{"ID":2844684,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06074","arxiv_id":"2511.06074","title":"Assessing On-Demand Mobility Services and Policy Impacts: A Case Study from Chengdu, China","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.","short_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 theo...","url_abs":"https://arxiv.org/abs/2511.06074","url_pdf":"https://arxiv.org/pdf/2511.06074v2","authors":"[\"Youkai Wu\",\"Zhaoxia Guo\",\"Qi Liu\",\"Stein W. Wallace\"]","published":"2025-11-08T17:08:07Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.CY\"]","methods":"[]","has_code":false}