Abstract
Driver and pedestrian behaviour significantly affect the safety and the flow of traffic at the microscopic and macroscopic levels. The driver behaviour models describe the driver decisions made in different traffic flow conditions. Modelling the pedestrian behaviour plays an essential role in the analysis of pedestrian flows in the areas such as public transit terminals, pedestrian zones, evacuations, etc. Driver behaviour models, integrated into simulation tools, can be divided into car-following models and lane-changing models. The simulation tools are used to replicate traffic flows and infer certain regularities. Particular model parameters must be appropriately calibrated to approximate the realistic traffic flow conditions. This paper describes the existing car-following models, lane-changing models, and pedestrian behaviour models. Further, it underlines the importance of calibrating the parameters of microsimulation models to replicate realistic traffic flow conditions and sets the guidelines for future research related to the development of new models and the improvement of the existing ones.
Highlights
The main aim of the paper is to stratify the existing car-following, lane-changing, and pedestrian behaviour models
This paper presents an update on the existing Car Following (CF) and Lane Change (LC) models with considerations of autonomous vehicles and pedestrian behaviours
The results have shown that Recurrent Neural Network (RNN) has more accurate results when it comes to predicting the trajectories of vehicles and predicting the aggressive, timid, and normal oscillations
Summary
The main aim of the paper is to stratify the existing car-following, lane-changing, and pedestrian behaviour models. Different simulation tools that integrate driver and pedestrian behaviour models are used to evaluate various traffic situations and various solutions and to facilitate the decision-making. Simulation models have the ability to closely replicate actual traffic situations by modelling each vehicle and pedestrian in the network. The fundamental element of microscopic simulation models is driver and pedestrian behaviour in a traffic network, i.e. Car Following (CF), Lane Change (LC), route choice, gap acceptance, and regarding pedestrians, avoiding other pedestrians and obstacles. None of them consider the car-following and lane-change behaviour of Autonomous Vehicles (AV) or pedestrian behaviour models which have an essential role in traffic. This paper presents an update on the existing CF and LC models with considerations of autonomous vehicles and pedestrian behaviours
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