A A systematic literature review of traffic control system implementations

Authors

DOI:

https://doi.org/10.26439/interfases2024.n19.6779

Keywords:

traffic control, methods, algorithms, models, YOLO, implementations

Abstract

Traffic congestion frequently occurs in highly populated cities and can result from poor civil planning or inadequate public transportation. This issue increases traffic accidents, air pollution, fuel loss, and public dissatisfaction. Therefore, implementing traffic control systems that improve traffic flow and reduce travel times becomes essential. This work conducts a systematic literature review to identify the most efficient methods, algorithms, and models for developing traffic control systems. The review identifies three methods and three algorithms that are highly efficient for these systems, highlighting Bayesian filters and convolutional neural networks. It also shows that You Only Look Once (YOLO) is the most efficient image processing model for these implementations.

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Author Biographies

  • Eduardo Rodrigo Wong Leon, Universidad Católica Sedes Sapientiae, Perú

    Estudiante de la Universidad Católica Sedes Sapientiae con una pasión destacada por el desarrollo de software y el análisis de datos. Actualmente, se encuentra inmerso en su formación universitaria, donde explora activamente las tecnologías de vanguardia aplicadas sobre estas disciplinas.

  • Marco Antonio Coral Ygnacio, Universidad Católica Sedes Sapientiae, Perú

    Es candidato a doctor en Ingeniería de Sistemas por la Universidad Nacional Mayor de San Marcos y magíster en Ciencias en Ingeniería de Sistemas y Computación. Es experto en temas de transformación digital en universidades. Ha sido responsable técnico del proyecto Cero Papel en la Universidad Nacional Mayor de San Marcos, donde trabajó con sistemas de gestión documentaria con firma digital, implementación de documentos digitales tales como grados, títulos y otros, integración de sistemas y generación de servicios para la universidad. Ha sido jefe de la Unidad de Tecnología Educativa, jefe de la Unidad de Servidores y Sistemas de Información de la Red Telemática-UNMSM y jefe de la Oficina de Calidad y Acreditación Académica de la FISI-UNMSM, responsable de la acreditación de los programas de posgrado e implementación de la norma ISO 9001:2015. Es investigador en temas de transformación digital con publicaciones enrevistas científicas y evaluador de proyectos de innovación tecnológica para CONCYTEC.

References

Aqib, M., Mehmood, R., Alzahrani, A., Katib, I., Albeshri, A., & Altowaijri, S. M. (2019). Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs. Sensors, 19(9). https://doi.org/10.3390/s19092206

Bao, Y., Huang, J., Shen, Q., Cao, Y., Ding, W., Shi, Z., & Shi, Q. (2023). Spatial–Temporal Complex Graph Convolution Network for Traffic Flow Prediction. Engineering Applications of Artificial Intelligence, 121. https://doi.org/10.1016/j.engappai.2023.106044

Chabchoub, A., Hamouda, A., Al-Ahmadi, S., & Cherif, A. (2021). Intelligent Traffic Light Controller using Fuzzy Logic and Image Processing. International Journal of Advanced Computer Science and Applications, 12(4), 396-399. https://doi.org/10.14569/IJACSA.2021.0120450

Chahal, A., Gulia, P., Gill, N. S., & Priyadarshini, I. (2023). A Hybrid Univariate Traffic Congestion Prediction Model for IoT-Enabled Smart City. Information, 14(5). https://doi.org/10.3390/info14050268

Chatterjee, K., De, A., & Chan, F. T. S. (2019). Real time traffic delay optimization using shadowed type-2 fuzzy rule base. Applied Soft Computing Journal, 74, 226-241. https://doi.org/10.1016/j.asoc.2018.10.008

Cheng, R., Qiao, Z., Li, J., & Huang, J. (2023). Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm. Sensors, 23(11). https://doi.org/10.3390/s23115157

Damadam, S., Zourbakhsh, M., Javidan, R., & Faroughi, A. (2022). An Intelligent IoT Based Traffic Light Management System: Deep Reinforcement Learning. Smart Cities, 5(4), 1293-1311. https://doi.org/10.3390/smartcities5040066

