A Study of Passenger Flow Prediction Based on IC Card Data

Xie Zhen-dong; Liu Xue-qin; Wu Jin-cheng; Leng Meng-tian

doi:10.12052/gdutxb.170143

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A Study of Passenger Flow Prediction Based on IC Card Data

更新时间：2026-05-27

- A Study of Passenger Flow Prediction Based on IC Card Data
- Vol. 35, Issue 1, Pages: 16-22(2018)
- 作者机构：
  
  1. 广东岭南通股份有限公司,广东,广州,510000
  2. 广东工业大学　自动化学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170143
  CLC： U491.1
- Online First：09 January 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
谢振东, 刘雪琴, 吴金成, 冷梦甜. 公交IC卡数据客流预测模型研究[J]. 广东工业大学学报, 2018, 35(1): 16-22.

Xie Zhen-dong, Liu Xue-qin, Wu Jin-cheng, et al. A Study of Passenger Flow Prediction Based on IC Card Data[J]. 2018, 35(1): 16-22.
谢振东, 刘雪琴, 吴金成, 冷梦甜. 公交IC卡数据客流预测模型研究[J]. 广东工业大学学报, 2018, 35(1): 16-22. DOI： 10.12052/gdutxb.170143.

Xie Zhen-dong, Liu Xue-qin, Wu Jin-cheng, et al. A Study of Passenger Flow Prediction Based on IC Card Data[J]. 2018, 35(1): 16-22. DOI： 10.12052/gdutxb.170143.

摘要

为解决我国大城市工作上下班高峰期公交线路客流不均衡的问题

提出以公交路线一卡通数据为研究对象

综合考虑天气类型、温度和风力多个因素对客流的影响

利用多元线性回归模型建立公交工作日高峰期各时间段客流模型. 最后通过一卡通大数据对模型进行了验证

证明多元线性回归模型能够准确且快速地预测高峰时间段的客流量

实现了通过公交线路客流预测缓解城市工作日高峰期出行拥堵的问题.

Abstract

To solve the unbalanced bus passenger flow during weekday rush hours in China’s big cities

a multiple linear regression model is established by collecting the bus IC card data

weather data and line data

considering the weather types

temperature and wind factors which influence the passenger flow. The results show that the model can forecast

quickly and precisely

the bus weekday passenger flow in each peak period

which may provide the basis for reasonably scheduling bus passenger flow.

关键词

Keywords

references

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