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    Short-Term Prediction of Traffic Volume in Urban Arterials

    Source: Journal of Transportation Engineering, Part A: Systems:;1995:;Volume ( 121 ):;issue: 003
    Author:
    Mohammad M. Hamed
    ,
    Hashem R. Al-Masaeid
    ,
    Zahi M. Bani Said
    DOI: 10.1061/(ASCE)0733-947X(1995)121:3(249)
    Publisher: American Society of Civil Engineers
    Abstract: This paper attempts to develop time-series models for forecasting traffic volume in urban arterials. The Box-Jenkins approach is used to estimate the time-series models. A 1-min data set representing traffic volume on five major urban arterials were available to estimate time-series models. The Box-Jenkins autoregressive integrated moving average (ARIMA) model of order (0, 1, 1) turned out to be the most adequate model in reproducing all original time series. The developed model is easy to understand and implement. Further, the model is computationally tractable, and only requires the storage of the last forecasted error and current traffic observation.
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      Short-Term Prediction of Traffic Volume in Urban Arterials

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    https://yetl.yabesh.ir/yetl1/handle/yetl/36857
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    • Journal of Transportation Engineering, Part A: Systems

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    contributor authorMohammad M. Hamed
    contributor authorHashem R. Al-Masaeid
    contributor authorZahi M. Bani Said
    date accessioned2017-05-08T21:03:12Z
    date available2017-05-08T21:03:12Z
    date copyrightMay 1995
    date issued1995
    identifier other%28asce%290733-947x%281995%29121%3A3%28249%29.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/36857
    description abstractThis paper attempts to develop time-series models for forecasting traffic volume in urban arterials. The Box-Jenkins approach is used to estimate the time-series models. A 1-min data set representing traffic volume on five major urban arterials were available to estimate time-series models. The Box-Jenkins autoregressive integrated moving average (ARIMA) model of order (0, 1, 1) turned out to be the most adequate model in reproducing all original time series. The developed model is easy to understand and implement. Further, the model is computationally tractable, and only requires the storage of the last forecasted error and current traffic observation.
    publisherAmerican Society of Civil Engineers
    titleShort-Term Prediction of Traffic Volume in Urban Arterials
    typeJournal Paper
    journal volume121
    journal issue3
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/(ASCE)0733-947X(1995)121:3(249)
    treeJournal of Transportation Engineering, Part A: Systems:;1995:;Volume ( 121 ):;issue: 003
    contenttypeFulltext
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