Transition Towards Fixed-Line Autonomous BusTransportation Systems

JONAS HATZENBÜHLER

Licentiate ThesisStockholm, Sweden 2020

TRITA-ABE-DLT-2011ISBN 978-91-7873-514-3

KTH Royal Institute of TechnologySchool of Architecture and the Build EnvironmentDepartment of Civil and Architectural Engineering

Division of Transport PlanningBrinellvägen 23, SE-100 44 Stockholm

SWEDEN

Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framläggestill offentlig granskning för avläggande av licentiatexamen i transportvetenskapMåndag den 25 Maj 2020 klockan 10:00 i K409, Brinellvägen 23, Kungl Tekniskahögskolan, Stockholm.

© Jonas Hatzenbühler, May 2020

Tryck: Universitetsservice US AB

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Abstract

In the last years the steady development of autonomous driving technologyhas enabled the deployment of more mature autonomous vehicles. Thesevehicles have been applied in several pilot projects worldwide, most commonlyin the form of small buses. At the same time, the amount of people travelingin especially urban areas is continuously growing, resulting in more trips inthe transportation system. An efficient transportation system is thereforerequired to serve the growing passenger demand. Autonomous buses (AB) areassumed to have lower operational costs and with that public transport (PT)systems can potentially be designed more efficiently to facilitate the increaseddemand better. In this study, an AB specific simulation-based optimizationframework is proposed which allows analyzing the impacts AB have on line-based PT systems. The thesis focuses on the transition from existing PTsystems towards line-based PT systems operated partially or exclusively byAB.

Existing work on PT service design is extended so that realistic AB sys-tems can be investigated. This is achieved by (i) using AB specific operatorcost formulations, (ii) integrating infrastructure costs required for AB oper-ations, (iii) utilizing a dynamic, stochastic and schedule-based passenger as-signment model for the simulation of PT networks and by (iv) formulating amulti-objective optimization problem allowing to investigate the stakeholder-specific impacts of AB.

In Paper I the effects of AB, concerning service frequency and vehicle ca-pacity, on fixed-line PT networks are investigated. Among other metrics, thechanges are evaluated based on differences in level of service and passengerflow. Additionally, the sequential introduction of AB in existing PT systemsis studied. The framework addresses a case study in Kista, Sweden. Thestudy confirmed the initial hypothesis that the deployment of AB leads toan increase in service frequency and a marginal reduction in vehicle capacity.Furthermore, it could be seen that the deployment of AB increases the pas-senger load on AB lines and that passengers can shift from other PT modestowards the AB services.

Paper II incorporates a multi-objective heuristic optimization algorithmin the simulation framework. The study investigates changes in transport net-work design based on the deployment of AB. The differences in user-focusedand operator-focused network design are analyzed and the impact of AB onthese is quantified. This study is applied to a case study in Barkarby, Swedenwhere a full-sized, line-based PT network is designed to exclusively operateAB. Among other findings, we show that the autonomous technology reducesthe number of served bus stops and reduces the total PT network size. Addi-tionally, average passenger waiting time can be reduced when deploying ABon user-focused PT networks, which in turn leads to a further reduction ofuser cost.

Keywords: Autonomous Buses, Public Transportation, Network Design,Resource Allocation, Simulation-based multi-objective Optimization

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Sammanfattning

De senaste årens framsteg inom autonom körteknik har lett till mer mog-na autonoma fordon. Dessa fordon har setts tillämpas i flera pilotprojekt överhela världen, oftast i form av små bussar. Samtidigt växer mängden männi-skor som reser, särskilt i stadsområden, kontinuerligt vilket resulterar i flerresor i transportsystemet. Därför krävs ett effektivt transportsystem för atttillgodose det växande antalet passagerare. Autonoma bussar (AB) antas halägre driftskostnader och därmed kan system för kollektivtrafik (public trans-port, PT) potentiellt utformas mer effektivt för att underlätta den ökadeefterfrågan bättre. I denna studie föreslås ett AB-specifikt simuleringsbaseratoptimeringsramverk som gör det möjligt att analysera effekterna AB har pålinjebaserade PT-system. Avhandlingen fokuserar på övergången från befint-liga PT-system till linjebaserade PT-system som delvis eller uteslutande drivsav AB.

Befintligt arbete med PT-tjänstdesign utvidgas så att realistiska AB-system kan undersökas. Detta uppnås genom att (i) använda AB-specifikaoperatörskostnadsformuleringar, (ii) integrera infrastrukturkostnader som krävsför AB-verksamhet, (iii) använda en dynamisk, stokastisk och schemabaseradmodell för att tilldela passagerare vid simulering av PT-nät samt genom att(iv) formulera ett multifunktionellt optimeringsproblem som gör det möjligtatt undersöka AB: s intressespecifika effekter.

I artikel I undersöks effekterna av AB, med avseende på servicefrekvensoch fordonskapacitet, på fasta linjer i PT-nät. Förändringar utvärderas blandannat utifrån skillnader i servicenivå och passagerarflöde. Dessutom studerasden sekventiella introduktionen av AB i befintliga PT-system. Det föreslagnaramverket tillämpas på en fallstudie i Kista, Sverige. Studien bekräftade deninitiala hypotesen att utplaceringen av AB leder till en ökning av service-frekvensen och en marginell minskning av fordonens kapacitet. Vidare kundeman se att utplaceringen av AB ökar passagerarbelastningen på AB-linjer ochatt passagerare kan skifta från andra PT-former mot AB-tjänsterna.

