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A compositional stochastic model for real time freeway traffic simulation

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  • Boel, René
  • Mihaylova, Lyudmila

Abstract

Traffic flow on freeways is a non-linear, many-particle phenomenon, with complex interactions between vehicles. This paper presents a stochastic model of freeway traffic at a time scale and of a level of detail suitable for on-line estimation, routing and ramp metering control. The freeway is considered as a network of interconnected components, corresponding to one-way road links consisting of consecutively connected short sections (cells). The compositional model proposed here extends the Daganzo cell transmission model by defining sending and receiving functions explicitly as random variables, and by also specifying the dynamics of the average speed in each cell. Simple stochastic equations describing the macroscopic traffic behavior of each cell, as well as its interaction with neighboring cells are obtained. This will allow the simulation of quite large road networks by composing many links. The model is validated over synthetic data with abrupt changes in the number of lanes and over real traffic data sets collected from a Belgian freeway.

Suggested Citation

  • Boel, René & Mihaylova, Lyudmila, 2006. "A compositional stochastic model for real time freeway traffic simulation," Transportation Research Part B: Methodological, Elsevier, vol. 40(4), pages 319-334, May.
    • Handle: RePEc:eee:transb:v:40:y:2006:i:4:p:319-334
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    References listed on IDEAS

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    1. Daganzo, Carlos F., 1994. "The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory," Transportation Research Part B: Methodological, Elsevier, vol. 28(4), pages 269-287, August.
    2. Hilliges, Martin & Weidlich, Wolfgang, 1995. "A phenomenological model for dynamic traffic flow in networks," Transportation Research Part B: Methodological, Elsevier, vol. 29(6), pages 407-431, December.
    3. Daganzo, Carlos F., 1995. "A finite difference approximation of the kinematic wave model of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 29(4), pages 261-276, August.
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    2. Siqueira, Adriano F. & Peixoto, Carlos J.T. & Wu, Chen & Qian, Wei-Liang, 2016. "Effect of stochastic transition in the fundamental diagram of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 87(C), pages 1-13.
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    6. Jahangirian, Mohsen & Eldabi, Tillal & Naseer, Aisha & Stergioulas, Lampros K. & Young, Terry, 2010. "Simulation in manufacturing and business: A review," European Journal of Operational Research, Elsevier, vol. 203(1), pages 1-13, May.
    7. Sumalee, A. & Zhong, R.X. & Pan, T.L. & Szeto, W.Y., 2011. "Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 507-533, March.
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    10. Osorio, Carolina & Flötteröd, Gunnar & Bierlaire, Michel, 2011. "Dynamic network loading: A stochastic differentiable model that derives link state distributions," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1410-1423.
    11. Bharathi, Dhivya & Vanajakshi, Lelitha & Subramanian, Shankar C., 2022. "Spatio-temporal modelling and prediction of bus travel time using a higher-order traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
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    13. Carey, Malachy & Bar-Gera, Hillel & Watling, David & Balijepalli, Chandra, 2014. "Implementing first-in–first-out in the cell transmission model for networks," Transportation Research Part B: Methodological, Elsevier, vol. 65(C), pages 105-118.
    14. Jabari, Saif Eddin & Liu, Henry X., 2013. "A stochastic model of traffic flow: Gaussian approximation and estimation," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 15-41.
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