Not minding the gap: Does ride-hailing serve transit deserts?

Abstract

Transit has long connected people to opportunities but access to transit varies greatly across space. In some cases, unevenly distributed transit supply creates gaps in service that impede travelers' abilities to cross space and access jobs or other opportunities. With the advent of ride-hailing services like Uber and Lyft, however, travelers now have a new potential to gain automobility without high car purchase costs and in the absence of reliable transit service. Research remains mixed on whether ride-hailing serves as a modal complement or substitute to transit or whether ride-hailing fills transit service needs gaps. This study measures transit supply in Chicago and compares it to ride-hailing origins and destinations to examine if ridehailing fills existing transit service gaps. Findings reveal clustering of ride-hailing pickups and drop-offs across the City of Chicago, but that the number of ride-hailing pickups and drop-offs was most strongly associated with high neighborhood median household income rather than measures of transit supply. In bivariate analyses, transit service was not associated with ridehailing trip ends. But after controlling for neighborhood socioeconomic status, transit dependency, population density, and employment density, we found fewer ride-hailing trips in neighborhoods where bus service dominated and significantly more ride-hailing trips where rail service was prevalent. Patterns were slightly different for overnight weekend ride-hailing pickups , where higher transit density predicted a greater number of trips in nearby tracts. Additional research and policy is needed to ensure that ride-hailing services provide travel options to those who need them the most and fill transit gaps in low-income communities when options to increase service are limited.

Sharing behavior in ride-hailing trips: A machine learning inference approach

“…3, since the EDA trips are longer and more expensive, lower price of shared trips has become a stronger factor encouraging riders to opt for sharing. In EDAs , the transit supply is also poor, suggesting that the convenience of door-to-door mobility combined with lower price of shared trips may be replacing active and public transit trips and come at the expense of expanding infrastructure and/or services to those areas (Barajas and Brown, 2021). This is consistent with Schaller (2021) which found sharing in ride-hailing is most popular in lieu of public transit and attracts passengers from existing shared modes (Schaller, 2021).…”

“…Chicago is one of the largest ride-hailing markets in the US, where ride-hailing make up about 3% of the total regional VMT (Balding et al, 2019). The publicly available ride-hailing data from the City of Chicago has provided an unpreceded opportunity for empirical understanding of ridehailing demand patterns (Ghaffar et al, 2020;Yan et al, 2020), relationship with transit services (Barajas and Brown, 2021), and neighborhood characteristics (Marquet, 2020). Since it has a unique feature of observing which trips were requested to be shared, recent studies with this data attempted to understand the determinants of WTS (Dean and Kockelman, 2021;Hou et al, 2020;Tu et al, 2021;Xu et al, 2021).…”

Ride-hailing and transit accessibility considering the trade-off between time and money

“…Other studies using travel surveys have shown that TNCs are increasingly used as first-last mile alternatives to integrate with transit (Bedoya-Maya et al, 2022;Brown et al, 2021;Sunitiyoso et al, 2022). Along these lines, there is also growing research on ride-hailing and transportation equity, looking at whether TNCs can effectively improve the mobility conditions of low-income and disadvantaged communities (Brown et al, 2022;Jiao & Wang, 2020;Jin et al, 2019) or whether ride-hailing services end up exacerbating the gap in the urban mobility and accessibility conditions of different socioeconomic groups (Abdelwahab et al, 2021;Barajas & Brown, 2021;Brown et al, 2021).…”

Joint Econometric Model Framework for Transportation Network Company Users’ Trip Fare and Destination Choice Analysis

“…However, the result is exactly opposite in the context of L stations. The variation might reflect the different neighborhood characteristics of census tracts with higher number of buses vis-a`-vis census tracts with higher number of L stations (62)(63)(64).…”