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Evaluation of the impacts of autonomous vehicles on the mobility of user groups by using agent-based simulation

    Jamil Hamadneh Affiliation
    ; Domokos Esztergar-Kiss Affiliation

Abstract

An agent-based transport simulation model is used to examine the impacts of Autonomous Vehicles (AVs) on the mobility of certain groups of people. In the state of the art, it has been found that the researchers primarily have simulation studies focusing on the impacts of AVs on people regardless of certain groups. However, this study focuses on assessing the impacts of AVs on different groups of users, where each group is affected variously by the introduction of different penetration levels of AVs into the market. The Multi-Agent Transport Simulation (MATSim) software, which applies the co-evolutionary algorithm and provides a framework to carry out large-scale agent-based transport simulations, is used as a tool for conducting the simulations. In addition to the simulation of all travellers, 3 groups of users are selected as potential users of AVs, as follow: (1) long commuters with high-income, (2) elderly people who are retired, and (3) part-time workers. Budapest (Hungary) is examined in a case study, where the daily activity plans of the households are provided. Initially, the existing daily activity plans (i.e., the existing condition) of each group are simulated and assessed before the introduction of AVs into the market. After that, the AVs are inserted into the road network, where different fleet sizes of AVs are applied based on the demand of each group. The marginal utility of the travel time spent in case of a transport mode, the AV fleet size, and the cost of the travel are the key variables that determine the use of a transport mode. The key variables are set based on the characteristics of the case study (i.e., demand) and the AVs. The results of the simulations suggest that the AVs have different degrees of influences on certain groups as demonstrated in the occurred changes on the modal share. The value of changes depends on the Value of Travel Time (VOT) of people and the used fleet size of AVs. Moreover, the influence of the traveller’s characteristics on the AVs is manifested, such as different values of fleet utilization. Furthermore, the study demonstrates that an increase in the fleet size of AVs beyond 10% of the demand does not significantly raise the modal share of AVs. The outcome of this paper might be used by decision-makers to define the shape of the AVs’ use and those groups who are interested in using AVs.

Keyword : agent-based modelling, autonomous vehicle, MATSim, activity chains, utility function

How to Cite
Hamadneh, J., & Esztergar-Kiss, D. (2022). Evaluation of the impacts of autonomous vehicles on the mobility of user groups by using agent-based simulation. Transport, 37(1), 1–16. https://doi.org/10.3846/transport.2022.16322
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Mar 15, 2022
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References

Acheampong, R. A.; Cugurullo, F. 2019. Capturing the behavioural determinants behind the adoption of autonomous vehicles: conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars, Transportation Research Part F: Traffic Psychology and Behaviour 62: 349–375. https://doi.org/10.1016/j.trf.2019.01.009

Al-Sahili, K.; Hamadneh, J. 2016. Establishing parking generation rates/models of selected land uses for Palestinian cities, Transportation Research Part A: Policy and Practice 91: 213–222. https://doi.org/10.1016/j.tra.2016.06.027

BetterTec Gmbh. 2021. Taxi Fares are Based on the Current Taxi Tariffs of Budapest. Available from Internet: https://www.bettertaxi.com/taxi-fare-calculator/budapest/

Bischoff, J.; Maciejewski, M.; Schlenther, T.; Nagel, K. 2019a. Autonomous vehicles and their impact on parking search, IEEE Intelligent Transportation Systems Magazine 11(4): 19–27. https://doi.org/10.1109/MITS.2018.2876566

Bischoff, J.; Führer, K.; Maciejewski, M. 2019b. Impact assessment of autonomous DRT systems, Transportation Research Procedia 41: 440–446. https://doi.org/10.1016/j.trpro.2019.09.074

Bischoff, J.; Maciejewski, M. 2016. Simulation of city-wide replacement of private cars with autonomous taxis in Berlin, Procedia Computer Science 83: 237–244. https://doi.org/10.1016/j.procs.2016.04.121

