Equitable? Exploring ridesourcing waiting time and its determinants

Abstract Waiting time (WT) is an important measure that can reflect accessibility to ridesourcing service. Previous studies explored the effects of built environment factors on WT based on estimated WT but did not control for trip-level characteristics, which may lead to biased parameter estimation. Thus, we further study this topic by using the actual WT recorded by the RideAustrin platform and considering trip-level variables. The single-level and multilevel proportional hazards models are constructed, and model comparison shows that the multilevel model performs better. We find that waiting time is positively correlated with trip-level characteristics such as traffic conditions, surge multiplier, and rainy weather. Regarding built environment factors, WT is positively related to distance to CBD and negatively related to road density, transit stop density, and land-use entropy. WT is also higher in areas with a high fraction of Hispanic/Latino and Black residents but lower in areas of low income.

[1]  Christo Wilson,et al.  On Ridesharing Competition and Accessibility: Evidence from Uber, Lyft, and Taxi , 2018, WWW.

[2]  Christopher R. Knittel,et al.  Racial and Gender Discrimination in Transportation Network Companies , 2016 .

[3]  Renbin Pan,et al.  Exploring the Equity of Traditional and Ride-Hailing Taxi Services during Peak Hours , 2020 .

[4]  Y. Nie.  How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China , 2017 .

[5]  Anne E. Brown.  Redefining Car Access , 2019, Journal of the American Planning Association.

[6]  Anae Sobhani,et al.  On the determinants of Uber accessibility and its spatial distribution: Evidence from Uber in Philadelphia , 2020, WIREs Data Mining Knowl. Discov..

[7]  S. C. Calvert,et al.  Quantifying the impact of adverse weather conditions on road network performance , 2016 .

[8]  Steven Farber,et al.  The Who, Why, and When of Uber and other Ride-hailing Trips: An Examination of a Large Sample Household Travel Survey , 2019, Transportation Research Part A: Policy and Practice.

[9]  Anne E. Brown,et al.  Hailing a change: comparing taxi and ridehail service quality in Los Angeles , 2020 .

[10]  Christopher R. Knittel,et al.  Racial discrimination in transportation network companies , 2020 .

[11]  H. S. Matthews,et al.  Socioeconomic and usage characteristics of transportation network company (TNC) riders , 2020, Transportation.

[12]  Roel Bosker,et al.  Multilevel analysis : an introduction to basic and advanced multilevel modeling , 1999 .

[13]  Chandra R. Bhat,et al.  A model of deadheading trips and pick-up locations for ride-hailing service vehicles , 2020 .

[14]  Chandra R. Bhat,et al.  The spatial analysis of activity stop generation , 2002 .

[15]  Zhixiang Fang,et al.  Understanding the Effect of an E-Hailing App Subsidy War on Taxicab Operation Zones , 2018, Journal of Advanced Transportation.

[16]  Anne E. Brown.  Prevalence and Mechanisms of Discrimination: Evidence from the Ride-Hail and Taxi Industries , 2019, Journal of Planning Education and Research.

[17]  Loren G. Terveen,et al.  Towards a Geographic Understanding of the Sharing Economy: Systemic Biases in UberX and TaskRabbit , 2022 .

[18]  Qing Shen,et al.  How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales , 2014 .

[19]  A. Golub,et al.  Assessing the barriers to equity in smart mobility systems: A case study of Portland, Oregon , 2019 .

[20]  G. Tiwari,et al.  Role of user's socio-economic and travel characteristics in mode choice between city bus and informal transit services: Lessons from household surveys in Visakhapatnam, India , 2020 .

[21]  Chuan Ding,et al.  Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach , 2017 .

[22]  H. S. Matthews,et al.  Are travelers substituting between transportation network companies (TNC) and public buses? A case study in Pittsburgh , 2020 .

[23]  Ryan C. Hughes,et al.  Transportation network company wait times in Greater Seattle, and relationship to socioeconomic indicators , 2016 .

[24]  Junfeng Jiao,et al.  Investigating Uber price surges during a special event in Austin, TX , 2018, Research in Transportation Business & Management.

[25]  Maya Abou-Zeid,et al.  Modeling the choice to switch from traditional modes to ridesourcing services for social/recreational trips in Lebanon , 2020, Transportation.

[26]  R. Cervero,et al.  TRAVEL DEMAND AND THE 3DS: DENSITY, DIVERSITY, AND DESIGN , 1997 .

[27]  A. Shalaby,et al.  Equity Analysis and New Mobility Technologies: Toward Meaningful Interventions , 2020 .

[28]  B. Taylor,et al.  Hate to Wait , 2011 .

[29]  J. Gill.  Hierarchical Linear Models , 2005 .

[30]  Zeina Wafa,et al.  Assessing the Impact of App-Based Ride Share Systems in an Urban Context: Findings from Austin , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[31]  Fred L. Mannering,et al.  HAZARD-BASED DURATION MODELS AND THEIR APPLICATION TO TRANSPORT ANALYSIS. , 1994 .

[32]  Carmelo Ardito,et al.  Empowering End Users to Customize their Smart Environments , 2017, ACM Trans. Comput. Hum. Interact..

[33]  Zhong-Ren Peng,et al.  Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression , 2019, Journal of Transport Geography.

[34]  Kerry Nield,et al.  An empirical analysis of taxi, Lyft and Uber rides: Evidence from weather shocks in NYC , 2018, Journal of Economic Behavior & Organization.

[35]  R. Cervero,et al.  Just a better taxi? A survey-based comparison of taxis, transit, and ridesourcing services in San Francisco , 2016 .

[36]  A. Bigazzi,et al.  What Can Publicly Available API Data Tell Us about Supply and Demand for New Mobility Services? , 2020 .

[37]  A. Sobhani,et al.  Impacts of trip characteristics and weather condition on ride-sourcing network: Evidence from Uber and Lyft , 2020 .

[38]  M. Keith Chen,et al.  Dynamic Pricing in a Labor Market: Surge Pricing and Flexible Work on the Uber Platform , 2016, EC.

[39]  Chandra R. Bhat,et al.  The Impact of Stop-Making and Travel Time Reliability on Commute Mode Choice , 2006 .

[40]  Hai Yang,et al.  Ridesourcing systems: A framework and review , 2019, Transportation Research Part B: Methodological.

[41]  D.,et al.  Regression Models and Life-Tables , 2022 .

[42]  Hongtai Yang,et al.  Equilibrium in taxi and ride-sourcing service considering the use of e-hailing application , 2021, Transportmetrica A: Transport Science.

[43]  Mingshu Wang,et al.  Spatial disparities of Uber accessibility: An exploratory analysis in Atlanta, USA , 2018, Comput. Environ. Urban Syst..