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

您当前的位置：

首页 >

文章列表页 >

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
- 网络出版日期：2021-03，
  
  纸质出版日期：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%.

关键词

Keywords

references

范娇娇 . 基于私家车轨迹的停等聚集效应分析研究 [D ] . 长沙:湖南大学 , 2019 .

FAN J J . Research on stop-and-wait aggregation effect based on private car trajectory data [D ] . Changsha:Hunan University , 2019 .

KONDOR D , ZHANG H , TACHET R , et al . Estimating savings in parking demand using shared vehicles for homework commuting [J ] . IEEE Transactions on Intelligent Transportation Systems , 2018 , 20 ( 8 ): 2903 - 2912 .

WANG D , FAN J J , XIAO Z , et al . Stop-and-wait:discover aggregation effect based on private car trajectory data [J ] . IEEE Transactions on Intelligent Transportation Systems , 2018 , 20 ( 10 ): 3623 - 3633 .

ZHANG W , LIU H , LIU Y , et al . Semi-supervised hierarchical recurrent graph neural network for city-wide parking availability prediction [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2020 , 34 ( 1 ): 1186 - 1193 .

GONG Y , LI Z , ZHANG J , et al . Online spatio-temporal crowd flow distribution prediction for complex metro system [J ] . IEEE Transactions on Knowledge and Data Engineering , 2020 ,doi:10.1109/TKDE.2020.2985952.

徐丰力 , 李勇 . 城市环境下的用户移动行为建模概述 [J ] . 通信学报 , 2020 , 41 ( 7 ): 18 - 28 .

XU F L , LI Y . Survey on user’s mobility behavior modelling in urban environment [J ] . Journal on Communications , 2020 , 41 ( 7 ): 18 - 28 .

许佳捷 , 郑凯 , 池明旻 , 等 . 轨迹大数据:数据、应用与技术现状 [J ] . 通信学报 , 2015 , 36 ( 12 ): 97 - 105 .

XU J J , ZHENG K , CHI M M , et al . Trajectory big data:data,applications and techniques [J ] . Journal on Communications , 2015 , 36 ( 12 ): 97 - 105 .

CHEN J , XIAO Z , WANG D , et al . Stay of interest:a dynamic spatiotemporal stay behavior perception method for private car users [C ] // IEEE 21st International Conference on High Performance Computing and Communications . Piscataway:IEEE Press , 2019 : 1526 - 1532 .

SHEN B L , LIANG X , OUYANG Y , et al . Stepdeep:a novel spatial-temporal mobility event prediction framework based on deep neural network [C ] // Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining . New York:ACM Press , 2018 : 724 - 733 .

LIU L , QIU Z , LI G , et al . Contextualized spatial-temporal network for taxi origin-destination demand prediction [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 20 ( 10 ): 3875 - 3887 .

SUN J , ZHANG J , LI Q , et al . Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks [J ] . IEEE Transactions on Knowledge and Data Engineering , 2020 ,doi:10.1109/TKDE.2020.3008774.

CUI Z , KE R , PU Z , et al . Learning traffic as a graph:a gated graph wavelet recurrent neural network for network-scale traffic prediction [J ] . Transportation Research Part C:Emerging Technologies , 2020 , 115 : 102620

JIN G Y , CUI Y , ZENG L , et al . Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network [J ] . Transportation Research Part C:Emerging Technologies , 2020 , 117 : 102665 .

ZHAO L , SONG Y , ZHANG C , et al . T-GCN:a temporal graph convolutional network for traffic prediction [J ] . IEEE Transactions on Intelligent Transportation Systems , 2019 , 21 ( 9 ): 3848 - 3858 .

CHAI D , WANG L , YANG Q . Bike flow prediction with multi-graph convolutional networks [C ] // Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems . New York:ACM Press , 2018 : 397 - 400 .

