基于稀疏浮动车数据的城市路网交通流速度估计

王晓蒙; 彭玲; 池天河

doi:10.11947/j.AGCS.2016.20150472

测绘学报 >

2016 , Vol. 45 >Issue 7: 866 - 873

DOI: https://doi.org/10.11947/j.AGCS.2016.20150472

地图学与地理信息

基于稀疏浮动车数据的城市路网交通流速度估计

王晓蒙 ,
彭玲 ,
池天河

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1. 中国科学院大学, 北京 100049;
2. 中国科学院遥感与数字地球研究所, 北京 100101

王晓蒙(1986-),男,博士,研究方向为地图学与地理信息系统、智慧城市。E-mail:wangxiaomeng1986@163.com

收稿日期: 2015-09-15

修回日期: 2016-01-08

网络出版日期: 2016-07-28

基金资助

国家科技支撑计划（2015BAJ02B00）；国家科技部政策引导类项目（2011FU125Z24）

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A Method of Urban Traffic Flow Speed Estimation Using Sparse Floating Car Data

WANG Xiaomeng ,
PENG Ling ,
CHI Tianhe

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1. University of Chinese Academy of Sciences, Beijing 100049, China;
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Received date: 2015-09-15

Revised date: 2016-01-08

Online published: 2016-07-28

Supported by

National Key Technology Support Program(No.2015BAJ02B00);Ministry of Science and Technology Policy Guidance Project(No.2011FU125Z24)

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摘要

浮动车数据在时空维度呈现较强的稀疏性，是其应用于城市路网交通流估计所面临的主要难题之一。本文通过分析路网交通流速度的时空特征，构建了一种基于朴素贝叶斯法的估计模型，实现对路网中未被样本覆盖路段交通流速度的估计。时间特征主要考虑目标路段相邻时段的交通流速度，空间特征根据路段间交通流相似关系进行分析，突破了传统基于欧氏空间或拓扑关系的度量方式。结果显示，模型能有效地估计出样本缺失路段的交通流速度，且在精度方面相对传统基于拓扑关系的算法优势显著，较好地解决了数据时空稀疏性问题，对基于浮动车数据的交通应用具有较强的实践意义。

关键词： 浮动车数据; 稀疏性; 交通估计; 交通流相似关系

本文引用格式

王晓蒙 , 彭玲 , 池天河 . 基于稀疏浮动车数据的城市路网交通流速度估计[J]. 测绘学报, 2016 , 45(7) : 866 -873 . DOI: 10.11947/j.AGCS.2016.20150472

Abstract

The sample spatio-temporalsparsity is one of the major challenges for traffic estimation when using floating car data (FCD).Spatio-temporal characteristics of road traffic flow are analysed and applied to build a naive Bayes-based traffic estimation model which is proposed to estimate the missing traffic state of the roads which are not covered by samples. In the model, the adjacent period traffic flow speed of the target road segment is considered for the representation of the time characteristic. And instead of Euclidean distance and topology relationship, urban traffic flow similarity relationships are mined to quantify the interior space features of urban traffic.The result demonstrates that the method is effective for missing traffic state estimation and more precision compared to traditional methods based on topology relationship.As a conclusion, the proposed model can solve the spatio-temporal sparsity problem effectively, which has a strong practical significance for traffic application based on FCD.

Key words： floating car data (FCD); sparsity; traffic estimation; traffic flow similarity relationship

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