An Integrative Modelling Language for Agent-Based Simulation of Traffic

Abstract

Alberto FERNÁNDEZ-ISABEL †a) , Nonmember and Rubén FUENTES-FERNÁNDEZ †b) , Member SUMMARYTraffic is a key aspect of everyday life. Its study, as it happens with other complex phenomena, has found in simulation a basic tool. However, the use of simulations faces important limitations. Building them requires considering different aspects of traffic (e.g. urbanism, car features, and individual drivers) with their specific theories, that must be integrated to provide a coherent model. There is also a variety of simulation platforms with different requirements. Many of these problems demand multidisciplinary teams, where the different backgrounds can hinder the communication and validation of simulations. The Model-Driven Engineering (MDE) of simulations has been proposed in other fields to address these issues. Such approaches develop graphical Modelling Languages (MLs) that researchers use to model their problems, and then semi-automatically generate simulations from those models. Working in this way promotes communication, platform independence, incremental development, and reutilisation. This paper presents the first steps for a MDE framework for traffic simulations. It introduces a tailored extensible ML for domain experts. The ML is focused on human actions, so it adopts an Agent-Based Modelling perspective. Regarding traffic aspects, it includes concepts commonly found in related literature following the Driver-Vehicle-Environment model. The language is also suitable to accommodate additional theories using its extension mechanisms. The approach is supported by an infrastructure developed using Eclipse MDE projects: the ML is specified with Ecore, and a model editor and a code generator tools are provided. A case study illustrates how to develop a simulation based on a driver's behaviour theory for a specific target platform using these elements. key words : traffic simulation, road behaviour, agent-based modelling, model-driven engineering, metamodel IntroductionRoad traffic has a great influence in modern societies. Its study in real settings is difficult, given its scale, complexity, and potential impact on the wellbeing of people. For this reason, researchers resort frequently to simulations [1]. However, these also present limitations [2]. The literature points out the difficulties with discussing and aligning simulation models at different levels of abstraction (e.g. social theory and code design), for people with heterogeneous backgrounds, or implemented in different platforms. These issues make it hard to guarantee that the resulting simulation faithfully reflects the initial abstract model [3].The use of model-driven approaches has been proposed to overcome those limitations [2]. Model-Driven Engineer- ing (MDE) [4] organises development projects around models, which are compliant with well-defined Modelling Languages (MLs). For traffic simulation, this approach implies developing MLs to specify simulations from different perspectives (e.g. driver's behaviour, car functioning, and environment) an...

Social-Aware Driver Assistance Systems for City Traffic in Shared Spaces

“…Regarding its focus, the SADAS-ML is linked to general social aspects in traffic. This is something not considered in those previous studies or only for some specific aspects (e.g., age and existence of companion in [25]). The base of the ML in the AT [23] provides a suitable theoretical framework for this modelling.…”

“…It defines primitives to represent that information (see Section 3.1.1) and its processing (see Section 3.1.2). For the information, it extends elements from the ITSML and UML-AT MLs (see Section 2) following the guidelines in [21,25]. For the processing, it mainly adopts the AT concept of activity [23] (which is already present in the UML-AT, see Section 2.2), and the new one of transformation .…”

“…For instance, the ITSML [11] endeavors to cover the specification of ITSs and their environment for different problems. It has been used in case studies using simulations of infrastructures such as smart roads [57], and in the context of MDE for integration with other types of systems [25]. Extension mechanisms for the MLs are considered in some studies, for instance in [25,56] through metamodel modification.…”