Statistical analysis and prediction of regional bus passenger flows

被引：9

作者：

Tan, Qian ^{[1
]}

Ling, Ximan ^{[1
]}

Chen, Meilin ^{[1
]}

Lu, Hengyu ^{[1
]}

Wang, Pu ^{[1
]}

Liu, Wusheng ^{[2
]}

机构：

[1] Cent South Univ, Sch Traff & Transportat Engn, 22 South Shaoshan Rd, Changsha 410075, Hunan, Peoples R China

[2] Changsha Univ Sci & Technol, Minist Educ, Engn Res Ctr Catastroph Prophylaxis & Treatment R, Changsha 410114, Hunan, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MODERN PHYSICS B | 2019年 / 33卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Statistical analysis; human mobility; passenger flow; machine learning; TRANSPORT; REGRESSION; NETWORK;

D O I：

10.1142/S0217979219501017

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

Urban bus transit is a major mode of transportation in modern cities and plays an important role in mitigating the traffic pressure in urban road networks. We used smartcard data collected by three-million bus passengers in Shenzhen, a major southern city of China, to study the statistical properties and dynamics of bus passenger flows. In this study, the recorded passenger flows were cross-grained into each 1 km x 1 km square grids to avoid large flow variations at a single bus station. The temporal and spatial patterns of passenger flows were analyzed and a machine learning-based model for passenger flow prediction was generated.

引用

页数：13

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