Traffic flow prediction for road intersection safety

Road safety is a significant issue in any intelligent transportation system (ITS). Intersections are the most complex part of the road network, as they involve various participants, such as vehicles and pedestrians. Therefore, providing an accurate traffic flow prediction model will enhance traffic efficiency and reduce common problems, such as accidents, congestion and air pollution. However, there are two challenges in the traffic flow prediction problem: first, traffic is a dynamic nonlinear problem due to nonrecurrent events, such as accidents and roadworks, that occur near intersections as an unexpected event will impact the accuracy of the prediction method. The second challenge is that there is a large amount of data which needs a scalable model to efficiently handle big data. To overcome the first issue, in this study, accidents and roadworks data are used, in addition to sensor data that are updated in real time. The datasets are published by VicRoads for the state of Victoria, Australia. Moreover, ensemble decision trees for regression, namely the gradient boosting regression trees (GBRT) and random forest (RF), are proposed. To address the second challenge, the extreme gradient boosting Tree (XGBoost) algorithm, which is a scalable system, is examined to explore its ability to handle traffic data. Finally, a comparative analysis of the proposed methods in terms of time and accuracy is presented.