Traffic Conflict-based Road Safety Analysis: Data Requirements and Evaluation of Safety Countermeasures

Abstract

Driven by the vision to eliminate road fatalities, Vision Zero initiatives have been widely adopted by many cities around the world, with significant investments of resources in various safety programs and countermeasures. Conflict-based traffic safety analysis is a burgeoning field, but many studies have failed to address the important question of how much data should be collected to make credible safety-related inferences and how the effectiveness of safety countermeasures could be quantified using conflict data. In this thesis research, a comprehensive framework based on power analysis is first proposed to determine the minimum sample size required for a conflict analysis study. Two case studies are investigated to illustrate how power analysis can be conducted for different types of conflict analysis study specifications, using the corresponding statistical tests. Power analysis is a well-established statistical tool used in many different scientific fields for determining an appropriate sample size. The power analysis exploits the significance criterion (α), power (1-β), and effect size (ES) such that the sample size is large enough to protect investigators from Type I and Type II errors to conventional thresholds of 95% and 80%, respectively. The minimum sample size is also the optimal sample size because it minimizes the observation period while maintaining acceptable protection from Type I and Type II errors. A case study is then conducted to assess the safety benefits of three Vision Zero safety countermeasures using data from the City of Toronto. By applying a combination of case-control and cross-sectional studies, the research attempts to quantify the safety effects of three commonly applied Vision Zero countermeasures, namely, Leading Pedestrian Interval (LPI), No Right Turn On Red (NRTOR), and installation of a dedicated Bicycle Lane (BL). The traffic interactions between vehicles and vulnerable road users (VRUs) were extracted using a video data processing platform and two surrogate measures of safety, including post-encroachment time (PET) and conflict speed, were obtained and then used to classify the conflict severity into different levels. A comparative analysis using mixed-effects negative binomial regression was conducted to quantify the impacts of different treatments on the frequency of traffic conflicts under specific road weather and traffic conditions. The results show that these three types of traffic countermeasures can effectively reduce the frequency of high-risk and moderate-risk traffic conflicts, moderated by various, traffic exposure, weather and environmental conditions, and accessible pedestrian signals (APS). These findings could help road safety engineers and decision makers make better informed decisions on their road safety initiatives and projects.