Study On Driver’s Path Choice Behavior And Traffic Allocation Model Under Forbidden Left

Urban roadway intersections often become bottlenecks for traffic flow,and implementing a no-left turn strategy is an effective way to reduce intersection congestion.The benefits of left-turn bans include improving straight lane capacity,reducing delay levels,and enhancing traffic safety while avoiding additional roadway engineering costs.However,implementing left-turn bans may require detours,and driver behavior can influence travel path choice and traffic distribution on detour paths.This study investigates the relationship between driver behavior and path choice by analyzing detour paths under left-turn ban conditions and proposes a generalized regret path choice model to target driver behavior.Additionally,a traffic assignment model under the no-left turn condition is developed based on the proposed model and verified through a case study.The study involves the following research work:(1)A survey was conducted to analyze the detour behavior of drivers under the noleft turn condition,and the characteristics of no-left traffic organization and vehicle detour methods were summarized.The results of a targeted questionnaire were statistically analyzed using Kendall correlation analysis and cross-tabulation analysis to identify the influence of key factors on detour path selection and investigate their nteractions in-depth.(2)The study proposes a generalized stochastic regret model to describe drivers’path selection behavior under the no-left turn condition.Based on the maximum utility rule and regret minimization rule,multiple decision rules for driver path choice are proposed.The constructed model includes the regulation of regret power parameters to better reflect the driver’s decision propensity and support the path utility calculation in traffic assignment.(3)A traffic assignment model that minimizes the generalized regret trip function as the objective function is developed to effectively describe the path benefits of driver behavior.The model incorporates the probability of driver path choice to enhance its accuracy while satisfying the original constraints.The solution algorithm uses a multiconditional judgment in the effective path search algorithm to improve the efficiency of the MSA algorithm solution.(4)The study applies the proposed model to the local no-left road network in Jinan,derives the OD demand using TransCAD,and determines the regret weight parameters and attribute regret parameter values of the model.The results of the solved model are compared and analyzed with the actual road network traffic.The effects of different OD distances and demands on the path selection probability and traffic assignment results are investigated.The trafic assignment results of the three models are compared by adjusting the parameters of the hybrid model.The traffic assignment model established in this study effectively portrays the traffic flow distribution under the no-left turn condition and has the advantages of semi-compensability and stability.In summary,this study investigates the relationship between driver behavior and path choice under the no-left turn condition and proposes a generalized regret path choice model to target driver behavior.The developed traffic assignment model effectively portrays the traffic flow distribution under the no-left turn condition and has practical value for traffic management and engineering design.