Stochastic link flow model for signalized traffic networks with uncertainty in demand

Reference:
S. Lin, T.L. Pan, W.H.K. Lam, R.X. Zhong, and B. De Schutter, "Stochastic link flow model for signalized traffic networks with uncertainty in demand," Proceedings of the 15th IFAC Symposium on Control in Transportation Systems (CTS 2018), Savona, Italy, pp. 458-463, June 2018.

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.