计算机应用 ›› 2020, Vol. 40 ›› Issue (1): 36-42.DOI: 10.11772/j.issn.1001-9081.2019061076

• 人工智能 • 上一篇    下一篇

基于朴素贝叶斯分类的居民出行起讫点识别方法

赵光华1, 赖见辉2, 陈艳艳2, 孙浩冬2, 张野2   

  1. 1. 中国建筑设计研究院有限公司 交通规划研究中心, 北京 100044;
    2. 北京工业大学 城市交通学院, 北京 100124
  • 收稿日期:2019-06-22 修回日期:2019-09-05 出版日期:2020-01-10 发布日期:2019-10-08
  • 通讯作者: 赖见辉
  • 作者简介:赵光华(1979-),男,吉林长春人,高级工程师,硕士,主要研究方向:交通规划;赖见辉(1986-),男,江西赣州人,助理研究员,博士,主要研究方向:智能交通;陈艳艳(1970-),女,河南郑州人,教授,博士,主要研究方向:智能交通;孙浩冬(1995-),男,安徽宿州人,博士研究生,主要研究方向:交通大数据挖掘方法;张野(1995-),男,河北秦皇岛人,硕士研究生,主要研究方向:交通大数据挖掘方法。
  • 基金资助:
    北京市科技计划项目(Z181100003918011)。

Residents' travel origin and destination identification method based on naive Bayes classification

ZHAO Guanghua1, LAI Jianhui2, CHEN Yanyan2, SUN Haodong2, ZHANG Ye2   

  1. 1. Transportation Planning Research Center, China Architecture Design and Research Group, Beijing 100044, China;
    2. College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
  • Received:2019-06-22 Revised:2019-09-05 Online:2020-01-10 Published:2019-10-08
  • Supported by:
    This work is partially supported by the Science and Technology Plan of Beijing (Z181100003918011).

摘要: 针对手机信令数据存在的精度不高、时间间隔大、信号"乒乓切换"等问题,提出一种基于朴素贝叶斯分类(NBC)的方法来利用手机定位数据识别居民出行起讫点(OD)。首先,利用80位志愿者连续1个月记录的出行活动数据,依据职住距离分类统计移动和停留状态下的条件概率分布;其次,建立用于表征用户移动停留状态的两个特征参数指标:方向夹角和最小覆盖圆直径;最后,依据NBC原理计算用户的移动或停留状态概率,将连续两个以上为移动状态的过程集聚为出行OD。利用厦门市移动的手机定位数据的分析结果表明:所提方法得到的人均出行次数的平均绝对百分比误差(MAPE)误差为7.79%,具备较高的精度,出行OD的分析结果可以较好地反映真实出行规律。

关键词: 手机定位数据, 朴素贝叶斯分类, 出行起讫点, 移动与停留状态, 职住距离

Abstract: Mobile signaling data has the characteristics of low accuracy, large time interval and the existence of signal "ping-pong switching". In order to identify residents' travel Origin and Destination (OD) using mobile location data, a method based on Naive Bayesian Classification (NBC) was proposed. Firstly, according to the distance between places of residence and working, the travel log data measured by 80 volunteers for one month were classified statistically, and the conditional probability distribution of moving and staying states was obtained. Then, the feature parameters used to represent the user's states of moving and staying were established, including angular separation and minimum covering circle diameter. Finally, the conditional probability distribution of moving and staying states was calculated according to NBC theory, the processes with more than two consecutive moving states were clustered into travel OD. The analysis results on Xiamen mobile location data indicate that the travel time per capita obtained by proposed method has the Mean Absolute Percentage Error (MAPE) of 7.79%, which has a high precision, and the analysis results of travel OD can better reflect real travel rules.

Key words: mobile location data, Naive Bayes Classification (NBC), travel Origin and Destination (OD), moving and staying states, distance between places of residence and working

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