The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas


  • Srishti Dikshit Noida Institute of Engineering and Technology
  • Areeba Atiq Noida Institute of Engineering and Technology
  • Mohammad Shahid Noida Institute of Engineering and Technology
  • Vinay Dwivedi Galgotias University image/svg+xml
  • Aarushi Thusu Noida Institute of Engineering and Technology



Artificial Intelligence, Vehicle Routing, Traffic Congestion, Urban Mobility, Sustainability, Machine Learning, Optimization, Transportation Efficiency


The swift urbanization of cities has given rise to an unparalleled surge in vehicular traffic, leading to substantial congestion, heightened pollution, and a diminished quality of life. This investigation explores the capacity of artificial intelligence (AI) to transform urban mobility by optimizing vehicle routing and alleviating traffic congestion. The objective is to create AI-powered solutions that augment transportation efficiency, diminish travel times, and mitigate environmental repercussions. This paper thoroughly scrutinizes existing AI algorithms, vehicle routing, and traffic management techniques. The study integrates real-time traffic data, road network characteristics, and individual travel patterns to formulate intelligent routing strategies. The proposed AI system adjusts to dynamic traffic conditions through machine learning and optimization algorithms, pinpointing optimal routes and redistributing traffic flows to minimize congestion hotspots. To assess the effectiveness of the AI-driven approach, extensive simulations and case studies are conducted in representative urban areas. Performance metrics, including travel time reduction, fuel consumption, and emissions reduction, are employed to quantify the impact of the proposed system on traffic congestion and environmental sustainability. Furthermore, the study evaluates the scalability, feasibility, and economic viability of implementing AI-based traffic management solutions on a larger scale. The outcomes of this research provide valuable insights into the potential advantages of AI in reshaping urban mobility. By optimizing vehicle routing and diminishing traffic congestion, the proposed AI-driven system has the potential to elevate overall transportation efficiency, reduce energy consumption, and contribute to a healthier urban environment. The findings carry substantial implications for policymakers, urban planners, and transportation authorities seeking innovative solutions to tackle the challenges of contemporary urbanization while promoting sustainable development.


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M. Jaiswal, N. Gupta and A. Rana: "Real-time Traffic Management in Emergency using Artificial Intelligence”, 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2020; pp. 699-702. DOI:

Kővári Bálint, Tettamanti Tamás, Bécsi Tamás: “Deep Reinforcement Learning based approach for Traffic Signal Control,” Transportation Research Procedia, 2022. pp 278-285. DOI:

G. M. Lingani, D. B. Rawat and M. Garuba: "Smart Traffic Management System using Deep Learning for Smart City Applications," IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2019. doi: 10.1109/CCWC.2019.8666539. pp. 0101-0106. DOI:

Mahmuda Akhtar and Sara Moridpour: A Review of Traffic Congestion Prediction Using Artificial Intelligence, Hindawi, Journal of Advanced Transportation 2021. DOI:

Z. Chen, Y. Jiang, D. Sun, and X. Liu: “Discrimination and prediction of traffic congestion states of urban road network based on spatio-temporal correlation,” IEEE Access. pp. 3330–3342, 2020. DOI:

J. F. Zaki, A. Ali-Eldin, S. E. Hussein, S. F. Saraya, and F. F. Areed, “Traffic congestion prediction based on Hidden Markov Models and contrast measure,” Ain Shams Engineering Journal. 2020. pp. 535 DOI:

Z. Shi: Advanced Artificial Intelligence, World Scientific, Singapore, 2011. DOI:

M. S. Ali, M. Adnan, S. M. Noman, and S. F. A. Baqueri, “Estimation of traffic congestion cost-A case study of a major arterial in karachi,” Procedia Engineering, 2014. vol. 77, pp. 37–44. DOI:

W. Cao and J. Wang, “Research on traffic flow congestion based on Mamdani fuzzy system,” AIP Conference Proceedings, 2019; vol. 2073. DOI:

