((1 + β) · c + b · d ) · Vc (9) capital cptl cptl 0 H

Lriding C = ((1 − η) · c + b · d ) · Vc (10) labor labor labor 0 H where,

d0 = γ · C (11)

1 Hkij = (12) Fkij

Equation (11) described the desired occupancy in terms of the vehicle capacity (C) and the desired parameter (γ ∈ [0, 1]). Moreover, equation (12) defines the headway as the inverse of the frequency. and, k ∈ periods i ∈ Originstop

22 j ∈ Destinationstop

Regarding the different times of the trip, the following equations give a definition on how to calculate them:

Lij RTkij = · Dkij and k ∈ periods, i ∈ Originstop, j ∈ Destinationstop (13) Vc

Dkij WTkij = ·Dkij and k ∈ periods, i ∈ Originstop, j ∈ Destinationstop (14) Hkij

L AT = accessing · D and k ∈ periods, i ∈ Originstop, j ∈ Destinationstop kij W kij (15)

L T ransferT = transfer ·D and k ∈ periods, i ∈ Originstop, j ∈ Destinationstop kij W kij (16)

Equation 13 explains the amount of time that the users spend in-vehicle while they are travelling per hour between i and j stops, which is independent of the headway in this case. Equation 14 defines the amount of time that passengers are waiting in the bus stop per hour, this is the main user value of study in terms the headway. Equation 15 reflects the time spent by passengers when are accessing from campus to bus stop or from bus stop to campus by hour. This parameter assumes that both origin and destination demand are set in the campuses. Equation 16 is related to the transfer time spent, this value is quite unique and not expected to be used in the proposed scenarios but in the current scenario (section 4.3.1).

23 4 Case Study

This section aims to get a deeper review of the case study. The studied area, as mentioned in the section 1, is located in Stockholm, Sweden and ranges the three main universities of the city (KTH, Karolinska Institutet and SU). Nowadays, different modes of transport converge in the area, the focus and interest of this overview is to review who is travelling from campus to campus, how do this target of people travel and the times spent to travel. This review will set the basis for a future computation of the current users and operator costs.

4.1 Location and current public transport

Research is done in order to precisely locate the case study, understand the area of interest and deeply review the current public transport in that region. As briefly presented in section 1, the area of study is named Vetenskapsstaden and converges between the campuses involved. It is important to understand the reduced area that shape the region, an average distance of 3.5 km between campuses and other data is recorded by the author 8. This distances are operated by metro and bus or in one case (origin-destination) by both. The following table gives a sight of the origin and destination of the trips and its related mode of transport and travel time. KI KTH SU KI X B6[16 min.] B6[16 min.]+t14[5 min.] KTH B6[19 min.] X t14 [4 min.] SU t14[4 min.]+B6[16 min.] t14[4 min.] X

Table 4.1: Transport modes and travel times between campuses

Regarding the Table 2.1, it will be understood B as Bus and t as Tunnelbana (metro). It is important to highlight the problematic when a transfer between transport modes appears as the case of Stockholm University to Karolinska Institutet and vice versa, that will lead to an amount of transfer time added to the in-vehicle travel time. The frequency between stops is also studied, so waiting times depend directly of it.

8https://maps.google.com/

24 Additionally, each campus have its own public transport stop and the location of it will lead to different distances from origin to public transport stop and public transport stop to destination (completing the whole trip). Thus, the next table is also describing origin and destination stops and its average accessing distances.

KI KTH SU Accessing distances 285 m. 250 m. 310 m.

Table 4.2: Campus accessing distances

The accessing distances in the different cases will end in accessing times for each campus. The author describes a trip between campuses as the following: walking time from origin campus to public transport stop, waiting time dependent on the mode of transport used in each case, riding time between origin and destination public transport stops and the final accessing time from destination top to the final destination in campus.

4.2 Demand target and definition

Understanding how are the campuses connected is as much important as detailing the amount of trips between the campuses. This section is a detailed description of the trip generation and a quantitative understanding of the case study demand.

Three campus universities area studied in the case study and each of them are education and research institutions where both professional and educational profiles converge. Estimating the amount of trips generated is quite a complex problem, so a study and survey is conducted to understand who is travelling, the amount of trips per day and the distribution over time and OD.

