Exploring the influence of built environment on Uber demand. (April 2020)

• Record Type: Journal Article
• Authors: Sabouri, Sadegh; Park, Keunhyun; Smith, Amy; Tian, Guang; Ewing, Reid
• Is Part Of: Transportation research. Volume 81(2020)
• Publication Date: 2020-04
• Subjects: Ride-sourcing services -- Transportation network companies -- Uber -- Built environment -- Trip distribution -- Multilevel modeling; Transportation -- Research -- Periodicals; Transportation -- Environmental aspects -- Periodicals; 354.76
• DOI: 10.1016/j.trd.2020.102296
• Languages: English
• ISSNs: 1361-9209

Abstract

Highlights: The built environment has an influence on Uber demand beyond simple socioeconomics. More Uber demand is expected as density, diversity, and distance to transit increase. Lower Uber demand is expected as accessibility and intersection density increase. Abstract: 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.