Analyzing single-vehicle and multi-vehicle freeway crashes with unobserved heterogeneity

Abstract

Freeways in China have developed rapidly in recent years. The large traffic volumes and high travel speeds have created a serious safety problem that is of growing concern, however. Accurate identification of factors influencing crashes is a prerequisite for implementing countermeasures, but unobserved heterogeneity in crash data can lead to erroneous inferences. To identify key factors influencing crash occurrence, this study used two data preparation and modeling approaches to account for unobserved heterogeneity. First, freeway traffic crashes were divided into single-vehicle (SV) and multi-vehicle (MV) crashes because of their different mechanisms of occurrence. Second, random parameter modeling and finite mixture modeling were used, and were compared with regard to their ability to account for unobserved heterogeneity. The results indicated that the finite mixture negative binomial regression model with two components (FMNB-2) produced a better goodness-of-fit and parameter estimation. Results of the FMNB-2 SV and MV models’ classification of crashes into two homogeneous subgroups showed that for both SV and MV, crashes in Component 1 were most affected by roadway geometric features, while in Component 2, crashes were more strongly associated with traffic operational conditions. These findings will help traffic managers implement more targeted countermeasures for freeway safety improvement.

Keywords:

• freeway safety
• unobserved heterogeneity
• finite mixture model
• crash type analysis

Declaration of interest statement

The study was approved by the Ethics Committee of Tongji University. All authors, Mingjie Feng, Xuesong Wang, and Yan li, declare that they have no conflict of interest or financial conflicts to disclose.

Additional information

Funding

This work was supported by the International Science & Technology Cooperation Program of China under Grant 2017YFE0134500; and National Natural Science Foundation of China under Grant 51878498.