Stochastic Link Flow Model for Signalized Traffic Networks with Uncertainty in Demand

被引：11

作者：

Lin, S. ^{[1
,2
]}

Pan, T. L. ^{[3
]}

Lam, W. H. K. ^{[3
]}

Zhong, R. X. ^{[4
]}

De Schutter, B. ^{[5
]}

机构：

[1] Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing, Peoples R China

[2] Minist Educ, Key Lab Syst Control & Informat Proc, Shanghai, Peoples R China

[3] Hong Kong Polytech Univ, Dept Civil & Struct Engn, Hong Kong, Hong Kong, Peoples R China

[4] Sun Yat Sen Univ, Sch Engn, Guangzhou, Guangdong, Peoples R China

[5] Delft Univ Technol, Delft Ctr Syst & Control, Delft, Netherlands

来源：

IFAC PAPERSONLINE | 2018年 / 51卷 / 09期

基金：

美国国家科学基金会; 北京市自然科学基金;

关键词：

Stochastic traffic model; Traffic signals; Urban traffic network; CELL TRANSMISSION MODEL; CONTROL FORMULATION;

D O I：

10.1016/j.ifacol.2018.07.075

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In order to investigate the stochastic features in urban traffic dynamics, we propose a Stochastic Link Flow Model (SLFM) for signalized traffic networks with demand uncertainties. In the proposed model, the link traffic state is described using four different link state modes, and the probability for each link state mode is determined based on the stochastic link states. The SLFM model is expressed as a finite mixture approximation of the link state probabilities and the dynamic link flow models for all the four link state modes. Using data from microscopic traffic simulator SUMO, we illustrate that the proposed model can provide a reliable estimation of the link traffic states, and as well as good estimations on the link state uncertainties propagating within a signalized traffic network. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：458 / 463

页数：6

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