Artikel II integrerar en multifunktionell heuristisk optimeringsalgoritm iramverket för simuleringen. Studien undersöker förändringar i transportnät-verkets design baserat på implementeringen av AB. Skillnaderna i användarfo-kuserad och operatörsfokuserad nätverksdesign analyseras och AB: s inverkanpå dessa kvantifieras. Denna studie tillämpas på en fallstudie i Barkarby, Sve-rige, där ett fullstort linjebaserat PT-nät är utformat för att exklusivt drivaAB. Vi visar bland annat att den autonoma tekniken reducerar antalet an-vända busshållplatser och reducerar den totala PT-nätstorleken. Dessutomkan implementeringen av AB på användarfokuserade PT-nät ytterligare för-bättra servicenivån främst genom att minska den genomsnittliga väntetidenper passagerare.

Nyckelord: autonoma bussar, kollektivtrafik, nätverksdesign, resursallo-kering, simuleringsbaserad multifunktionell optimering

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Acknowledgements

The help and support of numerous people made my time working on this thesis andconducting the research a very joyful and memorable learning experience. First ofall, a big thank you to my main supervisor Erik Jenelius and my assistant supervisorOded Cats for the open support and guidance during the work on this thesis. I amparticularly thankful for your supportive and easy to approach supervision style.I always appreciated our open discussions and the helpful advice I have receivedfrom you.

The research conducted in this thesis was part of the iQMobility project leadby Scania and financed through the Swedish Governmental Agency for InnovationSystems (Vinnova); both of which I would like to thank for the continued input,interest in the research and the cooperation along with the research project.

I would also like to thank the advance reviewer Markus Bohlin for taking thetime in assessing my thesis and giving me very valuable comments to further im-prove the quality of this thesis and the discussion about future research directions.

Special thanks go to all my colleagues at the division. You have managed totransform a simple lunch break into a fun event and supported me with my researchwhen I felt stuck. Thank you all for the laughter and support!

Finally, I would like to express my biggest thanks to my family and girlfriend.Thank you, mum and dad, for the constant support and open ear. And thank youEmelie, for putting up with my work hours and being the best partner one couldever wish for!

Jonas Hatzenbühler,Stockholm, April 2020

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List of papers

Papers included in the thesisI. Hatzenbühler, J., Cats, O., Jenelius, E., 2019. Transitioning towards the de-

ployment of line-based autonomous buses: Consequences for service frequencyand vehicle capacity. Under review at Transport Research Part A: Policy andPractice, January 2020.

II. Hatzenbühler, J., Cats, O., Jenelius, E., 2019. Fixed-line network design inlight of autonomous buses. Submitted to Transportation, February 2020.

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Declaration of contribution

The research motivation and the conceptualization of the research project weredone in close discussion with Erik Jenelius and Oded Cats. The generated ideas ledto the development of Paper I and Paper II. The research design, implementation,computational experiments, result analysis and writing were done mainly by me.Both, Erik Jenelius and Oded Cats, helped me with the development and criticalassessment of the chosen methodology as well as the interpretation and analysisof the results. They have also taken an active part in the internal revision of thepapers.

Contents

Contents x

List of Figures xi

List of Tables xii

List of Acronyms xiii

1 Introduction 11.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Research Objectives 7

3 Methodology 93.1 Mesoscopic Agent-Based Simulation . . . . . . . . . . . . . . . . . . 103.2 Heuristic Optimization in Network Design . . . . . . . . . . . . . . . 113.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 Scientific Contributions 134.1 Paper I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134.2 Paper II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

5 Implications and Future Directions 175.1 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175.2 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185.3 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Bibliography 19

viii

List of Figures

3.1 Conceptual overview of Paper I and Paper II. In orange the frameworkfor paper I is shown and in blue the framework for paper II. . . . . . . . 10

ix

List of Tables

4.1 Relationship between papers and research objectives . . . . . . . . . . . 13

x

List of Acronyms

AB Autonomous BusABC Artificial Bee ColonyAV Autonomous VehiclePT Public TransportRQ Research QuestionTNDFSP Transit Network Design and Frequency Setting Problem

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Chapter 1

Introduction

In recent years the number of pilot projects including driverless vehicles has steadilyincreased. This is mainly due to advances in technology that enable the vehicles todrive safely in real traffic situations and proactive policymakers who use the pilotprojects to study the acceptance among the public. In Ainsalu et al. (2018) the au-thors present an extensive overview of on-going and completed EU-based projectsutilizing autonomous vehicle technology. Most of the ongoing projects and researchabout the use of autonomous technology have focused on autonomous driving forcars on highways whereas replacing or substituting conventional public transport(PT) systems has been underrepresented. In the last years, however, a growingnumber of studies dedicated to the synergetic effects of autonomous vehicles (AV)and PT systems have been published (Liang et al., 2016; Scheltes and de AlmeidaCorreia, 2017; Abe, 2019; Tirachini and Antoniou, 2020). These studies investigateconcepts like shared autonomous last-mile services, feeder services, and others. Thepotential autonomous technology has in improving individual traffic systems wasshown by (Alonso-Mora et al., 2017). These studies are applied in areas with a largepopulation and a high demand density. The main effects are an increase in peopleper car and an increase in vehicle-km traveled, which implies that fewer vehiclesare required to serve the given demand. In rural areas, similar studies show thata large number of vehicles are required to serve the sparse demand, which in turnmeans that many vehicles are needed and the number of empty-vehicle kilometersdriven is high (Ben-Dor et al., 2019).

