Assessing On-Demand Mobility Services and Policy Impacts: A Case Study from Chengdu, China

• URL: http://arxiv.org/abs/2511.06074v2
• Date: Sat, 15 Nov 2025 15:08:04 GMT
• Title: Assessing On-Demand Mobility Services and Policy Impacts: A Case Study from Chengdu, China
• Authors: Youkai Wu, Zhaoxia Guo, Qi Liu, Stein W. Wallace,
• Abstract summary: This study integrates a graph theory-based trip-vehicle matching mechanism with real cruising taxi operations data to simulate ride-hailing services in Chengdu, China.<n>We examine the impacts of fleet size management, geofencing, and demand management, on the performance of ride-hailing services.
• Score: 3.8367373028524874
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The rapid expansion of ride-hailing services has significantly reshaped urban on-demand mobility patterns, but it still remains unclear how they perform relative to traditional street-hailing services and how effective are related policy interventions. This study presents a simulation framework integrating a graph theory-based trip-vehicle matching mechanism with real cruising taxi operations data to simulate ride-hailing services in Chengdu, China. The performances of the two on-demand mobility service modes (i.e., ride-hailing and street-hailing) are evaluated in terms of three key performance indicators: average passenger waiting time (APWT), average deadheading miles (ADM), and average deadheading energy consumption (ADEC). We further examine the impacts of spatiotemporal characteristics and three types of policies: fleet size management, geofencing, and demand management, on the performance of ride-hailing services. Results show that under the same fleet size and trip demand as street-hailing taxis, ride-hailing services without cruising achieve substantial improvements, reducing APWT, ADM, and ADEC by 81\%, 75\%, and 72.1\%, respectively. These improvements are most pronounced during midnight low-demand hours and in remote areas such as airports. Our analysis also reveals that for ride-hailing service, (1) expanding fleet size yields diminishing marginal benefits; (2) geofencing worsens overall performance while it improves the performance of serving all trips within the city center; and (3) demand-side management targeting trips to high-attraction and low-demand areas can effectively reduce passenger waiting time without increasing deadheading costs.

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