A total amount of 30.638 people involved in the three universities is recorded, there are 3 different types of profiles per university, professional employees, research staff and students. Part of this amount are potential users of the public transport network, but the survey dedicated to this study state that above 70% of the people involved at campuses use the public transport as a mode of access

25 to university. Moreover, the study of university activity through the year is also a point to understand in this case. As part of the school year, it can be differentiated two big periods: from latest August until earliest June, the activity at the campus is high and most of the case study target travel to the campus with high-frequency; on the other side, during summer (June to mid-August) the activity is low.

The studied periods are determined to be working hours. This is linked with the intention of taking realistic scenarios. The activity in the studied hubs is mainly produced during daily hours. To support this, the survey has showed to be sensitive to this idea because after 8 p.m the generated trips were minimum. Regarding the night shift, barely trips were produced. So, the simplification is understood to be consistent with the reality of the case.

Lastly, the target people where asked to answer if any trip was being done from campus to campus frequently, as part of the survey. The answers were summarized in two main figures: a time-series distribution of total trips from campus to campus (Figure 2.2) and a OD matrix of trips between campuses (Table 2.3). Additionally, the data is recorded through the working hours because of the nature of the campuses.

Figure 4.1: Distribution of daily trips over time from campus to campus

26 KI KTH SU KI X 46 29 KTH 60 X 181 SU 34 197 X

Table 4.3: OD matrix of trips generation.

The collected data defines a first approach to the case study, the recorded data leads to generate the user and operator costs of the current and used public transport network.

4.3 Description of scenarios

The aim of this section is to describe the different scenarios that the methodology will conduct, so the understanding will be completely founded.

4.3.1 Current Scenario

The current scenario is named for the actual public transport network of Stockholm in the studied region. This is described by Table 4.1, giving the optimum mode of transport between campuses and its travel times. The nowadays scenario is the established by Stockholm’s public transport agency. Hence, this infrastructures of transport are the basic scenario that is going to be questioned by other types of solutions involving the other scenarios.

4.3.2 Proposed Scenarios

The approach of this project have the need to study different possibilities for a potential solution. To do it so, two different proposed scenarios have been formulated with different characteristics. The basis of these are to create a first sight of the Automated bus line model. The routing have been done to respect the existing road infrastructure. Both scenarios will follow the current streets and avenues connecting the three university campus. The following sketch is describing the operating route of the proposed bus lines.

27 Figure 4.2: Description of the connection between the university campus.

Therefore, the green, red and blue lines describes the routing of the bus connecting the campuses.

4.3.2.1 One-line Scenario This is the first case option of study. A simple scenario with only one line going through the three studied campuses, operating in only one direction as described below:

28 Figure 4.3: Sketch of the first scenario of study.

For a better understanding, KI stop will be assigned as stop 1, KTH stop is named stop 2, and SU is called stop 3. The sense and direction of the route will be from stop 1 to stop 2, from stop 2 to stop 3 and from stop 3 stop 1. This creates a so called loop or circle network, where the start of the route is also the end of it.

In addition, the fact that there is only one direction of routing (already described it) makes this simple scenario specific when on-board passengers are calculated. This leads to a probable crowding problem, because there is no bus operating on the other sense of the line. For example, the demand going from stop 1 to stop 3 will be on-board during the trip from stop 1 to stop 2 and from stop 2 to stop 3. This can be described as an accumulative demand added to the two consecutive stops demand. Furthermore, the importance to highlight that the bus line will be operated by automated vehicles.

4.3.2.2 Three-line Scenario The second scenario of study is based on three different bus lines, each of it is working independently as a close system. So, each bus line is defined between stops. Figure 4.4 defines the system of bus lines:

29 Figure 4.4: Sketch of the second scenario of study.

The fact that each line acts independently from the others, make the lines offer a different service. Frequencies of service, fleet size and demand rate will differ from one line to the other.

4.4 Evaluation of the Case Study

The overall view of the case study is presented. Then, the evaluation of it should be placed, the following words explain how to proceed on that sense.

It is important to understand two different senses of the method, Vetenskapsstaden is already operated by different modes of transport. The travel times are known, the public transport stops are known, the demand is surveyed, the headway and frequency of lines are also known, and mean walking

30 distances are computed. As all the needed variables and parameters are known, no design needs to be done. The General costs of the transport infrastructure can be computed. The User costs and Operating costs are expected to be low because there is a Stockholm demand added to the transport modes, this leads to a better service and the costs of it can be supported. For the interest of the author, this is the public transport network to compete with in terms of General costs when a new solution for the area is designed.

