Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79706
Title: Stochastic link flow model for signalized traffic networks with uncertainty in demand
Authors: Lin, S
Pan, TL 
Lam, WHK 
Zhong, RX
De Schutter, B
Keywords: Stochastic traffic model
Traffic signals
Urban traffic network
Issue Date: 2018
Publisher: Elsevier
Source: IFAC-PapersOnLine, 2018, v. 51, no. 9, p. 458-463 How to cite?
Journal: IFAC-PapersOnLine 
Abstract: 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.
Description: 15th International-Federation-of-Automatic-Control (IFAC) Symposium on Control in Transportation Systems (CTS), ITALY, Jun 6-8, 2018
URI: http://hdl.handle.net/10397/79706
ISSN: 2405-8963
DOI: 10.1016/j.ifacol.2018.07.075
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