To connect high demand urban areas conventional public transport has provento be an efficient solution. These systems typically operate on a fixed-line networkand follow a schedule. The advantage of such operations is the high passenger vol-ume these systems can transport and the simplicity of usage for passengers. One ofthe main disadvantages of these systems is the low service quality and the inefficientuse of resources in low demand areas. Due to autonomous technology, the need forbus drivers is reduced and hence the labor cost reduced. This, in turn, has the

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2 CHAPTER 1. INTRODUCTION

potential to (1) increase the level of service through an increased service frequency,(2) reduce travel times through direct connections, (3) increase operating hours ofthe bus system and (4) reduce of travel costs, on line-based bus systems. Withthe introduction of autonomous and electrified driving technologies on line-basednew problems arise, e.g. facilitating charging spots, managing vehicle fleets andtransitioning towards autonomous bus (AB) operations. Especially the transitionscenarios towards AB services, the relation between reduction of operator cost dueto autonomous technology and impacts on the level of service has not been studiedmuch in the literature before. We have therefore identified the research gap in ex-ploring the impacts AB has on line-based services.

All of these studies show the potential of autonomous technology when operatedin traffic with exclusively autonomous vehicles. There has been a lack of studiesthat look into the transition towards autonomous induced transportation systems.Due to practical reasons and regulations, it is not realistic to assume a sudden andfast change from conventional to autonomously driven vehicles in the system, asit is assumed by most current studies. Therefore, a focus area of this work is theinvestigation of the transition phase towards autonomous vehicles in traffic systems.

This thesis aims to investigate the effect driverless technology has on fixed-linetransport systems concerning the level of service and the service design of suchsystems. The author believes that the fixed-line driverless buses will be the firstautonomous systems that will be in long-term operation since they are alreadyin advanced pilot phases, allow for a controlled transition and have lower techni-cal and regulatory burdens. More specifically, this study investigates the effectsautonomously operated buses have on the service frequency, vehicle capacity, pas-senger flow, and network design when operated as a fixed-line service. The changesin resource allocation, sequence of technology introduction and the alignment ofthe fixed-line network are compared with conventionally operated networks. Thepresented work focuses on the utilization of small to large-sized buses operating onpublic roads in mixed-traffic conditions. The knowledge gained through the pre-sented analysis benefits the understanding and impacts of autonomous buses fortransport authorities, transport operators, the users of public transport systemsand vehicle manufactures.

1.1 Literature Review

The relevant literature for this work is divided into three subcategories. First, stud-ies about the allocation of public transportation (PT) resources in terms of vehiclecapacity and service frequency are presented. Then relevant literature concern-ing the design of transportation networks is highlighted. Lastly, works developingpassenger assignment models are briefly discussed. The section closes with the de-

1.1. LITERATURE REVIEW 3

scription of the identified research contributions.

Resource AllocationThe allocation of vehicle capacity and service frequency on line-based PT networkshas been studied over a number of years. A categorization of the previous workscan be done based on the method and problem formulation. The majority of theearly studies use analytical approaches and single line networks to determine theoptimal allocation of vehicle resources. In his study Newell (1971) the optimalheadway is determined through a minimization of the passengers’ waiting time.Salzborn (1972) and Ceder (2007) build on this formulation. They propose formu-lations integrating trip chaining and variations on demand levels. An importantstudy was presented by Jansson (1980) and Gwilliam et al. (1985). Both authorsdevelop an analytical model to determine optimal vehicle capacities and servicefrequency, respectively. More specifically, Jansson (1980) derived a mathematicalexpression considering simplified user costs and operator costs to determine theoptimal resource allocation on simple line networks. This "square root formula"considers user costs and operator costs by integrating passenger flow, maximumcrowding level and peak hour duration among other factors. More recent workby Yu et al. (2011) proposes a comprehensive approach for the resource allocationproblem. The model considers in-vehicle and bus stop crowding effects, dwell timesand denied boardings on PT networks.

The research done on resource allocation of AB on line-based PT networks islimited. Wen et al. (2018) explore the impacts of AB on the modal split of the entirePT system. The authors focus on the last-/first mile use case of AB. The methodused is a combination of agent-based simulation and analytical demand model. Theauthors report that 82% of private car trips would be done with AB instead while18% of previous bus trips are shifted to AB. In Abe (2019) the potential benefitsof AB in Japan are investigated using a survey. The authors report that transitagencies and operators benefit from the automation of line-based systems, whilepassengers in an urban environment would benefit from flexible autonomous sys-tems. Similarly, Tirachini and Antoniou (2020) use a variation of the square rootformula (Jansson, 1980) in their study of the impacts of AB on PT networks. Singleline case studies in Germany and Chile are investigated. The authors show that abenefit for operators and users can be expected when operating AB on fixed-linesystems.

