# A Middleware-Based Approach for Multi-Scale Mobility Simulation
## 2 Literature Review  
### Scales in Mobility Simulations  
In [14], the authors provide an overview of the three possibilities for the orchestration of different simulations to perform a multi-scale analysis, which we describe in Figure 1. First, both models could be executed separately; then, the user performs a global analysis based on the results. In this independent orchestration, the simulations never interact with each others. Second, one model can be executed entirely first, then its output is used by a second model. This is a sequential orchestration. Third, both models could be executed together in a parallel orchestration. This is the most complex orchestration, since simulations exchange data during their execution. This paper focuses on this complex type of orchestration. In the literature, authors have proposed several methods to compose a pair of simulations, with several use cases. Vissim [15], which is the microscopic simulation model from PTV(r), proposes a sequential orchestration of multi-scale simulations. Indeed, the simulation offers the possibility to use a network topology and an origin-destination (OD) matrix that are imported from Visum, which is the macroscopic static model assignment from the same company. There are some older examples of a macroscopic static model whose output is used in a traffic simulation over a small sub-part of the global network as in [16]. In [17], the output of a macroscopic simulation is used to provide an OD matrix to a microscopic simulation of traffic. If the results of the microscopic simulation do not have an impact on the behavior of the macroscopic simulation, such a sequentially orchestrated multi-scale  
Figure 1: Simulations orchestrations (independent (**left**), sequential (**middle**), and parallel (**right**)). simulation is satisfactory. However, if the two represented areas are interconnected and influence each others, then the two simulations need to continuously interact. In a parallel multi-scale simulation, both models feed each other. For instance, AIM-SUN [18], a simulation-based traffic prediction solution with a macroscopic model and a microscopic model, proposes this kind of orchestration. A third (so-called mesoscopic) model can also be used in relation with the microscopic model, when the area to simulate is too large. Parallel orchestration is often studied in the literature, especially when the level of representation details is dynamic, i.e., when the scale of a given area can be dynamically changed during the simulation. For instance, the authors in [19] present a hybrid traffic simulation mixing individual drivers (represented as agents) and flows of drivers. The authors describe how to transform agents into flows and vice versa, when a \"zoom\" is performed on any area of the simulated zone. In [20], the authors state that the simulation scales should not be defined before the beginning of the simulation and that they should be always dynamically defined during execution. They define a hierarchy between the simulated elements (creating so called \"holonic\" systems), which allows for aggregating them at will, and to create the \"best\" scale depending on the context. The work reported in [21] is based on the same holonic idea but adapts this concept to multi-agent vehicle simulations. The authors in [22; 23] propose a method to aggregate and disaggregate agents of the simulation in groups such that the size of the groups and the level of aggregation can be chosen during the simulation. The state-of-the-art approaches for multi-scale simulations are generally either ad hoc or specific to certain simulations. However, some approaches are concerned with genericity and with the provision of general guidelines to a great number of existing simulations. The authors in [24], for instance, propose a framework to coupling any mesoscopic pedestrian simulation model with any microscopic simulation.