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

研究成果: Conference contribution

摘要

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.

原文English
主出版物標題Proceedings - 2020 21st IEEE International Conference on Mobile Data Management, MDM 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面310-315
頁數6
ISBN(電子)9781728146638
DOIs
出版狀態Published - 2020 六月
事件21st IEEE International Conference on Mobile Data Management, MDM 2020 - Versailles, France
持續時間: 2020 六月 302020 七月 3

出版系列

名字Proceedings - IEEE International Conference on Mobile Data Management
2020-June
ISSN(列印)1551-6245

Conference

Conference21st IEEE International Conference on Mobile Data Management, MDM 2020
國家France
城市Versailles
期間20-06-3020-07-03

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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