Congratulations to Dr. Amin Shaer for “satisfying the requirements for the award of the degree of Doctor of Philosophy at the University of Sydney.”
Thesis Title: Detecting Dangerous Driving
Lead Supervisor: Professor David Levinson.
Abstract:
Risky driving behaviors contribute to road crashes, which impose major social and economic burdens. To tackle this phenomenon, this thesis develops two main ideas. First, it examines what factors influence the choice to drive after visiting alcohol-serving establishments (ASEs), where driving may be riskier due to impairment, late-night travel, fatigue, and reduced attention. This then motivates the rest of the thesis, which uses computer vision to detect and measure dangerous and unusual driving behaviors from traffic-camera footage, linking this early-stage risk context to objective indicators of near-crashes and crash risk.
In this thesis, we begin by examining the factors influencing the choice to drive from alcohol-serving establishments (ASEs) using the Victorian Integrated Survey of Travel and Activity (VISTA) dataset. The thesis then shows how computer vision and existing roadside traffic cameras can be used to: (1) detect dangerous driving behaviors such as speeding, short headway, lane violations, and aggressive driving; (2) compare road safety indicators under different weather conditions; and (3) detect unusual driving behaviors such as slipping, running off the road, passing on the shoulder, and abrupt stopping.
The results of the first part of the thesis show that people tended to drive for longer ASEs and walk for shorter ones. Going home made driving more likely, while going to other social activities did not show the same pattern. Men were more likely than women to drive after visiting ASEs. In addition, short travel distances (<1 km) and 3–4 hour activity durations were associated with a greater chance of switching from driving to other travel modes for ASE trips.
With regard to the application of computer vision, first, data extracted from 258 hours of traffic-camera footage in Minnesota are combined with crash records from 2016–2022. The relationship between dangerous driving behavior indicators (DDBIs) and the number of events where time-to-collision (TTC) falls below two seconds (NTTC2) is examined using an Ordinary Least Squares model. A Negative Binomial Regression model then links NTTC2 to crash frequency, and Structural Equation Modeling is used to describe the broader links between driving behavior, near-crash risk, and crashes. Results show that short headway, speeding, and aggressive driving increase NTTC2, and higher NTTC2 is associated with more crashes.
Second, surrogate safety measures (modified TTC, short headway, and lane violations) are compared across weather conditions using 268 hours of traffic-camera footage collected in Minnesota. To better capture changing road-surface conditions, the road–tire friction coefficient is incorporated into the TTC and safe-gap calculations, providing a more realistic safety assessment for dry, wet, and snowy roads. The findings provide consistent evidence that adverse weather increases the risk of surrogate crashes, helping to clarify previously mixed results in the literature.
Third, this thesis develops a heuristic, trajectory-based algorithm to automatically detect unusual driving behaviors using vehicle trajectories extracted from traffic-camera video. The algorithm is applied to 360 hours of footage from nine highway segments in Minnesota under clear, rainy, and snowy conditions, covering 1,120,751 vehicles. Fifteen types of UDBs are identified, including running off the road, slipping in snow, abrupt pull-overs, and police-related stops. The results confirm that the proposed algorithm can effectively detect unusual driving behaviors from large-scale traffic-camera data.
By analyzing road traffic video footage, we demonstrated the potential of computer vision to detect dangerous and unusual driving behavior. Our findings provide a practical foundation for real-time traffic surveillance and proactive road safety monitoring.
The first publications from the Dissertation are:
SHAER, A., FIELBAUM, A., LEVINSON, D. (2026) Detecting Dangerous Driving Via Computer Vision: Linking Video-Based Indicators To Road Crashes. Journal of Transportation Safety and Security. [doi]
SHAER, A., FIELBAUM, A., LEVINSON, D. (2024) Choosing To Drive From Alcohol Serving Establishments. Traffic Injury Prevention. 25(8), 1013-1022. [doi]