Research on Intelligent Traffic Management Methods for Urban Traffic Congestion

Authors

  • Wenao Xiao

DOI:

https://doi.org/10.61173/p2bry907

Keywords:

traffic congestion, Internet of Vehicles, public transport optimization, driver behavior control, intelligent transportation systems

Abstract

In recent years, the problem of traffic congestion has become more and more serious as the urban population continues to increase and the number of private cars is also rising. Therefore, there is an urgent need to find methods that can effectively alleviate traffic congestion. Based on this, this paper analyzes and researches the causes of traffic congestion and the existing solutions, and analyzes in detail the three effective solutions to traffic congestion, Telematics, Driver Emotional Adjustment Control and Public Transportation Optimization, and finally puts forward several suggestions in combination with the existing problems of urban transportation system, hoping to provide certain references for the relevant departments.

References

[1] Qi Y, Cheng Z. Research on Traffic Congestion Forecast Based on Deep Learning. Information (2078-2489). 2023;14(2):108. doi:10.3390/info14020108.

[2] Li Xiaodong, Ran Bingbing. Research on the application of vehicle networking in smart city traffic management[J]. Automobile and Driving Maintenance,2024,(7): 21-23.

[3] Shouming Q, Liwei H, Xiaoyang D. Control Methods of Traffic Congestion Based on Drivers’ Behavior Characteristics. 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA), Intelligent Computation Technology and Automation (ICICTA), 2015 8th International Conference on. June 2015:333-336. doi:10.1109/ ICICTA.2015.90.

[4] Afrin, T., Yodo, N.: A survey of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability 12(11), 4660 (2020). https://doi. org/10.3390/ su12114660.

[5] Jarašūnienė A, Žemaitytė G. Identification of Problem Areas of Traffic Flow Management and Solutions in Vilnius. Transbaltica: Proceedings of the International Scientific Conference. 2022;13:502-509. doi:10.1007/978-3-031-25863- 3_47.

[6] Zhang Y, He Y, Chen R, Tiwari P, Saddik AE, Hossain MS. A Dual Channel Cyber–Physical Transportation Network for Detecting Traffic Incidents and Driver Emotion. IEEE Transactions on Consumer Electronics, Consumer Electronics, IEEE Transactions on, IEEE Trans Consumer Electron. 2024;70(1):1766-1774. doi:10.1109/TCE.2023.3325335.

[7] Mertens L, Wolbeck L-A, Rößler D, Xie L, Kliewer N. An overview of optimization approaches for scheduling and rostering resources in public transportation. 2023. Accessed October 17, 2024. https://search.ebscohost.com/login.aspx? direct=true&db=edsarx&AN=edsarx.2310.13425&site=edslive&scope=site.

[8] Xin Mantong, Tan Jinlin, Liu Yang, Li Xiaoshen. Analysis of public transportation problems and countermeasures in Harbin[J]. Construction and Budget,2021,(2): 98-100.

[9] Qin Minheng. Research on optimization of bus routes in Lanzhou city[J]. Inner Mongolia Science and Economy,2018,(21): 73-74, 103.

[10] Sriprateep K, Pitakaso R, Khonjun S, et al. Multi- Objective Optimization of Resilient, Sustainable, and Safe Urban Bus Routes for Tourism Promotion Using a Hybrid Reinforcement Learning Algorithm. Mathematics (2227-7390). 2024;12(14):2283. doi:10.3390/math12142283.

[11] Song Y, Jin Y, Li D. Optimization of Bus Routes at Urban Rail Transit Stations Based on Link Growth Probability. 2023 7th International Conference on Transportation Information and Safety (ICTIS), Transportation Information and Safety (ICTIS), 2023 7th International Conference on. August 2023:1-10. doi:10.1109/ICTIS60134.2023.10243843.

[12] Samuilovas A, Uspalyte-Vitkuniene R. Viesojo Transporto Elektroninio Bilieto Sistemos, Galimybes Ir Perspektyvos Lietuvoje /E Ticketing at Public Transport: Solutions, Advantages and Perspectives in Lithuania. Science - Future of Lithuania. 2023;15:1. doi:10.3846/mla.2023.19429.

[13] He X, Yang Z, Fan T, Gao J, Zhen L, Lyu J. Branch and price algorithm for route optimization on customized bus service. Annals of Operations Research. 2024;335(1):205-236. doi:10.1007/s10479-023-05474-4.

[14] Wang Y, Tian Y, Yang B, Wang J, Hu X, An S. Planning Flexible Bus Service as an Alternative to Suspended Bicycle- Sharing Service: A Data-Driven Approach. Journal of Advanced Transportation. 2023;2023:1-15. doi:10.1155/2023/3187654.

[15] Lv Chang 1, Zhang Chaoyong 1, Zhang Daode 2, Ren Yaping 1, Meng Leilei 3. Shared bicycle rebalancing problem based on two-layer forbidden search algorithm[J]. Computer Integrated Manufacturing Systems,2020,26(12): 3216-3228.

[16] Huang Fubin, Du Xin. Introduction to urban intelligent transportation management problems[J]. Science and Education Guide (Electronic Edition),2015,(32): 165.

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Published

2024-12-31