Exploring the influence of built environment on Uber demand

被引:71
作者
Sabouri, Sadegh [1 ]
Park, Keunhyun [2 ]
Smith, Amy [3 ]
Tian, Guang [4 ]
Ewing, Reid [1 ]
机构
[1] Univ Utah, Dept City & Metropolitan Planning, 375 S 1530 E,Room 235, Salt Lake City, UT 84112 USA
[2] Utah State Univ, Dept Landscape Architecture & Environm Planning, 4005 Old Main Hill, Logan, UT 84322 USA
[3] Uber Technol, 1455 Market St, San Francisco, CA 94103 USA
[4] Univ New Orleans, Dept Planning & Urban Studies, 2000 Lakeshore Dr, New Orleans, LA 70148 USA
关键词
Ride-sourcing services; Transportation network companies; Uber; Built environment; Trip distribution; Multilevel modeling; MODE CHOICE; LAND-USE; TRAVEL-TIME; SERVICES; ADOPTION;
D O I
10.1016/j.trd.2020.102296
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Ride-sourcing services have made significant changes to the transportation system, essentially creating a new mode of transport, arguably with its own relative utility compared to the other standard modes. As ride-sourcing services have become more popular each year and their markets have grown, so have the publications related to the emergence of these services. One question that has not been addressed yet is how the built environment, the so-called D variables (i.e., density, diversity, design, distance to transit, and destination accessibility), affect demand for ride-sourcing services. By having unique access to Uber trip data in 24 diverse U.S. regions, we provide a robust data-driven understanding of how ride-sourcing demand is affected by the built environment, after controlling for socioeconomic factors. Our results show that Uber demand is positively correlated with total population and employment, activity density, land use mix or entropy, and transit stop density of a census block group. In contrast, Uber demand is negatively correlated with intersection density and destination accessibility (both by auto and transit) variables. This result might be attributed to the relative advantages of other modes driving, taking transit, walking, or biking - in areas with denser street networks and better regional job access. The findings of this paper have important implications for policy, planning, and travel demand modeling, where decision-makers seek solutions to shape the built environment in order to reduce automobile dependence and promote walking, biking, and transit use.
引用
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页数:11
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