publication . Preprint . 2017

Impact of Mobility-on-Demand on Traffic Congestion: Simulation-based Study

Fiedler, David; Čáp, Michal; Čertický, Michal;
Open Access English
  • Published: 08 Aug 2017
Abstract
The increasing use of private vehicles for transportation in cities results in a growing demand for parking space and road network capacity. In many densely populated urban areas, however, the capacity of existing infrastructure is insufficient and extremely difficult to expand. Mobility-on-demand systems have been proposed as a remedy to the problem of limited parking space because they are able to satisfy the existing transportation demand with fewer shared vehicles and consequently require less parking space. Yet, the impact of large-scale vehicle sharing on traffic patterns is not well understood. In this work, we perform a simulation-based analysis of conse...
Subjects
free text keywords: Computer Science - Multiagent Systems
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[1] Todd Litman. Autonomous vehicle implementation predictions - implications for transport planning. Technical report, Victoria Transport Policy Institute, 2017.

[2] Kevin Spieser, Kyle Treleaven, Rick Zhang, Emilio Frazzoli, Daniel Morton, and Marco Pavone. Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: A case study in singapore. In Gereon Meyer and Sven Beiker, editors, Road Vehicle Automation, Lecture Notes in Mobility, pages 229-245. Springer International Publishing, 2014.

[3] Daniel J. Fagnant and Kara M. Kockelman. The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transportation Research Part C: Emerging Technologies, 40:1 - 13, 2014.

[4] Marczuk, Katarzyna A., Soh, Harold S.H., Azevedo, Carlos M.L., Lee, Der-Horng, and Frazzoli, Emilio. Simulation framework for rebalancing of autonomous mobility on demand systems. MATEC Web Conf., 81:01005, 2016.

[5] Rick Zhang and Marco Pavone. Control of Robotic Mobility-on-demand Systems. Int. J. Rob. Res., 35(1-3):186-203, January 2016.

[6] Rick Zhang, Federico Rossi, and Marco Pavone. Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms. ArXiv e-prints, March 2016.

[7] Javier Alonso-Mora, Samitha Samaranayake, Alex Wallar, Emilio Frazzoli, , and Daniela Rus. On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proceedings of the National Academy of Sciences of the United States of America, 114(3):462 - 467, 2016.

[8] Frank A. Haight (Eds.). Mathematical Theories of Traffic Flow. Mathematics in Science and Engineering. Elsevier Science, 1963.

[9] Boris S. Kerner (auth.). Introduction to Modern Traffic Flow Theory and Control: The Long Road to Three-Phase Traffic Theory. Springer-Verlag Berlin Heidelberg, 2009.

[10] Shin-ichi Tadaki, Macoto Kikuchi, Minoru Fukui, Akihiro Nakayama, Katsuhiro Nishinari, Akihiro Shibata, Yuki Sugiyama, Taturu Yosida, and Satoshi Yukawa. Critical Density of Experimental Traffic Jam, pages 505-511. Springer International Publishing, Cham, 2015.

[11] M. Cˇerticky´, J. Drchal, M. Cuchy´, and M. Jakob. Fully agent-based simulation model of multimodal mobility in European cities. In 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pages 229-236, June 2015. [OpenAIRE]

[12] David A. Hensher and Kenneth J. Button. Handbook of Transport Modelling: 2nd Edition. Emerald, Inc., 2007.

[13] Jan Drchal, Michal Cˇerticky´, and Michal Jakob. Data Driven Validation Framework for Multi-agent Activity-Based Models. In Multi-Agent Based Simulation XVI, pages 55-67. Springer, Cham, May 2015.

[14] Marco Pavone, Stephen L Smith, Emilio Frazzoli, and Daniela Rus. Robotic load balancing for mobility-on-demand systems. The International Journal of Robotics Research, 31(7):839-854, June 2012.

[15] Rick Zhang, Federico Rossi, and Marco Pavone. Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms. arXiv:1603.00939 [cs], March 2016. arXiv: 1603.00939.

