Cover Image

Identifying the Klang Valley rail riders' travel pattern for future expansion using social network analysis

As the population grows, the importance of public transportation increased rapidly as people can use them to travel within the city or town easily. Due to the immense complexity of any public rail, a rich set of information can be uncovered and mined using Social Network Analysis (SNA). In this rese...

Full description

Saved in:
Bibliographic Details
Main Authors: Yaacob, Muhammad Nor Hafiz, Zuhud, Daeng Ahmad Zuhri, Magalingam, Pritheega, Maarop, Norazean, Samy, Ganthan Narayana, Shanmugam, Mohana
Format: Conference or Workshop Item
Published: IEEE 2020
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/91108/
http://dx.doi.org/10.1109/ICIMU49871.2020.9243547
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.91108
record_format eprints
spelling my.utm.911082021-05-31T13:29:28Z http://eprints.utm.my/id/eprint/91108/ Identifying the Klang Valley rail riders' travel pattern for future expansion using social network analysis Yaacob, Muhammad Nor Hafiz Zuhud, Daeng Ahmad Zuhri Magalingam, Pritheega Maarop, Norazean Samy, Ganthan Narayana Shanmugam, Mohana T Technology (General) As the population grows, the importance of public transportation increased rapidly as people can use them to travel within the city or town easily. Due to the immense complexity of any public rail, a rich set of information can be uncovered and mined using Social Network Analysis (SNA). In this research, SNA is used to analyse Klang Valley's rail network and its daily riders' pattern. Klang Valley's rail network serves the area of Klang Valley and Greater Kuala Lumpur with the length of 555.7KM. SNA is a type of analysis that characterizes any phenomena within entities' relationships based on their source and destination. A directed graph was constructed based on a dataset consisting of riders' travel history containing 120 nodes and 5567 edges; the nodes represent stations and the edges are daily-commuters' travel routes between various stations. Temporal analysis is then performed on the dataset to understand the travel trends of residential and central business district (CBD) stations during peak and non-peak hours. Another analysis was also done to identify the rails' network performance when handling attacks on high degree stations; its robustness is also studied. It is found that Klang Valley's rail network is highly robust with more than 500 triad connection detected, ensuring it to recover easily if either one station fails. This research helps to provide new insight into future transportation planning and rail network traffic scheduling. IEEE 2020-08 Conference or Workshop Item PeerReviewed Yaacob, Muhammad Nor Hafiz and Zuhud, Daeng Ahmad Zuhri and Magalingam, Pritheega and Maarop, Norazean and Samy, Ganthan Narayana and Shanmugam, Mohana (2020) Identifying the Klang Valley rail riders' travel pattern for future expansion using social network analysis. In: 2020 8th International Conference On Information Technology And Multimedia (ICIMU), 24 August 2020, Selangor, Malaysia. http://dx.doi.org/10.1109/ICIMU49871.2020.9243547
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T Technology (General)
spellingShingle T Technology (General)
Yaacob, Muhammad Nor Hafiz
Zuhud, Daeng Ahmad Zuhri
Magalingam, Pritheega
Maarop, Norazean
Samy, Ganthan Narayana
Shanmugam, Mohana
Identifying the Klang Valley rail riders' travel pattern for future expansion using social network analysis
description As the population grows, the importance of public transportation increased rapidly as people can use them to travel within the city or town easily. Due to the immense complexity of any public rail, a rich set of information can be uncovered and mined using Social Network Analysis (SNA). In this research, SNA is used to analyse Klang Valley's rail network and its daily riders' pattern. Klang Valley's rail network serves the area of Klang Valley and Greater Kuala Lumpur with the length of 555.7KM. SNA is a type of analysis that characterizes any phenomena within entities' relationships based on their source and destination. A directed graph was constructed based on a dataset consisting of riders' travel history containing 120 nodes and 5567 edges; the nodes represent stations and the edges are daily-commuters' travel routes between various stations. Temporal analysis is then performed on the dataset to understand the travel trends of residential and central business district (CBD) stations during peak and non-peak hours. Another analysis was also done to identify the rails' network performance when handling attacks on high degree stations; its robustness is also studied. It is found that Klang Valley's rail network is highly robust with more than 500 triad connection detected, ensuring it to recover easily if either one station fails. This research helps to provide new insight into future transportation planning and rail network traffic scheduling.
format Conference or Workshop Item
author Yaacob, Muhammad Nor Hafiz
Zuhud, Daeng Ahmad Zuhri
Magalingam, Pritheega
Maarop, Norazean
Samy, Ganthan Narayana
Shanmugam, Mohana
author_facet Yaacob, Muhammad Nor Hafiz
Zuhud, Daeng Ahmad Zuhri
Magalingam, Pritheega
Maarop, Norazean
Samy, Ganthan Narayana
Shanmugam, Mohana
author_sort Yaacob, Muhammad Nor Hafiz
title Identifying the Klang Valley rail riders' travel pattern for future expansion using social network analysis
title_short Identifying the Klang Valley rail riders' travel pattern for future expansion using social network analysis
title_full Identifying the Klang Valley rail riders' travel pattern for future expansion using social network analysis
title_fullStr Identifying the Klang Valley rail riders' travel pattern for future expansion using social network analysis
title_full_unstemmed Identifying the Klang Valley rail riders' travel pattern for future expansion using social network analysis
title_sort identifying the klang valley rail riders' travel pattern for future expansion using social network analysis
publisher IEEE
publishDate 2020
url http://eprints.utm.my/id/eprint/91108/
http://dx.doi.org/10.1109/ICIMU49871.2020.9243547
_version_ 1702169646425178112
score 13.211869

Similar Items