Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection

被引:51
作者
Fan, Shu-Kai S. [1 ]
Su, Chuan-Jun [2 ]
Nien, Han-Tang [1 ]
Tsai, Pei-Fang [1 ]
Cheng, Chen-Yang [1 ]
机构
[1] Natl Taipei Univ Technol, Dept Ind Engn & Management, Taipei 10608, Taiwan
[2] Yuan Ze Univ, Dept Ind Engn & Management, Taoyuan 32003, Taiwan
关键词
Big data; Random forests; Electronic toll collection (ETC); Travel time prediction; Apache Hadoop; RANDOM FOREST; FREEWAY; MANAGEMENT; MAPREDUCE; FRAMEWORK; MODEL;
D O I
10.1007/s00500-017-2610-y
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As the technology in automation and computation advances, traffic data can be easily collected from multiple sources, such as sensors and surveillance cameras. To extract value from the huge volumes of available data requires the capability to process and extract patterns in large datasets. In this paper, a machine learning method embedded within a big data analytics platform is constructed by using random forests method and Apache Hadoop to predict highway travel time based on data collected from highway electronic toll collection in Taiwan. Various prediction models are then developed for highway travel time based on historical and real-time data to provide drivers with estimated and adjusted travel time information.
引用
收藏
页码:5707 / 5718
页数:12
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