Applying Situated Agents to Microscopic Traffic Modelling

Applying Situated Agents to Microscopic Traffic Modelling

Paulo A.F. Ferreira (University of Porto, Portugal), Edgar F. Esteves (University of Porto, Portugal), Rosaldo J.F. Rossetti (University of Porto, Portugal) and Eugénio C. Oliveira (University of Porto, Portugal)
Copyright: © 2009 |Pages: 16
DOI: 10.4018/978-1-60566-226-8.ch005


Trading off between realism and too much abstraction is an important issue to address in microscopic traffic simulation. In this chapter the authors bring this discussion forward and propose a multi-agent model of the traffic domain where integration is ascribed to the way the environment is represented and agents interoperate. While most approaches still deal with drivers and vehicles indistinguishably, in the proposed framework vehicles are merely moveable objects whereas the driving role is played by agents fully endowed with cognitive abilities and situated in the environment. The authors discuss on the role of the environment dynamics in supporting a truly emergent behaviour of the system and present an extension to the traditional car-following and lane-change models based on the concept of situated agents. A physical communication model is proposed to base different interactions and some performance issues are also identified, which allows for more realistic representation of drivers’ behaviour in microscopic models.
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Using simulation is imperative for planning and realising the correct relation among parameters of any application domain. In the traffic engineering domain, however, most analyses are carried out on an individual basis as an attempt to reduce the number of variables observed and to simplify the process of finding out their correlations. This brings about the issue of how different standpoints from which the domain is viewed could be coupled in the same model and simulation environment in order to allow for wider analysis perspectives (Barceló, 1991; Grazziotin et al., 2004; Rossetti & Bampi, 1999). This is not a recent concern, though. The basic general framework for a fully transportation theory identifies two different concepts, borrowed from Economics, which encompass all aspects related to demand formulation and supply dynamics within the framework, including multi-modal selection and activities planning (McNally, 2000).

Arguably, realistic models are the first instrument to allow the integration of different analysis perspectives in virtually any application. However, modelling is not an easy task and abstraction is often necessary in order to make thinks feasible. The autonomous agent metaphor has been increasingly used in this way and offers a great deal of abstraction while important cognitive and behavioural characteristics of the system entities are preserved. Also, advances in engineering environments for multi-agent systems (MAS) have fostered the idea of overall system behaviour that emerges from the interaction of microscopically modelled entities.

Some examples of MAS applied to the field of traffic and transportation engineering can be found in the literature (e.g. Bouchefra, Reynaud, & Maurin, 1995; Roozemond, 1999; Davidsson et al., 2005; Oliveira & Duarte, 2005, to mention some) and are further discussed elsewhere in this book. However, most of the applications are concerned with the control system, even though it is possible to recognise an increasing interest in the representation of the driver elements and the way they interact and communicate (e.g. Burmeister, Haddadi, & Matylis, 1997; Rossetti et al., 2000; Rossetti et al., 2002; Dia 2002). To increase complexity, transportation systems have recently evolved so quickly as Intelligent Transportation Systems (ITS) start to make part of everyone’s daily life. According to Chatterjee & McDonald (1999), the underlying concept of ITS is to ensure productivity and efficiency by making better use of existing transportation infrastructures. Now, a wide range of novel technologies is presented to the user and start to directly affect the way individuals perceive their surrounding environment and ultimately make decisions, which must also be taken into account.

In very basic terms, the moving element in this whole picture is the vehicle that moves from one point to another throughout the network. Disregarding the importance of pedestrians in the first stage of this work, we consider bicycles, motorcycles, automobiles, trucks and buses as examples of moving elements. However, they are actually moving objects steered by their drivers and sometimes occupied by many other passengers that are people with a trip purpose. Also, their decision concerning how the trip will be carried out in most cases seeks to minimize some individual sense of cost. Therefore, we make a clear distinction between travellers and vehicles, as we shall see later on in this chapter.

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