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Centralized simulated annealing for alleviating vehicular congestion in smart cities

Author

Listed:
  • Amer, Hayder M.
  • Al-Kashoash, Hayder
  • Hawes, Matthew
  • Chaqfeh, Moumena
  • Kemp, Andrew
  • Mihaylova, Lyudmila

Abstract

Vehicular traffic congestion is a serious problem arising in many cities around the world, due to the increasing number of vehicles utilizing roads of a limited capacity. Often the congestion has a considerable influence on the travel time, travel distance, fuel consumption and air pollution. This paper proposes a novel dynamic centralized simulated annealing based approach for finding optimal vehicle routes using a VIKOR type of cost function. Five attributes: the average travel speed of the traffic, vehicles density, roads width, road traffic signals and the roads' length are utilized by the proposed approach to find the optimal paths. The average travel speed and vehicles density values can be obtained from the sensors deployed in smart cities and communicated to vehicles and roadside communication units via vehicular ad hoc networks. The performance of the proposed algorithm is compared with four other algorithms, over two test scenarios: Birmingham and Turin city centres. These show the proposed method improves traffic efficiency in the presence of congestion by an overall average of 24.05%, 48.88% and 36.89% in terms of travel time, fuel consumption and CO2 emission, respectively, for a test scenario from Birmingham city in the UK. Additionally, similar performance patterns are achieved for the a test with data from Turin, Italy.

Suggested Citation

  • Amer, Hayder M. & Al-Kashoash, Hayder & Hawes, Matthew & Chaqfeh, Moumena & Kemp, Andrew & Mihaylova, Lyudmila, 2019. "Centralized simulated annealing for alleviating vehicular congestion in smart cities," Technological Forecasting and Social Change, Elsevier, vol. 142(C), pages 235-248.
    • Handle: RePEc:eee:tefoso:v:142:y:2019:i:c:p:235-248
    DOI: 10.1016/j.techfore.2018.09.013
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    References listed on IDEAS

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    1. Angelelli, E. & Arsik, I. & Morandi, V. & Savelsbergh, M. & Speranza, M.G., 2016. "Proactive route guidance to avoid congestion," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 1-21.
    2. Opricovic, Serafim & Tzeng, Gwo-Hshiung, 2004. "Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS," European Journal of Operational Research, Elsevier, vol. 156(2), pages 445-455, July.
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    Cited by:

    1. Zhen Chu & Mingwang Cheng & Ning Neil Yu, 2022. "Development potential of Chinese smart cities and its spatio‐temporal pattern: A new hybrid MADM method using combination weight," Growth and Change, Wiley Blackwell, vol. 53(4), pages 1546-1566, December.
    2. Leonardo Guevara & Fernando Auat Cheein, 2020. "The Role of 5G Technologies: Challenges in Smart Cities and Intelligent Transportation Systems," Sustainability, MDPI, vol. 12(16), pages 1-15, August.
    3. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.
    4. Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun, 2022. "Improving the Road and Traffic Control Prediction Based on Fuzzy Logic Approach in Multiple Intersections," Mathematics, MDPI, vol. 10(16), pages 1-16, August.

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