Accurate Bus Occupancy Estimation for WLAN Probing Utilizing Probabilistic Models

Research output: Contribution to journalJournal articleResearchpeer-review

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.
Original languageEnglish
JournalInternational Journal of Sensor Networks
Volume30
Issue number4
Pages (from-to)231-241
Number of pages11
ISSN1748-1279
DOIs
Publication statusPublished - 2019

Fingerprint

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

Keywords

  • Device counts
  • WLAN probing
  • bus occupancy
  • Probabilstic Models

Cite this

@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.",
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author = "Mikkelsen, {Lars M{\o}ller} and Madsen, {Tatiana Kozlova} and Hans-Peter Schwefel",
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Accurate Bus Occupancy Estimation for WLAN Probing Utilizing Probabilistic Models. / Mikkelsen, Lars Møller; Madsen, Tatiana Kozlova; Schwefel, Hans-Peter.

In: International Journal of Sensor Networks, Vol. 30, No. 4, 2019, p. 231-241.

Research output: Contribution to journalJournal articleResearchpeer-review

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AU - Madsen, Tatiana Kozlova

AU - Schwefel, Hans-Peter

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