Identifying Harsh Driving Behaviors and Contributing Factors Using Telematics Data: A Case Study in Oakland and Fresno, California
Project Description
Despite extensive safety countermeasures, vulnerable road users continue to face significant risks on urban roadways, resulting in a substantial loss of life. Safety frameworks like Vision Zero and the Safe System Approach call for proactive solutions that address these dangers before severe crashes occur. This proactive approach can be powered by surrogate safety measures, which use data on near-misses and risky behaviors to identify hazards. Harsh driving events—such as harsh braking or acceleration—serve as excellent indicators of elevated crash risk. These behaviors are influenced by a combination of factors, including roadway design, traffic flow, and the complex, unpredictable interactions between vehicles and other road users in dense urban environments. This study leverages high-resolution telematics data from the Cities of Oakland and Fresno to investigate the differential impacts of harsh driving behaviors on road safety.
We will construct and compare crash hotspots (e.g., high injury network) and harsh driving behavior hotspots to examine which types of harsh driving behaviors most strongly align with crashes involving vulnerable road users and latent crash risks. Additionally, by using statistical methods and explainable artificial intelligence techniques, we will analyze roadway characteristics (e.g., intersections, lane curvature, or slope), as well as traffic flow and surrounding conditions, to determine whether specific features are associated with increased prevalence of harsh driving events that, in turn, elevate crash risk.
By integrating the spatiotemporal patterns of crashes and telematics-based behavioral measures, along with infrastructure characteristics, this study aims to better understand how risky driving patterns contribute to vulnerable road user safety outcomes. The findings will provide actionable insights for prioritizing enforcement such as speed camera deployment, designing infrastructure countermeasures, and developing data-driven, proactive strategies to support safe transportation.
Outputs
In addition to a Final Research Report, this project will produce a policy brief providing data-driven recommendations related to risky driving behaviors to enhance road safety, particularly for vulnerable road users. The project also produces one or more peer-reviewed journal articles, along with at least one conference presentation, with insights into analyzing harsh driving behaviors using telematics data and contribute to discussions on effective countermeasures in urban areas.
Outcomes/Impacts
The project will identify high-risk areas for vulnerable road users before crashes occur, enabling proactive interventions. By integrating harsh driving and crash hotspot analyses with road geometry, traffic conditions, and neighborhood characteristics, the project will inform countermeasure placement and infrastructure improvements to reduce risky driving behaviors and increase vulnerable road user awareness. The findings will help prioritize safety efforts, promote safer transportation, and demonstrate how telematics data can support evidence-based proactive road safety planning. In the long term, the project will contribute to reducing fatal and serious injury crashes involving vehicles and other road users.
Dates
12/1/2025 to 11/30/2026
Universities
University of California Berkeley
Principal Investigator
Julia Griswold
juliagris@berkeley.edu
https://orcid.org/0000-0002-1125-3316
Project Partners
None
Research Project Funding
Federal: $113,400
Contract Number
69A3552348336
Project Number
25UCB01
Research Priority
Promoting Safety