Proactive crash risk prediction modeling for merging assistance system at interchange merging areas.

Conclusions: The results suggest that the nested logit model has the highest prediction accuracy. It is concluded that the merging speed, driving ability (i.e., lane-keeping instability), and the vehicle type in the target lane affect the crash risk. Finally, the implementation of the proposed prediction model for merging assistance system is designed. The findings from this study can have implications for the design of the merging assistance system for helping drivers make safe merging decisions and thus enhancing the safety of the interchange merging area. PMID: 32154738 [PubMed - as supplied by publisher]
Source: Traffic Injury Prevention - Category: Accident Prevention Authors: Tags: Traffic Inj Prev Source Type: research