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Exploring influential factors and endogeneity of traffic flow of different lanes on urban freeways using Bayesian multivariate spatial models

引用

Journal of Traffic and Transportation Engineering(English Edition) 2023年第1期10卷 104-115页

作者： Yongping Zhang Gurdiljot Singh Gill Wen Cheng paulina reina Mankirat Singh Department of Civil Engineering California State Polytechnic UniversityPomonaCA 91768USA Department of Civil and Environmental Engineering California State UniversityFullertonCA 91768USA

The traffic flow pertinent modelling is essential for distinct strategy formulations.However,the present literature illustrated some needed improvements for such models,especially those related to lane-specific flow.Given such context,this study aims to bridge the research gap by generating regression models to investigate influential factors for traffic flow based on the data collected from one multilane freeway.With the Full Bayesian specification,the hierarchical models were built by accounting for three types of random effects:the structured and unstructured spatial effects,and the one addressing the multivariate heterogeneity across multiple lanes.The endogenous relationship of traffic flow of adjacent lanes was also explored by utilizing the capability of multivariate correlation structure for simultaneous estimation of lane flow.The model estimates revealed the presence of endogeneity with statistical significance for the flow of neighbouring lanes for both directions of travel.The impact of flow was not only limited to the adjacent lanes but also to non-adjacent lanes.The multivariate specification also confirmed interdependency for lane flows.Compared to conventional approaches,the more accurate model estimation in the study indicates the advantage of incorporating the various correlation structures in the models.

关键词： Spatial interdependency Structured and unstructured random effects Multivariate specification Endogeneity Traffic flow

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