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SpatioConvGRU-Net for Short-Term Traffic Crash Frequency Prediction in Bogotá: A Macroscopic Spatiotemporal Deep Learning Approach with Urban Factors
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SpatioConvGRU-Net for Short-Term Traffic Crash Frequency Prediction in Bogotá: A Macroscopic Spatiotemporal Deep Learning Approach with Urban Factors
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SpatioConvGRU-Net for Short-Term Traffic Crash Frequency Prediction in Bogotá: A Macroscopic Spatiotemporal Deep Learning Approach with Urban Factors
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Journal Article
SpatioConvGRU-Net for Short-Term Traffic Crash Frequency Prediction in Bogotá: A Macroscopic Spatiotemporal Deep Learning Approach with Urban Factors
2025
Overview
Traffic crashes represent a major challenge for road safety, public health, and mobility management in complex urban environments, particularly in metropolitan areas characterized by intense traffic flows, high population density, and strong commuter dynamics. The development of short-term traffic crash prediction models represents a fundamental line of analysis in road safety research within the scientific community. Among these efforts, macro-level modeling plays a key role by enabling the analysis of the spatiotemporal relationships between diverse factors at an aggregated zonal scale. However, in cities like Bogotá, predicting short-term traffic crashes remains challenging due to the complexity of these spatiotemporal dynamics, underscoring the need for models that more effectively integrate spatial and temporal data. This paper presents a strategy based on deep learning techniques to predict short-term spatiotemporal traffic crashes in Bogotá using 2019 data on socioeconomic, land use, mobility, weather, lighting, and crash records across TMAU and TAZ zones. The results showed that the strategy performed with a model called SpatioConvGru-Net with top performance at the TMAU level, achieving R2 = 0.983, MSE = 0.017, and MAPE = 5.5%. Its hybrid design captured spatiotemporal patterns better than CNN, LSTM, and others. Performance improved at the TAZ level using transfer learning.
