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dc.contributor.author张洋
dc.contributor.author程恩
dc.date.accessioned2018-11-26T08:55:27Z
dc.date.available2018-11-26T08:55:27Z
dc.date.issued2017
dc.identifier.citation厦门大学学报. 自然科学版,2017,(3):442-448
dc.identifier.issn0438-0479
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/165751
dc.description.abstract选择$\varepsilon $-支持向量机回归($\varepsilon; $-SVR)算法预测快速公交(BRT)车辆的到站时间,以提高公共交通的准点性.分别对BRT的停靠时间和路段行驶时间建立模型.根据分析,在停靠站时; 间预测建模过程中选取车头时距、时段、天气等7维特征向量作为模型输入,采用人工调查法,对厦门BRT-1路的数据进行采集,归一化处理后建模.仿真结果; 显示该模型能够比较准确地预测厦门BRT-1路的运行路线到站时间,并验证天气因素对该线路的到站时间预测影响最大.
dc.description.abstractIn this article,we select $\varepsilon $-support vector machine; regression ($\varepsilon $-SVR) algorithm to predict the bus rapid; transit (BRT) arrival time,in order to improve the public transport on; time.The bus stop time and road travel time models are; established.During the modeling process,seven dimensional features such; as the headway, time, weather etc.,are chosen as model inputs.; Artificial investigation method is used to collect the data of Xiamen; BRT-1. These data are normalized during the modeling process. Simulation; results show that the model can accurately predict bus running time of; Xiamen BRT-1. Results verify that weather factors exert the highest; influence on the arrival time of Xiamen BRT-1.
dc.description.sponsorship福建省教育厅中青年教师教育科研项目
dc.language.isozh_CN
dc.subject支持向量机回归
dc.subject快速公交
dc.subject到站时间
dc.subject停靠时间
dc.subject车头时距
dc.subjectsupport vector regression
dc.subjectbus rapid transit
dc.subjectarrival time
dc.subjectstop time
dc.subjectheadway
dc.title基于$\varepsilon $-支持向量机回归的快速公交到站时间预测
dc.title.alternativeThe Bus Rapid Transit Arrival Time Prediction Based on $\varepsilon $-SVR
dc.typeArticle


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