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Predicting traffic flow using Bayesian networks

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  • Castillo, Enrique
  • Menéndez, José María
  • Sánchez-Cambronero, Santos

Abstract

This paper deals with the problem of predicting traffic flows and updating these predictions when information about OD pairs and/or link flows becomes available. To this end, a Bayesian network is built which is able to take into account the random character of the level of total mean flow and the variability of OD pair flows, together with the random violation of the balance equations for OD pairs and link flows due to extra incoming or exiting traffic at links or to measurement errors. Bayesian networks provide the joint density of all unobserved variables and in particular the corresponding conditional and marginal densities, which allow not only joint predictions, but also probability intervals. The influence of congested traffic can also be taken into consideration by combination of the traffic assignment rules (as SUE, for example) with the Bayesian network model proposed. Some examples illustrate the model and show its practical applicability.

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  • Castillo, Enrique & Menéndez, José María & Sánchez-Cambronero, Santos, 2008. "Predicting traffic flow using Bayesian networks," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 482-509, June.
    • Handle: RePEc:eee:transb:v:42:y:2008:i:5:p:482-509
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    1. Fisk, Caroline, 1980. "Some developments in equilibrium traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 243-255, September.
    2. Warren B. Powell & Yosef Sheffi, 1982. "The Convergence of Equilibrium Algorithms with Predetermined Step Sizes," Transportation Science, INFORMS, vol. 16(1), pages 45-55, February.
    3. Hazelton, Martin L., 2000. "Estimation of origin-destination matrices from link flows on uncongested networks," Transportation Research Part B: Methodological, Elsevier, vol. 34(7), pages 549-566, September.
    4. Cascetta, Ennio & Nguyen, Sang, 1988. "A unified framework for estimating or updating origin/destination matrices from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 22(6), pages 437-455, December.
    5. Maher, Michael J. & Zhang, Xiaoyan & Vliet, Dirck Van, 2001. "A bi-level programming approach for trip matrix estimation and traffic control problems with stochastic user equilibrium link flows," Transportation Research Part B: Methodological, Elsevier, vol. 35(1), pages 23-40, January.
    6. Doblas, Javier & Benitez, Francisco G., 2005. "An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix," Transportation Research Part B: Methodological, Elsevier, vol. 39(7), pages 565-591, August.
    7. Giulio Erberto Cantarella, 1997. "A General Fixed-Point Approach to Multimode Multi-User Equilibrium Assignment with Elastic Demand," Transportation Science, INFORMS, vol. 31(2), pages 107-128, May.
    8. Prashker, Joseph N. & Bekhor, Shlomo, 2000. "Some observations on stochastic user equilibrium and system optimum of traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 34(4), pages 277-291, May.
    9. Maher, M. J., 1983. "Inferences on trip matrices from observations on link volumes: A Bayesian statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 17(6), pages 435-447, December.
    10. Michael Patriksson, 2004. "Sensitivity Analysis of Traffic Equilibria," Transportation Science, INFORMS, vol. 38(3), pages 258-281, August.
    11. E. Castillo & A. J. Conejo & C. Castillo & R. Mínguez & D. Ortigosa, 2006. "Perturbation Approach to Sensitivity Analysis in Mathematical Programming," Journal of Optimization Theory and Applications, Springer, vol. 128(1), pages 49-74, January.
    12. Malachy Carey & Riccardo Revelli, 1986. "Constrained Estimation of Direct Demand Functions and Trip Matrices," Transportation Science, INFORMS, vol. 20(3), pages 143-152, August.
    13. Ross D. Shachter & C. Robert Kenley, 1989. "Gaussian Influence Diagrams," Management Science, INFORMS, vol. 35(5), pages 527-550, May.
    14. Carlos F. Daganzo & Yosef Sheffi, 1977. "On Stochastic Models of Traffic Assignment," Transportation Science, INFORMS, vol. 11(3), pages 253-274, August.
    15. Lo, H. P. & Zhang, N. & Lam, W. H. K., 1996. "Estimation of an origin-destination matrix with random link choice proportions: A statistical approach," Transportation Research Part B: Methodological, Elsevier, vol. 30(4), pages 309-324, August.
    16. Van Zuylen, Henk J. & Willumsen, Luis G., 1980. "The most likely trip matrix estimated from traffic counts," Transportation Research Part B: Methodological, Elsevier, vol. 14(3), pages 281-293, September.
