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An Industry-Focused Traffic System Utilising Internet of Things
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Author(s): Binay Kumar Pandey (Department of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, India), Vinay Kumar Nassa (Tecnia Institute of Advanced Studies, India), A. P. Mukundan (Champions Group, Singapore & Aligarh Muslim University, India), Darshan A. Mahajan (NICMAR University, India), Digvijay Pandey (Department of Technical Education, Government of Uttar Pradesh, India), A. Shaji George (Business System Department, Almarai Company, TSM, Riyadh, Saudi Arabia)and Pankaj Dadheech (Swami Keshvanand Institute of Technology, Management, and Gramothan, India)
Copyright: 2024
Pages: 14
Source title:
Emerging Engineering Technologies and Industrial Applications
Source Author(s)/Editor(s): Younes El Kacimi (Ibn Tofail University, Morocco)and Khaoula Alaoui (Ibn Tofail University, Morocco)
DOI: 10.4018/979-8-3693-1335-0.ch009
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Abstract
Radio frequency identification technology (RFID) and time series forecasts are used to create a dependable IoT-based traffic system. The system regulates city traffic. The suggested method estimates junction traffic volume over time using LSTM neural networks. RFID technology improves data collection accuracy and reliability. Data preparation includes outlier identification to remove anomalies. Training the LSTM model on preprocessed data reveals traffic volume trends. The trained model predicts traffic volume using historical data. Prediction performance is quantified by MAE, MAPE, and R2. The proposed approach is tested using four intersection traffic data. Results indicate that LSTM-based traffic volume estimation works. The optimal design is determined by evaluating system performance for 12-to-168-time steps. The experimental findings suggest that the proposed method can accurately anticipate traffic volume, helping traffic managers enhance flow. RFID and time series projections bolster traffic system reliability.
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