Integrating Geovisual Analytics with Machine Learning for Human Mobility Pattern Discovery

作者：Tong Zhang, Jianlong Wang, Chenrong Cui, Yicong Li, Wei He, Yonghua Lu, Qinghua Qiao · 发表于：ISPRS International Journal of Geo-Information · 年份：2019 · DOI：10.3390/ijgi8100434 · 被引用次数：14 · 研究领域：Human Mobility and Location-Based Analysis、Data-Driven Disease Surveillance、Complex Network Analysis Techniques

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 di...