Modelling the Impact of Individual Preferences on Traffic Policies

Abstract

Urban traffic is a system always prone to overload, often approaching breakdown during rush hour times. Well-adjusted modifications of traffic policies, with appropriate interventions, promise potential improvements by inducing change in both individual as well as global system behaviour. However, truly effective measures are hard to identify, and testing in vivo is at least expensive and often hardly feasible. Computer-based simulations have successfully been applied for studying effects of policies, and multi-agent systems are accepted tools for that purpose as they provide means to model individual behaviour. These simulations have primarily studied effects of policies by measuring performance indicators on social benefit, while effects on individuals are hardly considered. However, successful implementation of policies hinges on whether they are accepted by the common public. Thus, effects on individuals cannot be neglected. Evaluating effects on individuals requires a more detailed modelling that is able to capture individual preferences as determining factors of agent decisions. In this paper, we present a simulation framework that focuses on modelling of individuals and thus allows evaluation of effects of policies on both the individual as well as global system behaviour. We use semantic technology (OWL ontologies and SWRL rules) to model preferences and knowledge of agents in our simulation. Using AGADE Traffic simulator, we demonstrate modelling and simulation for a mobility scenario and discuss observed results.