Gap Acceptance Behavior In The Lane-Changing Model In Congested Traffic

Including heterogeneity in the driver populace can significantly improve the accuracy of microscopic traffic simulation models.Yet,compared to car-following models,heterogeneity in lane-changing models has not been ventured into enough.To integrate the overlooked heterogeneity into lane-changing models,this research will construct a Multinomial logit model and use AIC & BIC to regulate them.One dataset was mined from the NGSIM vehicle trajectory dataset.It is expected that there will be at least one recognized ideal model and they will be split evenly between multinomial logit models.Additional influencing factors may be statistically noteworthy in the multinomial logit models;this will indicate if the suggested model will be effective in data mining for extracting unseen relations.While various variables will be measured across the board,the class will be named matching to its common aspects.The multinomial models will be tested for results in precision & accuracy.The results of this study are crucial for possibly bettering traffic simulation,traffic safety and procedure,increasing our knowledge on microscopic traffic flow,and traffic operations & management.The key work of this study is to construct a multinomial logit models based on the extracted NGSIM trajectory data,examine the results of any produced models,and determine the characterization of the lane-changing behavior with integrated driver heterogeneity.This is done through the Alteryx Designer and SPSS software.The main contribution of this study show a defined class observed from the results of the multinomial logit model.The conclusion determines that the results can be used for different aspects of traffic solutions.