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How to Mitigate Traffic Congestion Based on Improved Ant Colony Algorithm: A Case Study of a Congested Old Area of a Metropolis

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  • Zhichao Li

    (Department of Public Administration, School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China)

  • Jilin Huang

    (Department of Environmental Science, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China, 2015120401007@std.uestc.edu.cn)

Abstract

Old areas of metropolises play a crucial role in their development. The main factors restricting further progress are primitive road transportation planning, limited space, and dense population, among others. Mass transit systems and public transportation policies are thus being adopted to make an old area livable, achieve sustainable development, and solve transportation problems. Identifying old areas of metropolises as a research object, this paper puts forth an improved ant colony algorithm and combines it with virtual reality. This paper predicts traffic flow in Yangpu area on the basis of data obtained through Python, a programming language. On comparing the simulation outputs with reality, the results show that the improved model has a better simulation effect, and can take advantage of the allocation of traffic resources, enabling the transport system to achieve comprehensive optimization of time, cost, and accident rates. Subsequently, this paper conducted a robustness test, the results of which show that virtual traffic simulation based on the improved ant colony algorithm can effectively simulate real traffic flow, use vehicle road and signal resources, and alleviate overall traffic congestion. This paper offers suggestions to alleviate traffic congestion in old parts of metropolises.

Suggested Citation

  • Zhichao Li & Jilin Huang, 2019. "How to Mitigate Traffic Congestion Based on Improved Ant Colony Algorithm: A Case Study of a Congested Old Area of a Metropolis," Sustainability, MDPI, vol. 11(4), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:4:p:1140-:d:207969
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    References listed on IDEAS

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    1. Arnott, Richard & Inci, Eren, 2006. "An integrated model of downtown parking and traffic congestion," Journal of Urban Economics, Elsevier, vol. 60(3), pages 418-442, November.
    2. O Holthaus & C Rajendran, 2005. "A fast ant-colony algorithm for single-machine scheduling to minimize the sum of weighted tardiness of jobs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(8), pages 947-953, August.
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    Cited by:

    1. Mariusz Korzeń & Maciej Kruszyna, 2023. "Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section," IJERPH, MDPI, vol. 20(6), pages 1-15, March.
    2. Zhanzhong Wang & Ruijuan Chu & Minghang Zhang & Xiaochao Wang & Siliang Luan, 2020. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 12(20), pages 1-22, October.
    3. Zhichao Li & Jilin Huang & Zhiping Hu, 2019. "Screening and Diagnosis of Chronic Pharyngitis Based on Deep Learning," IJERPH, MDPI, vol. 16(10), pages 1-15, May.
    4. Dražen Žgaljić & Edvard Tijan & Alen Jugović & Tanja Poletan Jugović, 2019. "Implementation of Sustainable Motorways of the Sea Services Multi-Criteria Analysis of a Croatian Port System," Sustainability, MDPI, vol. 11(23), pages 1-21, December.
    5. Boshuai Zhao & Juliang Zhang & Wenchao Wei, 2019. "Impact of Time Restriction and Logistics Sprawl on Urban Freight and Environment: The Case of Beijing Agricultural Freight," Sustainability, MDPI, vol. 11(13), pages 1-17, July.

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