Factors Influencing Willingness to Pool in Ride-Hailing Trips

Abstract

In the past decade, transportation network companies (TNCs) such as Uber, Lyft, and Via have established themselves as a viable transportation alternative to other modes. However, the popularity of these services has come with a fair share of criticism for their negative externalities such as increasing vehicle miles traveled and congestion in cities. Pooled ride-hailing trips, in which all or a part of two individual (or group) trips are combined in and served by a single vehicle, have the potential to reduce these externalities. Pooling of rides is an effective solution to reduce congestion and travel cost, but pooled rides still represent a small percentage of the total trips served (and miles driven) by TNCs relative to single-occupancy (and without customer) vehicle miles. Both TNCs and cities alike will benefit from understanding what factors encourage or deter pooling a ride-hailing trip. In this study, newly available Chicago transportation network provider data were explored to identify the extent to which different socioeconomic, spatiotemporal, and trip characteristics affect willingness to pool (WTP) in ride-hailing trips. Multivariate linear regression and machine-learning models were employed to understand and predict WTP based on location, time, and trip factors. The results show intuitive trends, with income level at drop-off and pickup locations and airport trips as the most important predictors of WTP. Results from this study can help TNCs and cities devise strategies that increase pooled ride-hailing, thereby reducing adverse transportation and energy impacts from ride-hailing modes.

Examining Customers’ Critical Acceptance Factors toward Ridepooling Services

“…The majority of the respondents in the sample were students, who already have discounted seasonal tickets for local transportation in the city, explaining why PV is not relevant to them for short and occasional trips. Most recently, Hou et al (31) investigated the willingness to pool in ridehailing trips. They found that the willingness to share rides is primarily a time-cost tradeoff.…”

“…The study of Lavieri and Bhat (30) set another focus and examined willingness to share and investigated the adoption of shared rides. Hou et al (31) investigated the willingness to pool in ridehailing trips with linear regression and machine-learning models. They found that the willingness to share rides is at first a time-cost tradeoff.…”

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

“…Leveraging the same Chicago dataset used in our study, Hou et al and Xu et al took a similar approach to study the ratio of shared trips to total trips between O-D pairs (binned by pickup and drop-off census tracts) as a regression-based ML problem (Hou et al, 2020;Xu et al, 2021). They found that that socio-demographic variables as well as pickup/drop-off in airport census tracts have the highest predictive power, but both reported relatively large unexplained variance and high sensitivity to outlier observations.…”

“…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). The results of these studies reveal key factors affecting WTS, but only focusing on the aggregate level between origin-destination (O-D) pairs of census tracts.…”

Quantifying Environmental Benefits of Ridesplitting based on Observed Data from Ridesourcing Services

“…Ridesplitting is a ridesourcing service that involves volunteering to share a ridesourcing ride with someone at a reduced cost, and the driver in a ridesplitting service is for-hire (instead of a traveler) (21). The current literature on ridesplitting mainly focuses on the following three distinctive directions: (a) analyzing the characteristics of ridesplitting through the method of comparison (20,21); (b) exploring the choice behavior and willingness of adopting ridesplitting (22)(23)(24)(25); and (c) discussing the potentiality of ridesplitting (26,27).…”