Assessing Accident Risk using Ordinal Regression and Multinomial Logistic Regression Data Generation

Robust and accurate modeling of motor vehicle accident and injury severities have significant impact on transportation safety and economy. The capability to assess accident risk based on external driving conditions (e.g., weather, road condition, etc.) and driver behavior and characteristics can reduce accident occurrences by alerting drivers to alleviated risk. In this paper, we propose a novel accident risk assessment framework driven by ordinal regression. One challenge of the risk assessment problem is that non-accident data are not collected by any agency in their study of transportation safety. Hence, we also propose a realistic negative data generation scheme based on feature weighs derived from multinomial logistic regression to overcome this challenge. Experimental results on two different real-world datasets from the US National Highway Traffic Safety Administration and UK Transport for Greater Manchester are used to demonstrate the feasibility and robustness of our proposed ordinal regression framework. Performance on four ordinal regression algorithms, namely: logistic all-threshold, logistic immediate-threshold, ordinal ridge, and least absolute deviations are compared. In addition, for US dataset, we investigate the effect of random oversampling and undersampling on the proposed risk assessment framework. We empirically show that bagging with random oversampling using logistic all-threshold ordinal regression method has the best prediction performance among ordinal regression models.