A Geosimulation Approach Involving Spatially-Aware Agents A Case Study on the Identification of Risky Areas for Trains

摘要

Modeling and simulating train behaviours in large scale geographic environments is a complex process. Such a process involves several heterogeneous actors evolving and interacting with their environment. The use of statistical and mathematical models does not satisfy the requirements of such a complex process where spatial data is of fundamental importance. On the other hand, the Agent-Based Geo-Simulation provides a flexible approach that can be used to easily simulate complex systems in large scale georeferenced environments. In this paper we present Train-MAGS, an agent-based geosimulation tool which simulates train behaviours and identifies risky areas in large scale geographic environments. We describe the way in which using the agent based simulation opens perspectives regarding the development of new functionalities to improve the risk assessment of railway networks.