Adaptive Traffic-Oriented Pathfinding Algorithm (ATOPA) for Enhancing Intelligent and Sustainable Smart Transportation Systems
Adaptive Traffic-Oriented Pathfinding Algorithm (ATOPA) for Enhancing Intelligent and Sustainable Smart Transportation Systems |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-7 |
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| Year of Publication : 2025 | ||
| Author : Jyoti Sunil More, Vijaya Umesh Pinjarkar, Vaishali V. Sarbhukan (Bodade), Divya Y. Chirayil | ||
| DOI : 10.14445/22315381/IJETT-V73I7P106 | ||
How to Cite?
Jyoti Sunil More, Vijaya Umesh Pinjarkar, Vaishali V. Sarbhukan (Bodade), Divya Y. Chirayil, "Adaptive Traffic-Oriented Pathfinding Algorithm (ATOPA) for Enhancing Intelligent and Sustainable Smart Transportation Systems," International Journal of Engineering Trends and Technology, vol. 73, no. 7, pp.52-60, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I7P106
Abstract
The increase in vehicle density and the push for autonomous mobility make it challenging for traditional transportation networks to adapt to changing conditions in real time. Smart transportation, which integrates real-time data, predictive modelling, and multi-objective optimisation, is necessary to enhance Autonomous Vehicle (AV) navigation inside Intelligent Transportation Systems (ITS). Route optimization based on traffic, energy use, and safety can not only cut down on travel time and fuel use, but it can also increase road safety. Using IoT sensors, environmental data, and public transit schedules, an Adaptive Traffic-Oriented Pathfinding Algorithm (ATOPA) is proposed that prioritizes safety while lowering trip time, energy consumption, and congestion. By addressing scalability, privacy, and infrastructure concerns, this algorithm positions itself as a crucial component in the development of intelligent, sustainable, and efficient smart transportation systems in the era of autonomous mobility.
Keywords
Smart transportation, Sustainability, Intelligent transportation system, Pathfinding algorithm, Adaptive routing algorithm.
References
[1] Akshita Abrol, Purnima Murali Mohan, and Tram Truong-Huu, “A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-Gen Networks,” ICC 2024 - IEEE International Conference on Communications, Denver, CO, USA, pp. 465-471, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Hongyan Dui et al., “IoT-Enabled Real-Time Traffic Monitoring and Control Management for Intelligent Transportation Systems,” IEEE Internet of Things Journal, vol. 11, no. 9, pp. 15842-15854, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Changhong Wang et al., “A Novel Reinforcement Learning Framework for Adaptive Routing in Network-on-Chips,” IEEE 23rd International Conference on High Performance Computing & Communications; 7th International Conference on Data Science & Systems; 19th International Conference on Smart City; 7th International Conference on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), Haikou, Hainan, China, vol. 25, no. 3, pp. 4321-4335, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Xueyan Yin et al., “Deep Learning on Traffic Prediction: Methods, Analysis, and Future Directions,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 6, pp. 4927-4943, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Anshdwip Kumar et al., “Navigating Urban Mobility: A Review of AI-Driven Traffic Flow Management in Smart Cities,” 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, pp. 1-5, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Peichen Li et al., “Optimizing Network Performance with AI-Enabled Adaptive Routing in SDN,” 2023 15th International Conference on Communication Software and Networks (ICCSN), Shenyang, China, pp. 14-18, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Fatma Aktas et al., “AI-Enabled Routing in Next-Gen Networks: A Brief Overview,” 2023 10th International Conference on Wireless Networks and Mobile Communications (WINCOM), Istanbul, Turkiye, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] R. Shyam Shankaran, and Logesh Rajendran, “Real-Time Adaptive Traffic Control System for Smart Cities,” 2021 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp. 1-6, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] K.M. Abubeker et al., “Computer Vision Assisted Artificial Intelligence Enabled Smart Traffic Control System for Urban Transportation Management,” 2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS), Kanjirapally, India, pp. 1-5, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Angel Jaramillo-Alcazar, Jaime Govea, and William Villegas-Ch, “Advances in the Optimization of Vehicular Traffic in Smart Cities: Integration of Blockchain and Computer Vision for Sustainable Mobility,” Sustainability, vol. 15, no. 22, pp. 1-23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Anurag Agrahari et al., “Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review,” Electronics, vol. 13, no. 19, pp. 1-23, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Umesh Kumar Lilhore et al., “Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities,” Sensors, vol. 22, no. 8, pp. 1-26, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Hyunjin Joo, and Yujin Lim, “Reinforcement Learning for Traffic Signal Timing Optimization,” 2020 International Conference on Information Networking (ICOIN), Barcelona, Spain, pp. 738-742, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Aniekan Essien et al., “A Deep-Learning Model for Urban Traffic Flow Prediction with Traffic Events Mined from Twitter,” World Wide Web, vol. 24, no. 4, pp. 1345-1368, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Maria Viorela Muntean, “Multi-Agent System for Intelligent Urban Traffic Management Using Wireless Sensor Networks Data,” Sensors, vol. 22, no. 1, pp. 1-15, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Siqi Lai et al., “LLMLight: Large Language Models as Traffic Signal Control Agents,” arXiv Preprint, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Anum Mushtaq et al., “Multi-Agent Reinforcement Learning for Traffic Flow Management of Autonomous Vehicles,” Sensors, vol. 23, no. 5, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ming Zhu et al., “Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems,” arXiv Preprint, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Reem Atassi, and Aditi Sharma, “Intelligent Traffic Management using IoT and Machine Learning,” Journal of Intelligent Systems and Internet of Things, vol. 8, no. 2, pp. 8-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Huan Yan, and Yong Li, “A Survey of Generative AI for Intelligent Transportation Systems,” arXiv Preprint, pp. 1-35, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Lucas Koch et al., “Adaptive Traffic Light Control with Deep Reinforcement Learning: An Evaluation of Traffic Flow and Energy Consumption,” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 12, pp. 15066-15076, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Martin Gregurić et al., “Application of Deep Reinforcement Learning in Traffic Signal Control: An Overview and Impact of Open Traffic Data,” Applied Sciences, vol. 10, no. 11, pp. 1-25, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Máté Kolat et al., “Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach,” Sustainability, vol. 15, no. 4, pp. 1-13, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Romain Ducrocq, and Nadir Farhi, “Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detection,” International Journal of Intelligent Transportation Systems Research, vol. 21, no. 1, pp. 192-206, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Yiming Bie, Yuting Ji, and Dongfang Ma, “Multi-agent Deep Reinforcement Learning Collaborative Traffic Signal Control Method Considering Intersection Heterogeneity,” Transportation Research Part C: Emerging Technologies, vol. 164, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Vaishnav Datey, and Sandeep Prabhu, “Smart Traffic Control and Prediction Model Empowered with 5G Technology, Artificial Intelligence and Machine Learning,” 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), Ballari, India, pp. 1-6, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Mohammad Peyman et al., “Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics,” Sensors, vol. 23, no. 1, pp. 1-25, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Fermin Orozco et al., “Federated Learning for Traffic Flow Prediction with Synthetic Data Augmentation,” arXiv Preprint, 2024.
[CrossRef] [Google Scholar] [Publisher Link]