Consequently, two different possible solutions are given to study. Two scenarios offer an alternative to the current transport network in the area. It must be said that the design of them is totally focused to provide a specific mode of transport to the ”campus inhabitants”. So, in this case the General costs are studied to be competitive with the current network scenario. The evaluation of the objective functions will be dependant on the frequency of service, the vehicle capacity and a sensitivity study will be carried out in terms of speed.

On the one hand, for the One-line scenario, as the network name shows it consists of one-line operating the area. This means that one frequency and one speed will be computed per period (through the methods already explained). Adding the fact that on-board passengers will need to go through another stop if origin and destination are not consecutive. This phenomenon will lead to higher frequencies. On the other hand, the Three-line scenario offers another approach to the problem. Three different frequencies, one for each bus line, and speeds are designed per period. The demand covered by the bus fleet is relatively lower than the one covered by the One-line scenario, this will conduct to low set of frequencies

(probably close to the minimum stated frequency (Fmk)).

The evaluation of User costs and Operator costs should be studied, where users will be directly affected by the designed frequency and the operator firm will be dependant on the vehicle size (slightly dependant) and the bus fleet size (number of vehicles per period).

31 5 Numerical Analysis and Results

This section evaluates the cost and benefits of autonomous buses relative to conventional buses under the different scenarios presented and its conditions relative to demand profile, speed values and vehicle sizes.

5.1 Location of bus stops

As explained in the methodology, new bus stops locations are designed in order to reduce the walking distances. This is the current situation of the different campus and its public transport stop location:

(a) Current Stop 1 location. (b) Current Stop 2 location. (c) Current Stop 3 location.

Figure 5.1: Current location of KI, KTH, SU campus stop.

Figure 5.1 gives a sight of the composition of the campus, its extension and where the main stop is located. The following table gives information about the current mean distances between Origin/Destination inside campus and the public transport stop.

KI KTH SU Mean waking distance to the stop 285 m. 310 m. 250m. Number of samples studied 11 17 16

Table 5.1: Data related to the current location of the stops and the walking distances to campus.

Once known, the walking distances of the current public transport network, the new bus stops are located and set as the methodology proceed. This are the results:

32 (a) New Stop 1 location. (b) New Stop 2 location. (c) New Stop 3 location.

Figure 5.2: New location of KI, KTH, SU campus stop.

Figure 5.2 shows the translation of the bus stop to an optimized location. The green mark shows approximately the location of the new bus stop. Hence, the new walking distances are described in the following table:

KI KTH SU Mean waking distance to the stop 90 m. 90 m. 75m. Number of samples studied 11 17 16

Table 5.2: Data related to the new location of the stops and the walking distances to campus.

Table 5.2 is showing the walking distances reduction by applying the methodology, the new locations of the bus stops result to be closer to the mean amount of users. It converges to be a 30% of the initial walking distances.

Finally, the following table summarize the new route length due to the extension of the in-vehicle ride (reduction of walking distance).:

KI KTH SU KI X 4.44 km 4.54 km KTH 4.44 km X 4.14 km SU 4.54 km 4.14 km X

Table 5.3: Riding distances between campus

5.2 Load profiles

The treatment of demand data is oriented to the maximum load method (frequency and vehicle size determination), the need of the load profiles is needed

33 in order to compute the decision variables. The load profiles are computed as the methodology states and this is the result.

Figure 5.3: Load profile for the One-line scenario.

Figure 5.3 represents the demand used for the One-line scenario, frequencies will be designed in terms of the three stop-stop trips. As it can be seen, the distribution of on-board passengers travelling between stops seems closely the same, but D34 (which consists on the passengers riding the trip from Stop3 to Stop1) seems to be determining on the definition of frequencies.

Figure 5.4 is describing the origin-destination demand where each color describes each line. For example, the color red is pointing the first line that travels from Stop1:KI to Stop2:KTH. From each pair of load profiles, a design frequency per period will be computed. Comparing pairs of demands on each line, we can detect the maximum loads on one of them. Also, it is important to highlight the high load of line 2 (between stops 2 and 3) in comparison with the other two. The load profiles are interesting to analyze because by a sight it is possible to comprehend which lines and OD trips could possibly be operated by the minimum frequency.

