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International Journal of Advances in Engineering & Scientific Research

LEVERAGING PREDICTIVE INSIGHTS FOR SMARTER TRANSPORTATION SYSTEMS

Mr. K. Karthick, Deepak Raj S, Sabareeswaran B, Joel S

Mr. K. Karthick, Deepak raj S, Sabareeswaran B, Joel S, Department of Computer Science and Engineering, Kongunadu College of Engineering and Technology, Tamilnadu, India

DOI : https://doi.org/10.5281/zenodo.14925127 Page No : 100-111

Published Online : 2025-02-25

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The rapid expansion of urban populations and the growing complexity of transportation systems necessitate intelligent, data-driven solutions to enhance efficiency and sustainability. Artificial Intelligence (AI) is revolutionizing transportation by leveraging predictive insights to optimize traffic management, reduce congestion, and improve mobility. By analyzing vast datasets from traffic cameras, GPS, sensors, and social media, AI can monitor real-time conditions and forecast future traffic patterns, enabling proactive decision-making. These predictive capabilities facilitate dynamic traffic signal adjustments, route optimization, and congestion reduction, leading to lower fuel consumption and emissions. Furthermore, AI-driven insights enhance infrastructure management by identifying potential bottlenecks, assessing road conditions, and predicting maintenance needs, ensuring optimal resource allocation and long-term cost efficiency. AI also supports the seamless integration of alternative transportation modes, such as electric vehicles, public transit, and bike-sharing, fostering a more connected and sustainable urban mobility ecosystem. Despite its transformative potential, widespread AI adoption in transportation planning faces challenges, including data privacy concerns, system interoperability, and cybersecurity risks. Addressing these challenges will be key to unlocking the full potential of predictive AI insights in building smarter, more efficient transportation systems.

Keywords: Predictive Insights, AI, Smart Transportation Systems, Traffic Optimization, Infrastructure Management, Sustainable Urban Mobility.

REFERENCES

Ahmed, S. (2025). Leveraging Artificial Intelligence for Sustainable Transportation Planning in Smart Cities. DOI: http://dx.doi.org/10.13140/RG.2.2.15301.15848

Avacharmal, R., (2024). Mitigating Annotation Burden in Active Learning with Transfer Learning and Iterative Acquisition Functions. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. DOI: http://dx.doi.org/10.1109/ICCCNT61001.2024.10724595

Barma MD, (2021). Inhibition of Streptococcus mutans, antioxidant property and cytotoxicity of novel nano-zinc oxide varnish. Arch Oral Biol. 2021 Jun; 126:105132. DOI: https://doi.org/10.1016/j.archoralbio.2021.105132

Khalil, et. al (2024). Advanced learning technologies for intelligent transportation systems: Prospects and challenges. IEEE Open Journal of Vehicular Technology. Retrieved From: https://ieeexplore.ieee.org/document/10444919

Li, Z., et. al (2024). Steering the future: Redefining intelligent transportation systems with foundation models. CHAIN, 1(1), 46-53. Retrieved From: https://ieeexplore.ieee.org/abstract/document/10595852

Mian, A. et. al (2024). Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions. ACM Computing Surveys. DOI: https://doi.org/10.1145/3716371

Siddique R, et. al (2019). Quantitative analysis for detection of toxic elements in various irrigants, their combination (precipitate), and para-chloroaniline: An inductively coupled plasma mass spectrometry study. J Conserv Dent. 2019 Jul-Aug;22(4):344-350. DOI: http://dx.doi.org/10.4103/JCD.JCD_95_19

Sreelekha, et. al (2024). Intelligent Transportation System for Sustainable and Efficient Urban Mobility: Machine Learning Approach for Traffic Flow Prediction. In International Conference on Multi-Strategy Learning Environment (pp. 399-412). Singapore: Springer Nature Singapore. DOI: https://doi.org/10.1007/978-981-97-1488-9_30

Suresh, K. et. al (2019). A novel key exchange algorithm for security in internet of things. Indones. J. Electr. Eng. Comput. Sci, 16(3), 1515-1520. DOI: http://dx.doi.org/10.11591/ijeecs.v16.i3.pp1515-1520

Yogapriya, J et. al (2018). A study of image retrieval system based on feature extraction, selection, classification and similarity measurements. Journal of Medical Imaging and Health Informatics, 8(3), 479-484. DOI: https://doi.org/10.1166/jmihi.2018.2326

Karthick K, Deepak Raj S, Saareeswaran B, Joel S. (2025), Leveraging Predictive Insights For Smarter Transportation Systems”, International Journal of Advances in Engineering & Scientific Research, Volume 12, Issue 1, 2025, pp 100-111. DOI : https://doi.org/10.5281/zenodo.14925127

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REFERENCES

Ahmed, S. (2025). Leveraging Artificial Intelligence for Sustainable Transportation Planning in Smart Cities. DOI: http://dx.doi.org/10.13140/RG.2.2.15301.15848

Mian, A. et. al (2024). Making Sense of Big Data in Intelligent Transportation Systems: Current Trends, Challenges and Future Directions. ACM Computing Surveys. DOI: https://doi.org/10.1145/3716371

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