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Title	A Machine Learning Based Approach For The Prediction Of Road Traffic Flow On Urbanised Arterial Roads
ID_Doc	2462
Authors	Bartlett Z.; Han L.; Nguyen T.T.; Johnson P.
Year	2019
Published	Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
DOI	http://dx.doi.org/10.1109/HPCC/SmartCity/DSS.2018.00215
Abstract	Urbanised arterial roads connect geographically important areas and are used for commuting and the movement of goods. Prediction of traffic flow on these roads is vital to aid in the mitigation of congestion. However, there is currently a lack of research in this area. In this work we have applied machine learning models to a real dataset for the prediction of road traffic congestion on urbanised arterial road. A comparative analysis was conducted on each machine learning model, examining the prediction accuracy and time-horizon sensitivity. Furthermore, we examined different input parameter settings (various classes of vehicles such as motorcycles, cars, vans, rigid goods lorries, articulated heavy goods vehicles (HGVs), and buses) to investigate how heterogeneous traffic flow can affect prediction. The experimental results show that the Artificial Neural Network Model outperforms other models at predicting short-term traffic flow on an urbanised arterial road based on the standard performance indicator: Root Mean Squared Error (RMSE). Additionally, it is found that different classes of vehicles can aid the improvement prediction. © 2018 IEEE.
Author Keywords	K Nearest Neighbours (KNN); Machine Learning; Short-term Traffic Prediction; Support Vector Machine (SVM) and Artificial Neural Networks (ANN); Support Vector Regression (SVR)

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