Assessing On-Demand Mobility Services and Policy Impacts: A Case Study from Chengdu, China
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909905102831616 |
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| author | Wu, Youkai Guo, Zhaoxia Liu, Qi Wallace, Stein W. |
| author_facet | Wu, Youkai Guo, Zhaoxia Liu, Qi Wallace, Stein W. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_06074 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessing On-Demand Mobility Services and Policy Impacts: A Case Study from Chengdu, China Wu, Youkai Guo, Zhaoxia Liu, Qi Wallace, Stein W. Optimization and Control Computers and Society 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. |
| title | Assessing On-Demand Mobility Services and Policy Impacts: A Case Study from Chengdu, China |
| topic | Optimization and Control Computers and Society |
| url | https://arxiv.org/abs/2511.06074 |