Exploring spatiotemporal patterns and influencing factors of ridesourcing and traditional taxi usage using geographically and temporally weighted regression method

References

• Alemi, F., G. Circella, S. Handy, and P. Mokhtarian. 2017. What Influences Travelers to Use Uber? Exploring the Factors Affecting the Adoption of On-Demand Ride Services. Proceedings of the 96th annual conference of the transportation research board, January, Washington DC, United States.
   Google Scholar
• Alemi, F., G. Circella, S. Handy, and P. Mokhtarian. 2018b. “What Influences Travelers to Use Uber? Exploring the Factors Affecting the Adoption of On-Demand Ride Services in California.” Travel Behaviour and Society 13: 88-104. doi:10.1016/j.tbs.2018.06.002.
   Web of Science ®Google Scholar
• Alemi, F., G. Circella, P. Mokhtarian, and S. Handy. 2018a. On-Demand Ride Services in California: Investigating the Factors Affecting the Frequency of Use of Uber/Lyft. Proceedings of the 97th annual conference of the transportation research board, January, Washington DC, United States.
   Google Scholar
• Bao, J., Z. Yang, W. Zeng, and X. Shi. 2021. “Exploring the Spatial Impacts of Human Activities on Urban Traffic Crashes Using Multi-Source Big Data.” Journal of Transport Geography 94: 103118. doi:10.1016/j.jtrangeo.2021.103118.
   Web of Science ®Google Scholar
• Cardozo, O. D., J. C. García-Palomares, and J. Gutiérrez. 2012. “Application of Geographically Weighted Regression to the Direct Forecasting of Transit Ridership at Station-Level.” Applied Geography 34: 548-558. doi:10.1016/j.apgeog.2012.01.005.
   Web of Science ®Google Scholar
• Chen, F., Z. Yin, Y. Ye, and D. Sun. 2020. “Taxi Hailing Choice Behavior and Economic Benefit Analysis of Emission Reduction Based on Multi-Mode Travel Big Data.” Transport Policy 97: 73-84. doi:10.1016/j.tranpol.2020.04.001.
   Web of Science ®Google Scholar
• Chen, X. M., M. Zahiri, and S. Zhang. 2017. “Understanding Ridesplitting Behavior of On-Demand Ride Services: An Ensemble Learning Approach.” Transportation Research Part C: Emerging Technologies 76: 51-70. doi:10.1016/j.trc.2016.12.018.
   Web of Science ®Google Scholar
• Chung, K. 1997. Estimating the Effects of Employment, Development Level, and Parking Availability on CTA Rapid Transit Ridership: From 1976 to 1995 in Chicago. In Metropolitan conference on public transportation research. Chicago, IL, USA: University of Illinois.
   Google Scholar
• Correa, D., K. Xie, and K. Ozbay. 2017. Exploring the Taxi and Uber Demand in New York City: An Empirical Analysis and Spatial Modeling. Proceedings of the 96th annual conference of the transportation research board, January, Washington DC, United States.
   Google Scholar
• Fotheringham, A.S., C. Brunsdon, and M. E. Charlton. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: Wiley.
   Google Scholar
• Huang, B., B. Wu, and M. Barry. 2010. “Geographically and Temporally Weighted Regression for Modeling Spatio-Temporal Variation in House Prices.” International Journal of Geographical Information Science 24(3): 383-401. doi:10.1080/13658810802672469.
   Web of Science ®Google Scholar
• Kong, H., X. Zhang, and J. Zhao. 2020. “Is Ridesourcing More Efficient Than Taxis?” Applied Geography 125: 102301. doi:10.1016/j.apgeog.2020.102301.
   Web of Science ®Google Scholar
• Lawson, A. 2009. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. London: Chapman and Hall.
   Google Scholar
• Li, W., Z. Pu, Y. Li, and X. J. Ban. 2019. “Characterization of Ridesplitting Based on Observed Data: A Case Study of Chengdu, China.” Transportation Research Part C: Emerging Technologies 100: 330-353. doi:10.1016/j.trc.2019.01.030.
   Web of Science ®Google Scholar
• Ma, X., J. Zhang, C. Ding, and Y. Wang. 2018. “A Geographically and Temporally Weighted Regression Model to Explore the Spatiotemporal Influence of Built Environment on Transit Ridership.” Computers, Environment and Urban Systems 70: 113-124. doi:10.1016/j.compenvurbsys.2018.03.001.
   Web of Science ®Google Scholar
• G. McKenzie. 2019. “Spatiotemporal Comparative Analysis of Scooter-Share and Bike-Share Usage Patterns in Washington, D.C.” Journal of Transport Geography 78: 19-28. doi:10.1016/j.jtrangeo.2019.05.007.
   Web of Science ®Google Scholar
• Narayan, J., O. Cats, N. van Oort, and S. P. Hoogendoorn. 2022. “On the Scalability of Private and Pooled On-Demand Services for Urban Mobility in Amsterdam.” Transportation Planning and Technology 45(1): 2-18. doi:10.1080/03081060.2021.2017214.
   Web of Science ®Google Scholar
• Nelson, L. J. 2016. Uber and Lyft Have Devastated LA’s Taxi Industry, City Records Show.LA Times. http://www.latimes.com/local/lanow/la-me-ln-uber-lyfttaxis-la-20160413-story.html.
   Google Scholar
• Nie, Y. 2017. “How Can the Taxi Industry Survive the Tide of Ridesourcing?” Transportation Research Part C: Emerging Technologies 79: 242-256. doi:10.1016/j.trc.2017.03.017.
