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Article

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

1
Institute of Geomatics, Department of Civil Engineering, Tsinghua University, Beijing 100084, China
2
Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China
3
Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(6), 281; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060281
Received: 12 April 2019 / Revised: 6 June 2019 / Accepted: 8 June 2019 / Published: 16 June 2019
(This article belongs to the Special Issue Geographic Complexity: Concepts, Theories, and Practices)
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. View Full-Text
Keywords: time-series; spatial–temporal data analysis; dynamic time warping; geographic grid; transportation data mining time-series; spatial–temporal data analysis; dynamic time warping; geographic grid; transportation data mining
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MDPI and ACS Style

Zhao, X.; Hu, C.; Liu, Z.; Meng, Y. Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System. ISPRS Int. J. Geo-Inf. 2019, 8, 281. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060281

AMA Style

Zhao X, Hu C, Liu Z, Meng Y. Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System. ISPRS International Journal of Geo-Information. 2019; 8(6):281. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060281

Chicago/Turabian Style

Zhao, Xiaofei, Caiyi Hu, Zhao Liu, and Yangyang Meng. 2019. "Weighted Dynamic Time Warping for Grid-Based Travel-Demand-Pattern Clustering: Case Study of Beijing Bicycle-Sharing System" ISPRS International Journal of Geo-Information 8, no. 6: 281. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8060281

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