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Urban open places with a public service function (e.g., urban parks) are likely to be populated in peak hours and during public events. To mitigate the risk of overcrowding and even events of stampedes, it is of considerable significance to realize a real-time full coverage estimate of the population density. The main challenge has been the limited deployment of crowd surveillance detectors in open public spaces, leading to incomplete data coverage and thus impacting the quality and reliability of the density estimation. To remedy this issue, this paper proposes a modified inverse distance weighting (IDW) method, named the inverse distance weighting based on path selection behavior (IDWPSB) method. The proposed IDWPSB method adjusts the distance decay effect according to visitors’ path selection behavior, which better characterizes the human dynamics in open spaces. By implementing the model in a real-world road network in the Shichahai scenic area in Beijing, China, the study shows a decrease in the absolute deviation by 17.62% comparing the results between the new method and the traditional IDW method, justifying the effectiveness of the new method for spatial interpolation in open public places. By considering the behavioral factor, the proposed IDWPSB method can provide insights into public safety management with the increasing availability of data derived from location-based services.

A Modified Inverse Distance Weighting Method for Interpolation in Open Public Places Based on Wi-Fi Probe Data