Reducing ridesourcing empty vehicle travel with future travel demand prediction

Kontou, Eleftheria; Garikapati, Venu; Hou, Yi

doi:10.1016/j.trc.2020.102826

Reducing ridesourcing empty vehicle travel with future travel demand prediction

Journal Article · Thu Oct 22 00:00:00 EDT 2020 · Transportation Research Part C: Emerging Technologies

DOI:https://doi.org/10.1016/j.trc.2020.102826· OSTI ID:1755752

Kontou, Eleftheria ^[1]; ^[2]; ^[2]

Univ. of Illinois at Urbana-Champaign, IL (United States)
National Renewable Energy Lab. (NREL), Golden, CO (United States)

Ridesourcing services provide alternative mobility options in several cities. Their market share has grown exponentially due to the convenience they provide. The use of such services may be associated with car-light or car-free lifestyles. However, there are growing concerns regarding their impact on urban transportation operations performance due to empty, unproductive miles driven without a passenger (commonly referred to as deadheading). This paper is motivated by the potential to reduce deadhead mileage of ridesourcing trips by providing drivers with information on future ridesourcing trip demand. Future demand information enables the driver to wait in place for the next rider’s request without cruising around and contributing to congestion. A machine learning model is employed to predict hourly and 10-minute future interval travel demand for ridesourcing at a given location. Using future demand information, we propose algorithms to (i) assign drivers to act on received demand information by waiting in place for the next rider, and (ii) match these drivers with riders to minimize deadheading distance. Real-world data from ridesourcing providers in Austin, TX (RideAustin) and Chengdu, China (DiDi Chuxing) are leveraged. Results show that this process achieves 68%–82% and 53%–60% reduction of trip-level deadheading miles for the RideAustin and DiDi Chuxing sample operations respectively, under the assumption of unconstrained availability of short-term parking. Deadheading savings increase slightly as the maximum tolerable waiting time for the driver increases. Further, it is observed that significant deadhead savings per trip are possible, even when a small percent of the ridesourcing driver pool is provided with future ridesourcing demand information.

View Accepted Manuscript (DOE)

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE; USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office

Grant/Contract Number:: AC36-08GO28308

OSTI ID:: 1755752

Alternate ID(s):: OSTI ID: 1809698

Report Number(s):: NREL-JA--5400-77806; MainId:30721; UUID:3b96b497-69e3-4f60-8ee2-8397e5a524aa; MainAdminID:19095

Journal Information:: Transportation Research Part C: Emerging Technologies, Journal Name: Transportation Research Part C: Emerging Technologies Vol. 121; ISSN 0968-090X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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