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Identifying the Klang Valley Rail Riders' Travel Pattern for Future Expansion using Social Network Analysis

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dc.contributor.authorHafiz Bin Yaacob M.N.en_US
dc.contributor.authorZuhri Bin Zuhud D.A.en_US
dc.contributor.authorMagalingam P.en_US
dc.contributor.authorMaarop N.en_US
dc.contributor.authorSamy G.N.en_US
dc.contributor.authorShanmugam M.en_US
dc.contributor.authorid57220804522en_US
dc.contributor.authorid57220807256en_US
dc.contributor.authorid35302809600en_US
dc.contributor.authorid45661569600en_US
dc.contributor.authorid35303350500en_US
dc.contributor.authorid36195134500en_US
dc.date.accessioned2023-05-29T08:08:19Z
dc.date.available2023-05-29T08:08:19Z
dc.date.issued2020
dc.descriptionDirected graphs; Landforms; Central business districts; Public transportation; Rail networks; Temporal analysis; Transportation planning; Travel patterns; Travel routes; Tourism industryen_US
dc.description.abstractAs 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. � 2020 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9243547
dc.identifier.doi10.1109/ICIMU49871.2020.9243547
dc.identifier.epage188
dc.identifier.scopus2-s2.0-85097642649
dc.identifier.spage183
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097642649&doi=10.1109%2fICIMU49871.2020.9243547&partnerID=40&md5=42f51591bc28ba7280d778be06cd6178
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25341
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020
dc.titleIdentifying the Klang Valley Rail Riders' Travel Pattern for Future Expansion using Social Network Analysisen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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