Evaluating Pedestrian Congestion Based on Missing Sensing Data

Abstract

Accurately evaluating pedestrian congestion is crucial for evidence-based improvements in various walking environments. Tracking pedestrian movements in real-world settings often leads to incomplete data collection. Despite this challenge, pedestrian congestion with missing data has not been extensively addressed in existing research. This study examined the impact of missing data on density, speed, and congestion number in the course of evaluating pedestrian congestion. While density is the most commonly used index, speed and congestion number proved more robust.