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Open AccessArticle

Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Shenzhen Investigation and Research Institute Co., Ltd, Shenzhen 518026, China
3
Chinese Academy of Surveying & Mapping, Beijing 100830, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 434; https://doi.org/10.3390/ijgi8100434
Received: 19 August 2019 / Revised: 23 September 2019 / Accepted: 27 September 2019 / Published: 30 September 2019
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
Understanding human movement patterns is of fundamental importance in transportation planning and management. We propose to examine complex public transit travel patterns over a large-scale transit network, which is challenging since it involves thousands of transit passengers and massive data from heterogeneous sources. Additionally, efficient representation and visualization of discovered travel patterns is difficult given a large number of transit trips. To address these challenges, this study leverages advanced machine learning methods to identify time-varying mobility patterns based on smart card data and other urban data. The proposed approach delivers a comprehensive solution to pre-process, analyze, and visualize complex public transit travel patterns. This approach first fuses smart card data with other urban data to reconstruct original transit trips. We use two machine learning methods, including a clustering algorithm to extract transit corridors to represent primary mobility connections between different regions and a graph-embedding algorithm to discover hierarchical mobility community structures. We also devise compact and effective multi-scale visualization forms to represent the discovered travel behavior dynamics. An interactive web-based mapping prototype is developed to integrate advanced machine learning methods with specific visualizations to characterize transit travel behavior patterns and to enable visual exploration of transit mobility patterns at different scales and resolutions over space and time. The proposed approach is evaluated using multi-source big transit data (e.g., smart card data, transit network data, and bus trajectory data) collected in Shenzhen City, China. Evaluation of our prototype demonstrates that the proposed visual analytics approach offers a scalable and effective solution for discovering meaningful travel patterns across large metropolitan areas. View Full-Text
Keywords: geovisual analytics; machine learning; smart card data; transit corridor; mobility community; trip geovisual analytics; machine learning; smart card data; transit corridor; mobility community; trip
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Zhang, T.; Wang, J.; Cui, C.; Li, Y.; He, W.; Lu, Y.; Qiao, Q. Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery. ISPRS Int. J. Geo-Inf. 2019, 8, 434.

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