An Efficient Method for Recommending Branch Locations to Reduce the Transportation Distance between Stations and Urban Events

Sheng Ting Chien, Fandel Lin, Chiunghui Tsai, Hsun Ping Hsieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Urban areas need to deploy a lot of services and stations. This work considers the issue of establishing new branches for a certain service. Given a number of stations we plan to construct, our goal is to recommend locations as deploy placements and transportation cost could be efficiently reduced by jointly considering road network, existing stations and spatial event data. Our model can be divided into four parts: 1) Adopting DBSCAN clustering method to find hot spots of spatial events. 2) Doing community detection for road network to split the road network to smaller components. 3) Exploiting a refined closeness centrality to identify a good candidate location in each community. 4) Developing a greedy-based distance minimized method to establish stations sequentially. The results show our solution is effective and efficient for a large crime event dataset of Chicago.

Original languageEnglish
Title of host publicationProceedings - 2020 21st IEEE International Conference on Mobile Data Management, MDM 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages310-315
Number of pages6
ISBN (Electronic)9781728146638
DOIs
Publication statusPublished - 2020 Jun
Event21st IEEE International Conference on Mobile Data Management, MDM 2020 - Versailles, France
Duration: 2020 Jun 302020 Jul 3

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
Volume2020-June
ISSN (Print)1551-6245

Conference

Conference21st IEEE International Conference on Mobile Data Management, MDM 2020
CountryFrance
CityVersailles
Period20-06-3020-07-03

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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