Volume 23, Issue 4 (IJIEPR 2012)                   IJIEPR 2012, 23(4): 253-259 | Back to browse issues page

XML Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Hasanpour Jesri S O, Ahmadi A, Karimi B, Akbarpour M. HIERARCHICAL DATA CLUSTERING MODEL FOR ANALYZING PASSENGERS’ TRIP IN HIGHWAYS. IJIEPR 2012; 23 (4) :253-259
URL: http://ijiepr.iust.ac.ir/article-1-449-en.html
HIERARCHICAL DATA CLUSTERING MODEL FOR ANALYZING PASSENGERS’ TRIP IN HIGHWAYS
Seyed Omid Hasanpour Jesri1 , Abbas Ahmadi 2, Behrooz Karimi1 , Mohsen Akbarpour1
1- Amirkabir University of Tehran
2- Amirkabir University of Tehran , abbas.ahmadi@aut.ac.ir
Abstract:   (14955 Views)
One of the most important issues in urban planning is developing sustainable public transportation. The basic condition for this purpose is analyzing current condition especially based on data. Data mining is a set of new techniques that are beyond statistical data analyzing. Clustering techniques is a subset of it that one of it’s techniques used for analyzing passengers’ trip. The result of this research shows relations and similarities in different segments that its usage is from strategic to tactical and operational areas. The approach in transportation is completely novel in the part of trip patterns and a novel process is proposed that can be implemented in highway analysis. Also this method can be applied in traffic and vehicle treats that need automatic number plate recognition (ANPR) for data gathering. A real case study has been studied here by developed process.
Keywords: Urban planning, data mining, traffic analysis, trip, public transportation
Full-Text [PDF 347 kb]   (3025 Downloads)    
Type of Study: Research | Subject: Information Processing and Engineering
Received: 2012/07/1 | Accepted: 2014/07/21 | Published: 2014/07/21
Add your comments about this article : Your username or Email:
CAPTCHA
Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.