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Traffic management systems: a survey of current solutions and emerging technologies

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Abstract

With the rapid increase in the number of vehicles on the road, the necessity for traffic management systems became apparent. In order to effectively control traffic, a number of technical solutions were proposed to solve traffic issues, such as eliminating traffic congestion and identifying the shortest routes. Researchers are motivated to use various data-driven solutions that assist decision-makers in making timely decisions due to the enormous amount of traffic data that has been gathered by utilizing sensors, traffic signals, and cameras. The primary objective of this research is to analyze the current traffic management systems from several technical perspectives. According to the technology employed, this study reviews recent traffic management system methodologies and classifies them into five main groups: machine learning-based, fuzzy logic-based, statistically-based, graph-based, and hybrid approaches. Each group is presented together with a thorough overview of its scope, main challenges, analysis type, and dataset. Researchers and practitioners are anticipated to use this study as a guide to develop new technical-based traffic management systems, as well as to propose new contributions or enhance current ones.

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  4. https://www.mdpi.com/.

References

  1. Afrin, T., & Yodo, N. (2020). A survey of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability, 12(11), 4660.

    Article  Google Scholar 

  2. Alsrehin, N. O., Klaib, A. F., & Magableh, A. (2019). Intelligent transportation and control systems using data mining and machine learning techniques: A comprehensive study. IEEE Access, 7, 49830–49857.

    Article  Google Scholar 

  3. Arshad, S., Raza, Q., & Hussain, M. (2019). VANETs embedded novel traffic management paradigm for smart city milieu. In Proceedings of the 3rd international conference on future networks and distributed systems, ACM, pp. 1–10

  4. Ata, A., Khan, M. A., Abbas, S., Ahmad, G., & Fatima, A. (2019). Modelling smart road traffic congestion control system using machine learning techniques. Neural Network World, 29(2), 99–110.

    Article  Google Scholar 

  5. Bihani, P., & Patil, S. (2014). A comparative study of data analysis techniques. International Journal of Emerging Trends & Technology in Computer Science, 3(2), 95–101.

    Google Scholar 

  6. Boukerche, A., & Wang, J. (2020). Machine learning-based traffic prediction models for intelligent transportation systems. Computer Networks, 181, 107530.

    Article  Google Scholar 

  7. Carlucci, G., Cicco, L. D., Holmer, S., & Mascolo, S. (2017). Congestion control for web real-time communication. IEEE/ACM Transactions on Networking, 25(5), 2629–2642.

    Article  Google Scholar 

  8. Chavhan, S., & Venkataram, P. (2020). Prediction based traffic management in a metropolitan area. Journal of Traffic and Transportation Engineering (English Edition), 7(4), 447–466.

    Article  Google Scholar 

  9. Chen, J., & Gonsalves, T. (2018). Autonomous highway car following system based on fuzzy control. In Proceedings of the 2018 2nd high performance computing and cluster technologies conference, ACM, pp. 98-101

  10. Chen, W. (2020). Research on data traffic model of internet of things based on statistical analysis. In Proceedings of the 2020 4th international conference on electronic information technology and computer engineering, ACM, pp. 99-103

  11. Chung, J. J., Rebhuhn, C., Yates, C., Hollinger, G. A., & Tumer, K. (2018). A multiagent framework for learning dynamic traffic management strategies. Autonomous Robots, 43(6), 1375–1391.

    Article  Google Scholar 

  12. de Souza, A. M., Brennand, C. A., Yokoyama, R. S., Donato, E. A., Madeira, E. R., & Villas, L. A. (2017). Traffic management systems: A classification, review, challenges, and future perspectives. International Journal of Distributed Sensor Networks, 13(4), 155014771668361.

    Article  Google Scholar 

  13. de Souza, A. M., Yokoyama, R., Boukerche, A., Maia, G., Cerqueira, E., Loureiro, A. A., & Villas, L. A. (2016). ICARUS: Improvement of traffic condition through an alerting and re-routing system. Computer Networks, 110, 118–132.

    Article  Google Scholar 

  14. Derbel, A. & Boujelbene, Y. (2015). Bayesian network for traffic management application: Estimated the travel time. In 2015 2nd world symposium on web applications and networking (WSWAN), IEEE, pp. 1–6

  15. Djahel, S., Doolan, R., Muntean, G.-M., & Murphy, J. (2015). A communications-oriented perspective on traffic management systems for smart cities: Challenges and innovative approaches. IEEE Communications Surveys & Tutorials, 17(1), 125–151.