Simulation models offer an alternative approach to economic assessments of PTsystems. These models have the advantage of capturing complex networks morerealistically in terms of the relations and passenger flows of multiple PT lines andsystem dynamics. In Chakroborty (2003) the authors describe the difficulty ofmodeling resource allocation as a mathematical program due to the discrete nature

4 CHAPTER 1. INTRODUCTION

and the complexity (transfers, waiting times, trip chaining). Transit simulationframeworks have therefore been developed to allow efficient assessment of PT net-works. In Cats and Glück (2019) an agent-based assignment model (BusMezzo)that captures uncertainties in supply and adaptive user decisions are proposed. Inthe last years widely used multi-agent simulation tools like (MATSim or SUMO)have been used in studies simulating autonomous vehicle systems. For example,in Leich and Bischoff (2019) the authors compare the level of service provided byfixed-line networks with shared autonomous vehicles (SAV) using MATSim. Theyreport minor improvements in terms of user costs when using SAV, while the costsfor operator are increased mainly because of increased vehicle-km. Similarly, Alaz-zawi et al. (2018) use SUMO to study how a AV fleets could be used to reduce trafficcongestion. The authors rate different scenarios based on vehicle-km traveled andfree-floating vehicle movements.

Network DesignThe research branch of public transport network design has been active for manydecades. An extensive overview of the conducted research in the fields of transitnetwork design and scheduling is given in Guihaire and Hao (2008a). The authorsidentify three sub-problems for the network design: A (i) transit network designand frequency setting problem (TNDFSP), (ii) transit network frequency settingand scheduling problem (TNSP) and (iii) transit network design and schedulingproblem (TNDSP), respectively. This work focuses on the first sub-problem, i.e.the TNDFSP.

One of the first works studies of the TNSDSP is done by Lampkin and d. Saal-mans (1967). In their framework, the authors optimize the travel time and vehiclecapacity on a single route network. The demand is modeled static and specific foreach origin-destination stop pair. The objective function is computed as the sum ofuser and operator costs, which are represented as monetary values respectively. Inmore advanced models additional constraints are considered. The authors in Zhaoand Zeng (2008); Chien et al. (2003); Chakroborty (2003) consider the number ofbus stops on a route, the total number of route transfers and the route directnessas constraints of the TNDFSP. With the increased problem complexity advancedoptimization algorithms have to be utilized. A variety of heuristic algorithms areimplemented in different TNDFSP studies. Fan and Machemehl (2006) presenta simulated annealing algorithm. The authors optimize the combined sum of thecosts for the user, operator, and unsatisfied demand. The problem constraints arethe maximum fleet size, route capacity, maximum number of routes and maximumtrip length. In a recent study by Szeto and Jiang (2012) the application of artificialbee colony optimization (ABC) algorithm is discussed. The authors minimize theweighted sum of total transfers and total travel time in the network, while the prob-lem is constrained by the vehicle capacity. In their model, the service frequency is

1.1. LITERATURE REVIEW 5

determined considering the maximum vehicles per route. In a second work (Szetoand Jiang, 2014) propose a bi-level model for the TNDFSP. In that model, thelower-level problem is the passenger assignment problem which is constrained byvehicle capacity. The upper-level problem is the minimization of passenger trans-fers. Similar to their previous study the authors use an ABC algorithm. The studyPT networks in Winnipeg, Canada, and Tin Shui Wai, Hong Kong.

Passenger AssignmentAn essential part of studying the impacts of changes in the PT network is themodeling of passenger decisions. In the past several assignment models have beendeveloped. The models differ can be categorized in frequency-based (Nguyen andPallottino, 1988; Spiess and Florian, 1989; Cepeda et al., 2006) or schedule-baseddecision models (Mark D. Hickman and David H. Bernstein, 1997; Tong and Wong,1998; Poon et al., 2004). In both categories, models have static or dynamic anddeterministic or stochastic decision-making processes. Static assignment modelsassume a constant demand level over a given time period. This prevents thesemodels to capture changes in e.g. waiting time or vehicle crowding over time.In dynamic models, these time-dependent changes are considered by computingwith-in day changes of e.g. travel demand. In deterministic assignment models,a uniform distribution of passenger arrival processes is assumed. The passengersare then assigned to the PT vehicles based on deterministic rules. For short ve-hicle headways the assumption of uniform arrival distribution has shown to be agood approximation (Jolliffe and Hutchinson, 1975), however if PT vehicles follow aless frequent schedule they arrival processes can not be assumed uniform anymore.To capture more complex and more realistic arrival patterns stochastic assignmentmodels have been developed. Hence stochastic assignment models capture betteruncertainties in the passenger boarding process. In more complex PT networks thestochastic passenger assignment leads to more realistic results, which is the reasonwhy in this study a dynamic, stochastic and schedule-based passenger assignmentmodel (Cats, 2013) is used.

