Random coefficient modeling research on short-term forecast of passenger flow into an urban rail transit station

Transport

Xun Sun¹  Tiantian Gan¹  Hemeizi Zhang¹  Xuesong Feng¹  Qipeng Sun²  Fei Ma² 

[1] MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, China;School of Economics and Management, Chang’an University, China

关键词: random coefficient modeling;    Bayesian estimation and updating;    short-term forecast;    passenger flow;    urban rail transit station;    operation safety;   

DOI  :  10.3846/16484142.2016.1128484

学科分类：航空航天科学

来源: Vilnius Gedinimas Technical University

【 摘 要 】

Taking a representative metro station in Beijing as example, this research has newly developed a random coefficient model to predict the short-term passenger flows with sudden increases sometimes into an urban rail transit station. The hierarchical Bayesian approach is iteratively applied in this work to estimate the new model and the estimation outcomes in each of the iterative calibrations are improved by sequential Bayesian updating. It has been proved that the estimation procedure is able to effectively converge to rational results with satisfying accuracies. In addition, the model application study reveals that besides sufficient preparations in manpower, devices, etc.; the information of the factors affecting the passenger flows into an urban rail transit station should be timely transferred in advance from important buildings, road intersections, squares and so on in neighborhood to this station. In this way, this station is able to cope with the unexpectedly sharp increases of the passenger flows into the station to ensure its operation safety.

【 授权许可】

CC BY