A Model of Ridesourcing Demand Generation and Distribution

Abstract

Ridesourcing has experienced exponential growth in recent years, yet its impact on individual travel are unclear and have not been adequately examined. Recently, an Austin-based ridesourcing company released a large dataset containing disaggregate trip-level information. In this research, we use this new dataset in tandem with several publicly available data sources to estimate two models: a spatially lagged multivariate count model, which is used to describe how many trips are generated in a specific zone on both weekdays and weekend days; and a fractional split model, which helps us identify the characteristics of zones that attract ridesourcing trips. Our results show spatial dependence in ridesourcing trips among proximally located zones, as well as correlation between weekday and weekend day trips originating in a zone. Another interesting finding is the identification of a possible substitution effect between ridesourcing and transit use for weekday trips. Moreover, our results suggest that different income segments in the population may use ridesourcing for different activity purposes. From a travel behavior researcher perspective, the results in this paper identify aggregate area-level variables impacting ridesourcing, which can guide future efforts to better understand the demand for ridesourcing as well as the demand for autonomous and connected vehicles.

Trends in Taxi Use and the Advent of Ridehailing, 1995–2017: Evidence from the US National Household Travel Survey

“…Young people use ridehailing more than older people; 20-29 and 30-39 year olds use ridehailing far more than 16-19 year olds; 50-59 year olds and all older groups use it less. Table 1 shows that 50-59 year olds use ridehailing at about the same rate as [16][17][18][19] year olds; the model results likely differ because 50-59 year olds do not use ridehailing as much as their higher incomes might predict.…”

“…Some questions can be answered using the origin-destination trip records retained by ridehailing companies themselves. In some markets, these data are public [17,19], and in others, researchers or governments may be able to obtain access (e.g., [22]). However, many interesting questions require information about users' demographics, trip purposes, or the other trips they make, which cannot be obtained from trip records.…”

“…Using detailed trip-level data, Schneider [17] documented the swift rise of ridehailing in New York City; as of February 2017, ridehailing companies serve more trips in New York than taxis do, and the difference is especially pronounced in the outer boroughs [17,18]. Built environment characteristics are also important, with more ridehailing use in denser areas [19].…”

“…Race has been inconsistently associated with ridehailing; Clewlow and Mishra [21] found that African-Americans use ridehailing more than other racial or ethnic groups. Brown [22] found that majority-White areas had higher use than their populations would predict, while Lavieri et al [19] found the opposite. There are mixed results regarding the relationship between income and ridehailing as well.…”

“…This decline was less in cities with higher quality transit. One study found that a higher density of transit stops was associated with increased ridehailing use [22], while another found that higher transit frequency was associated with decreased ridehailing use [19].…”