Optimizing Urban Traffic Management Through Advanced Machine Learning: A Comprehensive Study
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Published:
Dec 30, 2023
Keywords:
CatBoost, Classification, Predictio, Traffic
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Abstract
Urban transport networks are vital components of modern societies, influencing efficiency and safety. This research explores the potential of traffic data as a crucial information source for forecasting and interpreting traffic problems. Using advanced data processing, statistical analysis, and classification algorithms, the study aims to identify and forecast traffic scenarios. With an interdisciplinary approach integrating computer science, statistics, and transportation engineering, the research emphasizes a holistic perspective on traffic concerns. The study involves outlier detection, label encoding, and cutting-edge technologies like GridSearchCV and ensemble modeling. Inspired by flash flood susceptibility research, machine learning models, particularly LightGBM and CatBoost, are applied to predict traffic situations. DecisionTreeClassifier and CatBoostClassifier emerge as top performers, achieving remarkable accuracies. The evaluation goes beyond accuracy, emphasizing the nuanced understanding of algorithm strengths and limitations for effective urban transportation network management
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Izhari, F. ., & Dhany, H. W. (2023). Optimizing Urban Traffic Management Through Advanced Machine Learning: A Comprehensive Study. Journal of Intelligent Decision Support System (IDSS), 6(4), 223-230. https://doi.org/10.35335/idss.v6i4.167
References
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Aghaei, S., Gomez, A. and Vayanos, P. (2020). Learning Optimal Classification Trees: Strong Max-Flow Formulations. arXiv e-print arXiv:2002.09142.
Anthony Jnr, B. (2023). Sustainable mobility governance in smart cities for urban policy development – a scoping review and conceptual model. Smart and Sustainable Built Environment, ahead-of-print(ahead-of-print). https://doi.org/10.1108/SASBE-05-2023-0109
Arya, G., Bagwari, A., Saini, H., Thakur, P., Rodriguez, C., & Lezama, P. (2023). Explainable AI for Enhanced Interpretation of Liver Cirrhosis Biomarkers. IEEE Access, 11, 123729–123741. https://doi.org/10.1109/ACCESS.2023.3329759
Erfani, A., Cui, Q., Baecher, G., & Kwak, Y. H. (2023). Data-Driven Approach to Risk Identification for Major Transportation Projects: A Common Risk Breakdown Structure. IEEE Transactions on Engineering Management, 1–12. https://doi.org/10.1109/TEM.2023.3279237
Gunl ¨ uk, O. ¨ , Kalagnanam, J., Li, M., Menickelly, M. and Scheinberg, K. (2021). Optimal decision trees for categorical data via integer programming. Journal of Global Optimization 1–28.
Hong, S. R., Hullman, J., & Bertini, E. (2020). Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs. Proc. ACM Hum.-Comput. Interact., 4(CSCW1). https://doi.org/10.1145/3392878
Izhari, F., & Willa Dhany, H. (2020). COMPARISON OF AIR QUALITY DATA ACCURATION USING DECISION TREE AND NEURAL NETWORK METHOD. JURNAL IPTEKS TERAPAN (Research of Applied Science and Education), 14(2), 123–127.
Kigo, S. N., Omondi, E. O., & Omolo, B. O. (2023). Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model. Scientific Reports, 13(1), 17315. https://doi.org/10.1038/s41598-023-44326-w
Kursuncu, U., Gaur, M. and Sheth, A. (2020). Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning. In Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020) I.
Liu, Z., Lyu, C., Wang, Z., Wang, S., Liu, P., & Meng, Q. (2023). A Gaussian-Process-Based Data-Driven Traffic Flow Model and Its Application in Road Capacity Analysis. IEEE Transactions on Intelligent Transportation Systems, 24(2), 1544–1563. https://doi.org/10.1109/TITS.2022.3223982
Mr.Gugloth Ganesh Dr.Pilla Srinivas Rudrapati Mounika Dudekula Rabiya Begum Kaki Leela Prasad, Dr. K. V. K. (2023). Efficient Classification of Brain Tumor Images Using Neural Network Technique. Journal of Advanced Zoology, 44(S-2), 1381–1395. http://jazindia.com/index.php/jaz/article/view/974
Nallaperuma, D., Nawaratne, R., Bandaragoda, T., Adikari, A., Nguyen, S., Kempitiya, T., Silva, D. De, Alahakoon, D., & Pothuhera, D. (2019). Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4679–4690. https://doi.org/10.1109/TITS.2019.2924883
Pathak, S., & Upadhyay, R. K. (2023). Macro level performance study of Ahmadabad bus rapid transit system: Janmarg. Green Energy and Intelligent Transportation, 2(3), 100093. https://doi.org/https://doi.org/10.1016/j.geits.2023.100093
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022). Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistics Surveys, 16(none), 1–85. https://doi.org/10.1214/21-SS133
Rudin, C. and Schapire, R. E. (2009). Margin-Based Ranking and an Equivalence between AdaBoost and RankBoost. Journal of Machine Learning Research 10 2193–2232.
Saber, M., Boulmaiz, T., Guermoui, M., Abdrabo, K. I., Kantoush, S. A., Sumi, T., Boutaghane, H., Nohara, D., & Mabrouk, E. (2022). Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction. Geocarto International, 37(25), 7462–7487. https://doi.org/10.1080/10106049.2021.1974959
Su, Z. C., Chow, A. H. F., & Zhong, R. X. (2021). Adaptive network traffic control with an integrated model-based and data-driven approach and a decentralised solution method. Transportation Research Part C: Emerging Technologies, 128, 103154. https://doi.org/https://doi.org/10.1016/j.trc.2021.103154
Tiong, K. Y., Ma, Z., & Palmqvist, C.-W. (2023). A review of data-driven approaches to predict train delays. Transportation Research Part C: Emerging Technologies, 148, 104027. https://doi.org/https://doi.org/10.1016/j.trc.2023.104027
Xu, H., Sun, Z., Cao, Y., & Bilal, H. (2023). A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things. Soft Computing, 27(19), 14469–14481. https://doi.org/10.1007/s00500-023-09037-4
Ye, F.-F., Yang, L.-H., Wang, Y.-M., & Lu, H. (2023). A data-driven rule-based system for China’s traffic accident prediction by considering the improvement of safety efficiency. Computers & Industrial Engineering, 176, 108924. https://doi.org/https://doi.org/10.1016/j.cie.2022.108924
Zhan, L., Wang, S., Xie, S., Zhang, Q., & Qu, Y. (2023). Spatial path to achieve urban-rural integration development − analytical framework for coupling the linkage and coordination of urban-rural system functions. Habitat International, 142, 102953. https://doi.org/https://doi.org/10.1016/j.habitatint.2023.102953
Zhang, Y., Zong, R., Shang, L., & Wang, D. (2023). A crowd-AI dynamic neural network hyperparameter optimization approach for image-driven social sensing applications. Knowledge-Based Systems, 278, 110864. https://doi.org/https://doi.org/10.1016/j.knosys.2023.110864
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