Hao, S., Yang, L., Ding, L., & Guo, Y. (2019). Distributed Cooperative Backpressure-Based Traffic Light Control Method. Journal of Advanced Transportation, 2019(1). https://doi.org/10.1155/2019/7481489

Hao, W., Rong, D., Yi, K., Zeng, Q., Gao, Z., Wu, W., Wei, C., & Scepanovic, B. (2020). Traffic Status Prediction of Arterial Roads Based on the Deep Recurrent Q-Learning. Journal of Advanced Transportation, 2020(1). https://doi.org/10.1155/2020/8831521

Impedovo, D., Balducci, F., Dentamaro, V., & Pirlo, G. (2019). Vehicular traffic congestion classification by visual features and deep learning approaches: A comparison. Sensors, 19(23). https://doi.org/10.3390/s19235213

Islam, Z., Abdel-Aty, M., & Mahmoud, N. (2022). Using CNN-LSTM to predict signal phasing and timing aided by High-Resolution detector data. Transportation Research Part C: Emerging Technologies, 141. https://doi.org/10.1016/j.trc.2022.103742

Jin, J., & Ma, X. (2019). A non-parametric Bayesian framework for traffic-state estimation at signalized intersections.Information Sciences, 498, 21-40. https://doi.org/10.1016/j.ins.2019.05.032

Joo, H., & Lim, Y. (2021). Traffic Signal Time Optimization Based on Deep Q-Network. Applied Sciences, 11(21). https://doi.org/10.3390/app11219850

Kim, M., Schrader, M., Yoon, H. S., & Bittle, J. A. (2023). Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information. Sustainability, 15(9). https://doi.org/10.3390/su15097637

Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009). Systematic literature reviews in software engineering – A systematic literature review. Information and Software Technology, 51(1), 7-15. https://doi.org/10.1016/j.infsof.2008.09.009

Korecki, M., & Helbing, D. (2022). Analytically Guided Reinforcement Learning for Green It and Fluent Traffic. IEEE Access, 10, 96348-96358. https://doi.org/10.1109/ACCESS.2022.3204057

Kumaran, S. K., Mohapatra, S., Dogra, D. P., Roy, P. P., & Kim, B. G. (2019). Computer vision-guided intelligent traffic signaling for isolated intersections. Expert Systems with Applications, 134, 267-278. https://doi.org/10.1016/j.eswa.2019.05.049

Li, Y., Chen, Y., Yuan, S., Liu, J., Zhao, X., Yang, Y., & Liu, Y. (2021). Vehicle detection from road image sequences for intelligent traffic scheduling. Computers and Electrical Engineering, 95. https://doi.org/10.1016/j.compeleceng.2021.107406

Li, Z., Yu, H., Zhang, G., Dong, S., & Xu, C. Z. (2021). Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning. Transportation Research Part C: Emerging Technologies, 125. https://doi.org/10.1016/j.trc.2021.103059

Liu, W. L., Gong, Y. J., Chen, W. N., & Zhang, J. (2020). EvoTSC: An evolutionary computation-based traffic signal controller for large-scale urban transportation networks. Applied Soft Computing, 97. https://doi.org/10.1016/j.asoc.2020.106640

Mukhtar, H., Afzal, A., Alahmari, S., & Yonbawi, S. (2023). CCGN: Centralized collaborative graphical transformer multi-agent reinforcement learning for multi-intersection signal free-corridor. Neural Networks, 166, 396-409. https://doi.org/10.1016/j.neunet.2023.07.027

Phursule, R., Lal, D., Waghere, S., Mughni, M. A., Ransubhe, S., & Shiralkar, C. (2023). Enhancing Traffic Flow Using Computer Vision Based - Dynamic Traffic Light Control and Lane Management. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7S), 386-391. https://doi.org/10.17762/ijritcc.v11i7s.7014

Rasheed, F., Yau, K. L. A., Noor, R. M., & Chong, Y. W. (2022). Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control. Computers, Materials and Continua, 71(2), 2225-2247. https://doi.org/10.32604/cmc.2022.022952

