Work

Estimating emissions reductions with carpooling and vehicle dispatching in ridesourcing mobility

Year: 2024

Type: article

Abstract: Ride-hailing services provide on-demand transportation solutions by connecting passengers with nearby drivers through mobile applications. However, carpooling often fails to attract passengers as expected due to inefficient order-matching strategies. This study estimates emissions reductions with order matching and vehicle dispatching in ridesourcing mobility. An explainable machine learning with a hierarchical framework is constructed for arrival time prediction. Considering pick-up and drop-off locations within the expected departure time, on-demand order matching and vehicle dispatching optimization models are built to determine the minimum fleet size and efficient route planning. Real-world experiments are conducted with large-scale ridesharing orders in Beijing, China. In comparison to the current operations, a reduction of 25.25% in fleet size and a simultaneous decrease of 21.65% in pollutant emissions are achieved. Results demonstrate that carpooling and vehicle dispatching processes lead to a slight increase in passenger waiting time while enhancing the operational efficiency of ride-hailing services and reducing pollutant emissions.

Source: npj Sustainable Mobility and Transport

DOI: 10.1038/s44333-024-00015-3

Authors: Ximing Chang, Jianjun Wu, Zifan Kang, Jianju Pan, Huijun Sun, Der-Horng Lee

Institutions: Beijing Jiaotong University, Dalian University of Technology, University of Illinois Urbana-Champaign, Zhejiang University

Language: English

Topic: Transportation and Mobility Innovations

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,590