Modeling Chaotic Pedestrian Behavior Using Chaos Indicators and Supervised Learning

TL;DR

This paper presents a data-driven framework that combines chaos theory metrics and supervised learning to model and predict the unpredictable, chaotic behavior of pedestrians in urban environments, aiding safety and infrastructure planning.

Contribution

It introduces a novel approach integrating chaos indicators with machine learning to quantify pedestrian behavioral chaos from trajectory data.

Findings

CatBoost models achieved R^2 up to 0.8574 in predicting chaos.

Chaos scores identified key behavioral features like travel distance and speed variability.

The framework supports real-time risk assessment and urban planning applications.

Abstract

As cities around the world aim to improve walkability and safety, understanding the irregular and unpredictable nature of pedestrian behavior has become increasingly important. This study introduces a data-driven framework for modeling chaotic pedestrian movement using empirically observed trajectory data and supervised learning. Videos were recorded during both daytime and nighttime conditions to capture pedestrian dynamics under varying ambient and traffic contexts. Pedestrian trajectories were extracted through computer vision techniques, and behavioral chaos was quantified using four chaos metrics: Approximate Entropy and Lyapunov Exponent, each computed for both velocity and direction change. A Principal Component Analysis (PCA) was then applied to consolidate these indicators into a unified chaos score. A comprehensive set of individual, group-level, and contextual traffic features was engineered and used to train Random Forest and CatBoost regression models. CatBoost models consistently achieved superior performance. The best daytime PCA-based CatBoost model reached an R^2 of 0.8319, while the nighttime PCA-based CatBoost model attained an R^2 of 0.8574. SHAP analysis highlighted that features such as distance travel, movement duration, and speed variability were robust contributors to chaotic behavior. The proposed framework enables practitioners to quantify and anticipate behavioral instability in real-world settings. Planners and engineers can use chaos scores to identify high-risk pedestrian zones, apprise infrastructure improvements, and calibrate realistic microsimulation models. The approach also supports adaptive risk assessment in automated vehicle systems by capturing short-term motion unpredictability grounded in observable, interpretable features.