标题/作者	年份
Investigating objective and subjective factors influencing the adoption, frequency, and characteristics of ride-hailing trips PS Lavieri, CR Bhat Transportation Research Part C: Emerging Technologies 105, 100-125	2019
Trends in taxi use and the advent of ridehailing, 1995–2017: Evidence from the US National Household Travel Survey MW Conway, D Salon, DA King Urban Science 2 (3), 79	2018
Characterization of ridesplitting based on observed data: A case study of Chengdu, China W Li, Z Pu, Y Li, XJ Ban Transportation Research Part C: Emerging Technologies 100, 330-353	2019
Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression H Yu, ZR Peng Journal of Transport Geography 75, 147-163	2019
Mobility-on-demand: An empirical study of internet-based ride-hailing adoption factors, travel characteristics and mode substitution effects RA Acheampong, A Siiba, DK Okyere, JP Tuffour Transportation Research Part C: Emerging Technologies 115, 102638	2020
Travel and energy implications of ridesourcing service in Austin, Texas T Wenzel, C Rames, E Kontou, A Henao Transportation Research Part D: Transport and Environment 70, 18-34	2019
Socioeconomic and usage characteristics of transportation network company (TNC) riders R Grahn, CD Harper, C Hendrickson, Z Qian, HS Matthews Transportation 47, 3047-3067	2020
Who uses ride-hailing services in the United States? S Sikder Transportation research record 2673 (12), 40-54	2019
Mode choice modelling for hailable rides: An investigation of the competition of Uber with other modes by using an integrated non-compensatory choice model with probabilistic choice set formation KN Habib Transportation Research Part A: Policy and Practice 129, 205-216	2019
Using machine learning for direct demand modeling of ridesourcing services in Chicago X Yan, X Liu, X Zhao Journal of Transport Geography 83, 102661	2020
Identifying key factors associated with ridesplitting adoption rate and modeling their nonlinear relationships Y Xu, X Yan, X Liu, X Zhao Transportation Research Part A: Policy and Practice 144, 170-188	2021
Exploring the influence of built environment on Uber demand S Sabouri, K Park, A Smith, G Tian, R Ewing Transportation Research Part D: Transport and Environment 81, 102296	2020
Modeling determinants of ridesourcing usage: A census tract-level analysis of Chicago A Ghaffar, S Mitra, M Hyland Transportation Research Part C: Emerging Technologies 119, 102769	2020
Fusing multiple sources of data to understand ride-hailing use FF Dias, PS Lavieri, T Kim, CR Bhat, RM Pendyala Transportation Research Record 2673 (6), 214-224	2019
A stepwise interpretable machine learning framework using linear regression (LR) and long short-term memory (LSTM): City-wide demand-side prediction of yellow taxi and for-hire vehicle (FHV) service T Kim, S Sharda, X Zhou, RM Pendyala Transportation Research Part C: Emerging Technologies 120, 102786	2020
Pooled versus private ride-hailing: A joint revealed and stated preference analysis recognizing psycho-social factors S Kang, A Mondal, AC Bhat, CR Bhat Transportation Research Part C: Emerging Technologies 124, 102906	2021
Are travelers substituting between transportation network companies (TNC) and public buses? A case study in Pittsburgh R Grahn, S Qian, HS Matthews, C Hendrickson Transportation 48, 977-1005	2021
Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago MD Dean, KM Kockelman Journal of Transport Geography 91, 102944	2021
The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas H Yu, ZR Peng Urban Studies 57 (1), 152-175	2020
Spatial distribution of ride-hailing trip demand and its association with walkability and neighborhood characteristics O Marquet Cities 106, 102926	2020

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