Identify Optimal Pedestrian Flow Forecasting Methods in Great Britain Retail Areas: A Comparative Study of Time Series Forecasting on a Footfall Dataset

The UK retail landscape has undergone significant changes over the past decade, driven by factors such as the rise of online shopping, economic downturns, and, more recently, the COVID-19 pandemic. Accurately measuring pedestrian flows in retail areas with high spatial and temporal resolution is essential for selecting the most appropriate forecasting model for different retail locations. However, several studies have adopted a one-size-fits-all approach, overlooking important local characteristics that are only occasionally captured by the best global model. In this work, using data generated by the SmartStreetSensor project, a large network of sensors installed across UK cities that collect Wi-Fi probe requests generated by mobile devices, we examine the optimal forecasting method to predict pedestrian footfall in various retail areas across Great Britain. After assessing six representative time series forecasting models, our results show that the LSTM model outperforms traditional methods in most areas. However, pedestrian counts at certain locations with specific spatial characteristics are better forecasted by other algorithms.