Modelling heavy vehicle car-following in congested traffic conditions

Heavy vehicles and passenger cars differ in their manoeuvrability and acceleration capabilities. Heavy vehicles thus influence other traffic in a different manner to passenger vehicles, causing different levels of traffic instability. Increasing number and proportion of heavy vehicles in the traffic stream may result in quite different traffic flow characteristics. Car-following (CF) models are fundamental to replicating traffic flow and thus they have received considerable attention over the last few decades. They are in a continuous state of improvement due to their significant role in traffic micro-simulations, intelligent transportation systems and safety engineering models. However, model estimates of the traffic flow could be degraded since existing CF models do not consider the interactions between these vehicles and passenger cars drivers satisfactorily. This oversight was revealed through a comprehensive literature review conducted in this study in which the existing CF models are classified into classic and artificial intelligence models and are critically reviewed. This research investigates the different car-following behaviour of drivers in congested mixed traffic conditions. The congested traffic conditions refer to the level of service (LOS) “E” and “F” according to the Highway Capacity Manual (HCM 2010). More specifically, this study investigates whether the existence of heavy vehicles in the traffic stream influence car-following behaviour between heavy vehicles and passenger cars? A detailed data analysis is conducted to explore this question using a rich trajectory data set recorded from a 503-meter a segment of freeway in the USA. Four combinations of car-following are considered based on the classes of the vehicles involved in the car-following process. These include heavy vehicle following passenger car (H-C), passenger car following heavy vehicle (C-H), passenger car following passenger car (C-C), and heavy vehicle following heavy vehicle (H-H). This research investigates the headways between the vehicles, driver’s reaction time, relative speed-space headway between the vehicles, and analysis of the vehicle accelerations during car-following process. The study explores the stimuli which can affect driver’s car-following behaviours. It also reconstructs the car-following thresholds for different combinations. The findings showed the fundamental differences amongst the car-following combinations suggesting further investigation and model development. Two CF models are developed in this thesis: one classic model and one artificial intelligence model. This study develops a psychophysical CF model in which four sets of perceptual thresholds are considered to estimate drivers’ car-following behaviour. This means each car-following combination is associated with one specific set of thresholds. The model is calibrated by evolutionary algorithm which is implemented using traffic micro-simulation. A parallel particle swarm optimisation (Parallel PSO) algorithm is implemented in this study to reduce the execution time using multithread methodology. The results show the better performance of the developed model compared to the existing models to estimate traffic measurements used in the traffic micro-simulation. As an alternative model, a new artificial intelligence CF model was developed which specifically considered heavy vehicles. The model used the local linear model tree (LOLIMOT) approach to predict the car-following behaviour of drivers with consideration of the classes of their vehicle and the immediate vehicles in front. The model and the ways of defining the localities and training of the model are explained. The performance of the developed model is evaluated by an independent data set. This evaluation is conducted through the comparison between the predictions of the developed model and the actual traffic measurements. Additionally, the performance for the developed model is compared with the existing CF models. The results showed a good performance of the developed model. This method could be considered as a new approach to modelling car-following behaviour of drivers in mixed traffic providing the opportunity for incorporating human perceptual imperfections into a rigorous modelling framework. This study concludes that the consideration of vehicle heterogeneity in modelling longitudinal behaviours of derivers could result in better representation of traffic flow. This study could be useful for the researchers and transport planners who wish to consider heavy vehicles in traffic stream. The model could be used in traffic micro-simulations to enhance their accuracy and modelling capability

Modelling heavy vehicle car-following in congested traffic conditions

Heavy vehicles and passenger cars differ in their manoeuvrability and acceleration capabilities. Heavy vehicles thus influence other traffic in a different manner to passenger vehicles, causing different levels of traffic instability. Increasing number and proportion of heavy vehicles in the traffic stream may result in quite different traffic flow characteristics. Car-following (CF) models are fundamental to replicating traffic flow and thus they have received considerable attention over the last few decades. They are in a continuous state of improvement due to their significant role in traffic micro-simulations, intelligent transportation systems and safety engineering models. However, model estimates of the traffic flow could be degraded since existing CF models do not consider the interactions between these vehicles and passenger cars drivers satisfactorily. This oversight was revealed through a comprehensive literature review conducted in this study in which the existing CF models are classified into classic and artificial intelligence models and are critically reviewed. This research investigates the different car-following behaviour of drivers in congested mixed traffic conditions. The congested traffic conditions refer to the level of service (LOS) “E” and “F” according to the Highway Capacity Manual (HCM 2010). More specifically, this study investigates whether the existence of heavy vehicles in the traffic stream influence car-following behaviour between heavy vehicles and passenger cars? A detailed data analysis is conducted to explore this question using a rich trajectory data set recorded from a 503-meter a segment of freeway in the USA. Four combinations of car-following are considered based on the classes of the vehicles involved in the car-following process. These include heavy vehicle following passenger car (H-C), passenger car following heavy vehicle (C-H), passenger car following passenger car (C-C), and heavy vehicle following heavy vehicle (H-H). This research investigates the headways between the vehicles, driver’s reaction time, relative speed-space headway between the vehicles, and analysis of the vehicle accelerations during car-following process. The study explores the stimuli which can affect driver’s car-following behaviours. It also reconstructs the car-following thresholds for different combinations. The findings showed the fundamental differences amongst the car-following combinations suggesting further investigation and model development. Two CF models are developed in this thesis: one classic model and one artificial intelligence model. This study develops a psychophysical CF model in which four sets of perceptual thresholds are considered to estimate drivers’ car-following behaviour. This means each car-following combination is associated with one specific set of thresholds. The model is calibrated by evolutionary algorithm which is implemented using traffic micro-simulation. A parallel particle swarm optimisation (Parallel PSO) algorithm is implemented in this study to reduce the execution time using multithread methodology. The results show the better performance of the developed model compared to the existing models to estimate traffic measurements used in the traffic micro-simulation. As an alternative model, a new artificial intelligence CF model was developed which specifically considered heavy vehicles. The model used the local linear model tree (LOLIMOT) approach to predict the car-following behaviour of drivers with consideration of the classes of their vehicle and the immediate vehicles in front. The model and the ways of defining the localities and training of the model are explained. The performance of the developed model is evaluated by an independent data set. This evaluation is conducted through the comparison between the predictions of the developed model and the actual traffic measurements. Additionally, the performance for the developed model is compared with the existing CF models. The results showed a good performance of the developed model. This method could be considered as a new approach to modelling car-following behaviour of drivers in mixed traffic providing the opportunity for incorporating human perceptual imperfections into a rigorous modelling framework. This study concludes that the consideration of vehicle heterogeneity in modelling longitudinal behaviours of derivers could result in better representation of traffic flow. This study could be useful for the researchers and transport planners who wish to consider heavy vehicles in traffic stream. The model could be used in traffic micro-simulations to enhance their accuracy and modelling capability