Shehu, H. A., Sharif, M. H., & Ramadan, R. A. (2020). Distributed Mutual Exclusion Algorithms for Intersection Traffic Problems. IEEE Access, 8, 13 8277-13 8296. https://doi.org/10.1109/ACCESS.2020.3012573

Shin, J., Roh, S., & Sohn, K. (2019). Image-Based Learning to Measure the Stopped Delay in an Approach of a Signalized Intersection. IEEE Access, 7, 169888-169898. https://doi.org/10.1109/ACCESS.2019.2955307

Stoilova, K., & Stoilov, T. (2022). Model Predictive Traffic Control by Bi-Level Optimization. Applied Sciences, 12(9). https://doi.org/10.3390/app12094147

Suga, S., Fujimori, R., Yamada, Y., Ihara, F., Takamura, D., Hayashi, K., & Kurihara, S. (2023). Traffic information interpolation method based on traffic flow emergence using swarm intelligence. Artificial Life and Robotics, 28(2), 367-380. https://doi.org/10.1007/s10015-022-00847-7

Szoke, L., Aradi, S., & Bécsi, T. (2023). Traffic Signal Control with Successor Feature-Based Deep Reinforcement Learning Agent. Electronics, 12(6). https://doi.org/10.3390/electronics12061442

Tan, J., Yuan, Q., Guo, W., Xie, N., Liu, F., Wei, J., & Zhang, X. (2022). Deep Reinforcement Learning for Traffic Signal Control Model and Adaptation Study. Sensors, 22(22). https://doi.org/10.3390/s22228732

Tunc, I., & Soylemez, M. T. (2023). Fuzzy logic and deep Q learning based control for traffic lights. Alexandria Engineering Journal, 67, 343-359. https://doi.org/10.1016/j.aej.2022.12.028

Vélez-Serrano, D., Álvaro-Meca, A., Sebastián-Huerta, F., & Vélez-Serrano, J. (2021). Spatio-temporal traffic flow prediction in madrid: An application of residual convolutional neural networks. Mathematics, 9(9). https://doi.org/10.3390/math9091068

Wakkumbura, R. T., Hettige, B., & Edirisuriya, A. (2021). Real-time traffic controlling system using multi-agent technology. Journal Européen des Systèmes Automatisés, 54(4), 633-640. https://doi.org/10.18280/jesa.540413

Wang, H., Zhu, J., & Gu, B. (2023). Model-Based Deep Reinforcement Learning with Traffic Inference for Traffic Signal Control. Applied Sciences, 13(6). https://doi.org/10.3390/app13064010

Wu, Q., Wu, J., Shen, J., Du, B., Telikani, A., Fahmideh, M., & Liang, C. (2022). Distributed agent-based deep reinforcement learning for large scale traffic signal control. Knowledge-Based Systems, 241. https://doi.org/10.1016/j.knosys.2022.108304

Yuan, Y., Zhang, Z., Yang, X. T., & Zhe, S. (2021). Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation. Transportation Research Part B: Methodological, 146, 88-110. https://doi.org/10.1016/j.trb.2021.02.007

Zhang, L., Wang, L., & Zhao, Q. (2020). Traffic State Recognition of Intersection Based on Image Model and PCA Hashing. Journal of Advanced Transportation, 2020(1). https://doi.org/10.1155/2020/3828395

Zhao, P., Yuan, Y., & Guo, T. (2022). Extensible Hierarchical Multi-Agent Reinforcement-Learning Algorithm in Traffic Signal Control. Applied Sciences, 12(24). https://doi.org/10.3390/app122412783

Zheng, Q., Xu, H., Chen, J., Zhang, D., Zhang, K., & Tang, G. (2022). Double Deep Q-Network with Dynamic Bootstrapping for Real-Time Isolated Signal Control: A Traffic Engineering Perspective. Applied Sciences, 12(17). https://doi.org/10.3390/app12178641

Published

2024-07-31

Issue

Section

Review papers

How to Cite

Wong Leon, E. R., & Coral Ygnacio, M. A. . (2024). A A systematic literature review of traffic control system implementations. Interfases, 019, 157-178. https://doi.org/10.26439/interfases2024.n19.6779

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