Citation

Karri SL, De Silva LC, Lai DTC, Yong SY. SN Comput. Sci. 2021; 2(3): e209.

Copyright

(Copyright © 2021, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s42979-021-00588-7

PMID

unavailable

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.

Language: en