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Understanding the operating speed profile patterns using unsupervised machine learning approach: short-term naturalistic driving study

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dc.contributor.author Malaghan, Vinayak Devendra
dc.date.accessioned 2026-05-14T10:39:17Z
dc.date.available 2026-05-14T10:39:17Z
dc.date.issued 2022-12
dc.identifier.uri https://ascelibrary.org/doi/abs/10.1061/JTEPBS.TEENG-7440
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/21320
dc.description.abstract Several studies have measured the minimum operating speed on horizontal curves to model the operating speed to assess the geometric design consistency. Most of these studies approximated equal lengths of deceleration and acceleration in the operating speed profiles for the curves and assumed the minimum operating speed position at the midpoint of the curve. In contrast, a few recent studies showed different percentages of deceleration lengths on the curve and measured the minimum operating speed at the deceleration end on the curve to model the operating speed. A defined pattern of the operating speed profile on the horizontal curve was not reported in the previous studies and therefore presents opportunities to determine the patterns of the operating speed profiles on curves. In this study, the operating speed profiles of different drivers for the given features of the horizontal curve were studied, and the clustering technique was used to categorize the different patterns in the operating speed profiles on horizontal curves. The optimal number of clusters was determined using four methods: silhouette, elbow, gap statistic, and NbClust function. The different patterns observed from the clustering results are as follows: (1) complete deceleration on the curve, (2) complete acceleration on the curve, (3) deceleration length slightly greater or lower than acceleration length, and (4) longer deceleration/acceleration lengths followed by shorter acceleration/deceleration lengths, respectively. The study results imply that all operating speed profiles are not symmetric around the midpoint of the curve (MC), and the group of drivers exhibited defined patterns of the operating speed profiles on the curves. This study helps in understanding the different patterns of operating speed profiles exhibited by the drivers and the measurement of the minimum operating speed at the deceleration end to model the operating speed to assess the geometric design consistency. en_US
dc.language.iso en en_US
dc.publisher ASCE en_US
dc.subject Civil engineering en_US
dc.subject Operating speed profiles en_US
dc.subject Horizontal curves en_US
dc.subject Clustering analysis en_US
dc.subject Geometric design consistency en_US
dc.title Understanding the operating speed profile patterns using unsupervised machine learning approach: short-term naturalistic driving study en_US
dc.type Article en_US


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