Kayvan Aghabayk

Understanding the Dynamics of Heavy Vehicle Interactions in Car-Following

Heavy vehicles and passenger cars differ in their maneuverability and acceleration capabilities. Heavy vehicles thus influence other traffic in a different manner than 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. Over the last five decades many studies have investigated passenger car drivers’ car-following behavior. However, the existence of heavy vehicles in the traffic stream has not received the same attention. This paper investigates the different car-following behavior of drivers in congested heterogeneous traffic conditions. It specifically focuses on the existence of heavy vehicles in general traffic and their interaction with other vehicles. Four different combinations of passenger car and heavy vehicle were considered. These combinations include passenger car-following a passenger car, passenger car-following a heavy vehicle, heavy vehicle following a passenger car, and heavy vehicle following another heavy vehicle. A data set from a freeway in the United States was used to show the different car-following behavior of drivers for each combination. This study analyzes space and time headways, drivers’ reaction times, and vehicle accelerations. It also introduces different car-following thresholds for each car-following combination. It was found that the presence of heavy vehicles causes larger space and time headways, longer reaction time, and more robust car-following behavior. It also shows that the car-following thresholds are of the four combinations studies. The findings of this paper indicate that further research is required to develop a generalized car-following model that can be used when different vehicle types are in the traffic stream.

Comparing Heavy Vehicle and Passenger Car Lane-Changing Maneuvers on Arterial Roads and Freeways

The behavior of drivers of heavy vehicles when making a decision or executing a lane-changing maneuver on arterial roads and freeways is investigated. Vehicle lane-changing maneuvers often result in instability in traffic flow. The impact on traffic flow of lane changes by heavy vehicles can be even more serious than that of lane changes by passenger vehicles because of vehicle characteristics and driver behavior. This impact is being exacerbated because the number of heavy vehicles and their share in the traffic steam are increasing. Models of lane-changing behavior were reviewed. It was found that the lane-changing maneuver of heavy vehicles has not received appropriate attention. Real-world data were applied to a comparison of this behavior with the behavior of passenger car drivers. The behavior of drivers is explored for both arterial roads and freeways considering the impact of the size of vehicles on the lane-changing maneuver. The results indicate that the type and size of vehicles influence the lane-changing maneuver, in particular on arterial roads. A new area of study is opened for investigating the microscopic behavior of heavy vehicles in a traffic steam with more detail and leads toward a model that incorporates the characteristics of heavy vehicles.

Attribute selection for modelling driver's car-following behaviour in heterogeneous congested traffic conditions

This paper uses a real-world data set to investigate a drivers car-following behaviour of different class of vehicles in congested traffic conditions. The existing car-following models do not explicitly consider heavy vehicle (HV) interactions with the other vehicles. This could become problematic in future due to the increasing proportion of HVs in the traffic stream. Four types of vehicle combinations were considered in this study including car–car, car–HV, HV–car, and HV–HV. The results of detailed data analysis showed that the drivers behaviours differ in each car-following combination. Further the variables which could influence the car-following behaviour in each combination were identified. The potential variables were explored and the effective variables were selected through a combination of advanced statistical analysis. The findings specify that further research is needed to develop a car-following model which incorporates these behavioural differences.

A State-of-the-Art Review of Car-Following Models with Particular Considerations of Heavy Vehicles

Abstract Car-following (CF) models are fundamental in the replication of traffic flow and thus they have received considerable attention. This attention needs to be reflected upon at particular points in time. CF models are in a continuous state of improvement due to their significant role in traffic micro-simulations, intelligent transportation systems and safety engineering models. This paper presents a review of existing CF models. It classifies them into classic and artificial intelligence models. It discusses the capability of the models and potential limitations that need to be considered in their improvement. This paper also reviews the studies investigating the impacts of heavy vehicles in traffic stream and on CF behaviour. The findings of the study provide promising directions for future research and suggest revisiting the existing models to accommodate different behaviours of drivers in heterogeneous traffic, in particular, heavy vehicles in traffic.

Exploring a Local Linear Model Tree Approach to Car-Following

Car-following (CF) models are fundamental to replicating traffic flow and they have received considerable attention over the last 50 years. These models are in a continuous state of improvement since they play a significant role in traffic microsimulations, intelligent transportation systems, and safety engineering models. A local linear model tree (LOLIMOT) approach is used in this paper to model drivers CF behavior to incorporate human perceptual imperfections into a CF model. The model defines some localities in the input space. These localities are fuzzy and have overlaps with each other. Specific models for each of the localities are then defined and combined in a fuzzy manner to predict the final output. The model was developed using real world dynamic data sets. The paper uses three different data sets for training, testing, and validating the model. The results showed very close agreement between the real data and the LOLIMOT outputs presented in this paper. The performance of the model was compared to a number of existing CF models.

Impacts of different angles and speeds on behavior of pedestrian crowd merging

Many forms of complex pedestrian crowd behaviors, including merging, can be identified in built environments such as public transport stations and public buildings. Understanding and capturing this phenomenon in a robust model is a challenging task; it is also a significant opportunity for research, given the international demand for models of this type. Despite the frequent occurrence of merging of crowd streams, this complex behavior has not received enough attention so far. The literature that is related to crowd merging is limited to T-shaped intersections and studies conducted on staircases. In this study using experimental data, the crowd merging phenomenon was investigated. The impacts of different merging angles and different pedestrian speeds were investigated. The results showed that flow rates and headway distributions are affected by variety in pedestrian speeds and merging angles.

New Car-Following Model Considering Impacts of Multiple Lead Vehicle Types

In the past decade, the development and the application of traffic micro-simulation to replicate real-world traffic behavior have become pervasive among traffic and transport researchers. The modeling of a drivers car-following behavior, which forms the fundamental component of traffic microsimulation, has meanwhile been an important research direction leading to the sophistication of traffic microsimulation. However, recent studies have pointed out that a drivers following behavior varies when the lead vehicle is a passenger car as opposed to a heavy vehicle. Nevertheless, existing models do not precisely address those differences. This oversight could diversely affect the accuracy of traffic microsimulations, particularly with the current trend of an increasing number of heavy vehicles in the traffic stream. A novel car-following model that considered the heterogeneity of lead vehicles was developed. Two types of lead vehicles were considered in this study: passenger cars and heavy vehicles. The model was developed on the basis of the local linear model tree approach. This approach is able to incorporate human perceptual imperfections into a car-following model. The input space is partitioned incrementally, and a linear model is developed for each locality (partition). The final output is calculated by the fuzzy combination of local models according to the validity function of each model. For training and testing purposes, two real-world data sets were obtained from a U.S. freeway under congested traffic conditions. The results showed very close agreement between the real data and the outputs of the proposed model.