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数据分析与知识发现  2019, Vol. 3 Issue (11): 24-34     https://doi.org/10.11925/infotech.2096-3467.2019.0109
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于GPS轨迹数据的城市交叉路口识别 *
高原1,王东2,冯宏伟2(),施元磊2,段治州2
1 西北大学经济管理学院 西安 710127
2 西北大学信息科学与技术学院 西安 710127
Identifying Urban Intersections with GPS Trajectories
Yuan Gao1,Dong Wang2,Hongwei Feng2(),Yuanlei Shi2,Zhizhou Duan2
1 School of Economics & Management, Northwest University, Xi’an 710127, China;
2 School of Information Science & Technology, Northwest University, Xi’an 710127, China;
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摘要 

【目的】针对交通方式混合模式下城市居民移动产生的GPS时空轨迹数据, 实现城市道路交叉路口的自动识别。【方法】将交叉路口识别转化为一个有监督的分类学习问题。利用GeoHash算法对原始轨迹和轨迹活动区域进行编码和分格; 将编码轨迹与活动区域编码矩阵映射成二值化融合矩阵, 构建交叉路口特征集合; 最后利用带有滑动窗口的K近邻分类算法, 实现城市交通的交叉路口识别。【结果】在真实轨迹数据集GeoLife上的对比实验表明, 经过GeoHash编码转换, 数据集规模平均缩减率达到原有轨迹点数量的39%, 降低了计算的时间复杂度; 同时, 识别精度优于传统的基于转向角度的交叉路口识别方法, 当误差距离为50米时, 综合评价指数的F1-Measure达到0.82。【局限】需要在更多城市真实轨迹数据集上进一步检验该方法的有效性。【结论】本文所提方法不受交通模式变化而产生的GPS轨迹采样频率影响, 能解决混合交通模式数据集上的城市交叉路口自动识别问题, 具有较强的通用性。

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高原
王东
冯宏伟
施元磊
段治州
关键词 交叉路口自动识别GPS轨迹GeoHash编码K近邻算法    
Abstract

[Objective] This paper proposes a new supervised learning method to identify road intersections automatically based on GPS trajectory data generated by travelers in the mixed traffic mode. [Methods] Firstly, we encoded and partitioned the original trajectory data and their active regions with the GeoHash algorithm. Then, the coded trajectory and the coding matrix of active regions were mapped into a binary fusion matrix for characteristics of road intersections. Finally, we employed the K nearest neighbor classification algorithm with sliding window to identify the intersections. [Results] The proposed method was more efficient than the Latitude and Longitude Coordination based systems. Encoding with GeoHash algorithm helped us reduce the volume of datasets by 61%. It had better performance than the turning-angle based methods, and its F1-measure score was 0.82 with the distance measure of 50 meters. [Limitations] More real life GPS data is needed to better evaluate our method’s performance. [Conclusions] The proposed method is robust to the changing of sampling frequencies and could effectively identify the urban intersections based on GPS trajectory data.

Key wordsAutomatic Intersection Identification    GPS Trajectory    GeoHash Coding    K Nearest Neighbor Algorithm
收稿日期: 2019-01-24      出版日期: 2019-12-18
ZTFLH:  P209  
基金资助:*本文系教育部社会科学研究一般项目“基于大数据挖掘的文化旅游时空认知分析及演变模式研究”(项目编号: 18YJA630025)
通讯作者: 冯宏伟     E-mail: 59460688@qq.com
引用本文:   
高原,王东,冯宏伟,施元磊,段治州. 基于GPS轨迹数据的城市交叉路口识别 *[J]. 数据分析与知识发现, 2019, 3(11): 24-34.
Yuan Gao,Dong Wang,Hongwei Feng,Yuanlei Shi,Zhizhou Duan. Identifying Urban Intersections with GPS Trajectories. Data Analysis and Knowledge Discovery, 2019, 3(11): 24-34.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2019.0109      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2019/V3/I11/24
  基于GPS轨迹数据的城市交叉路口识别方法框架
  原始轨迹T转化为GeoHash编码轨迹$T\text{ }\!\!\_\!\!\text{ }Geo$示意图
字符串长度 误差(km) 字符串长度 误差(km) 字符串长度 误差(km)
1 ±2500 4 ±20 7 ±0.076
2 ±630 5 ±2.4 8 ±0.019
3 ±78 6 ±0.61 9 ±0.002
  GeoHash编码字符串长度与误差对应关系表(① https://en.wikipedia.org/wiki/Geohash.)
  活动区域的GeoHash编码和分格示意图
  带有滑动窗口的KNN算法示意图
纬度 经度 海拔(英尺) 转化日期 日期 时间
39.9066 116.3855 49 240 097 586516198 2009-10-11 14:04:30
39.9065 116.3856 49 240 097 586516203 2009-10-11 14:04:35
  GeoLife数据集中轨迹点数据结构样例
  轨迹点间间隔分布
  GPS轨迹T转化为GeoHash编码轨迹T_Geo
  原始轨迹数量和GeoHash编码数量对比
  交叉路口与非交叉路口特征矩阵和可视化示意图
  二值化融合矩阵可视化
  交叉路口识别结果示意图
  本文所提方法与传统距离角度方法的对比实验结果
P R F1-Measure
基于转向角度的方法 0.73 0.81 0.77.
本文提出方法 0.87 0.76 0.82
  实验结果统计表
  集合IFP中交叉路口的路网对比
  集合IFN中交叉路口的路网对比
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