Cluster-based lognormal distribution model for accident duration

Abstract

This study develops a cluster-based lognormal distribution model for the purpose of predicting accident duration. With Maryland I-95 freeway traffic accident data collected in 2010 and 2011, this study first uses a decision tree approach to split the entire sample data into three clusters which are then treated as additional variables in modelling accident duration. The results show that seven explanatory variables and cluster variables significantly affect the mean accident duration. With the cluster-based lognormal distribution model, the mean and the probability of an accident duration being unacceptable can be predicted from the base accident information. Such predictions can be utilised as a basis for making rational diversion in the event of an accident, which will help mitigate traffic congestion and improve travel time reliability.