IOT-BASED INTELLIGENT TRAFFIC MANAGEMENT SYSTEM WITH DYNAMIC GREEN CORRIDORS FOR EMERGENCY VEHICLE PRIORITIZATION
DOI:
https://doi.org/10.11113/aej.v15.23422Keywords:
ITMS, Traffic Control System, Traffic congestion, Arduino IDE, UWB, Green Corridors, Emergency VehiclesAbstract
Traffic congestion has become a critical issue in urban areas worldwide, leading to increased accidents and delays for emergency vehicles. This paper proposes a solution utilizing IoT-enabled technology to alleviate these challenges by implementing "Green Corridors" for emergency vehicles. By leveraging Intelligent Traffic Management Systems (ITMS), which integrate sensors, cameras, and data analysis, this research aims to evaluate the effectiveness of such systems in reducing congestion and improving road safety. A comprehensive evaluation framework will be employed to analyze the impact of ITMS implementation on traffic flow patterns and safety outcomes. The system utilizes UWB technology to identify emergency vehicles, triggering traffic lights to switch to green and notifying nearby vehicles to clear lanes, facilitating unimpeded passage for emergency services. This integrated approach addresses the dual challenges of traffic congestion and emergency response, offering valuable insights for policymakers and urban planners seeking effective solutions for smart city transportation management.
References
P. N, K. Rajesh, P. S, T. G. Kulkarni, S. C and V. M, 2024. "IoT Based Autonomous Emergency Vehicle Traffic Management System," 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS), Chikkaballapur, India, 1-4, DOI: 10.1109/ICKECS61492.2024.10616632.
Prakash Rosayyan, Jasmine Paul, Senthilkumar Subramaniam, Saravana Ilango Ganesan, 2023. An optimal control strategy for emergency vehicle priority system in smart cities using edge computing and IOT sensors, Measurement: Sensors, 26: 100697, ISSN 2665-9174, DOI: https://doi.org/10.1016/j.measen.2023.100697. (https://www.sciencedirect.com/science/article/pii/S2665917423000338)
Musa, A.A.; Malami, S.I.; Alanazi, F.; Ounaies, W.; Alshammari, M.; Haruna, S.I. 2023. Sustainable Traffic Management for Smart Cities Using Internet-of-Things-Oriented Intelligent Transportation Systems (ITS): Challenges and Recommendations. Sustainability 15: 9859. DOI: https://doi.org/10.3390/su15139859
A. Hazarika, N. Choudhury, M. M. Nasralla, S. B. A. Khattak and I. U. Rehman, 2024. "Edge ML Technique for Smart Traffic Management in Intelligent Transportation Systems," in IEEE Access, 12: 25443-25458, DOI: https://doi.org/10.1109/ACCESS.2024.3365930.
A. Ranjan, S. Sharma and H. R. Goyal, 2023."Artificial Intelligence Enabled Emergency Green Corridor Mechanism for Ambulance Services in Smart City," 2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS), Kanjirapally, India, 1-6, DOI: https://doi.org/10.1109/AICERA/ICIS59538.2023.10420235.
Sharma, B., Maherchandani, J.K. 2022. Review of Recent Developments in Sustainable Traffic Management System. In: Reddy, A.N.R., Marla, D., Favorskaya, M.N., Satapathy, S.C. (eds) Intelligent Manufacturing and Energy Sustainability. Smart Innovation, Systems and Technologies, 265. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-16-6482-3_40
P Phani Kumar, Judy Simon, K Durga Devi, M Aarthi Elaveini, N Kapileswar, 2023. Enhanced Traffic Management for Emergency Vehicle Information Transmission using Wireless Sensor Networks, Procedia Computer Science, 230: 798-807, ISSN 1877-0509, DOI: https://doi.org/10.1016/j.procs.2023.12.053. (https://www.sciencedirect.com/science/article/pii/S1877050923020434)
Hao, Z.; Wang, Y.; Yang, X. 2024. Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles, Sustainability 16: 2917. DOI: https://doi.org/10.3390/ su 16072917
A. Rao and B. S. Chaudhari, 2020, "Development of LoRaWAN based Traffic Clearance System for Emergency Vehicles," 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 217-221. DOI: https://doi.org/ 10.1109/I-SMAC49090.2020.9243341.
C. Pedraza, D. Silva, A. Arevalo and F. Vega, 2016. RFID framework for intelligent traffic monitoring, 2016 8th Euro American Conference on Telematics and Information Systems (EATIS), Cartagena. 1-6, DOI: https://doi.org/10.1109/EATIS.2016.7910377
A. C, M. Kumar and P. Kumar, 2009. City traffic congestion control in Indian scenario using wireless sensors network, 2009 Fifth International Conference on Wireless Communication and Sensor Networks (WCSN), Allahabad, 1-6. DOI: https://doi.org/10.1109/WCSN.2009.5434446
Williams, B. M., Durvasula, P. K. and Brown, D. E., 1998. ‘Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models’, Transportation Research Record. 132–141. DOI: https://doi.org/10.3141/1644-14.
Lv, Yisheng, et al. 2014.” Traffic flow prediction with big data: a deep learning approach.” IEEE Transactions on Intelligent Transportation Systems 16(2): 865-873. DOI: https://doi.org/10.1109/TITS.2014.2345663.
P. Agarwal, S. Sharma and P. Matta, 2021. "Components, Technologies, and Market of Road Traffic Management System in Global Scenarios: A Complete Study," 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), Chennai, India, 1-10. DOI: https://doi.org/10.1109/ICSES52305.2021.9633796.
K. Rajan, K. S. Kumar, T. Kannapiran, S. Khan, A. Al-Dmour and B. T. Sharef, 2022. "Intelligent Traffic Management System for Smart Cities Utilizing Reinforcement Learning Algorithm," 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), Manama, Bahrain, 170-177. DOI: https://doi.org/10.1109/ICETSIS55481.2022.9888885.
Chen, Y., Qiu, Y., Tang, Z. et al. 2024. Exploring the Synergy of Blockchain, IoT, and Edge Computing in Smart Traffic Management across Urban Landscapes. Journal of Grid Computing. 22: 45 DOI: https://doi.org/10.1007/s10723_024 09762 6 (https://link.springer.com/journal/10723)-.
P. Thakre, P. Bhalerao, A. Dongre, L. Bendey, I. Jaiswal and C. Anikhindi, 2023,"Design and Implementation of a Dynamic Traffic Signal System with Digital Circuit and IoT Integration for Efficient Traffic Management," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 1-7, DOI: https://doi.org/10.1109/ICCCNT56998.2023.10306612
X. -G. Luo, H. -B. Zhang, Z. -L. Zhang, Y. Yu and K. Li, 2019. "A New Framework of Intelligent Public Transportation System Based on the Internet of Things," in IEEE Access, 7: 55290-55304, DOI: 10.1109/ACCESS.2019.2913288.
S. T. Ahmed, S. M. Basha, M. Ramachandran, M. Daneshmand and A. H. Gandomi, 2023. "An Edge-AI-Enabled Autonomous Connected Ambulance-Route Resource Recommendation Protocol (ACA-R3) for eHealth in Smart Cities," in IEEE Internet of Things Journal, 10(13): 11497-11506. DOI: https://doi.org/10.1109/ JIOT.2023.3243235.
Sachan, A., Kumar, N. S-Edge: heterogeneity-aware, light-weighted, and edge computing integrated adaptive traffic light control framework. The Journal of Supercomputing 79: 14923–14953. DOI: https://doi.org/10.1007/s11227-023-05 (https://link.springer.com/journal/11227) (2023).
