T-SCORE Project M2: Multi-Agent Simulation: A Model of Ride-Hailing Driver Participation

Tenenboim, Einat; Xu, Yufei; Macfarlane, Gregory S; Erhardt, Gregory D; Peeta, Srinivas; Georgia Institute of Technology. School of Civil and Environmental Engineering; University of Kentucky; Brigham Young University

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T-SCORE Project M2: Multi-Agent Simulation: A Model of Ride-Hailing Driver Participation

2023-03-01
By Tenenboim, Einat ; Xu, Yufei ; Macfarlane, Gregory S ; ...

[PDF-869.43 KB]

English

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Alternative Title:

Multi-Agent Simulation [Title from Cover]
Creators:

Tenenboim, Einat ; Xu, Yufei ; Macfarlane, Gregory S ; Erhardt, Gregory D ; Peeta, Srinivas

Tenenboim, Einat ; Xu, Yufei ; Macfarlane, Gregory S ; Erhardt, Gregory D ; Peeta, Srinivas Less -
Corporate Creators:

Georgia Institute of Technology. School of Civil and Environmental Engineering ; University of Kentucky ; Brigham Young University

Georgia Institute of Technology. School of Civil and Environmental Engineering ; University of Kentucky ; Brigham Young University Less -
Corporate Contributors:

Transit – Serving Communities Optimally, Responsively, and Efficiently (T-SCORE) Center [UTC] ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

Transit – Serving Communities Optimally, Responsively, and Efficiently (T-SCORE) Center [UTC] ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Driving Behavior Public Participation Ride-hailing Ridership Ridesourcing Shared Mobility Simulation Transportation Network Companies
Publication/ Report Number:

TSCORE-M2
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report Aug 2020-Jan 2023
Corporate Publisher:

Transit – Serving Communities Optimally, Responsively, and Efficiently (T-SCORE) Center [UTC]
Abstract:

The upsurge in ride-hailing services and their rapidly growing demand have recently ignited considerable research interest. However, present research on ride-hailing is mainly focused on the demand for trips, while less attention is given to the supply enabling them, i.e., the drivers. To obtain a comprehensive understanding of ride-hailing demand, a good understanding of driver participation should be achieved. The present study aims to obtain a realistic representation of driver participation that will later be embedded in a multi-agent simulation. To date, ride-hailing research has been hampered by a lack of data, yet here we leverage a unique dataset of Lyft and Uber vehicle traces collected in San Francisco. Using a choice modeling approach, we model driver participation within four steps, estimating: (1) number of shifts on the working day, (2) shift duration, (3) shift start time, and (4) shift start location. Driver type (full-time, part-time, occasional) was found to be a strong determinant of driver participation. Full-time, but not occasional, drivers were found to work both more shifts and longer shifts. A higher number of shifts started in the downtown area (where population and employment are higher) and in higher income areas, while less shifts started in areas of high student density. Based on these modeling results, a driver fleet was generated for a typical weekday. This is one of the first studies to estimate ride-hailing driver behavior, offering insights that can potentially support transit agencies in effectively planning and regulating multi-modal transportation.
Format:

PDF
Funding:

USDOT Grant 69A3552047141
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:4adac163fd9a69cc597d9f4b98efa5e1ec9eff71175eddfaa07812c5e25146ae
Download URL:

https://rosap.ntl.bts.gov/view/dot/72993/dot_72993_DS1.pdf
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T-SCORE Project M2: Multi-Agent Simulation: A Model of Ride-Hailing Driver Participation

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