People often have to stand waiting in a queue, which they may find disagreeable. Facilities where people must wait, for example, food courts, banks and amusement parks, need to recognize and provide an estimated wait time to increase customer satisfaction. Hence, there is demand for wait time estimation methods. Also, the requirement for estimation accuracy changes with the length of the queue. In this paper, we propose a cooperative line wait time estimation method using Bluetooth low energy (BLE, marketed as Bluetooth Smart) on a smartphone. To estimate the wait time, we utilize the stay-times of users approaching and moving past two preinstalled receivers. The wait time is estimated by the difference between the two stay-times. Our stay-time estimation includes two methods: a direct-wave blocking method and a stay-time estimation method. We experimentally evaluated with our method in a passageway of our university campus for different values of the range value which is a parameter used in the stay-time estimation. It was found that when the range value was set to 4-8 dB, almost all of the devices estimated the wait time to be within 20 s of from the expected wait time. This result satisfied the requirement of all users according to our questionnaire about discontent with erroneously estimated wait times.
This paper proposes a congestion degree estimation method for train cars using wireless LAN (WLAN) in a simple manner because the public WLAN service area has recently been expanded. WLAN stations (STAs) frequently find a WLAN access point (AP) using WLAN control frames named probe request frames. In addition, many APs for public WLAN services are located in places such as railways and airports. In this paper, we investigate the congestion degree using WLAN control frames. Compared with the case of using the number of WLAN STAs obtained by probe request frames, we clarify that the correlation between the results of evaluation by visual observations and the received signal strength indicator (RSSI) values obtained by the beacon frames of a WLAN AP is higher.
In this paper, we propose an immediate cooperative line wait time estimation system using Bluetooth low energy (BLE, marketed as Bluetooth Smart) on a smartphone; this system is a modified version of our previous proposed method. To estimate the wait time, we utilize time stamps of when users approach and move past two preinstalled receivers. Our system comprises three main components: the receivers, a wait time estimator, and a database. The receivers record two types of data: the recorded time and the RSSI values. The wait time estimator uses the wait time estimation algorithm, which includes three main subroutines: the maximum RSSI decision, in-decision, and out-decision on the receivers for each user's smartphone. By calibrating and analyzing the recorded log data, the wait time estimator estimates the estimated wait time. This estimated wait time is stored in the database, then provided through the website to the queueing users. The experimental results showed that the difference between the estimated wait time and the expected wait time was within 10 s for all measurements.
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