基于多源异构时空数据融合的交通流量预测模型

安洋; 孙健玮; 李倩; 宫永顺

doi:10.11959/j.issn.2096-0271.2023042

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基于多源异构时空数据融合的交通流量预测模型

专题：跨域数据管理 | 更新时间：2024-06-03

- 基于多源异构时空数据融合的交通流量预测模型
- Urban traffic flow prediction based on the multisource heterogeneous spatio-temporal data fusion
- 大数据 2023年9卷第4期页码：69-82
- 作者机构：
  
  1. 山东大学软件学院，山东济南 250101
  2. 中国电子科技集团有限公司第十五研究所，北京 100083
- 作者简介：
  
  [ "安洋（1999- ），女，山东大学软件学院硕士生，主要研究方向为时空数据挖掘、深度学习" ]
  [ "孙健玮（1998- ），男，中国电子科技集团有限公司第十五研究所硕士生，主要研究方向为多模态视图、深度学习" ]
  [ "李倩（1990- ），女，博士，中国电子科技集团有限公司第十五研究所工程师，主要研究方向为人工智能、数据科学" ]
  [ "宫永顺（1990- ），男，博士，山东大学软件学院副研究员，主要研究方向为时空数据挖掘、城市计算、深度学习、机器学习方法" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(62202270);山东省自然科学基金资助项目;Shandong Provincial Natural Science Foundation(ZR2021QF034)
- DOI：10.11959/j.issn.2096-0271.2023042
  中图分类号： TP311
- 网络首发：2023-07，
  
  纸质出版：2023-07-15
- 稿件说明：
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安洋, 孙健玮, 李倩, 等. 基于多源异构时空数据融合的交通流量预测模型[J]. 大数据, 2023,9(4):69-82.

Yang AN, Jianwei SUN, Qian LI, et al. Urban traffic flow prediction based on the multisource heterogeneous spatio-temporal data fusion[J]. Big data research, 2023, 9(4): 69-82.
安洋, 孙健玮, 李倩, 等. 基于多源异构时空数据融合的交通流量预测模型[J]. 大数据, 2023,9(4):69-82. DOI： 10.11959/j.issn.2096-0271.2023042.

Yang AN, Jianwei SUN, Qian LI, et al. Urban traffic flow prediction based on the multisource heterogeneous spatio-temporal data fusion[J]. Big data research, 2023, 9(4): 69-82. DOI： 10.11959/j.issn.2096-0271.2023042.

摘要

交通流量预测问题具有多源异构性，未来时刻的流量不仅与之前时刻的流量相关，同时也受城市区域间关系、天气情况、兴趣点（point of interest，POI）等异构时空数据的影响。针对此问题，提出一种基于多源异构时空数据融合的交通流量预测模型MHF-STNet。首先使用聚类方法获得城市区域不同的流量模式，并使用拼接、权重相加、注意力机制等多种方式融合交通流量、城市区域间的位置关系、天气、POI、工作日、假期多个模态的时空数据，使用深度学习方法对异构数据统一建模，预测未来时刻的交通流量。在北京出租车、纽约出租车和纽约自行车3个流量数据集上进行实验，与经典的交通流量预测模型相比，MHFSTNet的预测准确度有所提升。结果验证了MHF-STNet对异构时空数据统一建模的有效性。

Abstract

The problem of traffic flow forecasting has multi-source heterogeneity.The traffic flow in the future is not only related to the flow at the previous moment

but also affected by heterogeneous spatio-temporal data such as the relationship between urban regions

weather conditions and POI (point of interest).To solve this problem

a traffic flow prediction model based on multi-source heterogeneous spatio-temporal data fusion was proposed

which was called MHFSTNet (multi-source heterogeneous fusion spatio-temporal network).Firstly

this model used clustering methods to obtain different traffic patterns in urban areas

and utilized various methods such as concatenation

weight addition

and attention mechanism to integrate spatio-temporal data of multiple modalities

including traffic flow

location relationships between urban areas

weather

POI and the time of day.Deep learning methods were used to uniformly model heterogeneous data and predict traffic flow in the future.Experiments were conducted on three real-world traffic datasets

TaxiBJ

TaxiNYC and BikeNYC datasets.The results showed that MHF-STNet achieved the best performance compared with some classic traffic flow prediction models

which verified the effectiveness of MHF-STNet for unified modeling of heterogeneous spatio-temporal data.

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references

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