Modeling and Simulation of Pedestrian Behavior in Traffic Environment: A Systematic Review of Approaches and Software

Abstract

Pedestrian behavior modeling and simulation play a critical role in traffic safety, urban planning, and transportation system optimization. In traffic environments, these models are used to predict and analyze pedestrian movements and interactions with surrounding infrastructure. Such simulations are essential for improving safety, guiding infrastructure design, and supporting crowd management strategies. This review systematically examines the major approaches and software tools for modeling and simulating pedestrian dynamics in traffic environments. Pedestrian behavior models are classified into theoretical, data-driven, and hybrid approaches, with hybrid models integrating the strengths of both for more comprehensive analysis. The review further evaluates the capabilities and limitations of current simulation platforms. Findings highlight the growing significance of agent-based modeling, along with the integration of real-time simulation, machine learning, and big data analytics. Overall, the review provides insights into the current state of pedestrian behavior simulation and offers guidance on model selection and software choice for both research and applied contexts.