UTHM Institutional Repository

The most important centrality measures in PLUS highway traffic network: an approach based on effective variance

Mohd Asrah, Norhaidah and Djauhari, Maman Abdurachman and Mohamad, Ismail (2017) The most important centrality measures in PLUS highway traffic network: an approach based on effective variance. In: Proceeding of the 25th National Symposium on Mathematical Sciences (SKSM25), 27–29 August 2017, Pahang, Malaysia.

Full text not available from this repository.

Abstract

Highway network is important to most developing countries. With the increase of traffic burden on highway network, it can be a benchmark for these developing countries to measure their level of development. It is hard to analyze the highway traffic network since it comes from a complex network. This network connected all toll plazas and comes out with thedynamic network. In this study, we will learn to understand the PLUS highway traffic network. The information in the network topology of PLUS highway network can be filtered with the application of minimum spanning trees (MST). The network topology will be based on in-coming and out-coming traffic burden from 2009 until 2013. The incoming and out-coming traffic burden of each toll plazas are referredonthe total number of vehicles thatcome in and out over the corresponding toll plazas. Centrality measures are used to interpret these filtered networks. Then, themost important centrality measures can be determined by using the effective variance (EV) and effective vector variance (EVV) approach. The results will help the PLUS highway management to improve their policies and services in the future.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TE Highway engineering. Roads and pavements
Divisions: Faculty of Applied Science and Technology > Department of Mathematics and Statistic
Depositing User: Mr. Mohammad Shaifulrip Ithnin
Date Deposited: 31 Jul 2019 00:59
Last Modified: 31 Jul 2019 00:59
URI: http://eprints.uthm.edu.my/id/eprint/11387
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item