{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T01:09:54Z","timestamp":1768698594448,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T00:00:00Z","timestamp":1641686400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Startup Fund for Advanced Talents of Nanjing Forestry University","award":["No. 163106065"],"award-info":[{"award-number":["No. 163106065"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Understanding the spatiotemporal variation of high-efficiency ride-hailing orders (HROs) is helpful for transportation network companies (TNCs) to balance the income of drivers through reasonable order dispatch, and to alleviate the imbalance between supply and demand by improving the pricing mechanism, so as to promote the sustainable and healthy development of the ride-hailing industry and urban transportation. From the perspective of TNCs for order management, this study investigates the spatiotemporal variation of HROs and common ride-hailing orders (CROs) for ride-hailing services using the trip data of Didi Chuxing in Haikou, China. Ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to examine the factors that affect the densities of HROs and CROs during different time periods, such as morning, evening, afternoon and night, with considering various built environment variables. The OLS models show that factors including road density, average travel time rate, companies and enterprises and transportation facilities have significant impacts on HROs and CROs for most periods. The results of the GWR models are consistent with the global regression results and show the local effects of the built environment on HROs and CROs in different regions.<\/jats:p>","DOI":"10.3390\/ijgi11010042","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T20:29:26Z","timestamp":1641760166000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Understanding the Spatiotemporal Variation of High-Efficiency Ride-Hailing Orders: A Case Study of Haikou, China"],"prefix":"10.3390","volume":"11","author":[{"given":"Mingyang","family":"Du","sequence":"first","affiliation":[{"name":"School of Transportation, Southeast University, Nanjing 211189, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuefeng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mei-Po","family":"Kwan","sequence":"additional","affiliation":[{"name":"Department of Geography and Resource Management, Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, Hong Kong, China"},{"name":"Department of Human Geography and Spatial Planning, Utrecht University, 3584 CB Utrecht, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingzong","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Big Data, Baoshan University, Baoshan 678000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0953-7050","authenticated-orcid":false,"given":"Qiyang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1080\/01441647.2018.1497728","article-title":"Shared ride services in North America: Definitions, impacts, and the future of pooling","volume":"39","author":"Shaheen","year":"2019","journal-title":"Transp. 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