International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Intelligent Traffic Prediction and Route Analysis System for Tamil Nadu Urban Roads Using XGBoost and Dijkstra Algorithm |
|---|---|
| ABSTRACT | Traffic congestion has become a major challenge in urban areas of India due to the rapid increase in the number of vehicles and limited road infrastructure. This paper proposes an Intelligent Traffic Prediction and Route Analysis System designed specifically for Tamil Nadu's urban road network. The system analyzes traffic data to study congestion patterns across different time periods using key parameters including vehicle count, average speed, time of day, and road conditions. The XGBoost machine learning model is employed for accurate traffic congestion prediction and classification into four levels. The Dijkstra algorithm computes shortest paths between locations, while Yen's K-Shortest Paths algorithm provides multiple alternative routes. Route visualization is achieved using the Leaflet.js map library integrated with a Flask backend and React/TypeScript frontend. The system features color-coded traffic visualization, voice-based alerts, time-based travel predictions, and a comprehensive traffic analytics dashboard. Results demonstrate high prediction accuracy and route computation within 100 milliseconds for all tested source-destination pairs. |
| AUTHOR | NAVIKA N, RAMYA N, THIRULOSHINI A, MOHANAPRIYA V, K ABINAYA Department of Artificial Intelligence and Data Science, Christ the King Engineering College, Coimbatore, Tamil Nadu, India Assistant Professor, Department of Artificial Intelligence and Data Science, Christ The King Engineering College, Coimbatore, Tamil Nadu, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405039 |
| pdf/39_Intelligent Traffic Prediction and Route Analysis System for Tamil Nadu Urban Roads Using XGBoost and Dijkstra Algorithm.pdf | |
| KEYWORDS | |
| References | [1] J. Tiwari, A. Deshmukh, G. Godepure, K. Upadhyaya, and U. Kolekar, "Real Time Traffic Management Using Machine Learning," in Proc. ic-ETITE, IEEE, 2020, pp. 1–6. [2] A. Moses and P. Rathi, "Vehicular Traffic Analysis and Prediction using Machine Learning Algorithms," in Proc. ic-ETITE, IEEE, 2020, pp. 1–6. [3] P. Chhatpar, N. Doolani, S. Shahani, and P. R. Lalwani, "Machine Learning Solutions to Vehicular Traffic Congestion," Int. J. Computer Applications, 2020. [4] G. Meena, D. Sharma, and M. Mahrishi, "Traffic Prediction for ITS using Machine Learning," in Proc. ICETCE-2020, IEEE, pp. 145–149. [5] T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proc. 22nd ACM SIGKDD, pp. 785–794, 2016. [6] E. W. Dijkstra, "A Note on Two Problems in Connexion with Graphs," Numerische Mathematik, vol. 1, no. 1, pp. 269–271, 1959. [7] J. Y. Yen, "Finding the K Shortest Loopless Paths in a Network," Management Science, vol. 17, no. 11, pp. 712–716, 1971. [8] V. Agafonkin, "Leaflet.js: An Open-Source JavaScript Library for Interactive Maps," 2023. [Online]. Available: https://leafletjs.com |