Abstract
The increasing use of private vehicles for transportation in cities results in a growing demand for parking space and road network capacity. In many densely populated urban areas, however, the capacity of existing infrastructure is insufficient and extremely difficult to expand. Mobility-on-demand systems have been proposed as a remedy to the problem of limited parking space because they are able to satisfy the existing transportation demand with fewer shared vehicles and consequently require less parking space. Yet, the impact of large-scale vehicle sharing on traffic patterns is not well understood. In this work, we perform a simulation-based analysis of conse...
Subjects
free text keywords: Computer Science - Multiagent Systems
Download from

[1] Todd Litman. Autonomous vehicle implementation predictions - implications for transport planning. Technical report, Victoria Transport Policy Institute, 2017.

[2] Kevin Spieser, Kyle Treleaven, Rick Zhang, Emilio Frazzoli, Daniel Morton, and Marco Pavone. Toward a systematic approach to the design and evaluation of automated mobility-on-demand systems: A case study in singapore. In Gereon Meyer and Sven Beiker, editors, Road Vehicle Automation, Lecture Notes in Mobility, pages 229-245. Springer International Publishing, 2014.

[3] Daniel J. Fagnant and Kara M. Kockelman. The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transportation Research Part C: Emerging Technologies, 40:1 - 13, 2014.

[4] Marczuk, Katarzyna A., Soh, Harold S.H., Azevedo, Carlos M.L., Lee, Der-Horng, and Frazzoli, Emilio. Simulation framework for rebalancing of autonomous mobility on demand systems. MATEC Web Conf., 81:01005, 2016.

[5] Rick Zhang and Marco Pavone. Control of Robotic Mobility-on-demand Systems. Int. J. Rob. Res., 35(1-3):186-203, January 2016.

[6] Rick Zhang, Federico Rossi, and Marco Pavone. Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms. ArXiv e-prints, March 2016.

[7] Javier Alonso-Mora, Samitha Samaranayake, Alex Wallar, Emilio Frazzoli, , and Daniela Rus. On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proceedings of the National Academy of Sciences of the United States of America, 114(3):462 - 467, 2016.

[8] Frank A. Haight (Eds.). Mathematical Theories of Traffic Flow. Mathematics in Science and Engineering. Elsevier Science, 1963.

[9] Boris S. Kerner (auth.). Introduction to Modern Traffic Flow Theory and Control: The Long Road to Three-Phase Traffic Theory. Springer-Verlag Berlin Heidelberg, 2009.

[10] Shin-ichi Tadaki, Macoto Kikuchi, Minoru Fukui, Akihiro Nakayama, Katsuhiro Nishinari, Akihiro Shibata, Yuki Sugiyama, Taturu Yosida, and Satoshi Yukawa. Critical Density of Experimental Traffic Jam, pages 505-511. Springer International Publishing, Cham, 2015.

[11] M. Cˇerticky´, J. Drchal, M. Cuchy´, and M. Jakob. Fully agent-based simulation model of multimodal mobility in European cities. In 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pages 229-236, June 2015. [OpenAIRE]

[12] David A. Hensher and Kenneth J. Button. Handbook of Transport Modelling: 2nd Edition. Emerald, Inc., 2007.

[13] Jan Drchal, Michal Cˇerticky´, and Michal Jakob. Data Driven Validation Framework for Multi-agent Activity-Based Models. In Multi-Agent Based Simulation XVI, pages 55-67. Springer, Cham, May 2015.

[14] Marco Pavone, Stephen L Smith, Emilio Frazzoli, and Daniela Rus. Robotic load balancing for mobility-on-demand systems. The International Journal of Robotics Research, 31(7):839-854, June 2012.

[15] Rick Zhang, Federico Rossi, and Marco Pavone. Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms. arXiv:1603.00939 [cs], March 2016. arXiv: 1603.00939.

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