Estimation of Vehicular Journey Time Distributions by Bayesian Data Fusion with General Mixture Model

作     者：Wu, Xinyue Chow, Andy H.F. Zhuang, Li Ma, Wei Lam, William Wong, S.C. 

作者机构：Department of Advanced Design and Systems Engineering City University of Hong Kong Kowloon Tong Hong Kong School of Cyber Science and Engineering Southeast University Nanjing China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hung Hom Hong Kong Department of Civil Engineering The University of Hong Kong Pokfulam Hong Kong 

出 版 物：《SSRN》 

年 卷 期：2022年

核心收录：

主　　题：Mixtures 

摘      要：This paper presents a Bayesian data fusion framework for estimating journey time distributions that uses a mixture distribution model to classify feeding data into different traffic states. Different from most studies, the proposed framework uses general statistical models to estimate the vehicular journey time variability. Feeding data collected from multiple data sources are classified based on the associated traffic conditions, and the corresponding estimation biases of the individual data sources are determined using a Gaussian distribution model. The proposed framework is implemented and tested on a real Hong Kong highway corridor with actual data collected from the field. The findings of the case study show significant improvement in the journey time estimations of the proposed method compared with the individual measurements. The results also highlight the benefit of incorporating a traffic state classifier in the fusion framework. This study contributes to the development of reliability-based intelligent transportation systems based on advanced traffic data analytics. © 2022, The Authors. All rights reserved.