Abstract
The internet of Vehicles (IoV) technologies have boosted diverse applications related to Intelligent Transportation System (ITS) and Traffic Information Systems (TIS), which have significant potential to advance management of complex and large-scale traffic networks. With the goal of adaptive coordination of a traffic network to achieve high network-wide traffic efficiency, this paper develops a bio-inspired adaptive traffic signal control for real-time traffic flow operations. This adaptive control model is proposed based on swarm intelligence, inspired from particle swarm optimization. It treats each signalized traffic intersection as a particle and the whole traffic network as the particle swarm, then optimizes the global traffic efficiency in a distributed and on-line fashion. Our simulation results show that the proposed algorithm can achieve the performance improvement in terms of the queuing length and traffic flow allocation.
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Acknowledgments
This research was supported by the National Key Research and Development Program of China (2017YFB0102500).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Tian, D. et al. (2018). Swarm Intelligence Inspired Adaptive Traffic Control for Traffic Networks. In: Chen, Y., Duong, T. (eds) Industrial Networks and Intelligent Systems. INISCOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 221. Springer, Cham. https://doi.org/10.1007/978-3-319-74176-5_1
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DOI: https://doi.org/10.1007/978-3-319-74176-5_1
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