The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas

Haitao Yu and Zhong-Ren Peng
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Haitao Yu: International Center for Adaptation Planning and Design (iAdapt), College of Design, Construction, and Planning, University of Florida, USA
Zhong-Ren Peng: International Center for Adaptation Planning and Design (iAdapt), College of Design, Construction, and Planning, University of Florida, USA; China Institute for Urban Governance, Shanghai Jiao Tong University, China

Urban Studies, 2020, vol. 57, issue 1, 152-175

Abstract: Recently, the explosive growth of ridesourcing, or on-demand ridesharing, has attracted a great deal of attention from researchers and planners. Despite its transformative impacts on mobility, limited studies have examined how built environment affects its use. In this study, we investigate the impacts of built environment on ridesourcing demand. We employ structural equation modelling to account for the complex relationships among study variables, and investigate the impacts at census block group level by using RideAustin data in Austin, Texas. Findings reveal strong impacts of built environment on ridesourcing demand and significant temporal heterogeneity. The models show that greater population/employment/service job densities, road density, pavement completeness, land use mix and job accessibility by transit produce more ridesourcing demand. Access to the commuter rail (MetroRail) also leads to greater demand. Furthermore, time-of-day (TOD) models demonstrate that these effects vary significantly according to the time of day. Our research has implications for policy making and for travel demand modelling of ridesourcing.

Keywords: built environment; planning; policy; smart cities; technology; transport; 建筑环境; 规划; 政策; 智慧城市; 技术; äº¤é€šè¿ è¾“ (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:urbstu:v:57:y:2020:i:1:p:152-175

DOI: 10.1177/0042098019828180

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