Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/119685
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical and Electronic Engineering | en_US |
| dc.creator | Jin, Z | en_US |
| dc.creator | Sun, X | en_US |
| dc.creator | Zhen, L | en_US |
| dc.creator | Gu, W | en_US |
| dc.creator | Tu, H | en_US |
| dc.date.accessioned | 2026-07-06T02:42:33Z | - |
| dc.date.available | 2026-07-06T02:42:33Z | - |
| dc.identifier.issn | 0968-090X | en_US |
| dc.identifier.uri | http://hdl.handle.net/10397/119685 | - |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier Ltd | en_US |
| dc.subject | Complement | en_US |
| dc.subject | Public transit | en_US |
| dc.subject | Ride-hailing | en_US |
| dc.subject | Spatio-temporal analysis | en_US |
| dc.subject | Substitution | en_US |
| dc.title | Substitution or complement? Uncovering the interplay between ride-hailing services and public transit | en_US |
| dc.type | Journal/Magazine Article | en_US |
| dc.identifier.volume | 189 | en_US |
| dc.identifier.doi | 10.1016/j.trc.2026.105747 | en_US |
| dcterms.abstract | The literature on transportation network companies (TNCs), also known as ride-hailing services, has often characterized these service providers as predominantly substitutive to public transit (PT). However, as TNC markets expand and mature, the complementary and substitutive relationships with PT may shift. To explore whether such a transformation is occurring, this study collected travel data from 96,716 ride-hailing vehicles during September 2022 in Shanghai, a city characterized by an increasingly saturated TNC market. An enhanced data-driven framework is proposed to classify TNC-PT relationships into four types: first-mile complementary, last-mile complementary, substitutive, and independent. Using baseline parameter settings, our findings reveal a substantial increase in the complementary ratio (9.22%) and a relative decline in the substitutive ratio (9.06%) compared to previous studies. Furthermore, to examine the nonlinear impact of various influential factors on these ratios, a machine learning method integrating categorical boosting (CatBoost) and Shapley additive explanations (SHAP) is proposed. The results show significant nonlinear effects in some variables, including the distance to the nearest metro station and the density of bus stops. Moreover, metro hubs and regular single-line stations exhibit distinct effects on first- or last-mile complementary ratios. These ratios’ relation to the distance to single-line stations shows an inverted U-shaped pattern, with effects rising sharply within 1.5 km, remaining at the peak between 1.5 and 3 km, and then declining as the distance increases to about 15 km. On the other hand, the distance to multi-line hubs initially maintains a positive influence on the complementary ratios within 9 km, which then stabilizes in the 9–18 km range. These findings offer valuable insights for policymakers to promote urban multimodal mobility systems integrating ride-hailing and public transit. | en_US |
| dcterms.accessRights | embargoed access | en_US |
| dcterms.bibliographicCitation | Transportation research. Part C, Emerging technologies, Aug. 2026, v. 189, 105747 | en_US |
| dcterms.isPartOf | Transportation research. Part C, Emerging technologies | en_US |
| dcterms.issued | 2026-08 | - |
| dc.identifier.scopus | 2-s2.0-105038632764 | - |
| dc.identifier.eissn | 1879-2359 | en_US |
| dc.identifier.artn | 105747 | en_US |
| dc.description.validate | 202607 bchy | en_US |
| dc.description.oa | Not applicable | en_US |
| dc.identifier.SubFormID | G001933/2026-06 | - |
| dc.description.fundingSource | RGC | en_US |
| dc.description.fundingSource | Others | en_US |
| dc.description.fundingText | This work was partially supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (PolyU P0051967/RGC 15237624). Dr. Xiaotong Sun would like to thank the support provided by the National Natural Science Foundation of China (72201073). The authors are grateful for the data support provided by the Shanghai Electric Vehicle Public Data Collecting, Monitoring, and Research Center. In addition, the authors express their sincere gratitude to Mr. Ruiguo Zhong in HKUST(GZ) for his invaluable suggestions and expertise in coding. | en_US |
| dc.description.pubStatus | Published | en_US |
| dc.date.embargo | 2028-08-31 | en_US |
| dc.description.oaCategory | Green (AAM) | en_US |
| Appears in Collections: | Journal/Magazine Article | |
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