基于兴趣点与导航数据的手机信令数据出行方式识别

钟舒琦; 邓如丰; 邓红平; 蔡铭

doi:10.13471/j.cnki.acta.snus.2020.03.010

您当前的位置：

首页 >

文章列表页 >

基于兴趣点与导航数据的手机信令数据出行方式识别

更新时间：2023-12-11

- 基于兴趣点与导航数据的手机信令数据出行方式识别
- Recognition of traffic mode of mobile phone data based on the combination of point of interest data and navigation data
- 中山大学学报(自然科学版)(中英文) 2020年59卷第3期页码：87-96
- 作者机构：
  
  1. 中山大学智能工程学院,广东,广州,510006
  2. 广东省智能交通重点实验室,广东,广州,510006
  3. 佛山市交通运行监测中心,广东,佛山,528000
- 作者简介：
- 基金信息：
- DOI：10.13471/j.cnki.acta.snus.2020.03.010
  中图分类号：
- 纸质出版日期：2020，
  
  网络出版日期：2020-5-25，
扫描看全文
钟舒琦, 邓如丰, 邓红平, 等. 基于兴趣点与导航数据的手机信令数据出行方式识别[J]. 中山大学学报(自然科学版)(中英文), 2020,59(3):87-96.

ZHONG Shuqi, DENG Rufeng, DENG Hongping, et al. Recognition of traffic mode of mobile phone data based on the combination of point of interest data and navigation data[J]. Acta Scientiarum Naturalium Universitatis SunYatseni, 2020,59(3):87-96.
钟舒琦, 邓如丰, 邓红平, 等. 基于兴趣点与导航数据的手机信令数据出行方式识别[J]. 中山大学学报(自然科学版)(中英文), 2020,59(3):87-96. DOI： 10.13471/j.cnki.acta.snus.2020.03.010.

ZHONG Shuqi, DENG Rufeng, DENG Hongping, et al. Recognition of traffic mode of mobile phone data based on the combination of point of interest data and navigation data[J]. Acta Scientiarum Naturalium Universitatis SunYatseni, 2020,59(3):87-96. DOI： 10.13471/j.cnki.acta.snus.2020.03.010.

摘要

手机信令数据具有样本量大、采集成本低、跟随性强等优势，基于手机信令数据的居民出行特征分析具有重要意义。文章针对手机信令数据，设计了一套用户出行特征分析的框架，包括数据清洗、轨迹点分析、出行链提取、兴趣点分析与出行方式识别；基于兴趣点、路网数据与导航数据将用户的出行方式划分为驾车、公交、步行与骑行四种模式。结果表明，结合兴趣点与导航数据后，用户出行方式的识别正确率得到明显提升，与仅使用导航数据的识别算法相比，所提出的结合兴趣点与导航数据的算法正确率提升超过10%，具有较高的识别准确率。

Abstract

Mobile phone data has many advantages

including large sample volume

low collection cost and strong following performance. Thus

analysis of citizens'trip characteristic based on mobile phone data is of significance. A framework of user trip characteristic analysis based on mobile phone data is designed

including data cleaning

trajectory recognition

trip chain extraction

point of interest (POI) analysis and traffic mode recognition. The user's traffic mode is divided into driving

transit

walking and riding based on POI data

road network data and navigation data. The result shows that

with the combination of POI data and navigation data

the precision of traffic mode recognition has a significantly better than only use navigation data

and the promotion is more than 10%. In general

the traffic mode recognition algorithm is high accuracy and suitable for citizens'trip characteristic analysis.

关键词

手机信令出行方式识别导航数据兴趣点

Keywords

mobile phone datatraffic mode recognitionnavigation datapoint of interest

references

浏览量

115

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据