Research ContributionThe contributions of this study are twofold. First, the work on evaluating theimpacts of autonomous buses on existing public transport systems in terms of pas-senger flow and passenger load is building on the existing research on resourceallocation on line based PT systems. Additionally, the work on the sequencingof autonomous bus deployment with the developed simulation framework is con-tributing to policy studies about temporal changes in transport planning inducesby autonomous buses. Through the study on the sequential introduction of AB intoa fixed-line public transport network, it is possible to identify where autonomous

6 CHAPTER 1. INTRODUCTION

buses should be deployed first and in what order. The second contribution is ex-tending the existing research in network design and frequency setting with an ABspecific simulation-based optimization method, where the simulation uses advanceddynamic passenger assignment models and the optimization algorithm allows for amulti-objective problem formulation.

1.2 Thesis Organization

The remainder of the thesis is organized into four chapters. First, the researchobjectives of this work are highlighted. Second, the methodology employed toanswer the research objectives is presented. Third follows a detailed discussionof the papers and their scientific contributions. The fourth chapter discusses andimplications of the key results of the research conducted and gives potential futureresearch directions. The two research papers are included as attachments.

Chapter 2

Research Objectives

The overarching goal of this thesis is to analyze operational changes in the fixed-lines public transport system when transitioning towards autonomous bus systems.Consequently, three research questions (RQ) have been identified and are formu-lated as follows:

RQ1 What are the impacts of AB on line-based PT servicedesign?

A direct consequence of the driver-less operation of buses is the reduction of oper-ation costs of these vehicles. Hence the impacts of reinvesting these operation costsavings optimally in the service considering user and operator costs are analyzed.The impacts of AB on PT networks are measured in terms of changes in servicefrequency, vehicle capacity, passenger flow and bus line load based on vehicle tech-nology.

RQ2 In what line sequence should AB be introduced in aline-based PT network?

This RQ is aiming at the introduction sequence of autonomous technology. Due tobudget, fleet size or legislative constraints the extension of an autonomously oper-ated fixed-lines service advances in steps. To maximize the expected profit and levelof service the sequence of the introduction is crucial to know to transition towardsa fully autonomously driven service. The different potential sequences are rankedbased on the combined user and operator costs. A distinction is made betweenpath-dependent and path-independent sequences.

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8 CHAPTER 2. RESEARCH OBJECTIVES

RQ3 How should a line-based AB network be designed to besttake advantage of the autonomous technology?Besides the greater flexibility in resource allocation (see RQ1) the reduction of op-erational costs might also influence the bus route layout and bus stop locations ofa public transport network. Due to lower operational costs, longer trips and morefrequent stops might be affordable for the operator to do. Since larger areas canbe served and the access and egress time to PT access points can be reduced, morepassengers might use the PT system. Therefore it is important to study changes inthe network design in light of AB and if the network design is sensitive to differentstakeholder requirements.

Chapter 3

Methodology

To assess the impacts of AB in PT networks on passenger flows, vehicle loads andlevel of service a detailed representation of the network is required. Therefore asimulation-based optimization framework is developed and utilized to evaluate pub-lic transport scenarios. The framework is AB specific by (i) considering AB relatedinfrastructure costs (e.g. road enhancements, bus stop layout); (ii) implementinga connectivity constraint, meaning that each AB in the network has to be ableto reach every point in the network without human interference and(iii) an ABspecific operator cost formulation. To investigate the optimal network design anefficient optimization logarithm is required which allows for large solution spaces.The methodology used to answer the RQ is based on a combination of mesoscopicsimulation software and optimization algorithm. Based on the simulation outputthe objective values are computed and each solution can be evaluated.

Challenges for the chosen methodology are mainly the impacts of stochasticityon the simulation results and the definition of convergence criteria. To cope withthese challenges convergence criteria tests as proposed by (Auger et al., 2012; Bier-laire, 2015) are performed. Stochastic variability in simulation results is reduced byaveraging across multiple simulation runs for each solution. The number of requiredsimulations is determined using the concepts from Burghout (2004).

The data needed to perform the simulation and optimization is acquired frommultiple data sources. Open-source data from OpenStreetMap (OpenStreetMapcontributors, 2017) are used for mapping and spatial representation of a network.Passenger demand data are based on recorded data (tap-in passenger registrations),demand prediction models (Trafikverket, 2015)and estimates from public transportoperators. Other data for vehicle operational cost parameters, optimization param-eters, and simulation specific parameters (e.g. value of time, transfer penalty, etc.)are chosen based on recently published literature and widely accepted standards.

In Figure 3.1 the overall framework can be seen. Paper I uses a brute forcealgorithm in combination with the mesoscopic simulation tool to compute resource

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10 CHAPTER 3. METHODOLOGY

Figure 3.1: Conceptual overview of Paper I and Paper II. In orange the frameworkfor paper I is shown and in blue the framework for paper II.

allocation for a given PT network. A single line analytical model is used to deter-mine the initial solution for the resource allocation problem. The decision variablesare service frequency, vehicle capacity, and vehicle technology. The paper alsoanalyses the sequence of autonomous technology introduction. In paper II the PTnetwork design problem is solved using service frequency, bus stop locations andthe bus line configuration (number of stops, length of a bus line, the sequence ofstops and the routes in-between) as the decision variables. The research problemis solved using a multi-objective ABC and mesoscopic simulation tool.

