Identifying characteristics that impact motor carrier safety using Bayesian networks. (July 2019) - Hwang, Steven; Boyle, Linda Ng; Banerjee, Ashis G.
Highlights: Bayesian networks are used to learn the relationships on carrier safety rating. Data from a motor carrier database (MCMIS) is used to examine safety ratings for interstate motor carriers. The relationships are observed to be complex and non-linear. Traffic violations had the strongest impact on safety ratings. Abstract: Problem Statement: In the U.S., a safety rating is assigned to each motor carrier based on data obtained from the Motor Carrier Management Information System (MCMIS) and an on-site investigation. While researchers have identified variables associated with the safety ratings, the specific direction of the relationships are not necessarily clear. Objective: The objective of this study is to identify those relationships involved in the safety ratings of interstate motor carriers, the largest users of the U.S. transportation network. Method: Bayesian networks are used to learn these relationships from data obtained from MCMIS for a 6-year period (2007–2012). Results: Our study shows that safety rating assignment is a complex process with only a subset of the variables having statistically significant relationship with safety rating. They include driver out-of-service violations, weight violations, traffic violations, fleet size, total employed drivers, and passenger & general carrier indicators. Application: The findings have both immediate implications and long term benefits. The immediate implications relate to better identification of unsafe motor carriers, and the long term benefits pertain to policies and crash countermeasures that can enhance carrier safety.