Analysing mobility and environmental impacts of automated ride-sharing services under mixed traffic
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Shared Automated Vehicles (SAVs) hold great promise for the future of urban mobility. Automated ride-sharing services are expected to alleviate traffic congestion, reduce traffic emissions, and significantly improve road safety by combining advanced connected and autonomous vehicle (CAV) technology with the ride and/or car-sharing concept. These benefits, however, are highly dependent on the deployment concept of the service and environment including network characteristics, CAV technology, traffic compositions, population acceptance, etc. This study aims to assess the mobility and environmental impacts of introducing a door-to-door automated ride-sharing (ARS) service under different deployment scenarios. Two calibrated and validated city-scale networks with different characteristics were used: a suburban area in the Greater Manchester (UK) and a city-centre area in Leicester (UK). An optimisation technique for the vehicle routing problem was developed to efficiently operate ARS at a network-level. The customers' preference for individual and shared rides with Willingness to Share (WTS) was investigated to gain a better understanding of the performance indicators (i.e., delay, travel time, speed, kilometres-driven and emissions) The introduction of ARS was investigated under two deployment scenarios: 1) mixed with conventional human-driven vehicles (HDVs) and 2) mixed with HDVs with varying CAV market penetration rates. Findings suggest that introducing ARS can adversely impact mobility and the environment under mixed traffic, especially in suburban areas, and the benefits of an automated ride-sharing system are highly dependent on WTS. The findings will assist local authorities in formulating automated ride-sharing policies to manage the traffic on roads.
Date Issued
2025-10-01
Date Acceptance
2025-06-09
Citation
Research in Transportation Business and Management, 2025, 62
ISSN
2210-5395
Publisher
Elsevier
Journal / Book Title
Research in Transportation Business and Management
Volume
62
Copyright Statement
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Subjects
AGENT-BASED SIMULATION
Automated ride sharing
Automated ridesharing
AUTONOMOUS VEHICLES
Business
Business & Economics
Connected and automated vehicles
DEMAND
Management
Mixed traffic
MODEL
OWNERSHIP
PICKUP
Science & Technology
Social Sciences
Technology
Traffic microsimulation
Transportation
Publication Status
Published
Article Number
101434
Date Publish Online
2025-06-17