TOWARD UNDERSTANDING THE IMPACT OF USER PARTICIPATION IN AUTONOMOUS RIDESHARING SYSTEMS

Autonomous ridesharing systems (ARS) promise many societal and environmental benefits, including decreased accident rates, reduced energy consumption and pollutant emissions, and diminished land use for parking. To unleash ARS’ potential, stakeholders must understand how the degree of passenger participation influences the ridesharing systems’ efficiency. To date, however, a careful study that quantifies the impact of user participation on ARS’ performance is missing. Here, we present the first simulation analysis to investigate how and to what extent user participation affects the efficiency of ARS. We demonstrate how specific configurations (e.g., fleet size, vehicle capacity, and the maximum waiting time) of a system can be identified to counter the performance loss due to users’ uncoordinated behavior on ridesharing participation. Our results indicate that stakeholders of ARS should base decisions regarding system configurations on insights from data-driven simulations and make tradeoffs between system efficiency and price of anarchy for desired outcomes.

[1]  Iyad Rahwan,et al.  Regulating Highly Automated Robot Ecologies: Insights from Three User Studies , 2017, HAI.

[2]  I. Rahwan,et al.  Regulating Self-Adaptive Multi-Agent Systems with Real-Time Interventions , 2013 .

[3]  Elias Koutsoupias,et al.  The price of anarchy of finite congestion games , 2005, STOC '05.

[4]  Carlos F. Daganzo,et al.  On Stochastic Models of Traffic Assignment , 1977 .

[5]  Paolo Santi,et al.  Supporting Information for Quantifying the Benefits of Vehicle Pooling with Shareability Networks Data Set and Pre-processing , 2022 .

[6]  J. Neyman.  On the Two Different Aspects of the Representative Method: the Method of Stratified Sampling and the Method of Purposive Selection , 1934 .

[7]  Emilio Frazzoli,et al.  On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment , 2017, Proceedings of the National Academy of Sciences.

[8]  Michael T. Gastner,et al.  Price of anarchy in transportation networks: efficiency and optimality control. , 2007, Physical review letters.

[9]  Patricia L. Mokhtarian,et al.  What Affects Commute Mode Choice: Neighborhood Physical Structure or Preferences Toward Neighborhoods? , 2005 .

[10]  Gerrit K. Janssens,et al.  Exact and meta-heuristic approach for a general heterogeneous dial-a-ride problem with multiple depots , 2014 .

[11]  Cristina V. Lopes,et al.  CADIS: Aspect-Oriented architecture for collaborative modeling and simulation , 2016, 2016 Winter Simulation Conference (WSC).

[12]  J. Jucker,et al.  An Empirical Study of Travel Time Variability and Travel Choice Behavior , 1982 .

[13]  Daniel B. Work,et al.  New York City Taxi Trip Data (2010-2013) , 2016 .

[14]  Fernando Ordóñez,et al.  Ridesharing: The state-of-the-art and future directions , 2013 .

[15]  Cristina V. Lopes,et al.  An Online Mechanism for Ridesharing in Autonomous Mobility-on-Demand Systems , 2016, IJCAI.

[16]  Cristina V. Lopes,et al.  Managing Autonomous Mobility on Demand Systems for Better Passenger Experience , 2015, PRIMA.

[17]  Jean-François Cordeau,et al.  A Branch-and-Cut Algorithm for the Dial-a-Ride Problem , 2006, Oper. Res..

[18]  Eduardo C. Xavier,et al.  Dynamic Taxi and Ridesharing: A Framework and Heuristics for the Optimization Problem , 2013, IJCAI.

[19]  Claudio Borean,et al.  Dynamic ride sharing service: are users ready to adopt it? , 2015 .

[20]  Da-Jie Lin,et al.  Spatial and Temporal Factors in Estimating the Potential of Ride-sharing for Demand Reduction , 1999 .

[21]  Michael J. North,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[22]  Cristina V. Lopes,et al.  Predicate Collection Classes , 2017, J. Object Technol..

[23]  Tim Roughgarden,et al.  Selfish routing and the price of anarchy , 2005 .