🤖 AI Summary
Traditional approaches struggle to differentiate between road segments and intersections in terms of their distinct crash exposure characteristics and connectivity patterns. This study proposes a Bayesian negative binomial node–edge model that, for the first time, jointly models intersections and segments as heterogeneous network units, explicitly capturing their divergent crash mechanisms. Integrating variables such as road network topology, land use, and signal control, the framework establishes an interpretable baseline for urban crash analysis. Empirical results indicate that four-legged or higher-order intersections and higher approach speeds significantly increase expected crash frequency. Among segment-level factors, road class and signalization exhibit the strongest associations with crash occurrence, whereas certain pavement types demonstrate limited predictive power.
📝 Abstract
Introduction: Crash counts on road segments and intersections exhibit differ- ent exposure and connectivity patterns that conventional analyses may obscure. Methodology: A Bayesian negative binomial node edge model was fitted to 8,169 road segments and 8,398 intersections in six central districts of Bogotá. Separate predictors represented road hierarchy, pavement, speed, signalization, intersection configuration, and land-use treatment. Model performance was examined through pre- dictive summaries and spatial diagnostics, while computational details are reported in the appendix. Results: Intersections with at least four incident segments and higher maximum incident speeds had higher expected crash counts. Land use treatment and signalized access intensity also showed posterior associations. Road hierarchy and signalization were the clearest segment-level factors; however, this component had weak raw scale predictive performance and numerical uncertainty for some pavement categories. Conclusion: Treating intersections and segments as distinct network elements provides an interpretable baseline for urban crash analysis, but the segment results require cautious interpretation and motivate spatially structured extensions.