Analyzing ridesourcing demand, equity and neighborhood characteristics in Chicago

Abstract

As the use of ridesourcing is only slated to increase it is critical for policymakers to understand the demand for these services and usage patterns so they can examine the role of transportation network providers (TNPs) within the broader transportation system. There is a lack of research on the ridesourcing users, especially the impact and usage patterns of lower income urban households. This deficiency in research can be attributed to the scarcity of publicly available data by TNPs. Trip level data from Chicago’s Open Data Portal was analyzed to determine how neighborhood characteristics relate to ridesourcing demand and usage patterns. This paper describes how cluster analysis was used to create a neighborhood typology to describe the different users of ridesourcing in Chicago. The cluster analysis resulted in three groups of ridesourcing behaviors: (1) Urban Elites: High usage, short distance trips in commercially dense areas with public transit access, (2) Underserved Communities: Low usage, often shared medium distance trips originating in residential areas, and (3) Suburban Car Commuters: Low usage, long distance trips to city destinations (airport or downtown) originating in residential areas. The findings show that low-income users of ridesourcing tend to take more shared rides, during commuting hours but overall have low usage. It appears that ridesourcing in Chicago asks a complement to public transit for underserved communities. It also highlights that while Underserved Communities have better transit access than Suburban Car Commuters there is still considerable demand for shared rides during peak commuting times. Given the lack of trip level ridesourcing data available, this clustering methodology of ridesourcing behaviors may allow policymakers and planners to estimate specific ridesourcing behaviors of different populations without access to trip level data.