Abstract
Traffic congestion is one of the most important problems with respect to people daily lives. As a consequence, a lot of environmental and economic problems were emerged. Several works were proposed to participate in problem solving. Traffic signal management introduced a promising solution by minimizing vehicles average travel times and hence decreasing traffic congestion. Studying vehicles’ activities on roads is non-deterministic by nature and contains several continuously changing parameters which makes it hard to find an optimal solution for the mentioned problem. Therefore, optimization techniques were intensively exploited with respect to Traffic Signal Scheduling (TSS) systems. In this work, we propose TSS control methodology based on Whale Optimization Algorithm (WOA) in order to minimize Average Travel Time (ATT). Experimental results show the superiority of WOA over other related algorithms specially with the case of large-scale benchmarks.
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Notes
- 1.
The RMACC Summit supercomputer, which is supported by the National Science Foundation (awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University. The Summit supercomputer is a joint effort of the University of Colorado Boulder and Colorado State University.
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Thaher, T., Abdalhaq, B., Awad, A., Hawash, A. (2020). Whale Optimization Algorithm for Traffic Signal Scheduling Problem. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_17
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