Predicting passengers in public transportation using smart card data

Mengyu Dou; Tieke He; Hongzhi Yin; Xiaofang Zhou; Zhenyu Chen; Bin Luo

doi:10.1007/978-3-319-19548-3_3

Predicting passengers in public transportation using smart card data

Mengyu Dou, Tieke He, Hongzhi Yin, Xiaofang Zhou, Zhenyu Chen^*, Bin Luo

^*Corresponding author for this work

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

14 Citations (Scopus)

Abstract

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 backpropagation 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.

Original language	English
Title of host publication	Databases Theory and Applications - 26th Australasian Database Conference, ADC 2015, Proceedings
Editors	Muhammad Aamir Cheema, Jianzhong Qi, Mohamed A. Sharaf
Publisher	Springer Verlag
Pages	28-40
Number of pages	13
ISBN (Print)	9783319195476
DOIs	https://doi.org/10.1007/978-3-319-19548-3_3
Publication status	Published - 2015
Externally published	Yes
Event	26th Australasian Database Conference, ADC 2015 - Melbourne, Australia Duration: 4 Jun 2015 → 7 Jun 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9093
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	26th Australasian Database Conference, ADC 2015
Country/Territory	Australia
City	Melbourne
Period	4/06/15 → 7/06/15

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Back-propagation neural network
Bag-of-words
Prediction
Smart card
Transportation

Access to Document

10.1007/978-3-319-19548-3_3

Cite this

Dou, M., He, T., Yin, H., Zhou, X., Chen, Z., & Luo, B. (2015). Predicting passengers in public transportation using smart card data. In M. A. Cheema, J. Qi, & M. A. Sharaf (Eds.), Databases Theory and Applications - 26th Australasian Database Conference, ADC 2015, Proceedings (pp. 28-40). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9093). Springer Verlag. https://doi.org/10.1007/978-3-319-19548-3_3

Dou, Mengyu ; He, Tieke ; Yin, Hongzhi et al. / Predicting passengers in public transportation using smart card data. Databases Theory and Applications - 26th Australasian Database Conference, ADC 2015, Proceedings. editor / Muhammad Aamir Cheema ; Jianzhong Qi ; Mohamed A. Sharaf. Springer Verlag, 2015. pp. 28-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{45828e4ab4ae42a3b98cb01254e0cf52,

title = "Predicting passengers in public transportation using smart card data",

abstract = "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 backpropagation 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.",

keywords = "Back-propagation neural network, Bag-of-words, Prediction, Smart card, Transportation",

author = "Mengyu Dou and Tieke He and Hongzhi Yin and Xiaofang Zhou and Zhenyu Chen and Bin Luo",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 26th Australasian Database Conference, ADC 2015 ; Conference date: 04-06-2015 Through 07-06-2015",

year = "2015",

doi = "10.1007/978-3-319-19548-3\_3",

language = "English",

isbn = "9783319195476",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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}

Dou, M, He, T, Yin, H, Zhou, X, Chen, Z & Luo, B 2015, Predicting passengers in public transportation using smart card data. in MA Cheema, J Qi & MA Sharaf (eds), Databases Theory and Applications - 26th Australasian Database Conference, ADC 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9093, Springer Verlag, pp. 28-40, 26th Australasian Database Conference, ADC 2015, Melbourne, Australia, 4/06/15. https://doi.org/10.1007/978-3-319-19548-3_3

Predicting passengers in public transportation using smart card data. / Dou, Mengyu; He, Tieke; Yin, Hongzhi et al.
Databases Theory and Applications - 26th Australasian Database Conference, ADC 2015, Proceedings. ed. / Muhammad Aamir Cheema; Jianzhong Qi; Mohamed A. Sharaf. Springer Verlag, 2015. p. 28-40 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9093).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Predicting passengers in public transportation using smart card data

AU - Dou, Mengyu

AU - He, Tieke

AU - Yin, Hongzhi

AU - Zhou, Xiaofang

AU - Chen, Zhenyu

AU - Luo, Bin

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - 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 backpropagation 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.

AB - 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 backpropagation 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.

KW - Back-propagation neural network

KW - Bag-of-words

KW - Prediction

KW - Smart card

KW - Transportation

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UR - https://openalex.org/W2189251073

UR - https://www.scopus.com/pages/publications/84959377829

U2 - 10.1007/978-3-319-19548-3_3

DO - 10.1007/978-3-319-19548-3_3

M3 - Conference Paper published in a book

SN - 9783319195476

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 28

EP - 40

BT - Databases Theory and Applications - 26th Australasian Database Conference, ADC 2015, Proceedings

A2 - Cheema, Muhammad Aamir

A2 - Qi, Jianzhong

A2 - Sharaf, Mohamed A.

PB - Springer Verlag

T2 - 26th Australasian Database Conference, ADC 2015

Y2 - 4 June 2015 through 7 June 2015

ER -

Dou M, He T, Yin H, Zhou X, Chen Z, Luo B. Predicting passengers in public transportation using smart card data. In Cheema MA, Qi J, Sharaf MA, editors, Databases Theory and Applications - 26th Australasian Database Conference, ADC 2015, Proceedings. Springer Verlag. 2015. p. 28-40. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-19548-3_3

Predicting passengers in public transportation using smart card data

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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