This paper presents an arterial speed estimation model using data from two distinct sources: mobile probe vehicles and inductive loop detectors. The model consists of three modules: (1) the probe vehicle module which measures arterial speed using vehicles equipped with differential global positioning system receivers; (2) the loop detector module which estimates the link speed using loop detector data, incorporating traffic signal timing parameters; and (3) the data fusion module which uses a neural network to combine outputs from the above two modules to improve the speed estimation accuracy. The computational procedures of the three modules are presented. This paper presents a validation test of the model using a set of data generated from a calibrated simulation model. Our test results show that, the probe vehicle and loop detector modules are capable of making speed estimation with 2-RMSE of less than 3.20 km/h. Using a neural network to fuse the estimates from the two sources reduces the 2-RMSE to less than 1.32 km/h.


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    Title :

    An arterial speed estimation model fusing data from stationary and mobile sensors


    Contributors:
    Cheu, Ruey-Long (author) / Lee, Der-Horng (author) / Chi Xie (author)


    Publication date :

    2001


    Size :

    6 Seiten, 16 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    An arterial speed estimation model fusing data from stationary and mobile sensors

    Ruey Long Cheu, / Der-Horng Lee, / Chi Xie, | IEEE | 2001


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    Calibration-Free Arterial Link Speed Estimation Model Using Loop Data

    Xie, C. / Cheu, R. L. / Lee, D.-H. | British Library Online Contents | 2001


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