Abstract
When the drivers approaching signalized intersections (onset of yellow signal), the drivers would enter into a zone, where they will be in uncertain mode assessing their capabilities to stop or cross the intersection. Therefore, any improper decision might lead to a right-angle or back-end crash. To avoid a right-angle collision, drivers apply the harsh brakes to stop just before the signalized intersection. But this may lead to a 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. In order to reduce this issue, this study’s primary objective is to identify the driving behaviour at signalized intersections based on the driving features (parameters). The secondary objective is to classify the outcome of driving behaviour (safe stopping and unsafe stopping) at the signalized intersection using a support vector machine (SVM) technique. Turning moments are used to identify the zones and label them accordingly for further classification. The classification of 50 instances is identified for training and testing using a 70%–30% rule resulted in an accuracy of 85% and 86%, respectively. Classification performance is further verified by random sampling using five cross-validation and 30 iterations, which gave an accuracy of 97% and 100% for training and testing. These results demonstrate that the proposed approach can help develop a pre-warning system to alert the drivers approaching signalized intersections, thus reducing back-end crash and accidents.
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Acknowledgements
This work was supported by Universiti Brunei Darussalam under the University Bursary Scholarship, Universiti Brunei Darussalam’s Research Grants (Nos, UBD/PNC2/2/RG/1(311) and UBD/RSCH/1.11/FICBF/2018/002).
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Soni Lanka Karri received the M. Eng. degree in computer networks at Department of Computer Science and Systems Engineering, Andhra University, India in 2016. She is now a Ph. D. degree candidate in computer science at Universiti Brunei Darussalam, Brunei Darussalam.
Her research interests include computer architecture, data mining and machine learning.
Liyanage Chandratilak De Silva received the B. Sc. Eng. (Hons) degree in electronics and telecommunication engineering from the University of Moratuwa, Sri Lanka in 1985, received the M. Phil. degree in optical fibre communication from The Open University of Sri Lanka, Sri Lanka in 1989, received the M. Eng. degree in electrical engineering and Ph. D. degree in information and communication engineering from the University of Tokyo, Japan in 1992 and 1995 respectively. From April 1995 to March 1997, he pursued his postdoctoral research as a post-doctoral researcher at Advanced Telecommunication Research (ATR) Laboratories, Japan. He joined National University of Singapore as a lecturer where he was an assistant professor till June 2003. He was with Massey University, New Zealand from 2003 to 2007 as a senior lecturer. Currently, he is a professor and the Dean of Faculty of Integrated Technologies, Universiti Brunei Darussalam, Brunei Darussalam.
Daphne Teck Ching Lai received the B. Sc. degree in computer science from Strathclyde University, UK in 2004, the M. Sc. degree in distributed systems and networks from University of Kent at Canterbury, UK in 2006, and the Ph. D. degree in computer science in 2014 from School of Computer Science, University of Nottingham, UK. She is a senior assistant professor at the Faculty of Science, Universiti Brunei Darussalam, Brunei Darussalam.
Her research interests include data mining, artificial intelligence and metaheuristics.
Shiaw Yin Yong received the B. Sc. degree in mathematics in Universiti Brunei Darussalam, Brunei Darussalam in 2002, received the M. Sc. degree in operations research and the Ph. D. degree in civil engineering from University of Southampton, UK in 2010 and 2014, respectively. She is now a lecturer in Department of Mathematical and Computational Sciences, Universiti Brunei Darussalam, Brunei Darussalam where she has been a faculty member since 2003.
Her research interests include operations research and intelligent traffic system, ranging from theory to design to implementation, with a focus on improving road safety and driver behaviour.
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Karri, S.L., De Silva, L.C., Lai, D.T.C. et al. Identification and Classification of Driving Behaviour at Signalized Intersections Using Support Vector Machine. Int. J. Autom. Comput. 18, 480–491 (2021). https://doi.org/10.1007/s11633-021-1295-y
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DOI: https://doi.org/10.1007/s11633-021-1295-y