34 Figure 5.4: Load profile for the Three-line scenario.

5.3 General Costs

The results of the three scenarios are presented in this section by calculating its general costs and evaluating them through the decision variables.

35 5.3.1 Parameters

Parameters VOT (€/hour) 5.53€/h

Waccess 1.2

Wwaiting 1.2

Wriding 1.0

Wtransfer 7.0 β 0.5 η 0.63

ccptl(€/veh) 1.40€/veh-h

bcptl(€/seats − veh) 0.099€/veh-h-seat

clabor(€/veh) 32.9€/veh-h

ccptl(€/veh) 0.073€/veh-h-seat W (km/h) 5km/h The values of the given parameters are obtained through the study of Zhang et al. [17] and the use of the ”National guidelines for transport system management in Australia” by the Australian Transport Council [5]. The values η and β are parameters describing the use of autonomous vehicles, on the one hand η defines the decrease of the labor costs when riding due to the no need of driver. On the other hand, β will affect the capital costs by increasing the cost of each vehicle due to its automation.

5.3.2 Impact of User and Operator costs

Once the related parameters have been given a value, the computation of the general costs can be determined. Following the expressions from the methodology (section 3.5), the costs of the basic (current public transport network) scenario are calculated. The General Costs for the current public transport network are GC = 2120 [€/day], this cost can be divided in User costs (UserCosts = 1417 [€/day]) and Operator costs (OperatorCosts = 703 [€/day]). The user cost is two times the operator cost which is greatly different from what it should be, operator usually carries out with the majority of the general costs.

36 The user costs of the ”basic” scenario are computed according to the amount of demand studied previously. However, the operator costs of the current public transport network cannot be computed per passenger, it is computed through the travelling time given by the sources of Google Maps. To sum up, the main concern is the computation of operational costs of a given demand when travelling times and headway are computed through an overall demand (Stockholm users). Thus, in order to solve that question, the author end up with the thought of rating the operational costs in terms of the demand. For example, when a bus is taken by an amount of demand (demand of interest), the operational costs link to this amount of demand are the weighted costs in terms of the vehicle size (Capacity).

D − Busrate = on board (17) d0bus

D − Metrorate = on board (18) d0metro

Through this assumption, a rate is applied to the calculation of the operational costs of the different lines from the current public transport network (Metro and Bus). This rate is linearly proportional of the operational costs (equations (19)(20)).

GCbus = UserCosts + OperatorCosts · Busrate (19)

GCmetro = UserCosts + OperatorCosts · Metrorate (20)

On this sense, the results are quite logic in terms of the assumption, the treated demand is really low in comparison with the total capacity of the vehicles riding.

General costs of the current public transport network are computed and can be compared with the two other scenarios, to do it so, the generalized costs for the potential implemented solutions should be calculated.

37 Following the methodology explained and the parameters value, the user and operator costs are determined. Different vehicle capacities and commercial speeds are evaluated to create an overview of the costs over the decision variables. The next heat-map explains this analysis.

Figure 5.5: Heat-map of general costs through vehicle capacity and speed for the 1 line scenario.

Figure 5.5 is describing the One-line scenario costs in terms of the decision variables. The boundary line in the heat-map represents the boundaries of the general costs related to the current general costs. For the slightly bright surface, the applied solution will end in a better cost performance.

The next heat-map (Figure 5.6) represents the three line scenario general costs. It is defined exactly the same way as the previous figure. In this case, the three- line scenario shows less irregularity in terms of general costs through speed and capacity. It can be said that the system is a more profitable scenario. The surface of profit is bigger than the One-line scenario.

38 Figure 5.6: Heat-map of general costs through vehicle capacity and speed for the 3 line scenario.

It is important to highlight that there are values of study that are not consistent with the assumptions made. On the one hand, the vehicle capacities can not be greater than 16-18 passengers per vehicle (mini-bus) in the case of study. On the other hand, the recommended speed for a bus is located around 25-30 km/h, speeds over 30 km/h are not realistic at all. However, the range of the decision variables has been highly unsettled in order to see the behavior of the model in terms of them.

