A passenger-to-driver matching model for commuter carpooling: Case study and sensitivity analysis

Abstract

For the transport sector, promoting carpooling to private car users could be an effective strategy over reducing vehicle kilometers traveled. Theoretical studies have verified that carpooling is not only beneficial to drivers and passengers but also to the environment. Nevertheless, despite carpooling having a huge potential market in car commuters, it is not widely used in practice worldwide. In this paper, we develop a passenger-to-driver matching model based on the characteristics of a private-car based carpooling service, and propose an estimation method for time-based costs as well as the psychological costs of carpooling trips, taking into account the potential motivations and preferences of potential carpoolers. We test the model using commuting data for the Greater London from the UK Census 2011 and travel-time data from Uber. We investigate the service sensitivity to varying carpooling participant rates and fee-sharing ratios with the aim of improving matching performance at least cost. Finally, to illustrate how our matching model might be used, we test some practical carpooling promotion instruments. We found that higher participant role flexibility in the system can improve matching performance significantly. Encouraging commuters to walk helps form more carpooling trips and further reduces carbon emissions. Different feesharing ratios can influence matching performance, hence determination of optimal pricing should be based on the specific matching model and its cost parameters.Disincentives like parking charges and congestion charges seem to have a greater effect on carpooling choice than incentives like preferential parking and subsidies. The proposed model and associated findings provide valuable insights for designing an effective matching system and incentive scheme for carpooling services in practice.

Passenger‐perception dynamic ridesharing service based on parallel technology

“…The early sharing mode mainly refers to simple carpooling among acquaintances, including family members, colleagues, and neighbours, and it does not scale or organize well [9, 10]. Owing to emerging information and communications technology, the sharing mobility mode has been developed into an organized and scaled service, based on which users can send requests from their mobile devices and soon obtain shared service.…”

Carpooling Platforms as Smart City Projects: A Bibliometric Analysis and Systematic Literature Review

“…The optimization of matching participants, routes, and schedules is widely and actively studied, as shown in Table 5. Several algorithms have been proposed to overcome traditional problems like passenger to driver matching [66] to minimize time and distance to the carpooling starting point and destination [67], to find participants within organizations [68], to determine the pricing schemes for the driver and the passengers [67], and to plan the route by integrating different means such as public transportation or bike rentals with carpooling [69]. Several studies examine the business models framework for services in smart cities.…”

A Study of Spatiotemporal Distribution of Mobility-On-Demand in Generating Pick-Up/Drop-Offs Location Placement

“…This optimization method considers both the rider and vehicle assignments that lead to a complex mathematical task involving the NP-hard problem [18]. As such, although numerous implementation strategies in determining pick-up and drop-off (PUDO) zones to improve taxi, ridesharing and ride hailing operation management, PUDO station location placement is not explored extensively [19].…”