Abstract
During the past years, there were so many researches focusing on traffic prediction and ways to resolve future traffic congestion; at the very beginning, the goal was to build a mechanism capable of predicting the traffic for short-term; meanwhile, others did focus on the traffic prediction using different perspectives and methods, in order to obtain better and more precise results. The main aim was to come up with enhancements to the accuracy and precision of the outcomes and get a longer-term vision, also build a prediction’s system for the traffic jams and solve them by taking preventive measures (Bolshinsky and Freidman in Traffic flow forecast survey 2012, [1]) basing on artificial intelligence decisions with the given predictions. There are many algorithms; some of them are using statistical physics methods; others use genetic algorithms… the common goal was to achieve a kind of framework that will allow us to move forward and backward in time to have a practical and effective traffic prediction. In addition to moving forward and backward in time, the application of the new framework allows us to locate future traffic jams (congestions). This paper reviews the evolution of the existing traffic prediction’s approaches and the edge given by AI to make the best decisions; we will focus on the model-driven and data-driven approaches. We start by analyzing all advantages and disadvantages of each approach to reach our goal in order to pursue the best approaches for the best output possible.
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
Bolshinsky, E., Freidman, R.: Traffic Flow Forecast Survey. Technion—Computer Science Department, Tech. Rep. (2012)
Matthews, S.E.: How Google Tracks Traffic. Connectivist (2013)
Ministry of Equipment, Transport, Logistics and Water (Roads Management) of Morocco (2017)
vom Brocke, J., Simons, A., Riemer, K., Niehaves, B., Plattfaut, R., Cleven, A.: Standing on the Shoulders of Giants: Challenges and Recommendations of Literature Search in Information Systems Research (2015)
Barbosa, H., Barthelemy, M., Ghoshal, G., James, C.R., Lenormand, M., Louail, T., Menezes, R., Ramasco, J.J., Simini, F., Tomasini, M.: Human mobility: models and applications. Phys. Rep. 734, 1–74 (2018)
Saberi, K.M., Bertini, R.L.: Empirical Analysis of the Effects of Rain on Measured Freeway Traffic Parameters. Portland State University, Department of Civil and Environmental Engineering, Portland (2009)
Zipf, G.K.: The p1p2/d hypothesis: on the intercity movement of persons. Am. Sociol. Rev. 11(6), 677–686 (1946)
Jung, W.S.: Gravity model in the korean highway. 81(4), 48005 (2008)
Feynman, R.: The Brownian movement. Feynman Lect. Phys. 1, 41–51 (1964)
Matyas, L.: Proper econometric specification of the gravity model. World Econ. 20(3), 363–368 (1997)
Kong, X., Xu, Z., Shen, G., Wang, J., Yang, Q., Zhang, B.: Urban traffic congestion estimation and pre- diction based on floating car trajectory data. Futur. Gener. Comput. Syst. 61, 97–107 (2016)
Anderson, J.E.: The gravity model. Nber Work. Papers 19(3), 979–981 (2011)
Barth ́elemy, M.: Spatial networks. Phys. Rep. 499(1), 1–101 (2011)
Lenormand, M., Bassolas, A., Ramasco, J.J.: Sys- tematic comparison of trip distribution laws and mod- els. J. Transp. Geogr. 51, 158–169 (2016)
Simini, F., Gonz ́alez, M.C., Maritan, A., Baraba ́si, A.L.: A universal model for mobility and migration patterns. Nature 484(7392), 96–100 (2012)
INRIX.: Who We Are. INRIX Inc. (2014)
Lopes, J.: Traffic prediction for unplanned events on highways (2011)
Ziliaskopoulos, A.K., Waller, S.: An Internet-based geographic information system that integrates data, models and users for transportation applications. Transp. Res. Part C: Emerg. Technol. 8(1–6), 427–444 (2000)
Ben-akiva, M., Bierlaire, M., Koutsopoulos, H., Mishalani, R.: DynaMIT: a simulation-based system for traffic prediction. DACCORD Short Term Forecasting Workshop, pp. 1–12 (1998)
Milkovits, M., Huang, E., Antoniou, C., Ben-Akiva, M., Lopes, J.A.: DynaMIT 2.0: the next generation real-time dynamic traffic assignment system. In: 2010 Second International Conference on Advances in System Simulation, pp. 45–51 (2010)
Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., Ma, W.Y.: Mining user similarity based on location history. In: ACM Sigspatial International Conference on Advances in Geographic Information Systems, page 34. ACM (2008)
Wheatley, M.: Big Data Traffic Jam: Smarter Lights, Happy Drivers. Silicon ANGLE (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Soussi Niaimi, BE., Bouhorma, M., Zili, H. (2022). The Evolution of the Traffic Congestion Prediction and AI Application. In: Ben Ahmed, M., Teodorescu, HN.L., Mazri, T., Subashini, P., Boudhir, A.A. (eds) Networking, Intelligent Systems and Security. Smart Innovation, Systems and Technologies, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-3637-0_2
Download citation
DOI: https://doi.org/10.1007/978-981-16-3637-0_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3636-3
Online ISBN: 978-981-16-3637-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)