X. Kong, Z. Xu, G. Shen, J. Wang, Q. Yang, and B. Zhang, “Urban traffic congestion estimation and prediction based on floating car trajectory data,” Future Generation Computer Systems, 2016; vol. 61, pp. 97–107. DOI:

Q. Yang, J. Wang, X. Song, X. Kong, Z. Xu, and B. Zhang, “Urban traffic congestion prediction using floating car trajectory data,” in Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing, Springer, Zhangjiajie, China, November; 2015; pp. 18– 30. DOI:

W. Zhang, Y. Yu, Y. Qi, F. Shu, and Y. Wang, “Short-term traffic flow prediction based on spatiotemporal analysis and CNN deep learning,” Transportmetrica A: Transport Science, 2019; vol. 15, no. 2, pp. 1688–1711. DOI:

T. Adetiloye and A. Awasthi, “Multimodal big data fusion for traffic congestion prediction,” Multimodal Analytics for Next-Generation Big Data Technologies and Applications, Springer, Berlin, Germany, 2019; pp. 319–335. DOI:

F. Wen, G. Zhang, L. Sun, X. Wang, and X. Xu, “A hybrid temporal association rules mining method for traffic congestion prediction,” Computers & Industrial Engineering, 2019; vol. 130, pp. 779–787. DOI:

J. Wang, Y. Mao, J. Li, Z. Xiong, and W.-X. Wang, “Predictability of road traffic and Congestion in urban areas,” 2015; PLoS One, vol. 10, no. 4, Article ID e0121825. DOI:

Z.He, G.Qi, L.Lu, and Y.Chen, “Network-wide identification of turn-level intersection congestion using only low-frequency probe vehicle data,” Transportation Research Part C: Emerging Technologies, 2019; vol. 108, pp. 320–339. DOI:

K. M. Nadeem and T. P. Fowdur, “Performance analysis of a real-time adaptive prediction algorithm for traffic congestion,” Journal of Information and Communication Technology, 2018; vol. 17, no. 3, pp. 493–511. DOI:

H. Zhao, X. Jizhe, L. Fan, L. Zhen, and L. Qingquan, “A peak traffic Congestion prediction method based on bus driving time,” Entropy, 2019; vol. 21, no. 7, p. 709. DOI:

F.-H. Tseng, J.-H. Hsueh, C.-W. Tseng, Y.-T. Yang, H.-C. Chao, and L.-D. Chou, “Congestion prediction with big data for real-time highway traffic,” IEEE Access, 2018; vol. 6, pp. 57311–57323. DOI:

Z. Zhang, A. Zhang, C. Sun et al., “Research on air traffic flow forecast based on ELM non-iterative algorithm,” Mobile Networks and Applications, 2020; pp. 1–15. DOI:

Y.-m. Xing, X.-j. Ban, and R. Liu, “A short-term traffic flow prediction method based on kernel extreme learning machine,” in Proceedings of the 2018 IEEE International Conference on Big Data and Smart Computing (Big Comp), 2018; pp. 533–536. DOI:

R. Ke, W. Li, Z. Cui, and Y. Wang, “Two-stream multi-channel Convolutional neural network for multi-lane traffic speed prediction Considering traffic volume impact,” Transportation Research Record: Journal of the Transportation Research Board, 2020; vol. 2674, no. 4, pp. 459–470. DOI:

T. Bogaerts, A. D. Masegosa, J. S. Angarita-Zapata, E. Onieva, and P. Hellinckx, “A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data,” Transportation Research Part C: Emerging Technologies, 2020; vol. 112, pp. 62–77. DOI:

V. Nourani, H. Go ̈kçeku ̧s, I. K. Umar, and H. Najafi, “An emotional artificial neural network for prediction of vehicular traffic noise,” Science of The Total Environment, 2020; vol. 707, p. 136134. DOI:




How to Cite

Dikshit S, Atiq A, Shahid M, Dwivedi V, Thusu A. The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas. EAI Endorsed Trans Energy Web [Internet]. 2023 Dec. 15 [cited 2024 Jun. 18];10. Available from: