Abstract
Distracted driving causes most road accidents and injuries. Cell phones, food, radios, and passenger conversations are all distractions. Distractions may slow a driver's response time and increase the risk of accidents. Studies reveal that even minor distractions may impair a driver's ability to drive safely. This study examines how distracted driving affects male drivers. Using US and Malaysian databases will do this. This research included drivers with at least two years of experience to guarantee a representative sample. Each dataset chose 35 and 58 drivers. Driver distraction level, a new class characteristic, has four levels: no, mild, moderate, and severe. Weka software was used for “data mining” to get insights from a vast dataset. Weka is a strong data mining and machine learning program including algorithms for data preparation, classification, regression, clustering, and visualization. We applied these algorithms on their datasets using its GUI or command-line parameters. Speed, braking, acceleration, steering, lane offset, lane position, and time were used to assess driving performance. Male drivers were more likely to be distracted driving based on their driving skills which is identified by the driving performance indicator (DPI).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Engelberg JK, Hill LL, Rybar J, Styer T (2015) Distracted driving behaviors related to cell phone use among middle-aged adults. J Transp Health 2(3):434–440. https://doi.org/10.1016/j.jth.2015.05.002
Thompson KR, Johnson AM, Emerson JL, Dawson JD, Boer ER, Rizzo M (2012) Distracted driving in elderly and middle-aged drivers. Accid Anal Prev 45:711–717. https://doi.org/10.1016/j.aap.2011.09.040
Lyon C, Mayhew D, Granie M-A, Robertson R, Vanlaar W, Woods-Fry H, Thevenet C, Furian G, Soteropoulos A (n.d.) Focusing on elderly and young drivers. IATSS Res J Int Assoc Traffic Safety Sci 2020(3):212–219. https://doi.org/10.1016/j.iatssr.2020.08.005
Choudhary P, Velaga NR (2017) Analysis of vehicle-based lateral performance measures during distracted driving due to phone use. Transp Res F Traffic Psychol Behav 44:120–133. https://doi.org/10.1016/j.trf.2016.11.002
Rostami H, Dantan J-Y, Homri L (n.d.) Review of data mining applications for quality assessment in manufacturing industry: support vector machines
Taamneh S, Tsiamyrtzis P, Dcosta M, Buddharaju P, Khatri A, Manser M, Ferris T, Wunderlich R, Pavlidis I (2017) A multimodal dataset for various forms of distracted driving. Sci Data 4:170110. https://doi.org/10.1038/sdata.2017.110
You HW, Abdul Rahman A, Hendri Dwisatrya LH (2020) Dataset of driving behaviours in Selangor, Malaysia. Data Brief 31. https://doi.org/10.1016/j.dib.2020.105783
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ganasan, S., Sazali, N. (2024). Classification of Distracted Male Driver Based on Driving Performance Indicator (DPI). In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_49
Download citation
DOI: https://doi.org/10.1007/978-981-99-8819-8_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8818-1
Online ISBN: 978-981-99-8819-8
eBook Packages: EngineeringEngineering (R0)