    Article  Google Scholar 

  16. Fang, Z., Pan, L., Chen, L., Du, Y., & Gao, Y. (2021). MDTP: A multi-source deep traffic prediction framework over spatio-temporal trajectory data. Proceedings of the VLDB Endowment, 14(8), 1289–1297.

    Article  Google Scholar 

  17. Furno, A., Faouzi, N.-E.E., Sharma, R., & Zimeo, E. (2021). Graph-based ahead monitoring of vulnerabilities in large dynamic transportation networks. PLOS One, 16(3), e0248764.

    Article  Google Scholar 

  18. Gao, Y., Zhou, C., Rong, J., Wang, Y., & Liu, S. (2022). Short-term traffic speed forecasting using a deep learning method based on multitemporal traffic flow volume. IEEE Access, 10, 82384–82395.

    Article  Google Scholar 

  19. Ghorbanzadeh, O., Blaschke, T., Gholamnia, K., Meena, S. R., Tiede, D., & Aryal, J. (2019). Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sensing, 11(2), 196.

    Article  Google Scholar 

  20. Gilmore, J. F., Elibiary, K. J., & Abe, N. (1993). Traffic management applications of neural networks. In Working notes, AAAI-93 Workshop on AI in intelligent vehicle highway systems, pp. 85–95

  21. Goyal, M., Priya, A., Kumar, C., Verma, V., & Hota, M. (2018). An ingenious traffic control system using fuzzy logic control with emergency override feature. In 2018 IEEE international conference on system, computation, automation and networking (ICSCA), IEEE, pp. 1–6

  22. Guidoni, D. L., Maia, G., Souza, F. S. H., Villas, L. A., & Loureiro, A. A. F. (2020). Vehicular traffic management based on traffic engineering for vehicular ad hoc networks. IEEE Access, 8, 45167–45183.

    Article  Google Scholar 

  23. Hu, Z., Shao, F., & Sun, R. (2022). A new perspective on traffic flow prediction: A graph spatial-temporal network with complex network information. Electronics, 11(15), 2432.

    Article  Google Scholar 

  24. Hung, C. -Y., Chen, W. -C., Lai, P. -T., Lin, C. -H., & Lee, C. -C. (2017). Comparing deep neural network and other machine learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database. In 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp. 3110–3113

  25. Jafari, S., Shahbazi, Z., & Byun, Y.-C. (2021). Traffic control prediction design based on fuzzy logic and lyapunov approaches to improve the performance of road intersection. Processes, 9(12), 2205.

    Article  Google Scholar 

  26. Jafari, S., Shahbazi, Z., & Byun, Y.-C. (2022). Improving the road and traffic control prediction based on fuzzy logic approach in multiple intersections. Mathematics, 10(16), 2832.

    Article  Google Scholar 

  27. Jiang, W., & Luo, J. (2022). Graph neural network for traffic forecasting: A survey. Expert Systems with Applications, 207, 117921.

    Article  Google Scholar 

  28. Jin, J., Rong, D., Pang, Y., Ye, P., Ji, Q., Wang, X., Wang, G., & Wang, F.-Y. (2022). An agent-based traffic recommendation system: Revisiting and revising urban traffic management strategies. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(11), 7289–7301.

    Article  Google Scholar 

  29. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.

    Article  Google Scholar 

  30. Joy, J. J., Bhat, M., Verma, N., & Jani, M. (2018). Traffic management through image processing and fuzzy logic. In 2018 Second international conference on intelligent computing and control systems (ICICCS), IEEE, pp. 52–55

  31. Ke, R., Li, Z., Kim, S., Ash, J., Cui, Z., & Wang, Y. (2017). Real-time bidirectional traffic flow parameter estimation from aerial videos. IEEE Transactions on Intelligent Transportation Systems, 18(4), 890–901.

    Article  Google Scholar 

  32. Keele, S., et al. (2007). Guidelines for performing systematic literature reviews in software engineering. Citeseer: Technical report

  33. Kwee, A. T., Chiang, M. -F., Prasetyo, P. K., & Lim, E. -P. (2018). Traffic-cascade: mining and visualizing lifecycles of traffic congestion events using public bus trajectories. In Proceedings of the 27th ACM international conference on information and knowledge management, pp. 1955-1958

  34. Lee, S., Kim, Y., Kahng, H., Lee, S.-K., Chung, S., Cheong, T., Shin, K., Park, J., & Kim, S. B. (2020). Intelligent traffic control for autonomous vehicle systems based on machine learning. Expert Systems with Applications, 144, 113074.