3.1 Mesoscopic Agent-Based Simulation

With the advantages in computational processing power, a combination of bothmicro- and macroscopic simulation is possible. Mesoscopic simulation tools allowfor the detailed analysis of large, real-world networks and are therefore a helpfultool for traffic prediction, planning, and transport engineering.

For this work the simulation of large transport systems including passenger flowand vehicle movement is important. Additionally, the simulation software has tobe capable of simulating public transport systems. This requires the considerationof vehicle schedules, bus routes, concepts for bus stops and the measurement ofpassenger experiences. OpenSource simulation tools or commercial software pack-

3.2. HEURISTIC OPTIMIZATION IN NETWORK DESIGN 11

ages tools like SUMO (Krajzewicz et al., 2012), MatSim (Horni et al., 2016) andVissim (PTV, 2004) all offer the capability to study traffic and transportation sys-tem. However, they cannot realistically simulate different passenger assignmentprocesses. Here the open-source simulation tool BusMezzo has the greatest advan-tage over the other mentioned simulation software and is therefore chosen in thiswork.

Busmezzo includes a dynamic PT passenger assignment and vehicle simulationmodel. This model is based on the road network and the PT network definition.These definitions include road intersections, junctions, lane information as well asinformation about bus stops, bus lines, bus routes, the level of demand and thevehicle schedule. Passenger boarding, alighting processes and vehicle run and dwelltimes are stochastic. The stochastic nature might impact the convergence andvariance of the objective function, therefore to account for the stochastic processesand minimize their impact on the results each network is simulated multiple times.The number of simulations is computed using the mean and standard deviationof individual simulation outputs following the process as proposed by Burghout(2004). The decision-making model is based on utility maximization for each pas-senger. For each passenger, the utility from its origin to destination for multiplepaths is computed. During their journey, passengers can reevaluate their paths andmake changes based on real-time information on the current traffic situation. Theutility is computed based on expected in-vehicle time, waiting times, number oftransfers and others. Besides the dynamic passenger assignment, BusMezzo comeswith a day-to-day learning option. This option allows for a learning process of thepassengers between days. Passengers store the utility of a previously experiencedjourney and can adapt their behavior according to this utility value. Over thecourse of multiple days, a passenger can adjust their journey based on these ex-periences which eventually results in a more realistic and robust network evaluation.

3.2 Heuristic Optimization in Network Design

The transit network design problem as discussed in the second paper is NP-hard(Guihaire and Hao, 2008b; Magnanti and Wong, 1984). To solve the problem ina reasonable time either several simplifications have to be made to the problemformulation or heuristic problem solution methods have to be applied. Past re-search has considered both approaches: Analytical models to solve the networkdesign problem in a more general way (Ceder and Wilson, 1986; Spiess and Flo-rian, 1989) and optimization models using heuristic algorithms (Chakroborty andWivedi, 2002; Hadi Baaj and Mahmassani, 1995; Chien et al., 2003; Szeto andJiang, 2014). Models using Simulated Annealing, Genetic Algorithm, Ant ColonyOptimization, NSGA-II, and Artificial Bee Colony Optimization have shown themost promising results in recent years.

12 CHAPTER 3. METHODOLOGY

With advances in computation and higher availability of transport-related datamore realistic and complex transit network models can be developed. In (Zou et al.,2011) the authors have compared different heuristic algorithms and have shownthat NSGA-II and ABC are promising algorithms for solving the multi-objectiveoptimization problems. In terms of solution diversity and solution convergence,NSGA-II solutions are dominated by ABC generated solutions. In this study, theABC algorithm is chosen because of its capability to efficiently examine the solu-tion space and because of the promising results reported in other studies. The ABCrequires a relatively large number of evaluations to converge to a solution. For thestrategic network design problem as presented in this work the computation timeis not crucial and therefore this disadvantage is not relevant.

To formulate the natural behavior of bees into an optimization algorithm thenotation of bees and the food source has to be conceptualized. In this work, abee is represented as one network design including information about the servicefrequency and vehicle capacity of each line. A food source is the resulting objectivevalues from this network design solution.

The ABC is an iterative algorithm. Each iteration includes the exploration ofmultiple neighborhoods each by one bee, all of the bees report the objective valuesto the onlooker bees. After the onlooker bees have received the objective valuesfrom all neighborhoods the next generation of employed bees is allocated to the foodsources with the best objective values. This mechanism focuses the search towardsgood quality food sources which helps the algorithm to converge efficiently. For thiswork, the general ABC, as described above, is adjusted to be able to solve multi-objective problems. To do this the key concepts of exploration and exploitationusing three bee types are preserved. Additional heuristics for mutation, ranking ofsolutions and fitness computation need to be implemented. The proposed adjust-ments to the multi-objective ABC (MOABC) is a consolidation of the works byZou et al. (2011) and Deb et al. (2002).

3.3 Summary

In summary, the methodology used in this work is a combination of multi-agent PTsimulation and heuristic optimization. The first paper uses a brute force approachto evaluate all feasible network scenarios, while the shortest path and myopic al-gorithms are implemented to extract the recommended sequence of autonomoustechnology introduction. The second paper utilizes the proposed simulation-basedoptimization framework where the same simulation software as in paper one is usedto evaluate different network designs while the MOABC algorithm allows for anefficient convergence to a solution.

