Accurate Bus Occupancy Estimation for WLAN Probing Utilizing Probabilistic Models

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

This paper obtains an enhanced estimator of number of people on buses using probabilistic models and WLAN-based device counts.
The improved estimator exploits probabilistic models of false positives and false negatives of a previously presented baseline estimator. False positives of the baseline estimator result from devices on the roadside outside the bus that fail to be filtered out by the used threshold approaches.
False negatives may be caused by low probe emission frequency, message losses due to collisions of the WLAN transmissions, MAC address randomization, WLAN channel selection, and occur in particular when passengers stay on the bus only for short duration of time.
The extensions of the baseline model by probabilistic models for false positives and false negatives enables to apply maximum likelihood estimation. Distribution parameters for false positives and false negatives are found from measurements. Field tests on buses in a Danish town are used to validate and quantify the gain of the enhanced estimator.
OriginalsprogEngelsk
TidsskriftInternational Journal of Sensor Networks
ISSN1748-1279
StatusAccepteret/In press - 14 feb. 2019

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Wireless local area networks (WLAN)
Roadsides
Maximum likelihood estimation
Statistical Models

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    @article{d85aa3c9e5344b2bbee4fde64da3b006,
    title = "Accurate Bus Occupancy Estimation for WLAN Probing Utilizing Probabilistic Models",
    abstract = "This paper obtains an enhanced estimator of number of people on buses using probabilistic models and WLAN-based device counts.The improved estimator exploits probabilistic models of false positives and false negatives of a previously presented baseline estimator. False positives of the baseline estimator result from devices on the roadside outside the bus that fail to be filtered out by the used threshold approaches. False negatives may be caused by low probe emission frequency, message losses due to collisions of the WLAN transmissions, MAC address randomization, WLAN channel selection, and occur in particular when passengers stay on the bus only for short duration of time.The extensions of the baseline model by probabilistic models for false positives and false negatives enables to apply maximum likelihood estimation. Distribution parameters for false positives and false negatives are found from measurements. Field tests on buses in a Danish town are used to validate and quantify the gain of the enhanced estimator.",
    keywords = "Device counts, WLAN probing, bus occupancy, Probabilstic Models",
    author = "Mikkelsen, {Lars M{\o}ller} and Madsen, {Tatiana Kozlova} and Hans-Peter Schwefel",
    year = "2019",
    month = "2",
    day = "14",
    language = "English",
    journal = "International Journal of Sensor Networks",
    issn = "1748-1279",
    publisher = "Inderscience Publishers",

    }

    Accurate Bus Occupancy Estimation for WLAN Probing Utilizing Probabilistic Models. / Mikkelsen, Lars Møller; Madsen, Tatiana Kozlova; Schwefel, Hans-Peter.

    I: International Journal of Sensor Networks, 14.02.2019.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    TY - JOUR

    T1 - Accurate Bus Occupancy Estimation for WLAN Probing Utilizing Probabilistic Models

    AU - Mikkelsen, Lars Møller

    AU - Madsen, Tatiana Kozlova

    AU - Schwefel, Hans-Peter

    PY - 2019/2/14

    Y1 - 2019/2/14

    N2 - This paper obtains an enhanced estimator of number of people on buses using probabilistic models and WLAN-based device counts.The improved estimator exploits probabilistic models of false positives and false negatives of a previously presented baseline estimator. False positives of the baseline estimator result from devices on the roadside outside the bus that fail to be filtered out by the used threshold approaches. False negatives may be caused by low probe emission frequency, message losses due to collisions of the WLAN transmissions, MAC address randomization, WLAN channel selection, and occur in particular when passengers stay on the bus only for short duration of time.The extensions of the baseline model by probabilistic models for false positives and false negatives enables to apply maximum likelihood estimation. Distribution parameters for false positives and false negatives are found from measurements. Field tests on buses in a Danish town are used to validate and quantify the gain of the enhanced estimator.

    AB - This paper obtains an enhanced estimator of number of people on buses using probabilistic models and WLAN-based device counts.The improved estimator exploits probabilistic models of false positives and false negatives of a previously presented baseline estimator. False positives of the baseline estimator result from devices on the roadside outside the bus that fail to be filtered out by the used threshold approaches. False negatives may be caused by low probe emission frequency, message losses due to collisions of the WLAN transmissions, MAC address randomization, WLAN channel selection, and occur in particular when passengers stay on the bus only for short duration of time.The extensions of the baseline model by probabilistic models for false positives and false negatives enables to apply maximum likelihood estimation. Distribution parameters for false positives and false negatives are found from measurements. Field tests on buses in a Danish town are used to validate and quantify the gain of the enhanced estimator.

    KW - Device counts

    KW - WLAN probing

    KW - bus occupancy

    KW - Probabilstic Models

    M3 - Journal article

    JO - International Journal of Sensor Networks

    JF - International Journal of Sensor Networks

    SN - 1748-1279

    ER -