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Soft Computing

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29.04.2017 | Focus

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

verfasst von: Shu-Kai S. Fan, Chuan-Jun Su, Han-Tang Nien, Pei-Fang Tsai, Chen-Yang Cheng

Erschienen in: Soft Computing | Ausgabe 17/2018

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Abstract

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.

Vorheriger Artikel Quadratic entropy of uncertain variables

Nächster Artikel Stable set of uncertain coalitional game with application to electricity suppliers problem

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Titel: Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection
verfasst von: Shu-Kai S. Fan
Chuan-Jun Su
Han-Tang Nien
Pei-Fang Tsai
Chen-Yang Cheng
Publikationsdatum: 29.04.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 17/2018
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-017-2610-y

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