Enhancing crowd monitoring system functionality through data fusion

Enhancing crowd monitoring system functionality through data fusion: Estimating flow rate from wi-fi traces and automated counting system data

Duives, D.C. (TU Delft Transport and Planning)
van Oijen, T.P. (TU Delft Transport and Planning)
Hoogendoorn, S.P. (TU Delft Transport and Planning)

Transport and Planning

2020

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.

ARMAX
Automated counting systems
Crowd management
Crowd monitoring system
Data fusion
Mass events
Pedestrian movement dynamics
RNN-LSTM
Wi-Fi sensor data

http://resolver.tudelft.nl/uuid:6f01a1d7-86a9-4033-8823-a6a3cb3ac1d4

https://doi.org/10.3390/s20216032

1424-8220

Sensors, 20 (21), 1-25

Institutional Repository

journal article

© 2020 D.C. Duives, T.P. van Oijen, S.P. Hoogendoorn