Near-on-Demand Mobility. The Benefits of User Flexibility for Ride-Pooling Services

Mobility-On-Demand (MoD) services have been transforming the urban mobility ecosystem. However, they raise a lot of concerns for their impact on congestion, Vehicle Miles Travelled (VMT), and competition with transit. There are also questions about their long-term survival because of inherent inefficiencies in their operations. Considering the popularity of the MoD services, increasing ride-pooling is an important means to address these concerns. Shareability depends not only on riders attitudes and preferences but also on operating models deployed by providers. The paper introduces an advance requests operating model for ride pooling where users may request rides at least H minutes in advance of their desired departure times. A platform with efficient algorithms for request matching, vehicle routing, rebalancing, and flexible user preferences is developed. A large-scale transportation network company dataset is used to evaluate the performance of the advance requests system relative to current practices. The impacts of various design aspects of the system (advance requests horizon, vehicle capacity) on its performance are investigated. The sensitivity of the results to user preferences in terms of the level of service (time to be served and excess trip time), willingness to share and place requests in advance, and traffic conditions are explored. The results suggest that significant benefits in terms of sustainability, level of service, and fleet utilization can be realized when advance requests are along with an increased willingness to share. Furthermore, even near-on-demand (relative short advance planning horizons) operations can offer many benefits for all stakeholders involved (passengers, operators, and cities).

[1]  Gregory D. Erhardt,et al.  Do transportation network companies decrease or increase congestion? , 2019, Science Advances.

[2]  Vaneet Aggarwal,et al.  DeepPool: Distributed Model-Free Algorithm for Ride-Sharing Using Deep Reinforcement Learning , 2019, IEEE Transactions on Intelligent Transportation Systems.

[3]  Yulin Liu,et al.  Dispatch of autonomous vehicles for taxi services: A deep reinforcement learning approach , 2020, Transportation Research Part C: Emerging Technologies.

[4]  Dawn B. Woodard,et al.  Dynamic pricing and matching in ride‐hailing platforms , 2019, Naval Research Logistics (NRL).

[5]  R. Jayakrishnan,et al.  Promoting Peer-to-Peer Ridesharing Services as Transit System Feeders , 2017 .

[6]  Zhe Xu,et al.  Large-Scale Order Dispatch in On-Demand Ride-Hailing Platforms: A Learning and Planning Approach , 2018, KDD.

[7]  Satish V. Ukkusuri,et al.  Efficient proactive vehicle relocation for on-demand mobility service with recurrent neural networks , 2020 .

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

[9]  Emilio Frazzoli,et al.  Robotic load balancing for mobility-on-demand systems , 2012, Int. J. Robotics Res..

[10]  Daniela Rus,et al.  Markov-based redistribution policy model for future urban mobility networks , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[11]  Patrick Jaillet,et al.  Online Vehicle Routing: The Edge of Optimization in Large-Scale Applications , 2019, Oper. Res..

[12]  Kris Braekers,et al.  Typology and literature review for dial-a-ride problems , 2017, Ann. Oper. Res..

[13]  Matthew J. Roorda,et al.  Vehicle relocation and staff rebalancing in one-way carsharing systems , 2015 .

[14]  G. Currie,et al.  Why most DRT/Micro-Transits fail – What the survivors tell us about progress , 2020 .

[15]  Frédéric Meunier,et al.  Bike sharing systems: Solving the static rebalancing problem , 2013, Discret. Optim..

[16]  R. Jayakrishnan,et al.  A Real-Time Algorithm to Solve the Peer-to-Peer Ride-Matching Problem in a Flexible Ridesharing System , 2017 .

[17]  John Paul Shen,et al.  Data Driven Analysis of the Potentials of Dynamic Ride Pooling , 2017, IWCTS@SIGSPATIAL.

[18]  Felipe F. Dias,et al.  Modeling Individual Preferences for Ownership and Sharing of Autonomous Vehicle Technologies , 2017 .

[19]  Niels A. H. Agatz,et al.  The Value of Optimization in Dynamic Ride-Sharing: A Simulation Study in Metro Atlanta , 2010 .

[20]  Kaishun Wu,et al.  Context-Aware Taxi Dispatching at City-Scale Using Deep Reinforcement Learning , 2022, IEEE Transactions on Intelligent Transportation Systems.

[21]  D. Gruyer,et al.  Improving ridesplitting services using optimization procedures on a shareability network: A case study of Chengdu , 2019, Technological Forecasting and Social Change.

[22]  Marco Pavone,et al.  Model predictive control of autonomous mobility-on-demand systems , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

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

[24]  Gilbert Laporte,et al.  The dial-a-ride problem: models and algorithms , 2006, Ann. Oper. Res..

[25]  Satish V. Ukkusuri,et al.  Impact of transportation network companies on urban congestion: Evidence from large-scale trajectory data , 2020 .

[26]  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).

[27]  Jonathan P. How,et al.  Predictive positioning and quality of service ridesharing for campus mobility on demand systems , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

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

[29]  Satish V. Ukkusuri,et al.  A Graph-Based Approach to Measuring the Efficiency of an Urban Taxi Service System , 2016, IEEE Transactions on Intelligent Transportation Systems.

[30]  Geoff Boeing,et al.  OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks , 2016, Comput. Environ. Urban Syst..

[31]  Robert C. Hampshire,et al.  Inventory rebalancing and vehicle routing in bike sharing systems , 2017, Eur. J. Oper. Res..

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

[33]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[34]  Jun Wang,et al.  Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning , 2019, WWW.

[35]  R. Tachet,et al.  Scaling Law of Urban Ride Sharing , 2016, Scientific Reports.

[36]  Emilio Frazzoli,et al.  Simulation Framework for Rebalancing of Autonomous Mobility on Demand Systems , 2016 .

[37]  Marco Pavone,et al.  Control of robotic mobility-on-demand systems: A queueing-theoretical perspective , 2014, Int. J. Robotics Res..

[38]  Niels A. H. Agatz,et al.  The Benefits of Meeting Points in Ride-Sharing Systems , 2015 .

[39]  Kang G. Shin,et al.  Spatio-temporal Adaptive Pricing for Balancing Mobility-on-Demand Networks , 2019, ACM Trans. Intell. Syst. Technol..

[40]  Nigel H. M. Wilson,et al.  A heuristic algorithm for the multi-vehicle advance request dial-a-ride problem with time windows , 1986 .

[41]  Javier Alonso-Mora,et al.  Predictive routing for autonomous mobility-on-demand systems with ride-sharing , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[42]  Satish V. Ukkusuri,et al.  Optimal assignment and incentive design in the taxi group ride problem , 2017 .

[43]  D. Woodard,et al.  Dynamic pricing and matching in ride‐hailing platforms , 2018, Naval Research Logistics (NRL).

[44]  C. Hwang Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[45]  Zhe Xu,et al.  Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning , 2018, KDD.

[46]  Jieping Ye,et al.  Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching , 2019, CIKM.

[47]  Simone Weikl,et al.  Relocation strategies and algorithms for free-floating Car Sharing Systems , 2013, 2012 15th International IEEE Conference on Intelligent Transportation Systems.