    Article  Google Scholar 

  35. Mondal, M. A. & Rehena, Z. (2019). Intelligent traffic congestion classification system using artificial neural network. In Companion proceedings of the 2019 world wide web conference, ACM, pp. 110-116

  36. Nallaperuma, D., Nawaratne, R., Bandaragoda, T., Adikari, A., Nguyen, S., Kempitiya, T., Silva, D. D., Alahakoon, D., & Pothuhera, D. (2019). Online incremental machine learning platform for big data-driven smart traffic management. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4679–4690.

    Article  Google Scholar 

  37. Nama, M., Nath, A., Bechra, N., Bhatia, J., Tanwar, S., Chaturvedi, M., & Sadoun, B. (2021). Machine learning-based traffic scheduling techniques for intelligent transportation system: Opportunities and challenges. International Journal of Communication Systems, 34(9), e4814.

    Article  Google Scholar 

  38. Ning, Z., Zhang, K., Wang, X., Obaidat, M. S., Guo, L., Hu, X., Hu, B., Guo, Y., Sadoun, B., & Kwok, R. Y. K. (2021). Joint computing and caching in 5g-envisioned internet of vehicles: A deep reinforcement learning-based traffic control system. IEEE Transactions on Intelligent Transportation Systems, 22(8), 5201–5212.

    Article  Google Scholar 

  39. Olayode, I. O., Tartibu, L. K., Okwu, M. O., & Severino, A. (2021). Comparative traffic flow prediction of a heuristic ANN model and a hybrid ANN-PSO model in the traffic flow modelling of vehicles at a four-way signalized road intersection. Sustainability, 13(19), 10704.

    Article  Google Scholar 

  40. Ouallane, A. A., Bahnasse, A., Bakali, A., & Talea, M. (2022). Overview of road traffic management solutions based on IoT and AI. Procedia Computer Science, 198, 518–523.

    Article  Google Scholar 

  41. Pachauri, S., Yadav, R., Maurya, S., Verma, G., Saxena, A., & Agarwal, N. K. (2021). Performance analysis of different techniques of traffic control system. In 2021 First international conference on advances in computing and future communication technologies (ICACFCT), IEEE, pp. 208–211

  42. Praveen, D. S., & Raj, D. P. (2020). RETRACTED ARTICLE: Smart traffic management system in metropolitan cities. Journal of Ambient Intelligence and Humanized Computing, 12(7), 7529–7541.

    Article  Google Scholar 

  43. Qin, L., Yufei, H., & Zhuoqun, W. (2018). Signal timing simulation of single intersection based on fuzzy-genetic algorithm. In Proceedings of the 10th international conference on computer modeling and simulation, ACM, pp. 28-32

  44. Raskar, C., & Nema, S. (2020). Modified fuzzy-based smart barricade movement for traffic management system. Wireless Personal Communications, 116(4), 3351–3370.

    Article  Google Scholar 

  45. Respati, S., Bhaskar, A., & Chung, E. (2018). Traffic data characterisation: Review and challenges. Transportation Research Procedia, 34, 131–138.

    Article  Google Scholar 

  46. Rout, M., Rout, J. K., & Das, H., eds (2020). Nature inspired computing for data science. Springer International Publishing

  47. Sankaranarayanan, M., Mala, C., & Mathew, S. (2017). Congestion rate estimation for VANET infrastructure using fuzzy logic. In Proceedings of the 2017 international conference on intelligent systems, metaheuristics & swarm intelligence - ISMSI’17, ACM Press, pp. 98–102

  48. Savithramma, R. M. & Sumathi, R. (2020). Road traffic signal control and management system : A survey. In 2020 3rd International conference on intelligent sustainable systems (ICISS), IEEE, pp. 104–110

  49. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.

    Article  Google Scholar 

  50. Selvan, C., & Balasundaram, S. R. (2021). Data analysis in context-based statistical modeling in predictive analytics. In Advances in Data Mining and Database Management, IGI Global, pp. 96–114

  51. Setiawan, E. K., & Budayasa, I. K. (2017). Application of graph theory concept for traffic light control at crossroad. In AIP Conference Proceedings, 1–11

  52. Shepelev, V., Aliukov, S., Nikolskaya, K., & Shabiev, S. (2020). The capacity of the road network: Data collection and statistical analysis of traffic characteristics. Energies, 13(7), 1765.

