{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:25:07Z","timestamp":1784564707729,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T00:00:00Z","timestamp":1595980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["61672442"],"award-info":[{"award-number":["61672442"]}]},{"name":"Nature Science Foundation of Fujian Province","award":["2020J01130815"],"award-info":[{"award-number":["2020J01130815"]}]},{"name":"Scientific Climbing Project of Xiamen University of Technology","award":["XPDKT18030"],"award-info":[{"award-number":["XPDKT18030"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>The rapid growth of transportation network companies (TNCs) has reshaped the traditional taxi market in many modern cities around the world. This study aims to explore the spatiotemporal variations of built environment on traditional taxis (TTs) and TNC. Considering the heterogeneity of ridership distribution in spatial and temporal aspects, we implemented a geographically and temporally weighted regression (GTWR) model, which was improved by parallel computing technology, to efficiently evaluate the effects of local influencing factors on the monthly ridership distribution for both modes at each taxi zone. A case study was implemented in New York City (NYC) using 659 million pick-up points recorded by TT and TNC from 2015 to 2017. Fourteen influencing factors from four groups, including weather, land use, socioeconomic and transportation, are selected as independent variables. The modeling results show that the improved parallel-based GTWR model can achieve better fitting results than the ordinary least squares (OLS) model, and it is more efficient for big datasets. The coefficients of the influencing variables further indicate that TNC has become more convenient for passengers in snowy weather, while TT is more concentrated at the locations close to public transportation. Moreover, the socioeconomic properties are the most important factors that caused the difference of spatiotemporal patterns. For example, passengers with higher education\/income are more inclined to select TT in the western of NYC, while vehicle ownership promotes the utility of TNC in the middle of NYC. These findings can provide scientific insights and a basis for transportation departments and companies to make rational and effective use of existing resources.<\/jats:p>","DOI":"10.3390\/ijgi9080475","type":"journal-article","created":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T07:31:45Z","timestamp":1596007905000},"page":"475","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Spatiotemporal Varying Effects of Built Environment on Taxi and Ride-Hailing Ridership in New York City"],"prefix":"10.3390","volume":"9","author":[{"given":"Xinxin","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Geography and Resource Management and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong 999077, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shunzhi","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,29]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"The competitive effects of the sharing economy: How is uber changing taxis","volume":"22","author":"Wallsten","year":"2015","journal-title":"Technol. Policy Inst."},{"key":"ref_2","unstructured":"Rayle, L., Shaheen, S., Chan, N., Dai, D., and Cervero, R. (2014). App-Based, on-demand Ride Services: Comparing Taxi and Ridesourcing Trips and User Characteristics in San Francisco University Of California Transportation Center (UCTC), University of California."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.trc.2017.03.017","article-title":"How can the taxi industry survive the tide of ridesourcing? Evidence from shenzhen, China","volume":"79","author":"Nie","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.tre.2016.05.011","article-title":"Pricing strategies for a taxi-hailing platform","volume":"93","author":"Wang","year":"2016","journal-title":"Transp. Res. Part E Logist. Transp. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1257\/aer.p20161002","article-title":"Disruptive change in the taxi business: The case of uber","volume":"106","author":"Cramer","year":"2016","journal-title":"Am. Econ. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1177\/0019793917717222","article-title":"An analysis of the labor market for uber\u2019s driver-partners in the united states","volume":"71","author":"Hall","year":"2018","journal-title":"ILR Rev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, M., Dong, L., Shen, Z., Lang, W., and Ye, X. (2017). Examining the interaction of taxi and subway ridership for sustainable urbanization. Sustainability, 9.","DOI":"10.3390\/su9020242"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1177\/0042098010379280","article-title":"What affects transit ridership? A dynamic analysis involving multiple factors, lags and asymmetric behaviour","volume":"48","author":"Chen","year":"2011","journal-title":"Urban Stud."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.jtrangeo.2017.10.021","article-title":"Analysis of washington, dc taxi demand using gps and land-use data","volume":"66","author":"Yang","year":"2018","journal-title":"J. Transp. Geogr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1145\/2543581.2543584","article-title":"From taxi gps traces to social and community dynamics: A survey","volume":"46","author":"Castro","year":"2013","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.tra.2015.05.016","article-title":"Factors affecting public transportation usage rate: Geographically weighted regression","volume":"78","author":"Chiou","year":"2015","journal-title":"Transp. Res. Part A Policy Pract."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.landurbplan.2012.02.012","article-title":"Urban land uses and traffic \u2018source-sink areas\u2019: Evidence from gps-enabled taxi data in shanghai","volume":"106","author":"Liu","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_13","unstructured":"Sadowsky, N., and Nelson, E. (2017, May 26). The impact of ride-hailing services on public transportation use: A discontinuity regression analysis. Available online: https:\/\/digitalcommons.bowdoin.edu\/econpapers\/13."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"45","DOI":"10.3141\/2542-06","article-title":"Spatiotemporal pattern analysis of taxi trips in new york city","volume":"2542","author":"Hochmair","year":"2016","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1111\/gean.12071","article-title":"Geographical and temporal weighted regression (gtwr)","volume":"47","author":"Fotheringham","year":"2015","journal-title":"Geogr. Anal."