In addition, the Figures 5.5 and 5.6 show the sensitivity of the model to the decision variables. It is possible to understand from the figures that General costs of both scenarios are not highly affected by the vehicle capacity whereas the rate of change in terms of the fleet speed is greater.

The results showed are representing the developed model for a given (current) demand. The fact that the implementations made on the Vetenskapsstaden area are studied should lead to solve one of the motivations of the author: the impact of the autonomous bus line implementation could generate trips, and if does so, could the implemented solution support higher demands and would be costly

39 beneficial? So, in order to pursue this motivation question, the model have been studied for several increasing levels of demand rate.

The following figure compares the general costs of the three public transport network systems. The speed is set to 20 km/h and vehicle capacity to 6 pass/veh.

Figure 5.7: General costs of the three studied scenarios studied over and increase of demand.

The bar chart from Figure 5.7 explicitly compares the general costs, the former bar-group is presenting the current demand. Furthermore, the defined

Demandfactor is increasing and the relative costs too. For a better understanding, it must said that Demandfactor is directly multiplying the ridership to study.

From Figure 5.7 can be seen that until Demandfactor= 2.5 both general cost scenario are not below the ”Basic” scenario. Afterwards, for really increased demands, the distances between the two proposed scenario costs and the ”basic” scenario costs gets greater. The evaluation of user costs and operator costs, and the decision variables linked to these is done through the cost model.

Firstly, both scenarios look to increase general costs by increasing its capacity. Moreover, Figure 5.9a defines having constant user costs through the increase

40 of capacity and linearly constant grow of the operator costs (as the vehicle size increases, the capital costs and labor increase also in terms of the number of vehicle seats) as the cost model has defined. On the case of Figure 5.9b, general costs experiment a drop on vehicle size of 8 pass/veh and 10 pass/veh. The succeeding vehicle size increase lead to increase of general costs. Additionally, user costs have a different shape than the former one, it increases until vehicle size of 12 pass/veh and then it converges to a constant user cost value. On the side of operator costs, it also experiments a drop on the second of the capacities studied and then a constant increase.

The enthralling analysis of Figures 5.9a and 5.9b is the effect of the designed headway and how related is it to user costs. Regarding the cost model presented in methodology section, it can be noticed the importance of headway on the user costs and how by increasing the vehicle capacity, the headway also increases. So, it makes a lot of sense that by enlarging the vehicle size, user costs increase. But, it has to pay attention to the maximum headway allowed (headway policy) and that will remain user cost constant.

Then, as observed in results of the 3-line scenario user costs, ridership costs stay steady and so does the headway on each case. Furthermore, for the three-line scenario, user costs increase from 6 pass/veh until 14 pass/veh which leads to the statement that headway is also increasing until the headway policy value has been reached(value that will make user costs be constant).

Once a low-demand profile is analyzed through user and operator costs, the general costs of a positive revenue demand over the ”basic” scenario is studied. For example, in Figure 5.7 when there are some demand cases to study. In first demand case, both scenarios have lower general costs than the ”basic” scenario has for Demandfactor= 2.5. So, the two bus network scenarios are studied for that given demand.

As explained, Figure 5.10 is describing the general costs for both implementations with an increased demand. In this case, figure 5.10a is giving a similar profile of overall costs as the figure 5.9a. Although user costs remain constant in both demands, operator costs will have slightly different grow. So, in the three-line scenario for the given vehicle speed (V=20 km/h), the optimal solution is found

41 when vehicle capacity is C=12 pass/veh. Regarding the user costs analysis, the fact that it stays steady through vehicle capacities gives the related headway policy again as a solution of the problem.

Concerning Figure 5.10b, the increase of demand has showed some profile difference from Figure 5.9b. Firstly, operator costs will be dropping down until higher capacities (C=26 pass/veh) are achieved and then growing again with a constant slope. In second term, user costs appear to be lower for small vehicle sizes and increase through capacity enlargement. The ridership costs converge to 5503 €/day when C=30 pass/veh. So, as a potential solution for this scenario and the frame variables, a given capacity of C=14 pass/veh will end giving minimum costs for the increased demand.

