The question of whether driver behavior, and speeding in particular, differs based on passenger(s) presence requires the use of large amounts of data, some of which may be difficult to accurately obtain. Traditional methods of obtaining driver behavior information result in datasets that either lack passenger information altogether (i.e., insurance companies using telematics) or rely on rough estimates of passenger age and gender obtained from blurred photos (i.e., naturalistic driving studies like the Second Strategic Highway Research Program). This research project represents a novel, data-driven approach to assessing passenger impact on speeding. Household travel survey demographic information and GPS traces were linked to HERE network speed limit to study the impact of vehicle occupancy on speeding. Survey responses from 11 study areas were cleaned, merged, and ultimately used in developing binomial logistic regression models. Of particular interest were the following driver groups: teenagers, adults driving with child passenger(s), and older drivers. The models suggest that drivers speed less when there is a passenger in the vehicle, particularly adult drivers with a child passenger(s).
- This project was the first of its kind to use household travel survey diary and GPS trace data to study drivers’ speeding behavior with regard to passenger presence and type.
- The project was able to complete two major data linkage tasks within a tight timeframe (approx. 1 year): travel diaries with GPS traces and GPS traces with roadway speed limits.
- This project was completed by a multidisciplinary team: urban planners, civil engineers, and an epidemiologist.
EWD & T2 Products
AVID Slides (pptx): These slides will be included in his transportation planning session slides to show how planning and safety are related and how data traditionally used for a transportation planning purpose were used for innovative research.
Learning Module Materials (pptx): This presentation may be used in an educational setting to introduce students to the project and dataset, and includes instructions for creating a model using R (code available for download below).
R code (zip): This code is available for download and use with the learning module produced by this project, available above.
Student Impact Statement – Rahul Mars (pdf): The student(s) working on this project provided an impact statement describing what the project allowed them to learn/do/practice and how it benefited their education.
2020 TRB Annual Meeting poster presentation (Martin, M., Green, L. L., Shipp, E., Chigoy, B., & Mars, R. (2020, January 13). Vehicle Occupants and Driver Behavior: A Novel Data Approach to Assessing Speeding. Washington, D.C.: Transportation Research Board.) (Accepted)
ITE Western District Meeting poster presentation (Rahul Mars, D. D. (2018). Speeding Behavior Modelling in the Presence of Passengers for Vulnerable User Groups. Keystone, CO: Institute of Transportation Engineers.) (Accepted)
Green, L.L. (2017, May). TxDOT Travel Survey Program Data: Exploring Avenues of Added Value. Presentation at the Transportation Planning Applications Conference, Raleigh, NC. (Published)
The final datasets for this project are located in the Safe-D Collection on the VTTI Dataverse; DOI: 10.15787/VTT1/YRTS1Z.
Research Investigators (PI*)
Start Date: 2017-06-01
End Date: 2018-10-31
Grant Number: 69A3551747115
Total Funding: $149,980
Source Organization: Safe-D National UTC
Project Number: 02-009
Safe-D Theme Areas
Safe-D Application Areas
Driver Factors and Interfaces
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC 20590 United States
Texas A&M University
Texas A&M Transportation Institute
College Station, Texas 77843-3135