Author(s)

Daniel Chukwu, Adanna Uche^*

Affiliation(s)

Department of Civil Engineering, University of Nigeria Nsukka, Nigeria.

Corresponding Author

Adanna Uche

ABSTRACT

This systematic review evaluates the implementation and effectiveness of machine learning (ML) techniques in traffic management systems through analysis of 286 peer-reviewed articles published between 2015 and 2024. Our comprehensive analysis encompasses 45 metropolitan implementations across 23 countries, focusing on real-world applications, methodological approaches, and quantifiable outcomes. The findings demonstrate that ML-based traffic management systems consistently outperform traditional methods, achieving travel time reductions ranging from 15% to 40% and operational cost savings between 20% and 35%. This review provides an in-depth analysis of current implementations, technical frameworks, challenges, and future directions in the field.

KEYWORDS

Machine learning; Intelligent transportation systems; Urban traffic management; Smart cities

CITE THIS PAPER

Daniel Chukwu, Adanna Uche. The rise of machine learning in traffic management: a journey through innovation and urban transformation. Educational Research and Human Development. 2024, 1(1): 1-5. DOI: https://doi.org/10.61784/adsj3002.

REFERENCES

[1] Mandhare P A, Kharat V, Patil C Y. Intelligent road traffic control system for traffic congestion: a perspective. International Journal of Computer Sciences and Engineering, 2018, 6(07): 2018.

[2] Chen X, Liu M, Niu Y, et al. Deep-Learning-Based Lithium Battery Defect Detection via Cross-Domain Generalization. IEEE Access, 2024.

[3] Fadlullah Z M, Tang F, Mao B, et al. State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2432-2455.

[4] Zhang X, Chen S, Shao Z, et al. Enhanced Lithographic Hotspot Detection via Multi-Task Deep Learning with Synthetic Pattern Generation. IEEE Open Journal of the Computer Society, 2024.

[5] Jovanovi? A, Nikoli? M, Teodorovi? D. Area-wide urban traffic control: A Bee Colony Optimization approach. Transportation Research Part C: Emerging Technologies, 2017, 77: 329-350.

[6] Li J, Fan L, Wang X, et al. Product Demand Prediction with Spatial Graph Neural Networks. Applied Sciences, 2024, 14(16): 6989.

[7] Sun T, Yang J, Li J, et al. Enhancing Auto Insurance Risk Evaluation with Transformer and SHAP. IEEE Access, 2024.

[8] Callari T C, McDonald N, Kirwan B, et al. Investigating and operationalising the mindful organising construct in an Air Traffic Control organisation. Safety Science, 2019, 120: 838-849.

[9] Ma Z, Chen X, Sun T, et al. Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization. Future Internet, 2024, 16(5): 163.

[10] Wang X, Wu Y C, Ma Z. Blockchain in the courtroom: exploring its evidentiary significance and procedural implications in US judicial processes. Frontiers in Blockchain, 2024, 7: 1306058.

[11] Roshan A N, Gokulapriyan B, Siddarth C, et al. Adaptive traffic control with TinyML. In 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE, 2021: 451-455.

[12] Wang X, Wu Y C. Empowering legal justice with AI: A reinforcement learning SAC-VAE framework for advanced legal text summarization. PloS one, 2024, 19(10): e0312623.

[13] Tahmid T, Hossain E. Density based smart traffic control system using canny edge detection algorithm for congregating traffic information. In 2017 3rd International Conference on Electrical Information and Communication Technology (EICT). IEEE, 2017: 1-5.

[14] Chen J, Cui Y, Zhang X, et al. Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach. Applied Sciences, 2024, 14(20): 9213.

[15] Kato N, Fadlullah Z M, Mao B, et al. The deep learning vision for heterogeneous network traffic control: Proposal, challenges, and future perspective. IEEE wireless communications, 2016, 24(3): 146-153.

[16] Zuo Z, Niu Y, Li J, et al. Machine learning for advanced emission monitoring and reduction strategies in fossil fuel power plants. Applied Sciences, 2024, 14(18): 8442.

[17] Amini S, Gerostathopoulos I, Prehofer C. Big data analytics architecture for real-time traffic control. In 2017 5th IEEE international conference on models and technologies for intelligent transportation systems (MT-ITS). IEEE, 2017: 710-715.

[18] Wang X, Zhang X, Hoo V, et al. LegalReasoner: A Multi-Stage Framework for Legal Judgment Prediction via Large Language Models and Knowledge Integration. IEEE Access, 2024.

[19] Aricò P, Borghini G I A N L U C A, Di Flumeri G, et al. A passive brain–computer interface application for the mental workload assessment on professional air traffic controllers during realistic air traffic control tasks. Progress in brain research, 2016, 228: 295-328.

[20] Liu M, Ma Z, Li J, et al. Deep-Learning-Based Pre-training and Refined Tuning for Web Summarization Software. IEEE Access, 2024.

[21] Safiullin R, Fedotov V, Marusin A. Method to evaluate performance of measurement equipment in automated vehicle traffic control systems. Transportation Research Procedia, 2020, 50: 20-27.

[22] Ramadass R, Venumula S, Shankar T S, et al. Application Reliable Traffic Control Method for Efficient Data Management in Wireless-aided Computer Applications. IIRJET, 2023, 8(3).