Pedestrian Movement Prediction and Pattern Analysis

Abstract. Understanding pedestrian movement patterns is essential for designing accessible and pedestrian-friendly urban environments. By analysing them, city planners can optimize resource allocation, enhance walkability, and improve infrastructure planning. This study examines pedestrian patterns in District Lozenets of Sofia city, Bulgaria, and develops a predictive model to estimate foot traffic based on environmental factors such as weather conditions and pedestrian density at different times of day.
The study utilizes data for pedestrian movement collected in the GATE Institute’s City Living Lab in District Lozenets, employing radar-based sensors and incorporating weather data to analyse pedestrian movement trends. Furthermore, it explores various statistical models, such as ARIMA and ETS models, to forecast pedestrian traffic. Results indicate strong seasonal trends, with weekday peaks during commuting hours and a decline in foot traffic due to adverse weather conditions such as rain and snow. The SARIMA model demonstrates high accuracy in predicting short-term pedestrian movement patterns, outperforming alternative models in capturing both seasonal variations and long-term trends.
The findings provide valuable insights for urban planners, event organizers, and policymakers, enabling data-driven decisions for infrastructure improvements, public safety strategies, and pedestrian-friendly city planning. The study also highlights the potential of digital twin technology in urban mobility research, demonstrating the benefits of integrating real-time pedestrian data for predictive analytics.