Traveler’s Route Selection Considering Indifference Threshold And Reference Dependence

With the acceleration of urbanization and the expansion of transportation networks,people are facing more and more path choices in urban travel.Different path choices often bring different traffic costs,time costs,and safety risks.Therefore,studying the path selection patterns and behavioral decisions of individual travelers is key to improving urban traffic efficiency and safety,optimizing traffic network layout.However,individual travelers’ path selection is influenced by many factors such as personal social attributes and road attributes.Therefore,by studying path selection considering factors such as indifference threshold and reference dependence can better understand and predict individual traveler’s behavioral decisions to optimize traffic network layout design for improved efficiency and sustainability.Firstly,commuters were selected as research subjects with consideration given to their personal characteristics as well as travel attributes that affect their choice of travel paths.A SP survey questionnaire was designed based on hypothetical scenarios which collected data for further statistical analysis.Secondly,latent class models were used to classify traveling individuals into categories while analyzing behavior characteristics among different groups.Probit models were calibrated using indifference thresholds without considering individual traveler’s properties versus those that do consider them;analyzing how different classification populations influence indifference thresholds.Finally,internal reference dependence was considered when establishing utility functions based on random regret minimization theory creating both classic regret minimization path selection model along with an improved version incorporating internal reference dependence.Combining these two models established a comprehensive utility-based route choice model discussing precision between classic random regret minimization versus improved random regret minimization under consideration of internal reference dependence providing case studies demonstrating inducing methods.