Paradigm Shifts in Transport Planning: From Four-Step Models to Agent-Based Simulation

Dear Colleagues,

The landscape of urban transportation is undergoing a profound transformation, driven by shifting travel behaviors, emerging mobility technologies, and the increasing complexity of urban infrastructure. Historically, transport planning has heavily relied on the traditional four-step model—an aggregate, macro-level approach that has been foundational in estimating travel demand. However, as modern transport systems face new challenges such as dynamic pricing, extreme weather disruptions, and the need for highly integrated public transit networks, the limitations of static, aggregate modeling become apparent. The paradigm is rapidly shifting towards disaggregate, micro-level approaches, particularly agent-based simulation and activity-based modeling, which can capture the heterogeneity of individual passenger choices and dynamic system interactions.

This Special Issue focuses on this critical paradigm shift in transport planning. By moving from traditional four-step models to agent-based simulations, researchers and planners can better understand complex travel behaviors, evaluate localized policy impacts, and simulate multi-modal transit environments with high fidelity. We aim to gather innovative research that explores methodological advancements, empirical case studies, and practical applications of agent-based modeling in contemporary transport systems. We are particularly interested in studies that bridge the gap between discrete passenger choice modeling and large-scale network simulations.

We seek high-quality papers and comprehensive review articles that present cutting-edge research in this transitional phase of transport planning. Topics include, but are not limited to, the following:

• Methodological transitions: Frameworks and challenges in shifting from aggregate (four-step) models to disaggregate (agent-based and activity-based) simulations.
• Behavioral modeling: Integration of discrete choice models to analyze passenger travel behavior, including responses to differentiated fare policies and dynamic pricing.
• Public transit operations: Agent-based simulation of urban public transit networks (e.g., subways and buses) and multi-modal system integration.
• Network resilience: Simulating the impact of disruptions, including extreme weather events, on passenger flow and transit network reliability using micro-simulation.
• Comparative analysis: Cross-regional or international comparative studies of transit systems and passenger mobility patterns utilizing agent-based approaches.
• Data-driven calibration: Utilizing emerging data sources (e.g., smart card data, survey data, and GPS) to calibrate, validate, and scale agent-based transport models.
• Policy evaluation: Assessing the long-term impacts of sustainable transport planning, infrastructure investments, and modern mobility services through agent-based scenarios.

We look forward to receiving your contributions.

Dr. Yadi Zhu
Dr. Zhipeng Zhang
Dr. Rui Yue
Guest Editors

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• transport planning
• agent-based simulation
• activity-based modeling
• four-step model
• public transit networks
• discrete choice modeling
• travel behavior
• network resilience