Chapter 4

Scientific Contributions

This chapter links the research questions to the respective papers included in thethesis (see Table 4). Additionally, the main contributions of each paper are high-lighted.

Table 4.1: Relationship between papers and research objectives

Notation Research questionsPapers

I II

RQ1 What are the impacts of AB on line-based PTservice design?

X X

RQ2 In what line sequence should AB be intro-duced in a line-based PT network?

X

RQ3 How should a line-based AB network be de-signed to best take advantage of the au-tonomous technology?

X

4.1 Paper I

This paper studies the effect autonomous driving technology has on the service fre-quency and vehicle capacity of fixed-line public transport operations. Additionally,the impacts on passenger flow between modes and consequences for policymakersare extracted. Besides that, a framework to determine the sequence of technologyintroduction is developed. The main contributions of the paper are addressing theproblem of introducing autonomous vehicle technology in a sequential process. Thispaper applies the methodology to the real-world setting of Kista, connected to the

13

14 CHAPTER 4. SCIENTIFIC CONTRIBUTIONS

AB pilot project in that area. This topic has not been discussed in the contextof transport service planning before. The sequence of introduction is especiallyinteresting and important for autonomous vehicles since currently, the deploymentof actual buses on public roads is a complicated and time-consuming process. Itis therefore important to introduce autonomous vehicles on roads and lines whichbring the highest advantages to the transportation system.

The results of this study help to further understand the system-level implicationsof AB and more specifically give a tool to manifest the notion of improved servicequality for users of public transport systems through autonomous buses. The de-veloped framework also shows reasonable computational efficiency for a strategicplanning tool, witch which realistic (number of routes, level of demand and vehiclefleet) transport scenarios can be investigated. In combination with optimizationalgorithms as presented in (Paper II) the area of the case study could further beincreased. The key findings of paper I are:

• The reduction in operating costs of AB improves the level of service for thePT users. This is primarily due to an increase in service frequency on theAB lines, which translates into shorter waiting times for the passengers. Thehigher service frequencies also make the lines more attractive for other usersand therefore increases the passenger load. Another direct consequence of theAB lines is a slight increase in the recorded transfers (RQ 1).

• The total travel time and the travel time per passenger is not substantiallyaffected. Hence it can be inferred that the deployment of AB on fixed-lineservices increases the vehicle-km traveled but does not reduce the total timespend by passengers in the PT system (RQ1).

• The best sequence of introduction is different for a user-focused or an operator-focused service design. The user-driven sequence follows two principles. First,deploy AB vehicles on high demand lines. Second, connect the high demandlines with the bus line which has the shortest trip duration. The operatordriven sequence follows two simple principles. First, automate the lines withthe highest service frequency. Second, automate bus lines with a long cycletime (RQ2).

In summary, this study has confirmed the initial hypothesis, that the reductionof operational costs leads to changed resource allocation for autonomous vehicles.The service frequency shifts towards higher frequencies, while the vehicle capacityonly experiences a minor reduction. AB attracts passengers from other modes liketrain or metro and therefore increases the load on automated lines. The vehicle-kmincreases with the deployment of AB.

4.2. PAPER II 15

4.2 Paper II

Paper II extents the previously presented framework with an optimization model.It is, therefore, possible to explore larger case studies and investigate more complexresearch questions. In other words, paper I reveals a shift in service frequency to-wards higher frequencies and in paper II the changed resource allocation conditionsare used to motivate a change in the spatial representation of an autonomouslyoperated PT network. The paper develops a multi-objective simulation-based op-timization framework to solve the AB specific network design problem. In additionto the network design aspect, the second paper gives insights into the effects of au-tonomous technology on the level of service for users of PT systems. In this study,a realistic real-world case study is analyzed. The case study is connected to a pilotproject situated in Barkarby, Stockholm. There AB is operated on public roadsand integrated into the PT system. In Barkarby new residential areas and metrolines are built in the future. Hence the AB network has to be redesigned to capturethese changed conditions. The contributions are summarized in the following list:

• An AB specific transit network design and frequency setting problem (TNDFSP)is proposed, including line connectivity constraints and bus stop position asa decision variable.

• Integration of infrastructure costs and AB specific operator cost formulationsin the multi-objective optimization network design problem.

• The network design problem is formulated as a multi-objective problem withuser cost, operator cost, and infrastructure costs as the three objectives. Thesolutions are ranked following distance measures and non-dominated charac-teristics of each solution.

To investigate the consequences of autonomous technology in terms of lengthof trips, number of transfers, waiting times and other PT network performanceindicators the study is conducted on a real-world test case and benchmark tests.The key results for this case study and the general findings of the second paper arepresented in the following list:

• The deployment of exclusively AB leads to an average reduction of opera-tion cost by approximately 55% compared to conventional buses. Due to theassumed AB specific infrastructure enhancements the costs for infrastructureincrease. The reduction in operation cost does not in general result in a reduc-tion in user cost. Even though the service frequency can be increased in mostnetwork design solutions in the case of AB deployment (RQ1). Line-basedAB services are characterized by shorter network length and fewer bus stops,this leads to an increase in access/egress time and more direct connections(RQ1). In the case study the AB operation led to an increase in transfers andan increase in perceived in-vehicle time.

