Unreliability in ridesharing systems: Measuring changes in users’ times due to new requests

Abstract On-demand systems in which several users can ride simultaneously the same vehicle have great potential to improve mobility while reducing congestion. Nevertheless, they have a significant drawback: the actual realization of a trip depends on the other users with whom it is shared, as they might impose extra detours that increase the waiting time and the total delay; even the chance of being rejected by the system depends on which travelers are using the system at the same time. In this paper we propose a general description of the sources of unreliability that emerge in ridesharing systems and we introduce several measures. The proposed measures are related to two sources of unreliability induced by how requests and vehicles are being assigned, namely how users’ times change within a single trip and between different realizations of the same trip. We then analyze both sources using a state-of-the-art routing and assignment method, and a New York City test case. Regarding same trip unreliability, in our experiments for different fixed fleet compositions and when reassignment is not restricted, we find that more than one third of the requests that are not immediately rejected face some change, and the magnitude of these changes is relevant: when a user faces an increase in her waiting time, this extra time is comparable to the average waiting time of the whole system, and the same happens with total delay. Algorithmic changes to reduce this uncertainty induce a trade-off with respect to the overall quality of service. For instance, not allowing for reassignments may increase the number of rejected requests. Concerning the unreliability between different trips, we find that the same origin-destination request can be rejected or served depending on the state of the fleet. And when it is served the waiting times and total delay are rarely equal, which remains true for different fleet sizes. Furthermore, the largest variations are faced by trips beginning at high-demand areas.

[1]  Kara M. Kockelman,et al.  Analyzing the dynamic ride-sharing potential for shared autonomous vehicle fleets using cellphone data from Orlando, Florida , 2018, Comput. Environ. Urban Syst..

[2]  Sebastián Raveau,et al.  A comprehensive perspective of unreliable public transport services’ costs , 2020 .

[3]  Andrés Fielbaum,et al.  Strategic Public Transport Design Using Autonomous Vehicles and Other New Technologies , 2020, Int. J. Intell. Transp. Syst. Res..

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

[5]  Antonio Gschwender,et al.  Optimal fleet size, frequencies and vehicle capacities considering peak and off-peak periods in public transport , 2017 .

[6]  Antonio Gschwender,et al.  Beyond the Mohring effect: Scale economies induced by transit lines structures design , 2020 .

[7]  D. Hensher,et al.  Willingness to pay for travel time reliability in passenger transport: A review and some new empirical evidence , 2010 .

[8]  Iis P. Tussyadiah,et al.  Attitudes Toward Autonomous on Demand Mobility System: The Case of Self-Driving Taxi , 2017, ENTER.

[9]  Ikjin Lee,et al.  Shared autonomous electric vehicle design and operations under uncertainties: a reliability-based design optimization approach , 2020 .

[10]  S. Hoogendoorn,et al.  Value of time and reliability for urban pooled on-demand services , 2020, Transportation Research Part C: Emerging Technologies.

[11]  Richard F. Hartl,et al.  A survey on dynamic and stochastic vehicle routing problems , 2016 .

[12]  Emmanouil Chaniotakis,et al.  The sustainability of shared mobility: Can a platform for shared rides reduce motorized traffic in cities? , 2020, Transportation Research Part C: Emerging Technologies.

[13]  Bart van Arem,et al.  Performance analysis and fleet requirements of automated demand-responsive transport systems as an urban public transport service , 2018, International Journal of Transportation Science and Technology.

[14]  Yazhe Wang,et al.  Understanding the effects of taxi ride-sharing - A case study of Singapore , 2018, Comput. Environ. Urban Syst..

[15]  Andrea Simonetto,et al.  Real-time city-scale ridesharing via linear assignment problems , 2019, Transportation Research Part C: Emerging Technologies.

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

[17]  Gabriela Beirão,et al.  Understanding attitudes towards public transport and private car: A qualitative study , 2007 .

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

[19]  Javier Alonso-Mora,et al.  If you are late, everyone is late: late passenger arrival and ride-pooling systems' performance , 2020, Transportmetrica A: Transport Science.

[20]  Michel Gendreau,et al.  A review of dynamic vehicle routing problems , 2013, Eur. J. Oper. Res..

[21]  Gilbert Laporte,et al.  Quality of service in dial-a-ride operations , 2009, Comput. Ind. Eng..

[22]  Alejandro Henao,et al.  The impact of ride-hailing on vehicle miles traveled , 2018, Transportation.

[23]  Tommy Gärling,et al.  PERCEIVED SERVICE QUALITY ATTRIBUTES IN PUBLIC TRANSPORT: INFERENCES FROM COMPLAINTS AND NEGATIVE CRITICAL INCIDENTS , 1998 .

[24]  S. Jara-Díaz,et al.  The effect of financial constraints on the optimal design of public transport services , 2009 .

[25]  Javier Alonso-Mora,et al.  Vehicle Rebalancing for Mobility-on-Demand Systems with Ride-Sharing , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[26]  Tomio Miwa,et al.  Dynamic shared autonomous taxi system considering on-time arrival reliability , 2019, Transportation Research Part C: Emerging Technologies.

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

[28]  W. Y. Szeto,et al.  A survey of dial-a-ride problems: Literature review and recent developments , 2018 .

[29]  Marija Jankovic,et al.  Shared Autonomous Vehicle Simulation and Service Design , 2019, Transportation Research Part C: Emerging Technologies.

[30]  Xiqun Chen,et al.  Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach , 2017 .

[31]  David M Levinson,et al.  Value of Travel Time Reliability: A Review of Current Evidence , 2010 .

[32]  Emilio Frazzoli,et al.  Model Predictive Control of Ride-sharing Autonomous Mobility-on-Demand Systems , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[33]  Marco Pavone,et al.  On the Interaction between Autonomous Mobility-on-Demand and Public Transportation Systems , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[34]  Herbert S. Levinson The Reliability of Transit Service: An historical Perspective , 2005 .

[35]  Hani S. Mahmassani,et al.  Joint Design of Multimodal Transit Networks and Shared Autonomous Mobility Fleets , 2019 .

[36]  Daniele Vigo,et al.  Models and algorithms for reliability-oriented Dial-a-Ride with autonomous electric vehicles , 2017, Eur. J. Oper. Res..

[37]  Hani S. Mahmassani,et al.  Operational benefits and challenges of shared-ride automated mobility-on-demand services , 2020 .

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

[39]  Alejandro Tirachini,et al.  Does ride-hailing increase or decrease vehicle kilometers traveled (VKT)? A simulation approach for Santiago de Chile , 2020, International Journal of Sustainable Transportation.

[40]  Matthew J. Roorda,et al.  Agent Based Model for Dynamic Ridesharing , 2016 .

[41]  T. Gärling,et al.  Quality attributes of public transport that attract car users : A research review , 2013 .

[42]  D. Sui,et al.  Uber, Public Transit, and Urban Transportation Equity: A Case Study in New York City , 2019, The Professional Geographer.

[43]  Martin W. P. Savelsbergh,et al.  Optimization for dynamic ride-sharing: A review , 2012, Eur. J. Oper. Res..