A Machine Learning approach for Short-Term Traffic Flow Prediction

Gajendragadkar, Aaditya Ravindra (2023) A Machine Learning approach for Short-Term Traffic Flow Prediction. Masters thesis, Dublin, National College of Ireland.

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Abstract

Traffic congestion presents a complex problem, impacting not just the convenience of commuters, but also imposing significant economic and environmental consequences. While various models have been employed in traffic prediction, there remains a need for accurate, robust, and practical solutions to aid traffic managers in understanding patterns, reducing congestion, and optimizing traffic management strategies. This research focuses on predicting short-term traffic flow using statistical, machine learning, and deep learning models on the PEMS-08 Dataset from San Bernardino, covering July to August 2016 in five-minute intervals. In three case studies, deep learning models of LSTM, CNN, RNN, and machine learning models like KNN, Random Forest, Gradient Boosting, and Decision Tree demonstrate commendable performance, especially Random Forest, Decision Tree, and KNN, outshining others and making them first choice with R2 values of 0.99,0.98 and 0.97 respectively with extremely low RMSE and MAE values. Deep learning models LSTM, CNN, and RNN follow closely with R2 values of 0.958,0.957, and 0.91 respectively but with slightly higher RMSE and MAE, and statistical models of SARIMA and ARIMA Performed well with R2 of 0.95 and 0.90 but with an extremely high RMSE and MAE values. K cross-validation is performed on each machine learning and deep learning model that confirms the model’s performance, robustness, and reliability. This research offers valuable insights to policymakers, presenting optimal models for developing proactive strategies. The findings contribute to fostering sustainable and efficient urban transportation systems by addressing dynamic traffic patterns.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rustam, Furqan UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TL Motor vehicles. Aeronautics. Astronautics Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Motor Industry
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	08 May 2025 10:32
Last Modified:	08 May 2025 10:32
URI:	https://norma.ncirl.ie/id/eprint/7512

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