TRASS: A Multi-Purpose Agent-Based Simulation Framework for Complex Traffic Simulation Applications

TRASS: A Multi-Purpose Agent-Based Simulation Framework for Complex Traffic Simulation Applications

Ulf Lotzmann (University of Koblenz, Germany)
Copyright: © 2009 |Pages: 29
DOI: 10.4018/978-1-60566-226-8.ch004
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Abstract

In this chapter an agent-based traffic simulation approach is presented which sees agents as individual traffic participants moving in an artificial environment. There is no restriction on types of players, such as car drivers or pedestrians. A concept is introduced which is appropriate to model different kinds of traffic participants and to have them interact with each other in one single scenario. The scenario may not only include roads, but also stadiums, shopping malls and any other situations where pedestrians or vehicles of any kind move around. Core theme of the chapter is an agent model that is founded on a layered architecture. Experiences with implementation and usage of the agent model within the universal multi-agent simulation framework TRASS will be explained by means of several application examples which also support discussion about validation of concept and implementation.
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Introduction

Since agent-based simulation began to be widely used to treat problems in the field of traffic and transportation, several approaches have been established based on different definitions of the term “agent”. This has led to a large number of implementations which are more or less narrowly coupled to dedicated software tools. There are two main kinds of implementations, both of which depend upon the user community:

  • In scientific research, the typical procedure is to build models for various components of a particular (type of) traffic system. Often general purpose programming languages are used, together with more or less universal development environments or frameworks, most of which are open source. These systems can be adapted to the requirements of the model or class of models under consideration. This approach requires a high level of computer science expertise.

  • Traffic planners and other practice-oriented users prefer the application of specialized, mainly commercial tools. Any adaptation of such a tool is restricted to representing the real-world target system with the available features. Often an integration with analytical methods and real-world traffic control systems is desired.

In any case, two components of traffic simulation with characteristic features can be identified:

  • A model of an environment as a network or a landscape, i.e. a topography with typical static entities such as streets, sidewalks, static obstacles,

  • A representation of the traffic flow within the modeled environment where attributes are the geometric forms and sizes of moving entities, and motion obeys physical laws (either macroscopically as flows or microscopically as individual entities).

The development of the TRASS framework presented in this chapter was guided by the goal to have a universal platform to design multi-agent-based simulations with maximum flexibility, representing the properties of traffic described, and its environment. Special attention was devoted to the agent model representing traffic participants. These are seen as human beings who participate in traffic in many different roles. This makes the integration of social and behavioral science aspects relevant for the agent model.

The following presents TRASS as a framework that can be used for research and practice-oriented purposes.

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Background

The literature offers a very broad spectrum of contributions on topics related to traffic simulation. Authors from diverse scientific backgrounds – social scientists, physicists, mathematicians, computer scientists and many others – are engaged in that field and hold even more diverse views on that theme.

In the following literature review we will present a few articles on traffic simulation, which represent the context of microscopic agent-based models and show some evolutions in this specific field. We pass on a description of important and well-known contributions on macroscopic (Lebacque, 2003) or Cellular Automata-based (Nagel & Schreckenberg, 1992) approaches.

One direction of evolution is the increasing complexity of simulation models and scenarios. An early conversion of a traditional approach on traffic simulation into an agent-based model was done by Klügl et al. (2000). Based on their simulation platform SeSAM the original Cellular Automaton design car following model by Nagel and Schreckenberg was implemented. Klügl et al. successfully replicated the original model behavior and predictions. This project marked a starting point for further activities in the field of traffic simulation (e.g. Klügl & Bazzan, 2004).

Since agent-based simulation became more established in the traffic simulation domain, numerous specific aspects of traffic systems were studied, employing different kinds of software tools.

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