   Web of Science ®Google Scholar
• Palan, N. 2010. Measurement of Specialization # The Choice of Indices. FIW Working Paper, No. 62, FIW - Research Centre International Economics, Vienna.
   Google Scholar
• Pan, Y., S. Chen, S. Niu, Y. Ma, and K. Tang. 2020. “Investigating the Impacts of Built Environment on Traffic States Incorporating Spatial Heterogeneity.” Journal of Transport Geography 83: 102663. doi:10.1016/j.jtrangeo.2020.102663.
   Web of Science ®Google Scholar
• Qian, X. W., and S. V. Ukkusuri. 2015. “Spatial Variation of the Urban Taxi Ridership Using GPS Data.” Applied Geography 59: 31-42. doi:10.1016/j.apgeog.2015.02.011.
   Web of Science ®Google Scholar
• Rayle, L., D. Dai, N. Chan, R. Cervero, and S. Shaheen. 2016. “Just a Better Taxi? A Survey-Based Comparison of Taxis, Transit, and Ridesourcing Services in San Francisco.” Transport Policy 45: 168-178. doi:10.1016/j.tranpol.2015.10.004.
   Web of Science ®Google Scholar
• Soltani, A., A. Allan, D. Pojani, F. Khalaj, and M. Mehdizadeh. 2022. “Users and Non-Users of Bikesharing: How Do They Differ?” Transportation Planning and Technology 45(1): 39-58. doi:10.1080/03081060.2021.2017215.
   Web of Science ®Google Scholar
• Sun, D., and X. Ding. 2019. “Spatiotemporal Evolution of Ridesourcing Markets Under the New Restriction Policy: A Case Study in Shanghai.” Transportation Research Part A: Policy and Practice 130: 227-239. doi:10.1016/j.tra.2019.09.052.
   Web of Science ®Google Scholar
• Sung, H., and J. T. Oh. 2011. “Transit-Oriented Development in a High-Density City: Identifying Its Association with Transit Ridership in Seoul, Korea.” Cities 28(1): 70-82. doi:10.1016/j.cities.2010.09.004.
   Web of Science ®Google Scholar
• Taylor, B. D., D. Miller, H. Iseki, and C. Fink. 2009. “Nature and/or Nurture? Analyzing the Determinants of Transit Ridership Across US Urbanized Areas.” Transportation Research Part A: Policy and Practice 43: 60-77. doi:10.1016/j.tra.2008.06.007.
   Web of Science ®Google Scholar
• Tu, M., W. Li, O. Orfila, Y. Li, and D. Gruyer. 2021. “Exploring Nonlinear Effects of the Built Environment on Ridesplitting: Evidence from Chengdu.” Transportation Research Part D: Transport and Environment 93: 102776. doi:10.1016/j.trd.2021.102776.
   Web of Science ®Google Scholar
• Wang, S., and R. B. Noland. 2021. “Variation in Ride-Hailing Trips in Chengdu, China.” Transportation Research Part D: Transport and Environment 90: 102596. doi:10.1016/j.trd.2020.102596.
   Web of Science ®Google Scholar
• Wang, X., C. Shao, C. Yin, and C. Dong. 2021. “Exploring the Effects of the Built Environment on Commuting Mode Choice in Neighborhoods Near Public Transit Stations: Evidence from China.” Transportation Planning and Technology 44(1): 111-127. doi:10.1080/03081060.2020.1851453.
   Web of Science ®Google Scholar
• Washington, S. P., M. G. Karlaftis, and F. L. Mannering. 2011. Statistical and Economic Methods for Transportation Data Analysis. London: Chapman and Hall.
   Google Scholar
• Yang, H., J. Huo, R. Pan, K. Xie, W. Zhang, and X. Luo. 2022. “Exploring Built Environment Factors That Influence the Market Share of Ridesourcing Service.” Applied Geography 142: 102699. doi:10.1016/j.apgeog.2022.102699.
   Web of Science ®Google Scholar
• Yang, H., Y. Liang, and L. Yang. 2021. “Equitable? Exploring Ridesourcing Waiting Time and Its Determinants.” Transportation Research Part D: Transport and Environment 93: 102774. doi:10.1016/j.trd.2021.102774.
   Web of Science ®Google Scholar
• Yu, H., and Z. Peng. 2019. “Exploring the Spatial Variation of Ridesourcing Demand and Its Relationship to Built Environment and Socioeconomic Factors with the Geographically Weighted Poisson Regression.” Journal of Transport Geography 75: 147-163. doi:10.1016/j.jtrangeo.2019.01.004.
   Web of Science ®Google Scholar
• Zha, L., Y. Yin, and Y. Du. 2018. “Surge Pricing and Labor Supply in the Ridesourcing Market.” Transportation Research Part B: Methodological 117: 708-722. doi:10.1016/j.trb.2017.09.010.
   Web of Science ®Google Scholar
• Zhang, W., T.V. Le, S. V. Ukkusuri, and R. Li. 2020. “Influencing Factors and Heterogeneity in Ridership of Traditional and App-Based Taxi Systems.” Transportation 47: 971-996. doi:10.1007/s11116-018-9931-2.
   Web of Science ®Google Scholar
• Zhang, Y., B. K. Teoh, L. Zhang, and J. Chen. 2022. “Spatio-Temporal Heterogeneity Analysis of Energy Use in Residual Buildings.” Journal of Cleaner Production 352: 131422. doi:10.1016/j.jclepro.2022.131422.
   Web of Science ®Google Scholar