Abstract
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.
Highlights
Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage large crowds objectively
Six monitoring techniques exist that provide real-time information regarding the movements of crowds, being camera systems, automatic counting systems, Radio Frequency Identification (RFID) sensors, Wi-Fi/Bluetooth sensors, Global Positioning System (GPS) sensors, and social media data
The Wi-Fi count time series serves as the main input for all data fusion models that are estimated in this study
Summary
Crowd monitoring systems (CMSs) provide a state-of-the-art solution to manage large crowds objectively. In order to limit operation costs of CMSs, data fusion techniques are required that utilize the strengths of the overlapping presence of complementary sensor types in a limited number of locations in order to derive additional crowd state information at locations where only one sensor type is present, in general the least expensive one. All models leverage data from multiple semantically distinct sensors (i.e., Wi-Fi and automated counting systems) in order to infer the flow rate for one particular sensor location. Other more sophisticated model types use the data from multiple data sources during application in order to improve the flow rate estimation at one specific location. This section introduces the technical details of the CMS’s sensors, the model types under investigation, and the goodness-of-fit metrics adopted to determine the best flow rate estimation model. This paper concludes with a summary of the main findings and several suggestions for further research
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