Deciphering Car Crash Dynamics in Greater Melbourne: a Multi-Model Machine Learning and Geospatial Analysis

In the continually evolving landscape of data-driven methodologies addressing car crash patterns, a holistic analysis remains critical to decode the complex nuances of this phenomenon. This study bridges this knowledge gap with a robust examination of car crash occurrence dynamics and the influencing variables in the Greater Melbourne area, Australia. We employed a comprehensive multi-model machine learning and geospatial analytics approach, unveiling the complicated interactions intrinsic to vehicular incidents. By harnessing Random Forest with SHAP (Shapley Additive Explanations), GLR (Generalized Linear Regression), and GWR (Geographically Weighted Regression), our research not only highlighted pivotal contributing elements but also enriched our findings by capturing often overlooked complexities. Using the Random Forest model, essential factors were emphasized, and with the aid of SHAP, we accessed the interaction of these factors. To complement our methodology, we incorporated hexagonalized geographic units, refining the granularity of crash density evaluations. In our multi-model study of car crash dynamics in Greater Melbourne, road geometry emerged as a key factor, with intersections showing a significant positive correlation with crashes. The average land surface temperature had variable significance across scales. Socio-economically, regions with a higher proportion of childless populations were identified as more prone to accidents. Public transit usage displayed a strong positive association with crashes, especially in densely populated areas. The convergence of insights from both Generalized Linear Regression and Random Forest's SHAP values offered a comprehensive understanding of underlying patterns, pinpointing high-risk zones and influential determinants. These findings offer pivotal insights for targeted safety interventions in Greater Melbourne, Australia.