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Title:

Mining Smart Card Data for Transit Riders’ Travel Patterns

Accession Number:

01479242

Record Type:

Component

Availability:

Transportation Research Board Business Office

500 Fifth Street, NW
Washington, DC 20001 United States

Abstract:

To mitigate congestion caused by the increasing number of privately owned automobiles, public transit is highly promoted by transportation agencies worldwide. With a better understanding of the travel patterns and regularity (the “magnitude” level of travel pattern) of transit riders, transit authorities can evaluate the current transit services to adjust marketing strategies, keep loyal customers and improve transit performance. However, it is fairly challenging to identify travel pattern for each individual transit rider in a large dataset. Therefore, this paper proposes an efficient and effective data-mining approach that models the travel patterns of transit riders using the smart card data collected in Beijing, China. Transit riders’ trip chains are identified based on the temporal and spatial characteristics of smart card transaction data. Based on the identified trip chains, the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to detect each transit rider’s historical travel patterns. The K-Means++ clustering algorithm and the rough-set theory are jointly applied to clustering and classifying the travel pattern regularities. The rough-set-based algorithm is compared with other classification algorithms, including Naïve Bayes Classifier, C4.5 Decision Tree, K-Nearest Neighbor (KNN) and three-hidden-layers Neural Network. The results indicate that the proposed rough-set-based algorithm outperforms other prevailing data-mining algorithms in terms of accuracy and efficiency.

Supplemental Notes:

This paper was sponsored by TRB committee AP030 Public Transportation Marketing and Fare Policy.

Monograph Accession #:

01470560

Report/Paper Numbers:

13-3460

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Ma, Xiaolei
Wu, Yao-Jan
Wang, Yinhai
Chen, Feng
Liu, Jianfeng

Pagination:

19p

Publication Date:

2013

Conference:

Transportation Research Board 92nd Annual Meeting

Location: Washington DC, United States
Date: 2013-1-13 to 2013-1-17
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; Maps; References; Tables

Subject Areas:

Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning

Source Data:

Transportation Research Board Annual Meeting 2013 Paper #13-3460

Files:

TRIS, TRB, ATRI

Created Date:

Feb 5 2013 12:41PM