Pedestrian Crossing Safety Model for Unsignalized Three-Leg Intersection Based on User Perception Data - Wildan Reza Pahelvi; Aine Kusumawati; Taufiq Nugroho (Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Gadjah Mada, 2026)
Recent statistics show an upward trend in road crashes in Indonesia, with pedestrians identified as the most vulnerable group of road users, thus addressing this issue requires evidence-based tools to support decision-making for pedestrian safety improvement. This study develops a perception-based Pedestrian Intersection Safety Index (PedISI) model using multiple linear regression to estimate safety levels at three-leg unsignalized intersections based on traffic and geometric characteristics. Unlike previous studies that rely on crash or behavioral data, this research employs user perception data, offering a lower-risk and more flexible means of capturing pedestrians’ subjective evaluations of safety. The study was conducted at 15 unsignalized three legged intersections comprising 42 observation points in Cimahi City, West Java, Indonesia. Data were collected on traffic volume, 85th percentile vehicle speed, lane width, and median width, alongside respondents’ safety ratings derived from on-site video-based surveys. The results indicate that traffic volume, 85th percentile speed, lane width, and median width significantly influence pedestrian perceptions of crossing safety. Application of the developed regression model shows that the average perception-based pedestrian safety index at these intersections is 2.96. Sensitivity analysis further reveals that reductions in vehicle speed yield the greatest improvements in perceived safety, suggesting that speed management should be prioritized in pedestrian safety interventions. While geometric factors such as lane and median width also play a role, these must be optimized within design standards to balance safety and traffic performance. The study highlights the potential of perception-based modeling as a complementary approach for pedestrian safety assessment in data-limited urban environments and provides a framework for future applications incorporating diverse environmental and behavioral contexts.