Finally, Figure 5.8 sums up the effect of the studied variables in the different costs, giving an overall idea of the impact of them. For a better understanding of Figure 5.8 a brief description of the elements is explained. The aim of the following table is to summarize the content of the results in a graphical and synthesized way. The increase of the variables studied (Vehicle speed and capacity and Demand) will affect to user and operator costs of each public transport network scenario, so the impact is explained by basic arrows and different colours. The direction of the arrows (up and down) describes the increase or decrease of the costs. If the costs remain barely constant, it will be indicated by an hyphen (-). Regarding colour description, yellow defines a small change whereas green and red explains high decrease or increase respectively. It is assumed from the author that the positive answer of the model would be a decrease of the costs.

Figure 5.8: Evaluation of User and Operator cost when decision variables are increased.

42 (a) User and Operator costs of Three-line scenario

(b) User and Operator costs of One-line scenario

Figure 5.9: Impact of vehicle size on the two analyzed scenarios costs. 43 (a) User and Operator costs of Three-line scenario of the increased demand

(b) User and Operator costs of One-line scenario of the increased demand

Figure 5.10: User and Operator cost of the proposed solutions over vehicle size with the new demand on Demandf actor=2.5.

44 6 Conclusions

This section is covering author’s best conclusions and a discussion of the positive effects and the drawbacks of the research project.

The idea of giving a solution to a potential future problematic or a more efficient implementation of the current public transport network have been chased during the whole research process. During these months of study, the method has been implemented and positive results have been achieved. The results show the response of the cost model implemented to different scenarios likely to be checked in section 5.3. First of all, great results have been found when bus stops were strategically located, the basic approach fulfilled the needs of the system and offer a solution to the ”last mile” problem. So it is that walking distances to destinations have been reduced by three with this implementation.

Secondly, the results for the two scenario costs showed a lightly robustness when current demand was studied. Considering a robust response because in this case, users are not experiencing great changes on their costs when capacity is checked. There is a main reason for that which could be explained commonly for both scenarios, it is due to the low-demand rate. The experienced low trips generated induces the model to work with minimum frequencies. The fact that the bus line is working through policy headways, leads to inefficient use of the system because it could operate in higher frequencies and transporting more passengers for the same cost. This is directly translated to higher costs.

Moreover, the system has also been checked for a higher demand on both scenarios and the results have partially changed. For the one bus line scenario, great changes have been noticed for low capacities where an optimum capacity of 14 pass/veh is resulting. Hence, noticing a better experience for the user because the minimum frequency was avoided. However, even though the three line scenario has also been tested for a higher demand, same headways have been achieved converging again to a low frequency system.

Another approach is the performance of the costs model through different commercial speeds. It is not surprisingly that by increasing the speed velocity, the user costs and operator costs tend to drop. The fact that riding times get reduced,

45 affect directly to the reduction of the ridership costs and also the reduction of the fleet size. However, it is a fact that actually high speeds are hard to perform when autonomous buses are operated.

Once user costs has been discussed, a view to the operator cost is done. Exceptionally, operator costs take a smaller part of the total costs. This is singular and also should be commented, the fact that the total amount of passengers through the day leads to a little fleet size. The fact that autonomous buses are taking a step forward on this problem, creates low labor costs (operational costs) through avoiding drivers but automated vehicles end up being more expensive. For this reason, the author believes that autonomous should ride over conventional buses when vehicle sizes are implemented and greater self-driven fleets.

Actually, the results do state the last of author’s believes. In addition, analyzing the overall costs between the two implemented scenarios, it can be concluded that the three-line system represent less costs than the unique bus line. So, it results to be a better solution. On regard to the current public transport network, the results seem to be lower than both studied implementations when the actual demand is computed. But further on, if the campus environment is able to generate, attract and impulse the interaction between universities by creating more daily trips, the model seems to give a proof of cost optimization.

6.1 Future Work

The results seem to be clear and a reflect of how would the system behave, but not sufficient. The author’s maintains that further studies could be developed studying the trips between single campus where different behavior have been noticed. The load profiles show that another solutions could be implemented between two campus or other scenarios could be analyzed. Another approach could positioning Albano as a key point of the network between campus, it is certainly true that will become more and more important to the synergy of the region.

Also, the use of other modes of transport could become a good solution to this

46 study case. Bike-sharing, electric scooters or other options of micro-mobility could be studied.

Finally, a deep analysis on how to induce, if there is the need, more interaction between campus which will surely generate trips between them.

47 References

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