Automated Vehicle Scenarios: Simulation of System-Level Travel Effects Using Agent-Based Demand and Supply Models in the San Francisco Bay Area

In much in the same way that the automobile disrupted horse and cart transportation in the 20th century, automated vehicles hold the potential to disrupt our current system of transportation in the 21st century. Experts predict that vehicles could be fully automated by as early as 2025 or as late as 2035. Methods are needed to help the public and private sector understand automated vehicle technologies and their system-level effects. First, we explore the effects of automated vehicles using the San Francisco Bay Area Metropolitan Transportation Commission’s activity-based travel demand model (MTC-ABM). The simulation is unique in that it articulates the size and direction of change on travel for a wide range of automated vehicles scenarios. Second, we simulate the effects of the introduction of an automated taxi service on conventional personal vehicle and transit travel in the San Francisco Bay Area region and use new research on the costs of automated vehicles to represent plausible per mile automated taxi fares. We use an integrated model for the San Francisco Bay Area that includes the MTC-ABM combined with the agent-based MATSim model customized for the region. This model set uses baseline travel demand data from the region’s official activity-based travel model and dynamically assigns vehicles on road and transit networks by the time of day. Third, we use the MTC-ABM and the MATSim dynamic assignment model to simulate different “first” mile transit access services, including ride-hailing (Uber and Lyft) and ridesharing (Uber Pool/Lyft Line and Via) with and without automated vehicles. The results provide insight into the relative benefits of each service and automated vehicle technology and the potential market for these services.View the NCST Project Webpage

[1]  Kara M. Kockelman,et al.  Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas , 2018 .

[2]  Michal Maciejewski,et al.  Towards a Testbed for Dynamic Vehicle Routing Algorithms , 2017, PAAMS.

[3]  Glenn Lyons,et al.  The Use of Travel Time by Rail Passengers in Great Britain , 2007 .

[4]  Barbara Lenz,et al.  Travel-Time Valuation for Automated Driving: A Use-Case-Driven Study , 2015 .

[5]  R. Cervero,et al.  Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco , 2016 .

[6]  Kara M. Kockelman,et al.  Operations of a Shared, Autonomous Electric Vehicle Fleet: Implications of Vehicle & Charging Infrastructure Decisions , 2016 .

[7]  Joschka Bischoff,et al.  CONGESTION EFFECTS OF AUTONOMOUS TAXI FLEETS , 2018, Transport.

[8]  Michael Sivak,et al.  A Survey of Public Opinion about Autonomous and Self-Driving Vehicles in the U.S., the U.K., and Australia , 2014 .

[9]  Kay W. Axhausen,et al.  The Multi-Agent Transport Simulation , 2016 .

[10]  Suzanne Childress,et al.  Using an Activity-Based Model to Explore the Potential Impacts of Automated Vehicles , 2015 .

[11]  Alejandro Henao,et al.  Impacts of Ridesourcing - Lyft and Uber - on Transportation Including VMT, Mode Replacement, Parking, and Travel Behavior , 2017 .

[12]  Kara M. Kockelman,et al.  The Travel and Environmental Implications of Shared Autonomous Vehicles, Using Agent-Based Model Scenarios , 2014 .

[13]  Lei Chen,et al.  Cooperative speed harmonization for efficient road utilization , 2014, 2014 7th International Workshop on Communication Technologies for Vehicles (Nets4Cars-Fall).

[14]  Bruce Schaller,et al.  Elasticities for taxicab fares and service availability , 1999 .

[15]  Monica Menendez,et al.  What about space?: A simulation based assessment of AVs impact on road space in urban areas , 2016 .

[16]  Michal Maciejewski,et al.  City-wide shared taxis: A simulation study in Berlin , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[17]  Wenwen Zhang,et al.  The Performance and Benefits of a Shared Autonomous Vehicles Based Dynamic Ridesharing System: An Agent-Based Simulation Approach , 2015 .

[18]  Michal Maciejewski,et al.  Simulation of City-wide Replacement of Private Cars with Autonomous Taxis in Berlin , 2016, ANT/SEIT.

[19]  Patricia L. Mokhtarian,et al.  How Do Activities Conducted while Commuting Influence Mode Choice? Testing Public Transportation Advantage and Autonomous Vehicle Scenarios , 2015 .

