交通运输系统工程与信息

交通运输系统工程与信息 ›› 2015, Vol. 15 ›› Issue (4): 78-84.

所属专题： 车路协同与智能化技术

• 智能交通系统与信息技术 • 上一篇    下一篇

基于公交IC 卡数据的上车站点推算研究

马晓磊1，刘从从1，刘剑锋2，陈锋3, 于海洋*1   

  1. 1. 北京航空航天大学车路协同与安全控制北京市重点实验室，北京100091；2. 北京城建集团有限责任公司，北京 100037；3. 北京交通发展研究中心，北京100073
  • 收稿日期:2015-03-10 修回日期:2015-03-31 出版日期:2015-08-25 发布日期:2015-08-25
  • 作者简介:马晓磊（1985-），男，北京人，副教授.
  • 基金资助:

    国家自然科学基金(51408019, 51308021)；北京市科技新星计划项目(z151100000315048).

Boarding Stop Inference Based on Transit IC Card Data

MAXiao-lei1 , LIU Cong-cong1, LIU Jian-feng2 , CHEN Feng3, YU Hai-yang1   

  1. 1. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang University, Beijing 100091, China; 2. Beijing Urban Construction Group Co., Ltd., Beijing 100037, China; 3. Beijing Transportation Research Center, Beijing 100073, China
  • Received:2015-03-10 Revised:2015-03-31 Online:2015-08-25 Published:2015-08-25

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被引次数

25

摘要：

为了分析城市公交乘客的出行特征，本文利用公交IC 卡及GPS数据对公交IC 卡乘客上车站点推算进行研究.针对安装车载GPS设备的车辆，运用GPS数据与IC 卡数据融合算法进行推算；对于无车载GPS设备的情况，为适应一票制IC 卡数据挖掘，对贝叶斯决策树算法进行改进，允许节点跳跃，推算上车站点，并且利用Markov 链特性降低算法的运算复杂度.同时，本文以北京公交数据为例，对提出的两种方法进行验证.结果表明，利用本文提出的方法推算上车站点，3 站之内误差的准确率达到90%以上，算法在兼顾算法精度的同时合理地控制了运算复杂度，可以实际运用于城市公交系统.

关键词: 城市交通, 公共交通, 上车站点推算, 贝叶斯决策树算法, IC卡数据, GPS 数据

Abstract:

In order to analyze urban bus passengers' travel characteristic, this paper proposes several data mining algorithms for boarding stop inference based on IC card and GPS data. For those buses with GPS devices, a data-fusion method with GPS data is developed to estimate individual passenger’s boarding stop. For those buses without GPS devices, an improved Bayesian decision tree algorithm with varying steps is presented to calculate the likelihood of each possible boarding stop. In addition, Markov Chain optimization technique is applied to reduce the computational complexity. Empirical data from Beijing transit route are used to validate the effectiveness of the proposed algorithms. The results demonstrate that the accuracy of identified boarding stop can be guaranteed and the algorithm complexity can be well controlled to meet the requirements of practical application. As a result, the methods can be widely adopted for urban public transportation system.

Key words: urban traffic, public transit, boarding stop inference, Bayesian decision tree algorithm, IC card data, GPS data

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