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    Machine Learning Approach to Decomposing Arterial Travel Time Using a Hidden Markov Model with Genetic Algorithm

    Source: Journal of Computing in Civil Engineering:;2018:;Volume ( 032 ):;issue: 003
    Author:
    Yang Shu;Chen Ming;Wu Yao-Jan;An Chengchuan
    DOI: 10.1061/(ASCE)CP.1943-5487.0000748
    Publisher: American Society of Civil Engineers
    Abstract: Bluetooth-based traffic detection is an emerging travel time collection technique; however, its use on arterials has been limited due to several challenges. In particular, data missing not at random (MNAR) is a common data set problem caused by system network failure or sensor malfunctioning. Solving the MNAR problem requires travel-time decomposition (TTD) using complete travel times spanning successive links. Previous work has focused on TTD methodologies that use probe vehicle data. However, these approaches may be unsuitable for Bluetooth-based data. Therefore, this study proposes a machine learning–based approach to decomposing Bluetooth-based travel time. A modified hidden Markov model was developed to model travel-time distributions and traffic-state transitions. A genetic algorithm (GA) was applied to solve a numerical optimal decomposition based on maximum likelihood. Two real-world travel-time data sets were used for validation of the approach. The proposed hidden Markov chain with GA (HMMGA) approach and Gaussian mixture model with GA (GMMGA) were compared with a benchmark approach using distance-based allocation. The results showed that the HMMGA significantly outperformed both the GMMGA and benchmark approaches. Using the HMMGA, the average mean absolute percentage error was up to 72% lower compared to the benchmark approach.
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      Machine Learning Approach to Decomposing Arterial Travel Time Using a Hidden Markov Model with Genetic Algorithm

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4250368
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    contributor authorYang Shu;Chen Ming;Wu Yao-Jan;An Chengchuan
    date accessioned2019-02-26T07:56:03Z
    date available2019-02-26T07:56:03Z
    date issued2018
    identifier other%28ASCE%29CP.1943-5487.0000748.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4250368
    description abstractBluetooth-based traffic detection is an emerging travel time collection technique; however, its use on arterials has been limited due to several challenges. In particular, data missing not at random (MNAR) is a common data set problem caused by system network failure or sensor malfunctioning. Solving the MNAR problem requires travel-time decomposition (TTD) using complete travel times spanning successive links. Previous work has focused on TTD methodologies that use probe vehicle data. However, these approaches may be unsuitable for Bluetooth-based data. Therefore, this study proposes a machine learning–based approach to decomposing Bluetooth-based travel time. A modified hidden Markov model was developed to model travel-time distributions and traffic-state transitions. A genetic algorithm (GA) was applied to solve a numerical optimal decomposition based on maximum likelihood. Two real-world travel-time data sets were used for validation of the approach. The proposed hidden Markov chain with GA (HMMGA) approach and Gaussian mixture model with GA (GMMGA) were compared with a benchmark approach using distance-based allocation. The results showed that the HMMGA significantly outperformed both the GMMGA and benchmark approaches. Using the HMMGA, the average mean absolute percentage error was up to 72% lower compared to the benchmark approach.
    publisherAmerican Society of Civil Engineers
    titleMachine Learning Approach to Decomposing Arterial Travel Time Using a Hidden Markov Model with Genetic Algorithm
    typeJournal Paper
    journal volume32
    journal issue3
    journal titleJournal of Computing in Civil Engineering
    identifier doi10.1061/(ASCE)CP.1943-5487.0000748
    page4018005
    treeJournal of Computing in Civil Engineering:;2018:;Volume ( 032 ):;issue: 003
    contenttypeFulltext
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