Cluster-based lognormal distribution model for accident duration
Author(s)
Weng, Jinxian
Qiao, Wenxin
Qu, Xiaobo
Yan, Xuedong
Griffith University Author(s)
Year published
2015
Metadata
Show full item recordAbstract
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 ...
View more >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.
View less >
View more >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.
View less >
Journal Title
Transportmetrica A: Transport Science
Volume
11
Issue
4
Subject
Civil engineering
Transport planning
Transportation, logistics and supply chains