    Article  Google Scholar 

  53. Shepelev, V., Glushkov, A., Bedych, T., Gluchshenko, T., & Almetova, Z. (2021). Predicting the traffic capacity of an intersection using fuzzy logic and computer vision. Mathematics, 9(20), 2631.

    Article  Google Scholar 

  54. Sheriff, F. (2021). ELMOPP: An application of graph theory and machine learning to traffic light coordination. Applied Computing and Informatics

  55. Sommer, M., Tomforde, S., & Hähner, J. (2013). Using a neural network for forecasting in an organic traffic control management system. In International workshop on embedded self-organizing systems (ESOS ’13), June 2l5, 2013, San Jose, CA, part of the 10th international comference on autonomic computing (ICAC ’13), pp. 1–6

  56. Stevanovic, A., Dakic, I., & Zlatkovic, M. (2016). Comparison of adaptive traffic control benefits for recurring and non-recurring traffic conditions. IET Intelligent Transport Systems, 11(3), 142–151.

    Article  Google Scholar 

  57. Tomar, A. S., Singh, M., Sharma, G., & Arya, K. (2018). Traffic management using logistic regression with fuzzy logic. Procedia Computer Science, 132, 451–460.

    Article  Google Scholar 

  58. Tran, Q. H., Fang, Y.-M., Chou, T.-Y., Hoang, T.-V., Wang, C.-T., Vu, V. T., Ho, T. L. H., Le, Q., & Chen, M.-H. (2022). Short-term traffic speed forecasting model for a parallel multi-lane arterial road using GPS-monitored data based on deep learning approach. Sustainability, 14(10), 6351.

    Article  Google Scholar 

  59. Wang, P., Lai, J., Huang, Z., Tan, Q., & Lin, T. (2021). Estimating traffic flow in large road networks based on multi-source traffic data. IEEE Transactions on Intelligent Transportation Systems, 22(9), 5672–5683.

    Article  Google Scholar 

  60. Wang, Z., Sun, P., & Boukerche, A. (2022a). A novel time efficient machine learning-based traffic flow prediction method for large scale road network. In ICC 2022 - IEEE international conference on communications, IEEE, pp. 3532–3537

  61. Wang, Z., Sun, P., Hu, Y., & Boukerche, A. (2022b). A novel mixed method of machine learning based models in vehicular traffic flow prediction. In Proceedings of the international conference on modeling analysis and simulation of wireless and mobile systems on international conference on modeling analysis and simulation of wireless and mobile systems, ACM, pp. 95-101

  62. Yang, X., Luo, S., Gao, K., Qiao, T., & Chen, X. (2019). Application of data science technologies in intelligent prediction of traffic congestion. Journal of Advanced Transportation, 2019, 1–14.

    Google Scholar 

  63. Yang, Y., Zhang, T., Jia, Q., Cheng, G., Yu, Q., & Jin, M. (2022). Optimal design of multimodal traffic strategies in emergency evacuation considering background traffic. IEEE Access, 10, 77158–77169.

    Article  Google Scholar 

  64. Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  Google Scholar 

  65. Zamri, M. A., & Hamzah, N. (2022). The implementation of intelligent traffic management system in solving traffic congestion: A survey of federal route 3214. Journal of Physics: Conference Series, 2319(1), 012032.

    Google Scholar 

  66. Zhang, C., Zhu, L., Ni, J., Huang, C., & Shen, X. (2020). Verifiable and privacy-preserving traffic flow statistics for advanced traffic management systems. IEEE Transactions on Vehicular Technology, 69(9), 10336–10347.

    Article  Google Scholar 

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  1. Princess Sumaya University for Technology, Amman, Jordan

    Wael Etaiwi

  2. Department of Computer Science and Applications, Faculty of Prince Al Huessien Bin Abdullah II for Information Technology, The Hashemite University, Zarqa, Jordan

    Sahar Idwan

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  1. Wael Etaiwi
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Etaiwi, W., Idwan, S. Traffic management systems: a survey of current solutions and emerging technologies. J Comput Soc Sc 8, 7 (2025). https://doi.org/10.1007/s42001-024-00340-0

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