Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data
Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd’s traffic state. Incorporating multiple functionally comple...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/21/6032 |
| _version_ | 1797550065999413248 |
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| author | Dorine C. Duives Tim van Oijen Serge P. Hoogendoorn |
| author_facet | Dorine C. Duives Tim van Oijen Serge P. Hoogendoorn |
| author_sort | Dorine C. Duives |
| collection | DOAJ |
| description | Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd’s traffic state. Incorporating multiple functionally complementary sensor types is expensive. CMSs are needed that exploit data fusion opportunities to limit the number of (more expensive) sensors. This research estimates a data fusion algorithm to enhance the functionality of a CMS featuring Wi-Fi sensors by means of a small number of automated counting systems. Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best. |
| first_indexed | 2024-03-10T15:23:11Z |
| format | Article |
| id | doaj.art-f1e419b5bc024bccb3cb72f5e595f390 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:23:11Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f1e419b5bc024bccb3cb72f5e595f3902023-11-20T18:18:17ZengMDPI AGSensors1424-82202020-10-012021603210.3390/s20216032Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System DataDorine C. Duives0Tim van Oijen1Serge P. Hoogendoorn2Department of Transport & Planning, Faculty of Civil Engineering & Geosciences, Delft University of Technology, Mekelweg 1, 2628 CD Delft, The NetherlandsDepartment of Transport & Planning, Faculty of Civil Engineering & Geosciences, Delft University of Technology, Mekelweg 1, 2628 CD Delft, The NetherlandsDepartment of Transport & Planning, Faculty of Civil Engineering & Geosciences, Delft University of Technology, Mekelweg 1, 2628 CD Delft, The NetherlandsCrowd monitoring systems (CMSs) provide a state-of-the-art solution to manage crowds objectively. Most crowd monitoring systems feature one type of sensor, which severely limits the insights one can simultaneously gather regarding the crowd’s traffic state. Incorporating multiple functionally complementary sensor types is expensive. CMSs are needed that exploit data fusion opportunities to limit the number of (more expensive) sensors. This research estimates a data fusion algorithm to enhance the functionality of a CMS featuring Wi-Fi sensors by means of a small number of automated counting systems. Here, the goal is to estimate the pedestrian flow rate accurately based on real-time Wi-Fi traces at one sensor location, and historic flow rate and Wi-Fi trace information gathered at other sensor locations. Several data fusion models are estimated, amongst others, linear regression, shallow and recurrent neural networks, and Auto Regressive Moving Average (ARMAX) models. The data from the CMS of a large four-day music event was used to calibrate and validate the models. This study establishes that the RNN model best predicts the flow rate for this particular purpose. In addition, this research shows that model structures that incorporate information regarding the average current state of the area and the temporal variation in the Wi-Fi/count ratio perform best.https://www.mdpi.com/1424-8220/20/21/6032crowd monitoring systemdata fusionWi-Fi sensor dataautomated counting systemspedestrian movement dynamicscrowd management |
| spellingShingle | Dorine C. Duives Tim van Oijen Serge P. Hoogendoorn Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data Sensors crowd monitoring system data fusion Wi-Fi sensor data automated counting systems pedestrian movement dynamics crowd management |
| title | Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data |
| title_full | Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data |
| title_fullStr | Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data |
| title_full_unstemmed | Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data |
| title_short | Enhancing Crowd Monitoring System Functionality through Data Fusion: Estimating Flow Rate from Wi-Fi Traces and Automated Counting System Data |
| title_sort | enhancing crowd monitoring system functionality through data fusion estimating flow rate from wi fi traces and automated counting system data |
| topic | crowd monitoring system data fusion Wi-Fi sensor data automated counting systems pedestrian movement dynamics crowd management |
| url | https://www.mdpi.com/1424-8220/20/21/6032 |
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