Comparison of WLAN Probe and Light Sensor-Based Estimators of Bus Occupancy Using Live Deployment Data
Open accessAbstract
Bus company operators are interested in obtaining knowledge about the number of passengers on their buses—preferably doing so at low deployment costs and in an automated manner, while keeping accuracy high. One solution, widely used in practice, involves deploying a light sensor-based system, counting the people entering and leaving the bus. The light sensor system is simple, but errors accumulate over time, because it is not capable of error correcting. For this reason, the light sensor-based system is compared to a WLAN probe-based system, which has entirely different characteristics. Inaccuracy with the WLAN estimator comes from a need to filter out mobile devices outside the bus and to map the number of detected devices to a number of people. The comparison is performed based on data collected from a real-life deployment in a medium sized German city. The comparison shows the trade-off in selecting either of the two methods. Furthermore, a novel approach for fusion of the light sensor and WLAN estimators is proposed which has a big potential in improving accuracy of both estimators. A fusion approach is proposed that utilizes the different error characteristics for error compensation by calculating compensation terms. The knowledge of Ground Truth is not required as part of this fusion approach for calibration; results show that the approach can find the optimal parameter settings and that it makes this occupancy estimation approach scalable and automated.
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Light sensor performs better in following ground truth in short term but suffers long term; WLAN estimator not good short term but follows general tendencies long term.
“It was proven that a light sensor performs better in following ground truth data in detail in the short term while suffering in the long term.The WLAN estimator is not good at following ground truth in detail in the short term, but in the long term it follows the general tendencies of ground truth, and there is still a lot of room for improvement.”
From A Review of Passenger Counting in Public Transport Concepts with Solution Proposal Based on Image Processing and Machine Learning (2024) · cites this paper as [34]
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- Authors
- Madsen, T., Schwefel, H.-P., Mikkelsen, L., & Burggraf, A.
- Affiliations
- Aalborg University (DK)
- Keysight AS, Alfred Nobels Vej 21E, 9200 Aalborg, Denmark
- Wolfsburg AG MobilitätsWirtschaft, Major-Hirst-Straße 11, 38442 Wolfsburg, Germany
- Corresponding author
- Madsen, Tatiana
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2022-05-28
- Volume
- 22
- Issue
- 11
- Pages
- 4111
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC9185501
- OpenAlex
- W2967930185
- Cited by
- 6
- References
- 20
- Citations by year
- 2026: 2 · 2024: 1 · 2023: 2 · 2022: 1
- FWCI
- 0.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Horizon 2020 (688038)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Transportation › Transportation Planning and Optimization
- Keywords
- passenger counting, error compensation, urban public transport
- MeSH terms
- Cities, Humans, Motor Vehicles
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