[20]  Steven E Shladover,et al.  Impacts of Cooperative Adaptive Cruise Control on Freeway Traffic Flow , 2012 .

[21]  Susan L Handy,et al.  On-Demand Ride Services in California: Investigating the Factors Affecting the Frequency of Use of Uber/Lyft , 2018 .

[22]  Lewis Fulton,et al.  Three Revolutions in Urban Passenger Travel , 2018 .

[23]  Don MacKenzie,et al.  Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles , 2016 .

[24]  Joschka Bischoff,et al.  Autonomous Taxicabs in Berlin – A Spatiotemporal Analysis of Service Performance , 2016 .

[25]  P Thakur,et al.  Urban form and function in the autonomous era , 2016 .

[26]  Kara M. Kockelman,et al.  Operations of Shared Autonomous Vehicle Fleet for Austin, Texas, Market , 2015 .

[27]  Kara M. Kockelman,et al.  Assessing Public Opinions of and Interest in New Vehicle Technologies: An Austin Perspective , 2016 .

[28]  Jennie Lioris,et al.  Platoons of connected vehicles can double throughput in urban roads , 2015, 1511.00775.

[29]  Daniel Sperling,et al.  Adoption of Uber and Lyft, Factors Limiting and/or Encouraging Their Use and Impacts on Other Travel Modes among Millennials and Gen Xers in California , 2018 .

[30]  S. Guhathakurta,et al.  Parking Spaces in the Age of Shared Autonomous Vehicles: How Much Parking Will We Need and Where? , 2017 .

[31]  K. Kockelman,et al.  Management of a Shared Autonomous Electric Vehicle Fleet: Implications of Pricing Schemes , 2016 .

[32]  Steven E. Underwood,et al.  Disruptive Innovation on the Path to Sustainable Mobility: Creating a Roadmap for Road Transportation in the United States , 2014 .

[33]  Nikhil Menon,et al.  Consumer Perception and Intended Adoption of Autonomous-Vehicle Technology: Findings from a University Population Survey , 2016 .

[34]  Michael Sivak,et al.  The Reasons for the Recent Decline in Young Driver Licensing in the United States , 2014, Traffic injury prevention.

[35]  Alireza Zolfaghari,et al.  The tension between autonomous cars' impacts on intersection level-of-service and their occupants' use of travel time for leisurely or economically-productive activities , 2015 .

[36]  Joe Doak,et al.  The transport studies unit , 1991 .

[37]  Joshua Auld,et al.  Assessing the regional energy impact of connected vehicle deployment , 2016 .

[38]  Bart van Arem,et al.  Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility , 2016 .

[39]  Regina R. Clewlow,et al.  Disruptive Transportation: The Adoption, Utilization, and Impacts of Ride-Hailing in the United States , 2017 .

[40]  John B. Heywood,et al.  Determinants of US passenger car weight , 2014 .

[41]  Phil. Goodwin,et al.  Empirical evidence on induced traffic , 1996 .

[42]  Itf.  Urban Mobility System Upgrade: How shared self-driving cars could change city traffic , 2015 .

[43]  Jeffrey Gonder,et al.  An Analysis of Possible Energy Impacts of Automated Vehicles , 2014 .

[44]  T. Arentze,et al.  Travelers’ Preferences in Multimodal Networks: Design and Results of a Comprehensive Series of Choice Experiments , 2013 .

[45]  Carlos Lima Azevedo,et al.  Microsimulation of Demand and Supply of Autonomous Mobility On Demand , 2016 .

[46]  D A Hensher.  Full information maximum likelihood estimation of a nested logit mode choice model , 1984 .

[47]  Concepción Román,et al.  Competition of high-speed train with air transport: The case of Madrid–Barcelona , 2007 .

[48]  Stephen D. Boyles,et al.  A general framework for modeling shared autonomous vehicles with dynamic network-loading and dynamic ride-sharing application , 2017, Comput. Environ. Urban Syst..

[49]  Corey D. Harper,et al.  Estimating potential increases in travel with autonomous vehicles for the non-driving, elderly and people with travel-restrictive medical conditions , 2016 .

[50]  Marlon G. Boarnet,et al.  Impacts of Gas Price on Passenger Vehicle Use and Greenhouse Gas Emissions Policy Brief , 2014 .