Crash hotspot identification using large-scale trajectory data: Insights from surrogate safety measures

Road crashes remain a major global concern, underscoring the importance of accurately identifying crash hotspots to support proactive safety management. The objective of this study is to examine the use of large-scale trajectories for crash hotspot identification. Five years of crash data and trajectory records totaling 36,531,563 observations from ten cities in China were analyzed. Four kinematic surrogate safety measures (SSMs), acceleration, deceleration, yaw rate, and jerk, were extracted from trajectories to generate trajectory-based hotspots using Kernel Density Estimation (KDE). The spatial consistency between trajectory-based hotspots and crash-derived hotspots was systematically evaluated to assess the effectiveness of different SSMs. The results show that trajectory-based hotspots substantially overlap with crash hotspots, demonstrating the feasibility of using trajectory-derived SSMs for hotspot identification. Among the evaluated measures, jerk consistently achieved the highest prediction accuracy and precision across cities. The contribution of this study is that it develops and empirically validates a trajectory-based crash hotspot identification framework and provides a systematic multi-city comparison of kinematic SSMs for hotspot identification. The findings provide practical implications for supporting proactive safety screening and informed decision-making in data-driven traffic safety management.