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 Adaptive Boom Barrier System for ETC Toll Plazas Using Real-Time Predictive Analytics |
|---|---|
| ABSTRACT | Efficient traffic management at Electronic Toll Collection (ETC) plazas is critical for reducing congestion, minimizing vehicle delay, and improving overall transportation efficiency. Conventional boom barrier systems operate on fixed or reactive mechanisms, which often lead to increased queue length and waiting time during peak traffic conditions. To address these challenges, this paper proposes an intelligent adaptive boom barrier system for ETC toll plazas using real-time predictive analytics. The proposed system leverages data collected from sensors and transaction logs to analyze traffic patterns and predict incoming vehicle flow. A predictive model is employed to dynamically adjust boom barrier operations, enabling optimized opening and closing times based on anticipated traffic conditions. The system integrates IoT-enabled devices, real-time data processing, and communication modules to ensure seamless coordination between toll infrastructure and control units. Additionally, the framework incorporates cloud-based monitoring for continuous data analysis and system scalability. The adaptive mechanism reduces congestion by prioritizing lanes with higher traffic density and minimizing idle barrier time. Experimental evaluation demonstrates that the proposed system significantly improves traffic throughput, reduces average waiting time, and enhances toll plaza efficiency compared to conventional methods. The proposed intelligent system provides a scalable and cost-effective solution for modern toll management, contributing to smarter transportation infrastructure and improved commuter experience. |
| AUTHOR | DR.V.PONNIYINSELVAN, CHANDRA SAKTHI M, JAYASURYA S, LOGAPRASATH S Professor, Dept. of Electronics and Communication Engineering (ECE), Mahendra College of Engineering, Salem, Tamil Nadu, India UG Students, Dept. of Electronics and Communication Engineering (ECE), Mahendra College of Engineering, Salem, Tamil Nadu, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404105 |
| pdf/105_Intelligent Adaptive Boom Barrier System for ETC Toll Plazas Using Real-Time Predictive Analytics.pdf | |
| KEYWORDS | |
| References | 1. P. Klaykul, W. Lee, K. Srisomboon, L. Pipanmekaporn, and A. Prayote, “Adaptive Boom Barrier Management at ETC Toll Plazas Using Predictive Analytics,” IEEE Access, vol. 13, pp. 64364–64389, 2025. 2. S. C. Pandey and V. K. P, “Adaptive AI-Driven Toll Management: Enhancing Traffic Flow and Sustainability Through Real-Time Prediction, Allocation, and Task Optimization,” Future Transportation, vol. 5, no. 1, 2025. 3. D. Bortolotti, F. Rossi, and G. Ferrari, “Predicting and Optimizing Traffic Flow at Toll Plazas,” Transportation Research Procedia, vol. 37, pp. 330–337, 2019 4. H. T. Hashmi, S. Ud-Din, M. A. Khan, J. A. Khan, M. Arshad, and M. U. Hassan, “Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies,” Infrastructures, vol. 9, no. 5, p. 87, 2024. 5. T. Zhou, Y. Sun, X. Wang, and X. Wu, “Proactive Integrated Traffic Control to Mitigate Congestion at Toll Plazas,” IET Intelligent Transport Systems, 2023. 6. Y. Zou, H. Li, and X. Wang, “Short-Term Expressway Traffic Prediction Using ETC Data,” Electronics, vol. 11, no. 10, p. 1613, 2022. 7. V. Kumar Bachu, S. Swargam, and U. Reddy G., “RFID Based Toll Plaza,” International Journal of Engineering Research & Technology (IJERT), vol. 2, no. 10, 2013. 8. T. Bhanu Teja, N. Hari Kumar, D. Sasi Raja Sekhar, and C. Shiva Kumar, “Automatic Toll Collection System Using RFID with Vehicle Classification Using Convolutional Neural Network,” International Journal of Engineering and Technology, vol. 13, no. 2, pp. 281–285, 2024. 9. R. Ahmed, S. C. Omi, M. S. Rahman, and N. R. Bhuiyan, “Automated Toll Management System Using RFID and Image Processing,” arXiv preprint arXiv:2412.01728, 2024. 10. V. Vohra, T. H. Agaria, and D. Patel, “Optimizing RFID-Based Toll Gate System: A Novel Approach to Reducing Latency and Enhancing Traffic Flow,” Research Trends in Electronics and Communication Systems, vol. 11, no. 3, pp. 41–50, 2024 11. R. Deepika, R. A. Kalpana, S. Meera, and R. SharmikhaSree, “Advanced Toll Plaza Monitoring by Utilization of WSN Modules,” International Journal of Engineering and Technology, vol. 7, 2018. 12. P. Song, N. N. Sze, O. Zheng, and M. Abdel-Aty, “Addressing Unobserved Heterogeneity at Road User Level for the Analysis of Conflict Risk at Tunnel Toll Plaza,” arXiv preprint arXiv:2207.06576, 2022. 13. K. Sivakoti, “Vehicle Detection and Classification for Toll Collection Using YOLOv11 and Ensemble OCR,” arXiv preprint arXiv:2412.12191, 2024. 14. Dr.J.Sampathkumar, S.Shehajath Aalam, P.Sridharan, Vishnu Raja A, S. Thirumoorthy, “An AI-Driven Unified Model for Traffic Sign and Lane Detection in Dynamic Road Environments”, International Journal of Innovative Research in Technology (IJIRT), vol 12, No.11, 2016. |
