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    Freeway Traffic Speed Estimation by Regression Machine-Learning Techniques Using Probe Vehicle and Sensor Detector Data

    Source: Journal of Transportation Engineering, Part A: Systems:;2020:;Volume ( 146 ):;issue: 012
    Author:
    Zhao Zhang
    ,
    Xianfeng Yang
    DOI: 10.1061/JTEPBS.0000455
    Publisher: ASCE
    Abstract: In the literature, machine-learning techniques have been extensively implemented to capture the stochastic characteristics of freeway traffic speed. The deployment of intelligent transportation systems (ITSs) in recent decades offers much enriched and a wider range of traffic data, which makes it possible to adopt a variety of machine-learning methods to estimate traffic speed. However, an understanding of what type of machine-learning models to select for such applications and how to use probe vehicle data to estimate traffic conditions are still lacking. To fill this research gap, this study aims to utilize regression machine-learning algorithms to estimate traffic speed using probe vehicle and sensor detector data; also, the performance of the utilized machine-learning algorithms is compared using a novel traffic speed estimation framework. The results show that the proposed framework can effectively capture time-varying traffic patterns and has a superior ability to accurately estimate traffic speed in a timely manner. Using sensor detector data as the benchmark, the comparison results show that a random forest achieves the best performance in terms of traffic speed estimation.
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      Freeway Traffic Speed Estimation by Regression Machine-Learning Techniques Using Probe Vehicle and Sensor Detector Data

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4268186
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    contributor authorZhao Zhang
    contributor authorXianfeng Yang
    date accessioned2022-01-30T21:25:50Z
    date available2022-01-30T21:25:50Z
    date issued12/1/2020 12:00:00 AM
    identifier otherJTEPBS.0000455.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4268186
    description abstractIn the literature, machine-learning techniques have been extensively implemented to capture the stochastic characteristics of freeway traffic speed. The deployment of intelligent transportation systems (ITSs) in recent decades offers much enriched and a wider range of traffic data, which makes it possible to adopt a variety of machine-learning methods to estimate traffic speed. However, an understanding of what type of machine-learning models to select for such applications and how to use probe vehicle data to estimate traffic conditions are still lacking. To fill this research gap, this study aims to utilize regression machine-learning algorithms to estimate traffic speed using probe vehicle and sensor detector data; also, the performance of the utilized machine-learning algorithms is compared using a novel traffic speed estimation framework. The results show that the proposed framework can effectively capture time-varying traffic patterns and has a superior ability to accurately estimate traffic speed in a timely manner. Using sensor detector data as the benchmark, the comparison results show that a random forest achieves the best performance in terms of traffic speed estimation.
    publisherASCE
    titleFreeway Traffic Speed Estimation by Regression Machine-Learning Techniques Using Probe Vehicle and Sensor Detector Data
    typeJournal Paper
    journal volume146
    journal issue12
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.0000455
    page10
    treeJournal of Transportation Engineering, Part A: Systems:;2020:;Volume ( 146 ):;issue: 012
    contenttypeFulltext
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