Abstract
This study provides a road traffic portrait in urban areas to compare the congestion level of certain sections. In view of a better exploitation, we proposed a Bayesian network (BN) analysis approach to modeling the probabilistic dependency structure of congestion causes on a particular road segment and analyzing the probability of traffic congestion. In this case, two steps are also necessary, the macroscopic traffic flow modeling and the traffic simulation for which empirical measurements can be developed and tested. The BN method is used to analyze the uncertainty and probability of traffic congestion, and is proved to be fully capable of representing the stochastic nature of road network situation. This approach is used to represent road traffic knowledge in order to build scenarios based on a practical case adapted in the city of Sfax.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ni, D.: Traffic Flow Theory, 1st edn. Butterworth-Heinemann, Oxford (2015)
Martins, C., da Conceição Fonseca, M., Pato, M.V.: Modeling the steering of international roaming traffic. Eur. J. Oper. Res. 261(2), 735–754 (2017)
Altheneyan, A.S., Menai, M.E.B.: Naïve Bayes classifiers for authorship attribution of Arabic texts. Comput. Inf. Sci. 26(4), 473–484 (2014)
Yang, M.C., Huang, C.S., Chen, J.H., Chang, R.F.: Whole breast lesion detection using Naive Bayes classifier for portable ultrasound. Ultrasound Med. Biol. 38(11), 1870–1880 (2012)
Fusco, G., Colombaroni, C., Isaenko, N.: Short-term speed predictions exploiting big data on large urban road networks. Transp. Res. Part C: Emerg. Technol. 73, 183–201 (2016)
Horvitz, E.J., Sarin, R., Liao, L.: Prediction, expectation, and surprise: methods, designs, and study of a deployed traffic forecasting service. Microsoft, Indrix, University of Washington (2006)
Kim, J., Wang, G.: Diagnosis and prediction of traffic congestion on urban road networks using Bayesian networks. Transp. Res. Rec. J. Transp. Res. Board 108–118 (2016)
Chen, C., Zhang, G., Wang, H., Yang, J., Jin, P., Walton, C.M.: Bayesian network-based formulation and analysis for toll road utilization supported by traffic information provision. Transp. Res. Part C: Emerg. Technol. 60, 339–359 (2015)
Yang, H., Shen, L., Xiang, Y., Yao, Z., Liu, X.: Freeway incident duration prediction using Bayesian network. In: 4th International Conference on Transportation Information and Safety (ICTIS), Canada, pp. 974 – 980 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Derbel, A., Boujelbene, Y. (2018). Road Congestion Analysis in the Agglomeration of Sfax Using a Bayesian Model. In: Boudriga, N., Alouini, MS., Rekhis, S., Sabir, E., Pollin, S. (eds) Ubiquitous Networking. UNet 2018. Lecture Notes in Computer Science(), vol 11277. Springer, Cham. https://doi.org/10.1007/978-3-030-02849-7_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-02849-7_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02848-0
Online ISBN: 978-3-030-02849-7
eBook Packages: Computer ScienceComputer Science (R0)