IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v45y2011i3p507-533.html
   My bibliography  Save this article

Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment

Author

Listed:
  • Sumalee, A.
  • Zhong, R.X.
  • Pan, T.L.
  • Szeto, W.Y.

Abstract

The paper proposes a first-order macroscopic stochastic dynamic traffic model, namely the stochastic cell transmission model (SCTM), to model traffic flow density on freeway segments with stochastic demand and supply. The SCTM consists of five operational modes corresponding to different congestion levels of the freeway segment. Each mode is formulated as a discrete time bilinear stochastic system. A set of probabilistic conditions is proposed to characterize the probability of occurrence of each mode. The overall effect of the five modes is estimated by the joint traffic density which is derived from the theory of finite mixture distribution. The SCTM captures not only the mean and standard deviation (SD) of density of the traffic flow, but also the propagation of SD over time and space. The SCTM is tested with a hypothetical freeway corridor simulation and an empirical study. The simulation results are compared against the means and SDs of traffic densities obtained from the Monte Carlo Simulation (MCS) of the modified cell transmission model (MCTM). An approximately two-miles freeway segment of Interstate 210 West (I-210W) in Los Ageles, Southern California, is chosen for the empirical study. Traffic data is obtained from the Performance Measurement System (PeMS). The stochastic parameters of the SCTM are calibrated against the flow-density empirical data of I-210W. Both the SCTM and the MCS of the MCTM are tested. A discussion of the computational efficiency and the accuracy issues of the two methods is provided based on the empirical results. Both the numerical simulation results and the empirical results confirm that the SCTM is capable of accurately estimating the means and SDs of the freeway densities as compared to the MCS.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transb:v:45:y:2011:i:3:p:507-533
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191-2615(10)00118-9
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Friesz, Terry L. & Mookherjee, Reetabrata & Yao, Tao, 2008. "Securitizing congestion: The congestion call option," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 407-437, June.
    2. 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.
    3. Peeta, Srinivas & Zhou, Chao, 2006. "Stochastic quasi-gradient algorithm for the off-line stochastic dynamic traffic assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 40(3), pages 179-206, March.
    4. Daganzo, Carlos F., 1995. "The cell transmission model, part II: Network traffic," Transportation Research Part B: Methodological, Elsevier, vol. 29(2), pages 79-93, April.
    5. Yibing Wang & Markos Papageorgiou & Albert Messmer, 2007. "Real-Time Freeway Traffic State Estimation Based on Extended Kalman Filter: A Case Study," Transportation Science, INFORMS, vol. 41(2), pages 167-181, May.
    6. Chen, Chao, 2003. "Freeway Performance Measurement System (PeMS)," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6j93p90t, Institute of Transportation Studies, UC Berkeley.
    7. Kim, T. & Zhang, H.M., 2008. "A stochastic wave propagation model," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 619-634, August.
    8. 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.
    9. Pham, Dinh Tuan, 1985. "Bilinear markovian representation and bilinear models," Stochastic Processes and their Applications, Elsevier, vol. 20(2), pages 295-306, September.
    10. Justin Geistefeldt & Werner Brilon, 2009. "A Comparative Assessment of Stochastic Capacity Estimation Methods," Springer Books, in: William H. K. Lam & S. C. Wong & Hong K. Lo (ed.), Transportation and Traffic Theory 2009: Golden Jubilee, chapter 0, pages 583-602, Springer.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Zheng, Fangfang & Jabari, Saif Eddin & Liu, Henry X. & Lin, DianChao, 2018. "Traffic state estimation using stochastic Lagrangian dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 143-165.
    3. Flötteröd, G. & Osorio, C., 2017. "Stochastic network link transmission model," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 180-209.
    4. Yeo, Hwasoo, 2008. "Asymmetric Microscopic Driving Behavior Theory," University of California Transportation Center, Working Papers qt1tn1m968, University of California Transportation Center.
    5. 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.
    6. Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.
    7. Storm, Pieter Jacob & Mandjes, Michel & van Arem, Bart, 2022. "Efficient evaluation of stochastic traffic flow models using Gaussian process approximation," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 126-144.
    8. 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.
    9. Qixiu Cheng & Zhiyuan Liu & Feifei Liu & Ruo Jia, 2017. "Urban dynamic congestion pricing: an overview and emerging research needs," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 21(0), pages 3-18, August.
    10. Gentile, Guido & Meschini, Lorenzo & Papola, Natale, 2007. "Spillback congestion in dynamic traffic assignment: A macroscopic flow model with time-varying bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 41(10), pages 1114-1138, December.
    11. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    12. Huanping Li & Jian Wang & Guopeng Bai & Xiaowei Hu, 2021. "Exploring the Distribution of Traffic Flow for Shared Human and Autonomous Vehicle Roads," Energies, MDPI, vol. 14(12), pages 1-21, June.
    13. Herrera, Juan C. & Bayen, Alexandre M., 2010. "Incorporation of Lagrangian measurements in freeway traffic state estimation," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 460-481, May.
    14. Bellei, Giuseppe & Gentile, Guido & Papola, Natale, 2005. "A within-day dynamic traffic assignment model for urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 39(1), pages 1-29, January.
    15. Georgia Perakis & Guillaume Roels, 2006. "An Analytical Model for Traffic Delays and the Dynamic User Equilibrium Problem," Operations Research, INFORMS, vol. 54(6), pages 1151-1171, December.
    16. Jorge A. Laval & Bhargava R. Chilukuri, 2014. "The Distribution of Congestion on a Class of Stochastic Kinematic Wave Models," Transportation Science, INFORMS, vol. 48(2), pages 217-224, May.
    17. Malachy Carey & Paul Humphreys & Marie McHugh & Ronan McIvor, 2018. "Consistency and Inconsistency Between the Fundamental Relationships on Which Different Traffic Assignment Models Are Based," Service Science, INFORMS, vol. 52(6), pages 1548-1569, December.
    18. Flötteröd, Gunnar & Rohde, Jannis, 2011. "Operational macroscopic modeling of complex urban road intersections," Transportation Research Part B: Methodological, Elsevier, vol. 45(6), pages 903-922, July.
    19. Canepa, Edward S. & Claudel, Christian G., 2017. "Networked traffic state estimation involving mixed fixed-mobile sensor data using Hamilton-Jacobi equations," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 686-709.
    20. Jiang, Chenming & Bhat, Chandra R. & Lam, William H.K., 2020. "A bibliometric overview of Transportation Research Part B: Methodological in the past forty years (1979–2019)," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 268-291.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transb:v:45:y:2011:i:3:p:507-533. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.