Abstract
Given the increasing population, urbanization, and industry, traffic congestion is particularly severe during peak hours, especially in cities. This leads to high fuel consumption, noise pollution, and a range of health problems. Consequently, traffic management is essential for managing the aforementioned concerns. This paper proposes a novel traffic prediction and control approach based on wireless sensor networks (WSNs). The proposed mode-search optimization is designed by blending the particular characteristics of squawks with the theoretical foundations of traffic prediction and control optimization. Initially, the sensors are used to collect velocity, acceleration, jitter, and priority data for the network’s vehicles, after which the mode-search optimization method is suggested based on the data to cluster the vehicles. Next, for traffic projections, the possible paths are identified using a multi-objective approach. The proposed mode-search based Deep Long Short-Term Memory is then used to forecast traffic once the Deep LSTM (Long Short-Term Memory) has been trained using the proposed mode-search optimization to reduce training loss. Additionally, the use of traffic signal control also improves traffic flow management.
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This article is part of the topical collection “Research Trends in Communication and Network Technologies” guest edited by Anshul Verma, Pradeepika Verma and Kiran Kumar Pattanaik.
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Chauhan, S.S., Kumar, D. Mode Search Optimization Algorithm for Traffic Prediction and Signal Controlling Using Bellman–Ford with TPFN Path Discovery Model Based on Deep LSTM Classifier. SN COMPUT. SCI. 4, 686 (2023). https://doi.org/10.1007/s42979-023-02140-1
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DOI: https://doi.org/10.1007/s42979-023-02140-1