Identify Optimal Pedestrian Flow Forecasting Methods in Great Britain Retail Areas
A Comparative Study of Time Series Forecasting on a Footfall Dataset
Bibliographic Data
| ID | 22032884 |
|---|---|
| Authors | Roberto Murcio (0000-0002-0674-0363, University of London, corresponding author), Yujue Wang (0000-0003-0785-8256, University of London) |
| Year | 2025 |
| Volume | 14 |
| Issue | 2 |
| Pages | 50 |
| Publication date | 2025-01-27 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi14020050 |
| OpenAlex | W4406865009 |
| Language | EN |
| References cited | 47 |
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
Econometrics · Economics · Machine learning · Operations research · Pedestrian · Time series · Transport engineering · Computer Science · Engineering · Evacuation and Crowd Dynamics · Mathematics · Traffic and Road Safety · Traffic Prediction and Management Techniques · Geology
Time series analysis
Deep Learning in Remote Sensing
Long Short-Term Memory
Pedestrian Flow Prediction in Open Public Places Using Graph Convolutional Network
Geographical Determinants of Regional Retail Sales
Using Wi-Fi probe requests from mobile phones to quantify the impact of pedestrian flows on retail turnover
Comparing Predictive Accuracy
| Highly cited | No |
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