Demand for shared mobility to complement public transportation: Human driven and autonomous vehicles

Recent advances in communication technologies and automated vehicles have opened doors for alternative mobility systems (taxis, carpool, demand-responsive services, peer-to-peer ridesharing, and car sharing, shared autonomous vehicles/shuttles). These new mobility services have gathered interest from researchers, public and private sectors as potential solutions to address last-mile problem--especially in low density areas where implementation of high frequency buses is not feasible. In this study we investigate the effects of ride-sharing service on travel demand and welfare, as it complements public transportation under different scenarios. Two types of management and vehicle types are considered: crowdsourced human driven vehicles (HDV) (e.g. Uber, Lyft) and centrally operated shared autonomous vehicles (SAV). The influence of fare discount on demand and mode shift is also investigated. A case study of Oakville road network in Ontario, Canada is conducted using real data. The results reveal that ride-sharing having the potential of increasing ridership by 76 per cent and decreasing wait time by 47 per cent under centrally operated shared autonomous vehicles with 50 per cent fare discount for sharing.

[1]  Yu Shen,et al.  Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in Singapore , 2018, Transportation Research Part A: Policy and Practice.

[2]  Amer Shalaby,et al.  Feasibility of Flex-Route as a Feeder Transit Service to Rail Stations in the Suburbs: Case Study in Toronto , 2012 .

[3]  Edward K. Morlok.  SHORT RUN SUPPLY FUNCTIONS WITH DECREASING USER COSTS , 1979 .

[4]  Xiugang Li,et al.  A methodology to derive the critical demand density for designing and operating feeder transit services , 2009 .

[5]  José Manuel Viegas,et al.  Assessing the impacts of deploying a shared self-driving urban mobility system: An agent-based model applied to the city of Lisbon, Portugal , 2017 .

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

[7]  Gary c. Thomas,et al.  AmERicAN PUbLic TRANsPORTATiON AssOciATiON , 2005 .

[8]  Joseph Y. J. Chow,et al.  An agent-based day-to-day adjustment process for modeling ‘Mobility as a Service’ with a two-sided flexible transport market , 2017 .

[9]  Kay W. Axhausen,et al.  Assessing the welfare impacts of shared mobility and Mobility as a Service (MaaS) , 2018 .

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

[11]  Joseph Ying Jun Chow,et al.  Agent-based day-to-day adjustment process to evaluate dynamic flexible transport service policies , 2017 .

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

[13]  Reijo Sulonen,et al.  Non-myopic vehicle and route selection in dynamic DARP with travel time and workload objectives , 2012, Comput. Oper. Res..

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

[15]  Xiugang Li,et al.  Feeder transit services: Choosing between fixed and demand responsive policy , 2010 .

[16]  C. Bhat,et al.  Modeling individuals’ willingness to share trips with strangers in an autonomous vehicle future , 2019, Transportation Research Part A: Policy and Practice.