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Revealing Mobility Regularities in Urban Rail Systems

Ambient Systems, Networks and Technologies (ANT)(2020)

Monash Univ | Kongju Natl Univ

Cited 1|Views11
Abstract
Studying mobility patterns in public transport systems is critical for multiple applications from strategic planning to operations control to information provision. Unveiling and understanding the underlying (unobserved) mechanism governing the generation of (observed) mobility patterns are challenging. Considering the recurrent human travel activities and system operations, typical mobility regularities (or variations) patterns of the system can be captured by travel demand. This paper proposes a new paradigm to investigate the regularity in macroscopic mobility patterns for urban rail applications using concepts from image processing and pattern recognition to identify distinct demand patterns. We analyse the within-day demand patterns and day-to-day comparisons by constructing the spatiotemporal eigen-demand images (faces). The case study showed that the entry demand of Hong Kong metro network over 6 months can be grouped into 5 distinct clusters. This new perspective of eigen-demand allows examining the internal essence of large amounts of mobility patterns over time and reveals a global daily demand pattern at the entire system.
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Pulic transport,eigen-demand,principal component analysis,clustering
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要点】:本文提出了一种新方法,利用图像处理和模式识别技术,研究城市轨道交通系统中宏观移动模式的规律性。

方法】:通过构建时空特征需求图像(面孔)来分析日内的需求模式和日间的比较。

实验】:以香港地铁网络6个月的入口需求为例,结果显示可以分为5个不同的群组,这一方法揭示了大量的移动模式随时间的内在本质,并显示了整个系统的全局每日需求模式。