Boesch, P. M.; Ciari, F.; Axhausen, K. W. 2016. Autonomous vehicle fleet sizes required to serve different levels of demand, Transportation Research Record: Journal of the Transportation Research Board 2542: 111–119. https://doi.org/10.3141/2542-13

Bösch, P. M.; Becker, F.; Becker, H.; Axhausen, K. W. 2018. Cost-based analysis of autonomous mobility services, Transport Policy 64: 76–91. https://doi.org/10.1016/j.tranpol.2017.09.005

Bösch, P. M.; Ciari, F. 2017. MacroSim – a macroscopic Mobsim for MATSim, Procedia Computer Science 109: 861–868. https://doi.org/10.1016/j.procs.2017.05.406

Charypar, D.; Nagel, K. 2005. Generating complete all-day activity plans with genetic algorithms, Transportation 32(4): 369–397. https://doi.org/10.1007/s11116-004-8287-y

Choi, J. K.; Ji, Y. G. 2015. Investigating the importance of trust on adopting an autonomous vehicle, International Journal of Human–Computer Interaction 31(10): 692–702. https://doi.org/10.1080/10447318.2015.1070549

Das, S.; Sekar, A.; Chen, R.; Kim, H. C.; Wallington, T. J.; Williams, E. 2017. Impacts of autonomous vehicles on consumers time-use patterns, Challenges 8(2): 32. https://doi.org/10.3390/challe8020032

Etzioni, S.; Hamadneh, J.; Elvarsson, A. B.; Esztergár-Kiss, D.; Djukanovic, M.; Neophytou, S. N.; Sodnik, J.; Polydoropoulou, A.; Tsouros, I.; Pronello, C.; Thomopoulos, N.; Shiftan, Y. 2020. Modeling cross-national differences in automated vehicle acceptance, Sustainability 12(22): 9765. https://doi.org/10.3390/su12229765

Fagnant, D. J.; Kockelman, K. 2015. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations, Transportation Research Part A: Policy and Practice 77: 167–181. https://doi.org/10.1016/j.tra.2015.04.003

Fagnant, D. J.; Kockelman, K. M.; Bansal, P. 2016. Operations of shared autonomous vehicle fleet for Austin, Texas, market, Transportation Research Record: Journal of the Transportation Research Board 2563: 98–106. https://doi.org/10.3141/2536-12

Godoy, J.; Pérez, J.; Onieva, E.; Villagrá, J.; Milanés, V.; Haber, R. 2015. A driverless vehicle demonstration on motorways and in urban environments, Transport 30(3): 253–263. https://doi.org/10.3846/16484142.2014.1003406

Greenblatt, N. A. 2016. Self-driving cars and the law, IEEE Spectrum 53(2): 46–51. https://doi.org/10.1109/MSPEC.2016.7419800

Hamadneh, J.; Esztergár-Kiss, D. 2019. Impacts of shared autonomous vehicles on the travelers’ mobility, in 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 5–7 June 2019, Cracow, Poland. https://doi.org/10.1109/MTITS.2019.8883392

Hamadneh, J.; Esztergár-Kiss, D. 2021. Potential travel time reduction with autonomous vehicles for different types of travellers, Promet – Traffic & Transportation 33(1): 61–76. https://doi.org/10.7307/ptt.v33i1.3585

Hao, M.; Yamamoto, T. 2017. Analysis on supply and demand of shared autonomous vehicles considering household vehicle ownership and shared use, in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 16–19 October 2017, Yokohama, Japan. https://doi.org/10.1109/ITSC.2017.8317920

Harper, C. D.; Hendrickson, C. T.; Mangones, S.; Samaras, C. 2016. Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions, Transportation Research Part C: Emerging Technologies 72: 1–9. https://doi.org/10.1016/j.trc.2016.09.003

HCSO. 2021. Population Census, 2011. Hungarian Central Statistical Office (HCSO). Available from Internet: https://www.ksh.hu/nepszamlalas/copyright_eng

Hörl, S.; Erath, A.; Axhausen, K. W. 2016. Simulation of Autonomous Taxis in a Multi-Modal Traffic Scenario with Dynamic Demand. Working Paper. 16 p. https://doi.org/10.3929/ethz-b-000118794

