Centralized simulated annealing for alleviating vehicular congestion in smart cities
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DOI: 10.1016/j.techfore.2018.09.013
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References listed on IDEAS
- 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.
- 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:
- 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.
- 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.
- 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.
- 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.
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Keywords
Traffic congestion control; Simulated annealing; IoV applications; Multi-attribute decision making;All these keywords.
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