Dynamic Hybrid Traffic Flow Modeling

TL;DR

This paper introduces a dynamic hybrid traffic flow modeling approach that integrates micro and macro models in a single, adaptable framework to better simulate large-scale road networks and emergent phenomena like congestion.

Contribution

It presents a multi-level agent-based simulator, JAM-FREE, capable of dynamically adjusting levels of detail during simulation for improved accuracy and flexibility.

Findings

Enables simulation of large road networks with dynamic level adjustments.

Improves detection and analysis of congestion formation.

Utilizes the SIMILAR framework for multi-level agent-based modeling.

Abstract

A flow of moving agents can be observed at different scales. Thus, in traffic modeling, three levels are generally considered: the micro, meso and macro levels, representing respectively the interactions between vehicles, groups of vehicles sharing common properties (such as a common destination or a common localization) and flows of vehicles. Each approach is useful in a given context: micro and meso models allow to simulate road networks with complex topologies such as urban area, while macro models allow to develop control strategies to prevent congestion in highways. However, to simulate large-scale road networks, it can be interesting to integrate different representations, e.g., micro and macro, in a single model. Existing models share the same limitation: connections between levels are fixed a priori and cannot be changed at runtime. Therefore, to be able to observe some emerging phenomena such as congestion formation or to find the exact location of a jam in a large macro section, a dynamic hybrid modeling approach is needed. In 2013 we started the development of a multi-level agent-based simulator called JAM-FREE within the ISART project. It allows to simulate large road networks efficiently using a dynamic level of detail. This simulator relies on a multi-level agent-based modeling framework called SIMILAR.