Queuing Time Prediction Using WiFi Positioning Data in an Indoor Scenario

Abstract

Queuing is common in urban public places. Automatically monitoring and predicting queuing time can not only help individuals to reduce their wait time and alleviate anxiety but also help managers to allocate resources more efficiently and enhance their ability to address emergencies. This paper proposes a novel method to estimate and predict queuing time in indoor environments based on WiFi positioning data. First, we use a series of parameters to identify the trajectories that can be used as representatives of queuing time. Next, we divide the day into equal time slices and estimate individuals’ average queuing time during specific time slices. Finally, we build a nonstandard autoregressive (NAR) model trained using the previous day’s WiFi estimation results and actual queuing time to predict the queuing time in the upcoming time slice. A case study comparing two other time series analysis models shows that the NAR model has better precision. Random topological errors caused by the drift phenomenon of WiFi positioning technology (locations determined by a WiFi positioning system may drift accidently) and systematic topological errors caused by the positioning system are the main factors that affect the estimation precision. Therefore, we optimize the deployment strategy during the positioning system deployment phase and propose a drift ratio parameter pertaining to the trajectory screening phase to alleviate the impact of topological errors and improve estimates. The WiFi positioning data from an eight-day case study conducted at the T3-C entrance of Beijing Capital International Airport show that the mean absolute estimation error is 147 s, which is approximately 26.92% of the actual queuing time. For predictions using the NAR model, the proportion is approximately 27.49%. The theoretical predictions and the empirical case study indicate that the NAR model is an effective method to estimate and predict queuing time in indoor public areas.

Crowd Forecasting Based on WiFi Sensors and LSTM Neural Networks

“…Authors in [22] use WiFi signal detectors for crowd count estimation and an autoregressive integrated moving average (ARIMA) model for forecasting. Another work on indoor crowd queuing time estimation is presented in [23]. The authors used WiFi positioning data and nonstandard autoregressive (NAR) model to predict the queuing time.…”

Application of queuing theory to optimize the triage process in a tertiary emergency care (“ER”) department

“…[614] It is a mathematical model used in “waiting lines” that allows analyzing variables such as waiting times and dead times. [3481315161718] When applying this theory to the triage process, it is assumed that there are three fundamental variables that affect the total attention times: (1) number of patients, (2) time used by the nurse to classify a patient, and (3) times of wait.…”

Crowd Monitoring: State-of-the-Art and Future Directions

“…Authors in [55] use WiFi signal detectors for crowd counting to estimate shopper volume in a store. Another work on indoor crowd queuing time estimation is presented in [56]. The authors used WiFi positioning data and statistical time series analysis to predict the queuing time.…”

“…Authors in [55] use WiFi signal detectors for crowd count estimation and an autoregressive integrated moving average (ARIMA) model for forecasting. Another work on indoor crowd queuing time estimation is presented in [56]. [101].…”