16 CHAPTER 4. SCIENTIFIC CONTRIBUTIONS

• High service frequencies, a large network, and many bus stops are indicatorsof user-focused network design. As a consequence, the network has low wait-ing times, low access and egress times but high in-vehicle times and a highnumber of transfers. Operator focused PT networks have only a few linesoperated with low frequencies. The network size is small and the bus routeonly connects the high demand sections (RQ3).

• Introducing AB on user-focused PT networks can promote further improve-ment of the level of service. The main cost driver is the reduction of wait-ing time through an increased service frequency. The operation of AB onoperator-focused design does not result in a reduction in user cost. In thecase study, the waiting time could not be reduced through the increase inservice frequency, which could be because of an increased demand on the busline which results in longer boarding processes and crowded bus platforms(RQ2, RQ3). The autonomous technology has, therefore, the biggest impactin terms of user cost on larger networks, in that case, the higher service fre-quencies lead to network-wide improvements. On single line networks, theassumed operator cost savings cannot translate to a significant user cost re-duction.

The main learning from paper II can be divided into two parts. First, it canbe said that the operation of AB indeed leads to different network designs. Theautonomous technology leads to fewer bus stops and shorter network length. Itcan also result in an better level of service through the reduction of waiting time.Second, user-focused network and operator-focused network characteristics in thecase of AB deployment are similar to the network characteristics in the conventionalcase. User-focused design is based on high number of frequencies and number oflines, which gives low waiting times and a many transfers through indirect con-nections. Operator-focused design is characterized by short and direct bus linesserving the OD pairs with the highest demand. Long waiting times and low totalserved passengers are the consequences.

Chapter 5

Implications and Future Directions

In this chapter, the implications of the results for different stakeholders, the method-ological limitations, possible extensions, and future research directions are dis-cussed.

5.1 Implications

The stakeholders considered in this work are users of PT services, PT operators,policymakers and manufacturers. For the user, the biggest impact of AB is theincreased service frequencies which in turn can lead to a reduction in waiting time.However, due to the more attractive service, it can be expected that the passengerload on these lines is increased. Additionally, the number of bus stops and networksize are slightly reduced compared to conventionally operated networks which leadsto slightly longer access and egress times in the case of AB operation. In generaluser costs can be reduced most efficiently by increasing the number of lines servingthe area of interest and increasing the service frequency. This is valid for both,conventional and autonomous bus PT networks. The proposed framework can beused by planners and operators to design AB services. For the operators of ABfleets, the main consequence is the shift in service frequency and the potentiallylonger operation times. Additionally, the increase in service frequency may inducea reduction in vehicle capacity on shorter, low demand lines. Furthermore, theoperation of AB attracts passengers from other PT services, e.g. parallel bus lines.These network effects may impact the vehicle fleet characteristics, i.e. a change inthe number and size of the vehicle fleet required.

Policymakers and authorities should consider the network-wide impacts thathigh-frequency lines have on the existing public transport network due to the redis-tribution of passengers among the PT lines. In combination with the sequential in-troduction, path-dependent planning process are preferred over path-independent.

17

18 CHAPTER 5. IMPLICATIONS AND FUTURE DIRECTIONS

The consequences of this study for manufacturers of AB are twofold. First, thestudy is in line with existing research which proposes a trend towards smaller buscapacities, which in turn requires smaller bus vehicles and leads to changed man-ufacturing needs. Second, in areas with high PT demand and especially betweenhigh demand OD pairs bigger buses will still be required to facilitate all passengerjourneys efficiently. A complete shift towards small buses is therefore not likely.

The increase in passenger load on certain lines and platforms leads to highercapacity requirements for the PT infrastructure. The size of the platforms, stresson the roads and space requirements for potential dedicated lines impact the futureurban planning process. A holistic process is therefore advisable in case of success-ful AB integration.

5.2 Discussion

The results in this thesis are subject to some limitations, which are due to theframework design, the methodology chosen and the data sets used. In both pre-sented studies the assumptions for the operator costs of AB are based on reportedprojects around the world. Due to the limited number amount of published datathese assumptions lack robustness. A sensitivity analysis concerning the estimatedcost parameters is performed. The result shows that the results are fairly insen-sitive to parameter uncertainty. The used simulation tool incorporates stochasticdecision-making processes. The number of replications has therefore been carefullychosen to minimize the risk of statistical outliers. The case studies used to gen-erate the results are located in Stockholm. Consequently, the results cannot begeneralized to all other cases without further adjustment of the input and modelparameters. The proposed framework is flexible and generally applicable, whereasthe detailed results and numerical values are case-specific.

5.3 Future Directions

The proposed framework can be extended with the integration of sustainabilityconsiderations. A future transportation system should not only be designed forusers and operators but also improve the environmental footprint. The reformula-tion of the optimization problem could be a first start to finding more sustainablePT systems using AB. Another possible direction of research is the combination offreight and passenger transport. The integrated transport of passengers and freighthas the potential to reduce the costs and environmental footprint. The presentedframework could be extended with a second layer of freight flow to study the sym-biosis effects of these two streams.

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