A new study presents a generic modeling framework that integrates agent-based models (ABM), specifically HUMAT and MASS-GT, to address the complexity of last-mile delivery (LMD) systems. This framework simulates and evaluates the dynamics of urban goods flow across different cities, focusing on consumer behavior and delivery logistics, which are crucial for optimizing LMD strategies.
Key points include:
- The framework is flexible and adaptable to various urban environments and logistical scenarios, demonstrating its value in planning and decision-making.
- The study highlights the interdependence between freight transport agents and consumers, showing that consumer decisions directly influence delivery services’ efficiency, congestion, and emissions.
- The HUMAT model provides insights into how social networks and personal beliefs impact delivery choices.
- The framework can be adapted to emerging technologies and challenges in urban logistics, such as drones and AI-driven logistics.
- It is designed with modularity, standardized interfaces, and precise documentation, allowing for quick adaptation to simulate the adoption of social innovation studies in various Living Labs.
The framework consists of two main elements: Setup and Execution. The setup phase involves creating a synthetic population and generating parcel demand. The execution phase includes assigning parcel trips, determining the delivery services agents choose, and facilitating communication between agents.
The case studies demonstrate the framework’s effectiveness across different urban settings and logistical scenarios, showing that consumer behavior significantly impacts the efficiency of LMD systems.