The adoption of Connected Automated Transport (CAT) in Smart Logistics Nodes (SLNs) is hindered by the lack of systematic and reusable tools for quantitative ex-ante evaluation of its operational impact in complex, multi-stakeholder environments. To address this, we propose a reusable discrete-event simulation framework. The framework couples a library of modular simulation objects—a multimodal network generator, a multi-agent fleet manager, and control logic for parking, decoupling, and container dispatch—with agent-based and microscopic traffic modeling. This design enables systematic analysis of CAT’s impact on inbound flows, hub-to-hub transport, and stakeholder collaboration across diverse scenarios. We validate the framework by building detailed 3D models of four Dutch logistics nodes with open-source data and stakeholder input, and by performing a simulation study on CAT adoption for container transport. The models reproduce real infrastructure, freight itineraries, and peak-hour dynamics, confirming the framework’s fidelity and suitability for quantitative impact studies and stakeholder engagement. Results indicate 10%–40% improvements in logistics efficiency and emission reductions via connectivity, automation, and electrification of container transport. By unifying discrete event, agent-based and traffic-level perspectives, the framework supports evaluations of collaborative logistics strategies, automated and electric vehicle integration, and a broader process redesign in SLNs, thus providing a foundation for future research on logistics automation and digitization. • Simulation framework evaluates emerging technologies at logistics nodes. • Framework generates multimodal infrastructure networks using crowd-sourced data. • Agent-based control of shared, automated, and electric freight vehicles. • Framework is validated by modeling Port of Moerdijk; generates accurate freight flows. • Study shows connectivity and automation reduce dwell time, lateness and emissions.
Brunetti et al. (Wed,) studied this question.