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Congestion in the bus degrades travel experience for passengers who cannot get their seats. Uneven loading of passengers is a critical issue that may intensify the congestion in bus. To alleviate the congestion, it is necessary to balance the population of passengers inside the bus as well as those waiting at the bus stop. As passengers boarding the bus may simply be detected at the entrance door, this paper focuses on methods to analyze the population waiting at the bus stop. Wi-Fi packet sniffer has been developed to detect and record Wi-Fi activities, and the collected data is examined to precisely grasp the pedestrian flows around the bus stop. At the bus stop, unnecessary signals from passing cars and bicycles, surrounding buildings, etc. can also be detected even though they are not related to the potential passengers. To eliminate such signals, filtering parameters must be examined carefully to accurately extract only the bus passengers' data. Linear regression is used to find the correlation between the filtered data and the ground truth data collected by observation. The results show that the appropriate parameters can be determined using correlation coefficient, which can improve the accuracy better than setting parameters only from observable information, such as distance of bus stop zone and time intervals of arriving buses.

Filter Efficiency Analysis for Extracting Mobile Device Signals to Estimate Bus Passengers Population