面向间断流行程时间预测的浮动车数据挖掘

计算机工程与应用 ›› 2012, Vol. 48 ›› Issue (32): 9-13.

面向间断流行程时间预测的浮动车数据挖掘

李慧兵，杨晓光

同济大学交通运输工程学院，上海 201804

出版日期:2012-11-11 发布日期:2012-11-20

Urban travel time prediction model based on floating car data mining

LI Huibing, YANG Xiaoguang

School of Transportation Engineering, Tongji University, Shanghai 201804, China

Online:2012-11-11 Published:2012-11-20

摘要/Abstract

摘要： 间断流行程时间预测是交通流诱导系统和交通控制系统研究的一项重要内容。指出传统浮动车行程时间预测模型的局限性，提出一个模糊回归模型，该模型克服了传统预测模型的局限性，考虑了相邻路段交通状态（行程时间）的连续性，仅需要少量数据就可以对间断流行程时间进行较准确的预测。利用杭州市的实测数据对行程时间进行了预测分析，结果证明该模型是有效的。

关键词: 模糊回归, 间断流, 浮动车数据, 行程时间预测

Abstract: Link travel time prediction for the interrupted flow is an essential subject for traffic flow guidance system and traffic control system. After pointing out the shortcomings of traditional link Travel Time Prediction（TTP） models based on floating car data, the paper brings forward an interrupted flow TTP model based on fuzzy regression. The new model can overcome the drawbacks of traditional models and take traffic state（or link travel time） continuity between two adjacent links into consideration. At the same time, it only requires a small amount of data to implement the model. The paper utilizes the field data from Hangzhou to validate the proposed model, and the results prove to be quite satisfactory.

Key words: fuzzy regression, interrupted flow, floatingcar data, travel time prediction

李慧兵，杨晓光. 面向间断流行程时间预测的浮动车数据挖掘[J]. 计算机工程与应用, 2012, 48(32): 9-13.

LI Huibing, YANG Xiaoguang. Urban travel time prediction model based on floating car data mining[J]. Computer Engineering and Applications, 2012, 48(32): 9-13.