多源异构数据融合的城市私家车流量预测研究

刘晨曦; 王东; 陈慧玲; 李仁发

doi:10.11959/j.issn.1000-436x.2021018

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多源异构数据融合的城市私家车流量预测研究

学术论文 | 更新时间：2024-06-05

- 多源异构数据融合的城市私家车流量预测研究
- Study of forecasting urban private car volumes based on multi-source heterogeneous data fusion
- 通信学报 2021年42卷第3期页码：54-64
- 作者机构：
  
  湖南大学信息科学与工程学院，湖南长沙 410082
- 作者简介：
  
  [ "刘晨曦（1999- ），女，回族，安徽亳州人，湖南大学博士生，主要研究方向为智能交通、轨迹数据挖掘、城市计算。" ]
  [ "王东（1964- ），男，江西九江人，博士，湖南大学教授、博士生导师，主要研究方向为车载网络、智能交通、网络性能测试、无线网络协议与性能分析。" ]
  [ "陈慧玲（1997- ），女，湖南衡阳人，湖南大学硕士生，主要研究方向为轨迹数据挖掘、信息管理、自然语言处理。" ]
  [ "李仁发（1957- ），男，湖南郴州人，博士，湖南大学教授、博士生导师，主要研究方向为计算机体系结构、嵌入式计算、无线网络、虚拟与仿真。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61272061);地理信息工程国家重点实验室开放基金资助项目(SKLGIE2018-M-4-3)
- DOI：10.11959/j.issn.1000-436x.2021018
  中图分类号： TP399
- 网络首发：2021-03，
  
  纸质出版：2021-03-25
- 稿件说明：
移动端阅览
刘晨曦, 王东, 陈慧玲, 等. 多源异构数据融合的城市私家车流量预测研究[J]. 通信学报, 2021,42(3):54-64.

Chenxi LIU, Dong WANG, Huiling CHEN, et al. Study of forecasting urban private car volumes based on multi-source heterogeneous data fusion[J]. Journal on Communications, 2021, 42(3): 54-64.
刘晨曦, 王东, 陈慧玲, 等. 多源异构数据融合的城市私家车流量预测研究[J]. 通信学报, 2021,42(3):54-64. DOI： 10.11959/j.issn.1000-436x.2021018.

Chenxi LIU, Dong WANG, Huiling CHEN, et al. Study of forecasting urban private car volumes based on multi-source heterogeneous data fusion[J]. Journal on Communications, 2021, 42(3): 54-64. DOI： 10.11959/j.issn.1000-436x.2021018.

摘要

通过有效地捕获城市私家车出行的时空特征，提出一种多源异构数据融合的私家车流量预测模型。首先，融合私家车轨迹和城市区域数据表征城市私家车的出行分布；其次，通过多视角时空图建模私家车出行和城市区域之间的动态关联，设计了多图卷积-注意力网络以提取车流量演变的时空特征；最后，进一步融合时空特征与天气等外部特征，联合预测私家车流量。在长沙市和深圳市采集的真实数据上进行验证，实验结果表明，与现有的模型相比，所提模型的均方根误差约降低了11.3%～20.3%，平均绝对百分误差约降低了10.8%～36.1%。

Abstract

By effectively capturing the spatio-temporal characteristics of urban private car travel

a multi-source heterogeneous data fusion model for private car volume prediction was proposed.Firstly

private car trajectory and area-of-interest data were integrated.Secondly

the spatio-temporal correlations between private car travel and urban areas were modeled through multi-view spatio-temporal graphs

the multi-graph convolution-attention network (MGC-AN) was proposed to extract the spatio-temporal characteristics of private car travel.Finally

the spatio-temporal characteristics and external characteristics such as weather were integrated for joint prediction.Experiments were conducted on real datasets

which were collected in Changsha and Shenzhen.The experimental results show that

compared with the existing prediction model

the root mean square error of the MGC-AN is reduced 11.3%～20.3%

and the average absolute percentage error is reduced 10.8%～36.1%.

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Keywords

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