Modelling heavy vehicle car‐following behaviour in congested traffic conditions |
| |
| Authors: | Kayvan Aghabayk Majid Sarvi Nafiseh Forouzideh William Young |
| |
| Affiliation: | Institute of Transport Studies, Department of Civil Engineering, Monash University, Victoria, Australia |
| |
| Abstract: | This study develops a car‐following model in which heavy vehicle behaviour is predicted separately from passenger car. Heavy vehicles have different characteristics and manoeuvrability compared with passenger cars. These differences could create problems in freeway operations and safety under congested traffic conditions (level of service E and F) particularly when there is high proportion of heavy vehicles. With increasing numbers of heavy vehicles in the traffic stream, model estimates of the traffic flow could be degrades because existing car‐following models do not differentiate between these vehicles and passenger cars. This study highlighted some of the differences in car‐following behaviour of heavy vehicle and passenger drivers and developed a model considering heavy vehicles. In this model, the local linear model tree approach was used to incorporate human perceptual imperfections into a car‐following model. Three different real world data sets from a stretch of freeway in USA were used in this study. Two of them were used for the training and testing of the model, and one of them was used for evaluation purpose. The performance of the model was compared with a number of existing car‐following models. The results showed that the model, which considers the heavy vehicle type, could predict car‐following behaviour of drivers better than the existing models. Copyright © 2013 John Wiley & Sons, Ltd. |
| |
| Keywords: | traffic flow car‐following truck heavy vehicles driving behaviour congested traffic condition artificial intelligence local linear model tree (LOLIMOT) neuro‐fuzzy |
|
|
