A shared ride matching approach to low-carbon and electrified ridesplitting

Abstract

Ridesplitting has the potential to enhance transportation efficiency and reduce CO2 emissions from ridesourcing services by matching multiple shared rides to the same trip. However, an inappropriate matching can significantly diminish the environmental performance of ridesplitting. This study aims to propose a shared ride matching approach to low-carbon and electrified ridesplitting. Firstly, feasible ridesplitting trips within time constraints are identified and represented using a shareability network. Subsequently, the CO2 emission reduction of each feasible ridesplitting trip is calculated by comparing it with its corresponding single ride alternative. To maximize the total CO2 emission reduction from ridesplitting trips, an optimization model for shared ride matching is established and solved using a graph-theoretic algorithm. Additionally, a priority assignment policy is designed to preferentially assign available electric vehicles to feasible ridesplitting trips that have greater potential for emission reductions. Comparative experiments conducted in Chengdu, China show that the proposed approach can achieve higher levels of CO2 emission reduction without compromising service quality in ridesplitting operations. Furthermore, significant improvements in emission reductions can be achieved by implementing the priority assignment policy instead of a random assignment policy for electrified ridesplitting trips, particularly when electric vehicles are limited. Finally, the effects of maximum tolerable delay and electric vehicle penetration are further analyzed to provide valuable insights for the government and ridesourcing companies aiming to enhance both the economic and environmental benefits associated with ridesplitting.