Agent-based digital traffic model generation for regions facing data scarcity using aggregated cellphone data: a case study for Brussels

The adoption of agent-based models (ABMs) in transport research has highlighted their potential in digitally simulating travel behaviour from a bottom-up perspective. However, generating ABMs remains a complex multi-step process, often suffering from the lack of model representation or reliance on proprietary data, thereby limiting ABMs' transferability, especially in regions facing data scarcity. This research presents a systematic ABM generation algorithm for Brussels, Belgium, that generates high-quality ABMs more representative of real-life travel patterns, including incorporating external travel demand and allowing multiple destinations for the same activity types in activity chaining. Additionally, our algorithm's input data only contains social-spatial data and aggregated cellphone matrices. Due to the ubiquitous nature of such data worldwide, our algorithm demonstrates significant transferability to other regions for ABM generation while preserving individual privacy. We demonstrate the input data and the proposed algorithm step by step. The ABM generated using MATSim is validated against real-world data regarding mode share, distance share, travel time distribution and traffic counts. Overall, this research would serve as valuable guidance for ABM modellers in data collection and specific modelling setups, lowering the entry barrier of ABM research towards more efficient, representative and reproducible ABMs.