On Modeling and Verification of Agent-Based Traffic Simulation Properties in Alloy

On Modeling and Verification of Agent-Based Traffic Simulation Properties in Alloy

Junia Valente (Department of Computer Science, University of Texas at Dallas, Richardson, TX, USA), Frederico Araujo (Department of Computer Science, University of Texas at Dallas, Richardson, TX, USA) and Rym Z. Wenkstern (Department of Computer Science, University of Texas at Dallas, Richardson, TX, USA)
Copyright: © 2012 |Pages: 23
DOI: 10.4018/jats.2012100103
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Abstract

The advances in Intelligent Transportation Systems (ITS) call for a new generation of traffic simulation models that support connectivity and collaboration among simulated vehicles and traffic infrastructure. In this paper we introduce MATISSE, a complex, large scale agent-based framework for the modeling and simulation of ITS and discuss how Alloy, a modeling language based on set theory and first order logic, was used to specify, verify, and analyze MATISSE’s traffic models.
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2. Traffic Simulation

There are two major approaches to simulate traffic scenarios. Macroscopic models (Babin, Florian, James-Lefebvre, & Spiess, 1982; Lieu, Santiago, & Kanaan, 1992) describe traffic as a physical flow of fluid and make use of mathematical equations relating macroscopic quantities (e.g., traffic density, flow rate and average velocity). These models assume rational driving behavior and fairly consistent traffic streams and thus are unfit to model real traffic operations.

In contrast, microscopic models consider the characteristics of individual traffic elements (e.g., vehicles, traffic lights, traffic signals, driver behavior) and their interactions. Typical microscopic models are based on analytical techniques such as queuing analysis and shock-wave analysis (Helbing & Tilch, 1998). They assume traffic elements with predefined behavioral models. This is a limitation since realistic traffic simulation scenarios call for the modeling of unexpected behavior and unforeseen environmental conditions. The multi-agent paradigm alleviates this limitation by providing means to address non-deterministic behavior in non-deterministic, unpredictable environments.

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