Time-Series Data Mining in Transportation: A Case Study on Singapore Public Train Commuter Travel Patterns

Lee, Roy Ka-Wei; Kam, Tin Seong

doi:10.7763/ijet.2014.v6.737

Cited by 10 publications

(1 citation statement)

References 5 publications

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“…With the wide spread of monitors and automated fare-collection systems, public-transport operators have collected a mass of user-transaction data. User-generated data can be transferred to time series, which provide the operators and planners with opportunities to learn about the behaviors and travel patterns of transit users [17].…”

Section: Introductionmentioning

confidence: 99%

Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System

Zhao

Liu

et al. 2019

IJGI

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Many kinds of spatial–temporal data collected by transportation systems, such as user order systems or automated fare-collection (AFC) systems, can be discretized and converted into time-series data. With the technique of time-series data mining, certain travel-demand patterns of different areas in the city can be detected. This study proposes a data-mining model for understanding the patterns and regularities of human activities in urban areas from spatiotemporal datasets. This model uses a grid-based method to convert spatiotemporal point datasets into discretized temporal sequences. Time-series analysis technique dynamic time warping (DTW) is then used to describe the similarity between travel-demand sequences, while the clustering algorithm density-based spatial clustering of applications with noise (DBSCAN), based on modified DTW, is used to detect clusters among the travel-demand samples. Four typical patterns are found, including balanced and unbalanced cases. These findings can help to understand the land-use structure and commuting activities of a city. The results indicate that the grid-based model and time-series analysis model developed in this study can effectively uncover the spatiotemporal characteristics of travel demand from usage data in public transportation systems.

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Section: Introductionmentioning

confidence: 99%