Hybrid CNN LSTM Deep Learning Model for Spatiotemporal Traffic Accident Risk Prediction in Medan City with Localized Urban Traffic Patterns

Abstract

Traffic accidents remain a persistent challenge for urban safety, particularly in Medan City, Indonesia, where heterogeneous road networks, mixed traffic flows, and varying environmental conditions complicate risk prediction. Existing statistical and classical machine learning methods often fail to capture the nonlinear spatial–temporal dependencies inherent in such environments, creating a gap in accurate, context-specific accident risk forecasting for developing countries. This study addresses this gap by proposing a Hybrid CNN–LSTM Deep Learning Model that jointly learns spatial features from georeferenced accident maps, road networks, and traffic density heatmaps, and temporal dependencies from historical accident logs, GPS traces, meteorological data, and road surface conditions. Unlike prior models, the proposed framework is explicitly tailored to localized urban traffic patterns in Medan, enabling robust performance under heterogeneous and data-imbalanced conditions. Data preprocessing included cleaning, normalization, and categorical encoding, followed by model training with an Adam optimizer and tuned hyperparameters. Experimental evaluation against baseline models—pure CNN, pure LSTM, and Random Forest—demonstrated statistically significant improvements (p < 0.05), with the hybrid CNN–LSTM achieving an accuracy of 96.8% (95% CI: 96.5–97.1%), precision of 96.5%, recall of 96.7%, and F1-score of 96.6%, outperforming baselines by up to 5% in predictive accuracy. The model effectively identified high-risk spatial clusters and peak accident periods, offering actionable intelligence for targeted safety interventions. These findings highlight the model’s potential for integration into intelligent transportation systems to support real-time monitoring, proactive policymaking, and enhanced urban traffic safety management.