Defining LOS criteria of urban streets using GPS data: k-means and k-medoid clustering in Indian context

Defining LOS criteria of urban streets using GPS data: k-means and k-medoid clustering in Indian context

The objective of this study is to classify urban streets into a number of classes and to define the speed ranges of levels of service (LOS) categories in Indian context. In this purpose, average travel speed on street segments is used as the measure of effectiveness, which has been obtained from se...

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Main Authors: Prasanta Kumar Bhuyan, Kurra Venkata Krishna Rao
Format: Article
Language:English
Published: Vilnius Gediminas Technical University 2012-06-01
Series:Transport
Subjects:
level of service
urban streets
GPS
clustering
cluster validation measures
Online Access:https://journals.vgtu.lt/index.php/Transport/article/view/4927
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spelling doaj-daa5059fc89d4ab9a24591316ccb77362021-07-02T01:25:43ZengVilnius Gediminas Technical UniversityTransport1648-41421648-34802012-06-0127210.3846/16484142.2012.692354Defining LOS criteria of urban streets using GPS data: k-means and k-medoid clustering in Indian contextPrasanta Kumar Bhuyan0Kurra Venkata Krishna Rao1Dept of Civil Engineering, National Institute of Technology, 769008 Rourkela, IndiaDept of Civil Engineering, Indian Institute of Technology Bombay, 400076 Mumbai, India The objective of this study is to classify urban streets into a number of classes and to define the speed ranges of levels of service (LOS) categories in Indian context. In this purpose, average travel speed on street segments is used as the measure of effectiveness, which has been obtained from second-wise speed data collected using Global Positioning System (GPS) receiver. Midsized vehicle (car) was used to collect travel speed data on five urban road corridors comprising of 100 street segments in the city of Mumbai and two major road corridors of Kolkata city in India. Both k-means and k-medoid clustering methods and several cluster validation measures have been employed in the classification of urban streets and LOS categories. It is found that k-medoid clustering is more suitable in Indian context and speed ranges of level of service categories are significantly different from that values mentioned in HCM 2000. https://journals.vgtu.lt/index.php/Transport/article/view/4927level of serviceurban streetsGPSclusteringcluster validation measures
collection DOAJ
language English
format Article
sources DOAJ
author Prasanta Kumar Bhuyan
Kurra Venkata Krishna Rao
spellingShingle Prasanta Kumar Bhuyan
Kurra Venkata Krishna Rao
Defining LOS criteria of urban streets using GPS data: k-means and k-medoid clustering in Indian context
Transport
level of service
urban streets
GPS
clustering
cluster validation measures
author_facet Prasanta Kumar Bhuyan
Kurra Venkata Krishna Rao
author_sort Prasanta Kumar Bhuyan
title Defining LOS criteria of urban streets using GPS data: k-means and k-medoid clustering in Indian context
title_short Defining LOS criteria of urban streets using GPS data: k-means and k-medoid clustering in Indian context
title_full Defining LOS criteria of urban streets using GPS data: k-means and k-medoid clustering in Indian context
title_fullStr Defining LOS criteria of urban streets using GPS data: k-means and k-medoid clustering in Indian context
title_full_unstemmed Defining LOS criteria of urban streets using GPS data: k-means and k-medoid clustering in Indian context
title_sort defining los criteria of urban streets using gps data: k-means and k-medoid clustering in indian context
publisher Vilnius Gediminas Technical University
series Transport
issn 1648-4142
1648-3480
publishDate 2012-06-01
description The objective of this study is to classify urban streets into a number of classes and to define the speed ranges of levels of service (LOS) categories in Indian context. In this purpose, average travel speed on street segments is used as the measure of effectiveness, which has been obtained from second-wise speed data collected using Global Positioning System (GPS) receiver. Midsized vehicle (car) was used to collect travel speed data on five urban road corridors comprising of 100 street segments in the city of Mumbai and two major road corridors of Kolkata city in India. Both k-means and k-medoid clustering methods and several cluster validation measures have been employed in the classification of urban streets and LOS categories. It is found that k-medoid clustering is more suitable in Indian context and speed ranges of level of service categories are significantly different from that values mentioned in HCM 2000.
topic level of service
urban streets
GPS
clustering
cluster validation measures
url https://journals.vgtu.lt/index.php/Transport/article/view/4927
work_keys_str_mv AT prasantakumarbhuyan definingloscriteriaofurbanstreetsusinggpsdatakmeansandkmedoidclusteringinindiancontext
AT kurravenkatakrishnarao definingloscriteriaofurbanstreetsusinggpsdatakmeansandkmedoidclusteringinindiancontext
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