Intelligent Transportation Systems: IoT and Sensor-Based Solutions for Traffic Management

Dr. Vijay Dattatray Gaikwad; Dr. Rajeev Kumar Singh; Prof. Dr. Rajamohan Parthasarathy; Dr. Geetika Parmar; Gohil narendrasinh babubha; Qaim Mehdi Rizvi

doi:10.29284/w95hb832

Authors

Dr. Vijay Dattatray Gaikwad Associate Professor, Vishwakarma Institute of Technology, Pune, Maharashtra, India
Dr. Rajeev Kumar Singh Lecturer (Computer Science), DNS- Regional Institute of Cooperative Management, Patna, National Council for Cooperative training (NCCT), New Delhi
Prof. Dr. Rajamohan Parthasarathy Professor, Department of Computer Applications, School of Engineering, Aditya University, Aditya Nagar, Surampalem, Kakinada (District), Andhra Pradesh, India
Dr. Geetika Parmar Assistant Professor, Department of Computer Science and Applications, Dr. Vishwanath Karad MIT World Peace University Pune, Maharashtra, India, Pincode: 411045
Gohil narendrasinh babubha Associate professor Government engineering college
Qaim Mehdi Rizvi Associate Professor, Department of (CSE) CS&DS, Brainware University, West Bengal, India

DOI:

https://doi.org/10.29284/w95hb832

Keywords:

Intelligent Transportation Systems; Spatio-Temporal Graph Neural Networks; Traffic Forecasting; IoT Sensor Networks; Congestion Detection; Graph Signal Processing; Multi-Horizon Prediction; Robust Traffic Modeling.

Abstract

The high rate at which IoT-based traffic sensing systems are being developed has heightened the need of precise and robust long-horizon traffic prediction in intelligent transport systems. This paper suggests the implementation of a spatio-temporal graph neural network (STGNN) model to predict traffic speeds on a multi-horizon and detect congestion based on massive highway sensor data. The suggested solution represents traffic sensors as nodes in a correlation-based graph, which allows the transmission of spatial information between the interconnected segments of roads, and the time dynamics is represented by gated recurrent units. The framework is assessed at 15, 30 and 60 minutes forecasting horizons and compared with persistence and time only baselines. Experimental evidence shows that the model proposed has statistically significant congestion detection performance improvements in terms of forecasting error at longer horizons and better congestion detection performance in proactive traffic management applications. The robustness analysis under the simulated sensor dropout conditions also demonstrates better resilience, as compared to the baseline models, which indicates the appropriateness of the method to real-world IoT deployments where the incompleteness of data is a common occurrence. The results validate the assumption that the combination of the spatial sensor interdependencies with temporal sequence modelling improves predictive stability, classification reliability and system robustness. This paper introduces a scalable and deployment-friendly spatio-temporal modeling framework that bridges the gap between graph-based signal processing and intelligent traffic management to aid the process of making data-driven decisions in the contemporary intelligent transportation systems.

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