@article{ref1,
title="TCC-HDL: a hybrid deep learning based traffic congestion control system for VANET",
journal="Indian journal of science and technology",
year="2023",
author="Assistant Professor, Department of CSE, B.S Abdur Rahman Crescent Institute of Science and Technolog,  and Ahamed, Vm Niaz and Prakash, Arul and Ziyath, Mohamed",
volume="16",
number="32",
pages="2548-2559",
abstract="OBJECTIVES: This Study is centered on developing suitable method to reduce road accidents and improve individual traffic management as a part of smart cities development. <br><br>METHODS: A new hybrid deep learning-based model which uses a hybrid deep learning technique (TCC-HDL) is proposed to collect data on traffic patterns and send vehicles along the most efficient routes. The data are collected from kaggle about 8,000 roadside of 12-hour manual counts. From the extracted data, traffic congestion is predicted by new hybrid deep learning approach such as Recurrent capsule networks (CapsRNN), Fuzzy Interface System (FIS) and Optimized Bi-LSTM (O-Bidirectional Long short Memory). The proposed model TCC-HDL has been analyzed in terms of Accuracy, Precision, F-Measure and Recall with the standard algorithms like Bi-LSTM, CapsRNN, GRU, and LSTM. The information comes from the Highway Traffic Crash Dataset. Statistical features, higher-order statistical features, correlation-based features, and database features are used to extract information from the collected data. <br><br>FINDINGS: The work achieved 0.0102 to 0.1043% improvement in terms of accuracy, 0.0088% to 0.2133% of Precision, 0.039% to 0.2364% of Recall and 0.0056% to 0.083% of F-Measure. Novelty: New hybrid deep learning approach for predicting the situation of heavy traffic CapsRNN algorithm which has the better action recogoization and Bi-LSTM is the long term prediction of data which optimized using RSOA can fused together and it is fed as input to Fuzzy Interface System (FIS).<p /> <p>Language: en</p>",
language="en",
issn="0974-6846",
doi="10.17485/IJST/v16i32.1319",
url="http://dx.doi.org/10.17485/IJST/v16i32.1319"
}