Predicting passengers in public transportation using smart card data

Dou, Mengyu, He, Tieke, Yin, Hongzhi, Zhou, Xiaofang, Chen, Zhenyu and Luo, Bin (2015). Predicting passengers in public transportation using smart card data. In: Mohamed A. Sharaf, Muhammad Aamir Cheema and Jianzhong Qi, Databases Theory and Applications. 26th Australasian Database Conference (ADC), Melbourne Australia, (28-40). 4-7 June 2015. doi:10.1007/978-3-319-19548-3_3


Author Dou, Mengyu
He, Tieke
Yin, Hongzhi
Zhou, Xiaofang
Chen, Zhenyu
Luo, Bin
Title of paper Predicting passengers in public transportation using smart card data
Conference name 26th Australasian Database Conference (ADC)
Conference location Melbourne Australia
Conference dates 4-7 June 2015
Proceedings title Databases Theory and Applications   Check publisher's open access policy
Journal name Databases Theory and Applications   Check publisher's open access policy
Series Lecture Notes in Computer Science
Place of Publication Heidelberg, Germany
Publisher Springer
Publication Year 2015
Sub-type Fully published paper
DOI 10.1007/978-3-319-19548-3_3
Open Access Status Not Open Access
ISBN 9783319195476
9783319195483
ISSN 0302-9743
Editor Mohamed A. Sharaf
Muhammad Aamir Cheema
Jianzhong Qi
Volume 9093
Start page 28
End page 40
Total pages 13
Language eng
Abstract/Summary Transit prediction has long been a hot research problem, which is central to the public transport agencies and operators, as evidence to support scheduling and urban planning. There are several previous work aiming at transit prediction, but they are all from the macro perspective. In this paper, we study the prediction of individuals in the context of public transport. Existing research on the prediction of individual behaviour are mostly found in information retrieval and recommender systems, leaving it untouched in the area of public transport. We propose a NLP based back-propagation neural network for the prediction job in this paper. Specifically, we adopt the concept of “bag of words” to build user profile, and use the result of clustering as input of back-propagation neural network to generate predictions. To illustrate the effectiveness of our method, we conduct an extensive set of experiments on a dataset from public transport fare collecting system. Our detailed experimental evaluation demonstrates that our method gets good performance on predicting public transport individuals.
Subjects 2614 Theoretical Computer Science
1700 Computer Science
Keyword Transportation
Prediction
Smart card
Bag-of-words
Back-propagation neural network
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Sub-type: Fully published paper
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 3 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Sun, 20 Dec 2015, 10:15:16 EST by System User on behalf of Scholarly Communication and Digitisation Service