# On the Use of the Multi-Agent Environment for Mobility Applications
## 1 Introduction  
Large-scale and complex systems, in different socio-demographic contexts and various land-use configurations, could be analysed by modelling the behaviour and interactions of a large number of self-interested \"agents\". The multi-agent paradigm provides a high level of detail and allows for the representation of non-linear phenomena and patterns that would be difficult to tackle with analytical approaches [1]. The multi-agent paradigm is a powerful model to design and implement transportation and mobility applications. Indeed, the multi-agent approach deals with systems consisting of many physically or logically distributed interacting components that possess some level of autonomy. These components can perceive their environment and react to changes following their goals. The authors in [2] listed several reasons for the preferred use of multi-agent systems in these applications, such as the natural and intuitive problem solving, the ability of autonomous agents to model heterogeneous systems, the ability to capture complex constraints linking all phases of problem-solving, etc. For [PERSON] [3], \"agent-based modelling is most appropriate for domains characterised by a high degree of localisation and distribution\", which is the case for complex and dynamic transportation applications. The agent concept is well suited to represent travellers in transit or road traffic scenarios, for instance [4, 5]. They are autonomous entities situated in an environment, adapt their behaviours to the dynamics they perceive, and interact with other agents to achieve specific goals. There is a growing awareness in the multi-agent community that the multi-agent environment should be considered a primary design abstraction of equal importance to that of the agents. Models and architectures were proposed in the literature for the design of multi-agent environments, validated in a variety of application domains [6]. We believe that one of the domains of choice for modelling multi-agent environments is the transportation domain, especially the mobility applications. Indeed, transportation and mobility applications always have some representation of the environment, typically transportation networks of all modes. The environment in transportation applications hasits own dynamics (e.g., traffic conditions, dynamic rules, weather, disruptions, etc. ), which argues for its independent and explicit representation. Besides the intuitive representation of the transportation networks as the multi-agent environment, there are several other potential uses of the environment. In this paper, we illustrate different angles of multi-agent environment design in the context of transportation applications. The design of the environment can be considered at several levels of the system construction. It can represent a means of interaction between the agents. It can coordinate and synchronise the activities of the agents. It can also be designed as a mental model for agents to use in their reasoning and planning activities. In Figure 1, we provide three main layers for the design of the multi-agent environment in mobility applications. In the first layer (bottom figure), the environment is a spatial reference on which the agents act. It is also a reference for the resources available to agents, either for sensing or actuation purposes. The second layer (middle figure) is the spatio-temporal environment which extends the spatial environment with the time dimension for planning purposes. It also serves for historical data analysis, learning behaviours, and predicting future events. The third layer (top figure) is the communication environment is not related to the physical environment of the agents and is used for general-purpose communication between the agents. The first level corresponds to the traditional environment understanding and will not be detailed. In the remainder of this paper, we will describe the two other levels, we provide example models to specify them and examples of mobility applications to illustrate them.