Mohd SadatSyed Aqeel Ahmad,Mehmet Ali Silgu2024-10-252024979836931348010.4018/979-8-3693-1347-3.ch014http://136.232.12.194:4000/handle/123456789/954AI and Machine Learning Impacts in Intelligent Supply Chain By Binay Kumar Pandey, Uday Kumar Kanike, A. Shaji George, Digvijay PandeyTraffic modelling has gained importance due to the adoption of intelligent transportation systems and software based on traffic models providing a platform to test and improve such systems. Modelling mixed traffic has proved to be a challenging task due to variations in vehicle dimensions and composi- tion along with non-lane-based driving. Most of the simulation software is based on the car following models and lane change models which were originally developed for lane-based traffic. Several attempts have been made to adapt these models for mixed traffic by extending them to include new parameters.This study summarizes lane change models used along with car following for mixed traffic. It can be concluded from past studies that lateral manoeuvre varies with the longitudinal speed in a non-linear manner. Sub-models or specific parameters are needed to model the lateral behaviour of each class of vehicle. Trajectory data analysis and subsequent models have also pointed towards the need for vehicle pair-dependent parameters.en-USCivil EngineeringArtificial Intelligence (AI)Machine Learning (ML)Book chapter