Analysis of Factors Affecting the Over-Representation of Sequential Crashes in Freeway Tunnels: Using Rule-Based Data Mining Method

Abstract

The paper provides an empirical analysis of road/tunnel design, traffic volume, and environmental factors associated with the increased likelihood of sequential crashes in freeway tunnels. The association rule mining and decision tree methods are employed since both of them are capable of identifying complicated interactions among variables and expressing them in the form of rules. Results show that tunnel length, traffic congestion, time of day, season, and vehicle type are the significant factors influencing the likelihood of sequential crashes in freeway tunnels. More importantly, association rule mining and decision tree analysis reveal that a combination of road/tunnel design, traffic, and environmental factors produces even a higher likelihood of sequential crashes, leading to a series of hazardous situations. For example, when factors including long tunnel and grade ≤ 2%, fourth level, and winter are combined, the proportion of sequential crashes is more than twice the average proportion of sequential crashes in the complete tunnel crash database. Traffic safety management should pay more attention to monitoring these hazardous situations which are more likely to be linked to sequential crashes.