An AI-Based Framework for Predicting Accident Rates in Freight Transportation along Urban Logistics Corridors

Freight transportation along urban logistics corridors is exposed to many risk factors that lead to accident events which have a large economic, operational, social and safety impact. The urban mobility systems are growing in complexity and so there is the need for a predictive approach that can accommodate a wide range of unknown variables when predicting accidents in freight transport. To this end, we propose a multidimensional artificial intelligence framework for accident prediction in freight transportation with a human, operational, vehicular, environmental, technology, and infrastructure-based approach. We have developed a process of identifying and operationalizing variables, mathematical modeling, and machine learning to predict accidents from the comprehensive perspective to achieve this objective. To evaluate the technical feasibility of the proposed framework, we conducted a proof of concept implementation on a synthetic dataset of 10,000 simulated freight transportation operations. We created an accident occurrence with a logistic model which includes the risk factors and interaction effects. Two machine learning algorithms, Logistic Regression and Random Forest were trained and evaluated using the usual classification parameters: Accuracy, Precision, Recall, F1-score and AUC-ROC. The results showed good predictive power in the simulation environment with Logistic Regression with an AUC-ROC of 0.989 and Random Forest with an AUC-ROC of 0.940. We also explored the driving hours, digitalization level and vehicle condition in our multidimensional model. Though these findings are not empirical proof-of-concept in practice, they highlight the internal consistency and technical feasibility of how the proposed approach works. Such research will establish the basis for future work that integrates artificial intelligence, transportation safety and Logistics 4.0 principles with freight transportation data.