    17. Cascetta, Ennio, 1984. "Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator," Transportation Research Part B: Methodological, Elsevier, vol. 18(4-5), pages 289-299.
    18. Fisk, C. S., 1988. "On combining maximum entropy trip matrix estimation with user optimal assignment," Transportation Research Part B: Methodological, Elsevier, vol. 22(1), pages 69-73, February.
    19. Yai, Tetsuo & Iwakura, Seiji & Morichi, Shigeru, 1997. "Multinomial probit with structured covariance for route choice behavior," Transportation Research Part B: Methodological, Elsevier, vol. 31(3), pages 195-207, June.
    20. Maher, M. J. & Hughes, P. C., 1997. "A probit-based stochastic user equilibrium assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 31(4), pages 341-355, August.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    2. Yanbing Li & Wei Zhao & Huilong Fan, 2022. "A Spatio-Temporal Graph Neural Network Approach for Traffic Flow Prediction," Mathematics, MDPI, vol. 10(10), pages 1-14, May.
    3. Castillo, Enrique & Menéndez, José María & Jiménez, Pilar, 2008. "Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 455-481, June.
    4. Fu, Hao & Lam, William H.K. & Shao, Hu & Ma, Wei & Chen, Bi Yu & Ho, H.W., 2022. "Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 19-47.
    5. Coogan, Samuel & Flores, Christopher & Varaiya, Pravin, 2017. "Traffic predictive control from low-rank structure," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 1-22.
    6. Siripirote, Treerapot & Sumalee, Agachai & Ho, H.W. & Lam, William H.K., 2015. "Statistical approach for activity-based model calibration based on plate scanning and traffic counts data," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 280-300.
    7. Yulong Pei & Songmin Ran & Wanjiao Wang & Chuntong Dong, 2023. "Bus-Passenger-Flow Prediction Model Based on WPD, Attention Mechanism, and Bi-LSTM," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    8. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    9. Ng, ManWo, 2012. "Synergistic sensor location for link flow inference without path enumeration: A node-based approach," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 781-788.
    10. Kumar, Anshuman Anjani & Kang, Jee Eun & Kwon, Changhyun & Nikolaev, Alexander, 2016. "Inferring origin-destination pairs and utility-based travel preferences of shared mobility system users in a multi-modal environment," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 270-291.
    11. Mínguez, R. & Sánchez-Cambronero, S. & Castillo, E. & Jiménez, P., 2010. "Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 282-298, February.
    12. Julio, Nikolas & Giesen, Ricardo & Lizana, Pedro, 2016. "Real-time prediction of bus travel speeds using traffic shockwaves and machine learning algorithms," Research in Transportation Economics, Elsevier, vol. 59(C), pages 250-257.
    13. Zheng Zhu & Xiqun Chen & Chenfeng Xiong & Lei Zhang, 2018. "A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice," Transportation, Springer, vol. 45(5), pages 1499-1522, September.
    14. Yinglian Zhou & Jifeng Chen, 2021. "Traffic Change Forecast and Decision Based on Variable Structure Dynamic Bayesian Network: Traffic Decision," International Journal of Decision Support System Technology (IJDSST), IGI Global, vol. 13(2), pages 1-17, April.
    15. Beatriz Molina Serrano & Nicoleta González-Cancelas & Francisco Soler-Flores & Samir Awad-Nuñez & Alberto Camarero Orive, 2018. "Use of Bayesian Networks to Analyze Port Variables in Order to Make Sustainable Planning and Management Decision," Logistics, MDPI, vol. 2(1), pages 1-16, January.
    16. Maosheng Li & Qian Luo & Jing Fan & Qingyan Ning, 2023. "Impact Analysis of Smart Road Stud on Driving Behavior and Traffic Flow in Two-Lane Two-Way Highway," Sustainability, MDPI, vol. 15(15), pages 1-20, July.
    17. Wen, Tzai-Hung & Chin, Wei-Chien-Benny & Lai, Pei-Chun, 2017. "Understanding the topological characteristics and flow complexity of urban traffic congestion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 473(C), pages 166-177.
    18. Mrinal Kanti Sen & Subhrajit Dutta & Golam Kabir, 2021. "Flood Resilience of Housing Infrastructure Modeling and Quantification Using a Bayesian Belief Network," Sustainability, MDPI, vol. 13(3), pages 1-24, January.
    19. Abdullah Alshehri & Mahmoud Owais & Jayadev Gyani & Mishal H. Aljarbou & Saleh Alsulamy, 2023. "Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    20. Ma, Tao & Zhou, Zhou & Abdulhai, Baher, 2015. "Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 27-47.

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