From dawn to dusk: daily fluctuations in pedestrian traffic in the city center

Abstract

Pedestrian traffic in a city is subject to fluctuations throughout the day due to a variety of factors. The understanding of these variations can be achieved using properly calibrated agent-based simulation models that capture the dynamics of pedestrian movement. However, despite their significance, such models are currently underrepresented in scientific discussions. In addition, acquiring real-world pedestrian localization data for model calibration poses challenges. To address these issues, this paper presents an agent-based model specifically designed to examine pedestrian traffic fluctuations at a mesoscale level. The model uses popular times data from the Google Places service and population data from the Geographic Information System (GIS) for accurate calibration. As a result, it effectively captures the real-world dynamics of pedestrian movement in the city center. By harnessing the advantages of agent-based modeling (ABM), the model generates several valuable insights into daily pedestrian traffic. It estimates the capacity and speed of pedestrian flows and determines the daily load within the simulated area. Moreover, it enables the identification of bottlenecks and areas characterized by varying levels of pedestrian density. The model’s validation process involves comparing its output with empirical studies and pedestrian traffic data from selected points of interest (POIs). The model successfully captures key aspects associated with fundamental diagrams of pedestrian flow. Furthermore, the dynamics of pedestrians closely align with Google Places popular times data for the chosen POIs. Overall, this research contributes to advancing pedestrian traffic management and optimizing public transport organization by employing empirically calibrated agent-based simulation models.