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1177\/0361198106197200113","article-title":"Transit ridership model based on geographically weighted regression","volume":"1972","author":"Chow","year":"2006","journal-title":"Transp. Res. Rec."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.apgeog.2012.01.005","article-title":"Application of geographically weighted regression to the direct forecasting of transit ridership at station-level","volume":"34","author":"Cardozo","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_18","unstructured":"Qian, X., and Ukkusuri, S.V. (2015, January 11\u201315). Exploring spatial variation of urban taxi ridership using geographically weighted regression. Proceedings of the Transportation Research Board 94th Annual Meeting, Washington, DC, USA."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1007\/s00168-015-0660-6","article-title":"Exploring, modelling and predicting spatiotemporal variations in house prices","volume":"54","author":"Fotheringham","year":"2015","journal-title":"Ann. Reg. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.rse.2016.07.015","article-title":"Viirs-based remote sensing estimation of ground-level pm2. 5 concentrations in beijing\u2013tianjin\u2013hebei: A spatiotemporal statistical model","volume":"184","author":"Wu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_21","unstructured":"Taylor, B.D., Miller, D., Iseki, H., and Fink, C. (2003). Analyzing the Determinants of Transit Ridership Using a Two-Stage Least Squares Regression on a National Sample of Urbanized Areas, University of California Transportation Center."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1080\/13658810802672469","article-title":"Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices","volume":"24","author":"Huang","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2017.12.018","article-title":"Satellite-based mapping of daily high-resolution ground pm 2.5 in china via space-time regression modeling","volume":"206","author":"He","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_24","first-page":"1","article-title":"Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression","volume":"65","author":"Chu","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1080\/13658816.2013.878463","article-title":"A geographically and temporally weighted autoregressive model with application to housing prices","volume":"28","author":"Wu","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ecoinf.2017.12.005","article-title":"Extending geographically and temporally weighted regression to account for both spatiotemporal heterogeneity and seasonal variations in coastal seas","volume":"43","author":"Du","year":"2018","journal-title":"Ecol. Inform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.compenvurbsys.2018.03.001","article-title":"A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership","volume":"70","author":"Ma","year":"2018","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, X., Huang, B., and Zhu, S. (2019). Spatiotemporal influence of urban environment on taxi ridership using geographically and temporally weighted regression. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8010023"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, Y., Ji, Y., Shi, Z., and Gao, L. (2018). The influence of the built environment on school children\u2019s metro ridership: An exploration using geographically weighted poisson regression models. Sustainability, 10.","DOI":"10.3390\/su10124684"},{"key":"ref_30","first-page":"175","article-title":"Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.).(telegraphic reviews)(book review)","volume":"67","author":"Peruggia","year":"2002","journal-title":"J. Wildl. Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10766-008-0082-5","article-title":"Matlab\u00ae: A language for parallel computing","volume":"37","author":"Sharma","year":"2009","journal-title":"Int. J. Parallel Program."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yazici, M.A., Kamga, C., and Singhal, A. (2013, January 6\u20139). A big data driven model for taxi drivers\u2019 airport pick-up decisions in New York city. Proceedings of the 2013 IEEE International Conference on Big Data, Silicon Valley, CA, USA.","DOI":"10.1109\/BigData.2013.6691775"},{"key":"ref_33","unstructured":"Neter, J., Kutner, M.H., Nachtsheim, C.J., and Wasserman, W. (1996). Applied Linear Statistical Models, McGraw-Hill Irwin."},{"key":"ref_34","unstructured":"King, D.A., Peters, J.R., and Daus, M.W. (2012, January 22\u201326). Taxicabs for improved urban mobility: Are we missing an opportunity?. Proceedings of the Transportation Research Board 91st Annual Meeting, Washington, DC, USA."},{"key":"ref_35","unstructured":"Kamga, C., Yazici, M.A., and Singhal, A. (2013, January 13\u201317). Hailing in the rain: Temporal and weather-related variations in taxi ridership and taxi demand-supply equilibrium. Proceedings of the Transportation Research Board 92nd Annual Meeting, Washington, DC, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Davidov, G. (2016). The status of uber drivers: A purposive approach. Span. Labour Law Employ. Relat. J. Forthcom. (2017), Forthcoming.","DOI":"10.1093\/acprof:oso\/9780198759034.001.0001"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1111\/gean.12084","article-title":"The multiple testing issue in geographically weighted regression","volume":"48","author":"Fotheringham","year":"2016","journal-title":"Geogr. Anal."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Poulsen, L.K., Dekkers, D., Wagenaar, N., Snijders, W., Lewinsky, B., Mukkamala, R.R., and Vatrapu, R. (July, January 27). Green cabs vs. Uber in New York city. Proceedings of the 2016 IEEE International Congress on Big Data (BigData Congress), San Francisco, CA, USA.","DOI":"10.1109\/BigDataCongress.2016.35"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1080\/13658816.2018.1521523","article-title":"Fast geographically weighted regression (fastgwr): A scalable algorithm to investigate spatial process heterogeneity in millions of observations","volume":"33","author":"Li","year":"2019","journal-title":"Int. J. Geogr. Inf. Sci."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/8\/475\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:52:32Z","timestamp":1760176352000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/8\/475"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,29]]},"references-count":39,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2020,8]]}},"alternative-id":["ijgi9080475"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9080475","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,29]]}}}