Apriori-based text mining method for the advancement of the transportation management plan in expressway work zones

This study contributes to knowledge by advancing the transportation management plan (TMP) development efforts for expressway work zones. Using text mining techniques to a large-scale transportation data set that contains descriptively narrated texts, this research analyzes the association between words related to the type of work being performed and the type of lane closure in expressway work zone areas. It found that recurrent everyday tasks and bridge repair works tend to cause shoulder lane closure, while works—such as tunnel repair, night work, pavement, median barrier, road surface repair, and line marking—are more associated with main lane closure. Moreover, the findings further clarify the characteristic patterns shared between the number of closed lanes, and the respective lane position in two- and three-lane expressways. These offer significant insights into the decision-making process for the development of work zone TMPs, which can further be integrated into the various components of TMP to make the plan more effective and, at the same time, ensure an efficient throughput flow throughout the work zone, reduced congestion, and improved safety.