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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.
Highlights
Queuing is a pervasive phenomenon in urban public places, such as supermarkets, restaurants, banks, transportation terminals and amusement parks [1]
We focus on queuing time in indoor public areas
The results of a case study at Beijing Capital International Airport show that the method we proposed is feasible for real-time queuing time prediction
Summary
Queuing is a pervasive phenomenon in urban public places, such as supermarkets, restaurants, banks, transportation terminals and amusement parks [1]. Long-duration queuing reflects the imbalance between the supply and demand of public services, which takes up many individuals’ time and leads to negative experiences and burdens management and reduces the capacity of such services. Research has shown that, in addition to the length of waiting time [2], an important factor affecting individuals’ service satisfaction is whether the waiting time is Sensors 2016, 16, 1958; doi:10.3390/s16111958 www.mdpi.com/journal/sensors. The ability to estimate and predict queuing time reduces the queuing time through self-planning and relieve anxiety. It promotes the responsible use of public resources and enhances the level of management and public security. Real-time queuing time measurement and prediction in public places is of significant importance to the community
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