Passenger Flow Prediction in Bus Transportation System Using ARIMA Models with Big Data

被引：11

作者：

Ye, Yinna ^{[1
]}

Chen, Li ^{[1
]}

Xue, Feng ^{[2
]}

机构：

[1] Xian Jiaotong Liverpool Univ, Dept Math Sci, Suzhou, Peoples R China

[2] Xiongdi Shenzhen Emperor Technol Co, Shenzhen, Peoples R China

来源：

2019 INTERNATIONAL CONFERENCE ON CYBER-ENABLED DISTRIBUTED COMPUTING AND KNOWLEDGE DISCOVERY (CYBERC) | 2019年

关键词：

passenger flow volume prediction; bus transportation system; time series analysis; ARMA model; ARIMA model;

D O I：

10.1109/CyberC.2019.00081

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The objective of this research is to predict the daily bus passenger flow volume in a given bus line and compare the prediction performances in the case using whole weekday data against the case using weekday-only data. Based on the real data collected from the bus IC card payment devices in Jiaozuo City, we firstly obtained time series plots on the daily passenger volume and then proposed ARIMA models to do the prediction. The results show that the operation of including weekend data is necessary to improve the prediction performance.

引用

页码：436 / 443

页数：8

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