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    Artificial Neural Network–Based Traffic State Estimation Using Erroneous Automated Sensor Data

    Source: Journal of Transportation Engineering, Part A: Systems:;2017:;Volume ( 143 ):;issue: 008
    Author:
    Shrikant Fulari
    ,
    Lelitha Vanajakshi
    ,
    Shankar C. Subramanian
    DOI: 10.1061/JTEPBS.0000058
    Publisher: American Society of Civil Engineers
    Abstract: Capturing real-time traffic system characteristics is a primary step in any intelligent transportation system (ITS) application. The majority of the traffic sensors used to capture real-time data have been developed for homogeneous and lane-disciplined traffic conditions. Hence many of them may not perform accurately under heterogeneous and less-lane-disciplined traffic conditions, ultimately leading to reduced estimation accuracy of end applications. The present study addresses this issue by developing an artificial neural network (ANN)–based estimation scheme that can handle these errors and still generate reasonably accurate results. The estimation of location-based speed, stream-based density, and stream speed is carried out using erroneous data as inputs to an ANN trained with accurate data. The same is also performed under varying ranges of errors in inputs. The results show that the ANN can handle the errors in automated data and produce accurate traffic state estimates when trained with good-quality data, hence demonstrating its efficacy for real-time ITS implementation under such traffic conditions.
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      Artificial Neural Network–Based Traffic State Estimation Using Erroneous Automated Sensor Data

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4244726
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorShrikant Fulari
    contributor authorLelitha Vanajakshi
    contributor authorShankar C. Subramanian
    date accessioned2017-12-30T13:01:44Z
    date available2017-12-30T13:01:44Z
    date issued2017
    identifier otherJTEPBS.0000058.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4244726
    description abstractCapturing real-time traffic system characteristics is a primary step in any intelligent transportation system (ITS) application. The majority of the traffic sensors used to capture real-time data have been developed for homogeneous and lane-disciplined traffic conditions. Hence many of them may not perform accurately under heterogeneous and less-lane-disciplined traffic conditions, ultimately leading to reduced estimation accuracy of end applications. The present study addresses this issue by developing an artificial neural network (ANN)–based estimation scheme that can handle these errors and still generate reasonably accurate results. The estimation of location-based speed, stream-based density, and stream speed is carried out using erroneous data as inputs to an ANN trained with accurate data. The same is also performed under varying ranges of errors in inputs. The results show that the ANN can handle the errors in automated data and produce accurate traffic state estimates when trained with good-quality data, hence demonstrating its efficacy for real-time ITS implementation under such traffic conditions.
    publisherAmerican Society of Civil Engineers
    titleArtificial Neural Network–Based Traffic State Estimation Using Erroneous Automated Sensor Data
    typeJournal Paper
    journal volume143
    journal issue8
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.0000058
    page05017003
    treeJournal of Transportation Engineering, Part A: Systems:;2017:;Volume ( 143 ):;issue: 008
    contenttypeFulltext
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