Identifying and Intervening with High-Risk Drivers

Research suggests that many dangerous drivers are simply not aware of: (1) the fact that they are driving unsafely; (2) the risk associated with their dangerous driving; and (3) how far out of the norm their dangerous driving is. Automated notifications have also been shown to increase desirable behavior and reduce undesirable behavior across many contexts, including driving. For example, in a study of teenage drivers, alerting both the teenagers and the parents of teenage drivers of risky behavior occurring in their cars can reduce risky driving. The District does something similar, by using Automated Traffic Enforcement (ATE) systems to enforce traffic safety and regulations for red light and speeding violations. ATE systems do this by automatically taking photographs of the rear of the vehicle and its license plate if the driver violates regulations, then sends a citation and fine to the registered vehicle owner’s address. However, these are reactive measures towards reducing risky driving behavior. Our study proposes to build upon this system further by targeting proactive measures to risky drivers to reduce crashes. District of Columbia Department of Transportation (DDOT) and The Lab will collaborate to design the modeling and intervention for this project. There are two key components to the intervention: (1) analysis of data from the District’s ATE systems and MPD crash data, to predict a driver’s likelihood of being involved in a crash; and (2) proactive intervention(s) to reduce risky behavior for drivers likely to be involved in a crash.

Language

• English

Project

• Status: Active
• Funding: $95200
• Sponsor Organizations:

  District Department of Transportation

  250 M Street, SE
  Washington, DC  United States  20003
• Project Managers:

  Bailey, Linda

• Performing Organizations:

  The Lab @ DC, Office of the City Administrator

  1350 Pennsylvania Avenue, NW
  Suite 533
  Washington, DC  United States  20004
• Start Date: 20211001
• Expected Completion Date: 20231031
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Automated enforcement; Crash risk forecasting; High risk drivers
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01781557
• Record Type: Research project
• Source Agency: District Department of Transportation
• Files: RIP, STATEDOT
• Created Date: Sep 13 2021 12:42PM