Abstract
Road accidents occur at traffic signals, even though traffic patrolling and awareness are increased. Accidents occurred at traffic signal junctions are a significant proportion in the overall reported road accidents. Usually, when the drivers approaching the traffic signals, at the onset of yellow, the drivers would enter into a dilemma zone, where they will be in confusion mode assessing their capabilities to cross the intersection or stop. So, any improper decision might lead to a collision. To avoid right-angle crash, drivers apply harsh brake to stop before the traffic signals. But this may lead to back-end crash, when the following driver encounters the former’s sudden stopping decision. This situation gets multifaceted when the traffic is heterogeneous containing various types of vehicles. So, the main objective of this study is to assess the performance using machine learning techniques. In this study, support vector machine (SVM) and K-nearest neighbors’ (KNN) techniques are implemented to validate the classification of the driving behaviour in terms of safe stopping/unsafe stopping at the signalized junctions at the onset of the yellow signal.
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Funding
Soni L Karri’s PhD is funded by Universiti Brunei Darussalam under the University Bursary Scholarship. This research is also partially funded by Universiti Brunei Darussalam’s research grants (UBD/PNC2/2/RG/1(311) & UBD/RSCH/1.11/FICBF/2018/002).
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This article is part of the topical collection “Data Science and Communication” guest edited by Kamesh Namudri, Naveen Chilamkurti, Sushma S J and S. Padmashree.
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Karri, S.L., De Silva, L.C., Lai, D.T.C. et al. Classification and Prediction of Driving Behaviour at a Traffic Intersection Using SVM and KNN. SN COMPUT. SCI. 2, 209 (2021). https://doi.org/10.1007/s42979-021-00588-7
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DOI: https://doi.org/10.1007/s42979-021-00588-7