Abstract
Ride-hailing data is sparingly available throughout the U.S., which limits researchers’ understanding of the mode. Chicago is one of a few cities that have mandated ride-hailing companies to submit detailed trip data to their local transportation agency. The dataset is one of the few to contain trip-level attributes such as fare, travel time, and trip length. Most research using the Chicago dataset has focused on understanding why people use ride-hailing. This study focuses on why ride-hailing passengers choose shared over private trips and what influences the shared trips to be matched. Trips to/from airports are less likely to be shared. Trips to/from low-income areas are more likely to be shared. Longer shared trips are more likely to be matched, shared trips to/from dense areas are more likely to be matched, and shared trips between areas with a high number of shared trips are more likely to be matched. Matching an additional shared trip with another adds approximately 4 min to a trip. Ride-hailing users’ value of time is found to be $48.23 per hour. Understanding travel behavior is important for all modes of transportation including ride-hailing. The results of this paper can be applied to guide polices aiming to promote more sustainable transportation modes.
Original language | English |
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Pages (from-to) | 293-306 |
Number of pages | 14 |
Journal | Transportation Research Record |
Volume | 2678 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2024 |
Bibliographical note
Publisher Copyright:© National Academy of Sciences: Transportation Research Board 2023.
Keywords
- behavior analysis
- innovative public transportation services and technologies
- planning and analysis
- public transportation
- ride-hailing data
- ride-hailing/ridesharing
- transportation network companies
ASJC Scopus subject areas
- Civil and Structural Engineering
- Mechanical Engineering