Passenger Flow Prediction Using Weather Data For Metro Systems

被引：4
|
作者：
Liu, Lijuan [1 ]
Chen, Rung-Ching [2 ]
Zhu, Shunzhi [1 ]
机构：
[1] Xiamen Univ Technol, Coll Comp & Informat Engn, Xiamen, Fujian, Peoples R China
[2] Chaoyang Univ Technol, Dept Informat Management, Taichung, Taiwan
来源：
2018 CONFERENCE ON TECHNOLOGIES AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE (TAAI) | 2018年
基金：
中国国家自然科学基金;
关键词：
Passenger flow; prediction; metro system; weather; RNN; IMPACT; TRAVEL;
D O I：
10.1109/TAAI.2018.00024
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Metro systems play an important role in reducing traffic congestion in large cities. In this paper, inspired by the potential impact of weather on passenger flow, we have developed an RNN-based model for metro passenger flow prediction with historical passenger flow data, the corresponding temporal data and weather data. A case study of passenger flow prediction model at Taipei Main Station is performed. The experimental results verify that adding the weather data to construct a passenger flow prediction model is contributory to improve the results.
引用
收藏
页码：70 / 73
页数：4
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