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应用支持向量机预测公交车运行时间 被引量：31

Bus Arrival Time Prediction Using Support Vector Machines
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摘要 采用一种新颖的神经网络-支持向量机(SVM),来预测公交车的到站时间,其目的是要验证SVM在运行时间预测领域的可行性.该模型采用了时间段、天气、路段以及当前路段的运行时间和下一路段的最新运行时间5个输入变量.最后,应用大连市开发区4路公交线对该模型进行了校验,并得到若干结论. Effective prediction of bus arrival time is central to many advanced traveler information system. This paper presents support vector machines （SVM）, a new neural network algorithm, to predict bus arrival time. The objective of this paper is to examine the feasibility and applicability of SVM in vehicle travel time forecasting area. Time-of-day, weather, segment, the travel time of current segment and the latest travel time of next segment are taken as five input features. Bus arrival time predicted by the SVM is assessed with the data of transit route number 4 in Dalian economic and technological development zone in China and some conclusions are drawn.
作者 于滨 杨忠振 林剑艺
机构地区 大连海事大学交通规划研究所 大连理工大学土木水利学院
出处 《系统工程理论与实践》 EI CSCD 北大核心 2007年第4期160-164,176,共6页 Systems Engineering-Theory & Practice
基金 高等学校博士学科点专项科研基金(20050151007)
关键词 预测 公交车到站时间 SVM prediction bus arrival time support vector machine
分类号 U121 [交通运输工程]
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参考文献22

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二级参考文献8

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共引文献81

同被引文献235

引证文献31

二级引证文献204

系统工程理论与实践
2007年 第4期

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