Improved Spatio-Temporal Residual Networks for Bus Traffic Flow Prediction

被引:14
|
作者
Liu, Panbiao [1 ]
Zhang, Yong [1 ]
Kong, Dehui [1 ]
Yin, Baocai [1 ]
机构
[1] Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Multimedia & Intelligent Software, Beijing 100124, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 04期
基金
中国国家自然科学基金;
关键词
spatio-temporal; residual networks; bus traffic flow prediction;
D O I
10.3390/app9040615
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Buses, as the most commonly used public transport, play a significant role in cities. Predicting bus traffic flow cannot only build an efficient and safe transportation network but also improve the current situation of road traffic congestion, which is very important for urban development. However, bus traffic flow has complex spatial and temporal correlations, as well as specific scenario patterns compared with other modes of transportation, which is one of the biggest challenges when building models to predict bus traffic flow. In this study, we explore bus traffic flow and its specific scenario patterns, then we build improved spatio-temporal residual networks to predict bus traffic flow, which uses fully connected neural networks to capture the bus scenario patterns and improved residual networks to capture the bus traffic flow spatio-temporal correlation. Experiments on Beijing transportation smart card data demonstrate that our method achieves better results than the four baseline methods.
引用
收藏
页数:12
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