Horni, A.; Nagel, K.; Axhausen, K. W. 2016. The Multi-Agent Transport Simulation MATSim. Ubiquity Press. 620 p. https://doi.org/10.5334/baw

Huang, S.; Ren, W.; Chan, S. C. 2000. Design and performance evaluation of mixed manual and automated control traffic, IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans 30(6): 661–673. https://doi.org/10.1109/3468.895889

JOSM. 2021. Java OpenStreetMap Editor. Available from Internet: https://josm.openstreetmap.de/wiki/Download#Java

Koryagin, M. 2018. Urban planning: a game theory application for the travel demand management, Periodica Polytechnica Transportation Engineering 46(4): 171–178. https://doi.org/10.3311/PPtr.9410

Krueger, R.; Rashidi, T. H.; Rose, J. M. 2016. Preferences for shared autonomous vehicles, Transportation Research Part C: Emerging Technologies 69: 343–355. https://doi.org/10.1016/j.trc.2016.06.015

Laidlaw, K. 2017. What’s Steering Consumer Preferences for Autonomous Vehicles in the Greater Toronto and Hamilton Area? MSc Thesis. Ryerson University, Toronto, ON, Canada. 71 p. https://doi.org/10.32920/ryerson.14664711.v1

Levin, M. W.; Boyles, S. D. 2015. Effects of autonomous vehicle ownership on trip, mode, and route choice, Transportation Research Record: Journal of the Transportation Research Board 2493: 29–38. https://doi.org/10.3141/2493-04

Litman, T. A. 2021. Autonomous Vehicle Implementation Predictions: Implications for Transport Planning. Victoria Transport Policy Institute, Victoria, BC, Canada. 48 p. Available from Internet: https://www.vtpi.org/avip.pdf

Litman, T. A. 2009. Transportation Cost and Benefit Analysis: Techniques, Estimates and Implications. Victoria Transport Policy Institute, Victoria, BC, Canada. Available from Internet: https://www.vtpi.org/tca/

Luo, L.; Troncoso Parady, G.; Takami, K.; Harata, N. 2019. Evaluating the impact of autonomous vehicles on accessibility using agent-based simulation – a case study of Gunma Prefecture, Journal of JSCE 7(1): 100–111. https://doi.org/10.2208/journalofjsce.7.1_100

Maciejewski, M.; Bischoff, J. 2018. Congestion effects of autonomous taxi fleets, Transport 33(4): 971–980. https://doi.org/10.3846/16484142.2017.1347827

Maciejewski, M.; Nagel, K. 2013. Simulation and Dynamic Optimization of Taxi Services in MATSim. Working Paper. 34 p. Available from Internet: https://svn.vsp.tu-berlin.de/repos/public-svn/publications/vspwp/2013/13-05/2013-06-03_Maciejewski_Nagel.pdf

Maciejewski, M.; Nagel, K. 2011. Towards multi-agent simulation of the dynamic vehicle routing problem in MATSim, Lecture Notes in Computer Science 7204: 551–560. https://doi.org/10.1007/978-3-642-31500-8_57

Menon, N.; Barbour, N.; Zhang, Y.; Pinjari, A. R.; Mannering, F. 2019. Shared autonomous vehicles and their potential impacts on household vehicle ownership: an exploratory empirical assessment, International Journal of Sustainable Transportation 13(2): 111–122. https://doi.org/10.1080/15568318.2018.1443178

Meyer, J.; Becker, H.; Bösch, P. M.; Axhausen, K. W. 2017. Autonomous vehicles: the next jump in accessibilities?, Research in Transportation Economics 62: 80–91. https://doi.org/10.1016/j.retrec.2017.03.005

Nourinejad, M.; Bahrami, S.; Roorda, M. J. 2018. Designing parking facilities for autonomous vehicles, Transportation Research Part B: Methodological 109: 110–127. https://doi.org/10.1016/j.trb.2017.12.017

Ortega, J.; Hamadneh, J.; Esztergár-Kiss, D.; Tóth, J. 2020. Simulation of the daily activity plans of travelers using the park-and-ride system and autonomous vehicles: work and shopping trip purposes, Applied Sciences 10(8): 2912. https://doi.org/10.3390/app10082912

OSM. 2021. Open Street Map. Available from Internet: https://www.openstreetmap.org/#map=9/47.3509/19.1409

Pinjari, A. R.; Augustin, B.; Menon, N. 2013. Highway Capacity Impacts of Autonomous Vehicles: an Assessment. Center for Urban Transportation Research, University of South Florida, Tampa, FL, US. 15 p.

