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

Abstract

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.