GENG X , LI Y , WANG L , et al . Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting [C ] // Proceedings of the AAAI Conference on Artificial Intelligence . Palo Alto:AAAI Press , 2019 : 3656 - 3663 .

LIU J , SUN L , LI Q , et al . Functional zone based hierarchical demand prediction for bike system expansion [C ] // Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM Press . 2017 : 957 - 966 .

XIA T , LI Y . Revealing urban dynamics by learning online and offline behaviours together [J ] . Proceedings of the ACM on Interactive,Mobile,Wearable and Ubiquitous Technologies , 2019 , 3 ( 1 ): 1 - 25 .

LI Y , REN W , JIN D , et al . Potential predictability of vehicular staying time for large-scale urban environment [J ] . IEEE Transactions on Vehicular Technology , 2014 , 63 ( 1 ): 322 - 333 .

GUO W , LI L , LI Z , et al . Exploring human stay time patterns from mobile phone data [C ] // 2018 21st International Conference on Intelligent Transportation Systems . Piscataway:IEEE Press , 2018 : 1378 - 1383 .

MOOSAVI S , SAMAVATIAN M H , NANDI A , et al . Short and long-term pattern discovery over large-scale geo-spatiotemporal data [C ] // Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining . New York:ACM Press , 2019 : 2905 - 2913 .

XIAO Z , LI P T , HAVYARIMANA V , et al . GOI:a novel design for vehicle positioning and trajectory prediction under urban environments [J ] . IEEE Sensors Journal , 2018 , 18 ( 13 ): 5586 - 5594 .

刘倩 . 城市中规律出行的私家车行驶特征分析方法研究 [D ] . 长沙:湖南大学 , 2018 .

LIU Q . Study on the method of analyzing the driving characteristics of private cars which have regularity in urban environment [D ] . Changsha:Hunan University , 2018 .

钱振 , 周侗 , 陶菲 , 等 . 基于聚类的AOI出入口位置提取方法研究 [J ] . 地理与地理信息科学 , 2020 , 36 ( 2 ): 1 - 7 .

QIAN Z , ZHOU T , TAO F , et al . Cluster-based extraction method of entrances and exits in AOI [J ] . Geography and Geo-Information Science , 2020 , 36 ( 2 ): 1 - 7 .

TOBLER W R . A computer movie simulating urban growth in the Detroit region [J ] . Economic Geography , 1970 : 234 - 240 .

ZHANG Y , LI Y H , ZHOU X , et al . TrafficGAN:off-deployment traffic estimation with traffic generative adversarial networks [C ] // 2019 IEEE International Conference on Data Mining.Piscataway:IEEE Press . 2019 : 1474 - 1479 .

ZHANG X Y , DU S H , ZHENG Z J . Heuristic sample learning for complex urban scenes:application to urban functional-zone mapping with VHR images and POI data [J ] . ISPRS Journal of Photogrammetry and Remote Sensing , 2020 , 161 : 1 - 12 .

WANG D , WANG C C , XIAO J H , et al . Bayesian optimization of support vector machine for regression prediction of short-term traffic flow [J ] . Intelligent Data Analysis , 2019 , 23 ( 2 ): 481 - 497 .

THOMAS N K , MAX W . Semi-supervised classification with graph convolutional networks [C ] // 2017 5th International Conference on Learning Representations . Piscataway:IEEE Press , 2017 : 1 - 14 .

LI Y , YU R , SHAHABI C , et al . Diffusion convolutional recurrent neural network:Data-driven traffic forecasting [C ] // 2018 6th International Conference on Learning Representations . Piscataway:IEEE Press , 2018 : 1 - 16 .

浏览量

653

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向WSN异常节点检测的融合重构机制与对比学习方法

基于预训练与新型时序图神经网络的智能合约漏洞检测方法

新增未知攻击场景下的工业互联网恶意流量识别方法

移动边缘计算中基于图到序列深度强化学习的复杂任务部署策略

基于图神经网络的账户余额模型区块链地址分类方法