Poletti, F.; Bösch, P. M.; Ciari, F.; Axhausen, K. W. 2017. Public Transit Route Mapping for Large-Scale Multimodal Networks, ISPRS International Journal of Geo-Information 6(9): 268. https://doi.org/10.3390/ijgi6090268

Popovici, E.; Bucci, A.; Wiegand, R. P.; De Jong, E. D. 2012. Coevolutionary principles, in G. Rozenberg, T. Bäck, J. N. Kok (Eds.). Handbook of Natural Computing, 987–1033. https://doi.org/10.1007/978-3-540-92910-9_31

Pudāne, B.; Molin, E. J. E.; Arentze, T. A.; Maknoon, Y.; Chorus, C. G. 2018. A time-use model for the automated vehicle-era, Transportation Research Part C: Emerging Technologies 93: 102–114. https://doi.org/10.1016/j.trc.2018.05.022

Sadat Lavasani Bozorg, S. M. A. 2016. Potential Implication of Automated Vehicle Technologies on Travel Behavior and System Modeling. PhD Dissertation. Florida International University, Miami, FL, US. 171 p. https://doi.org/10.25148/etd.FIDC001241

Simoni, M. D.; Kockelman, K. M.; Gurumurthy, K. M.; Bischoff, J. 2019. Congestion pricing in a world of self-driving vehicles: an analysis of different strategies in alternative future scenarios, Transportation Research Part C: Emerging Technologies 98: 167–185. https://doi.org/10.1016/j.trc.2018.11.002

Small, K. A. 2012. Valuation of travel time, Economics of Transportation 1(1–2): 2–14. https://doi.org/10.1016/j.ecotra.2012.09.002

Steck, F.; Kolarova, V.; Bahamonde-Birke, F.; Trommer, S.; Lenz, B. 2018. How autonomous driving may affect the value of travel time savings for commuting, Transportation Research Record: Journal of the Transportation Research Board 2672(46): 11–20. https://doi.org/10.1177/0361198118757980

Török, Á.; Szalay, Z.; Uti, G.; Verebélyi, B. 2020. Rerepresenting Autonomated Vehicles in a Macroscopic Transportation Model, Periodica Polytechnica Transportation Engineering 48(3): 269–275. https://doi.org/10.3311/PPtr.13989

Transitfeeds.com. 2018. BKK GTFS. Available from Internet: https://transitfeeds.com/p/bkk/42

Yap, M. D.; Correia, G.; Van Arem, B. 2016. Preferences of travellers for using automated vehicles as last mile public transport of multimodal train trips, Transportation Research Part A: Policy and Practice 94: 1–16. https://doi.org/10.1016/j.tra.2016.09.003

Zhang, W.; Guhathakurta, S.; Khalil, E. B. 2018. The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT generation, Transportation Research Part C: Emerging Technologies 90: 156–165. https://doi.org/10.1016/j.trc.2018.03.005

Zhong, H.; Li, W.; Burris, M. W.; Talebpour, A.; Sinha, K. C. 2020. Will autonomous vehicles change auto commuters’ value of travel time?, Transportation Research Part D: Transport and Environment 83: 102303. https://doi.org/10.1016/j.trd.2020.102303

Zmud, J.; Sener, I. N.; Wagner, J. 2016. Self-driving vehicles: determinants of adoption and conditions of usage, Transportation Research Record: Journal of the Transportation Research Board 2565(1): 57–64. https://doi.org/10.3141/2565-07