Learning about Urban Mobility: Experiences with a Multiagent-System Model

Multiagent systems (MASs) constitute a powerful methodological platform for dealing with the decision-making process underlying individual travel behaviour. A major challenge is the implementation of a strong notion of agency, through the building of agents capable of drawing information from their environment, processing what is important, and engaging in activities. This paper explores this issue. The notion of mental worlds—that is, cognitive entities involving both syntactic and semantic components—is invoked for accounting the way in which individuals understand their environment. A MAS model (CogMob) is illustrated. It has been developed to simulate the application of a palette of cognitive strategies in urban mobility, that is, habitual behaviour: agents choose the most familiar route; learning by instruction: agents secure information about the less congested routes; reasoning: agents select the most convenient route from a set of those previously used; visioning: agents explore a novel route. CogMob has been implemented on a SWARM simulation platform and considers a fictitious urban environment. Some results of its application to simulate the use of the proposed cognitive strategies are compared and discussed briefly. Three (not unexpected) key findings emerged: (1) there is no single most effective strategy, (2) agents can modify their strategies to adapt to changing travel conditions, and (3) an agent's personality matters when the need to modify a strategy arises. It is submitted that these aspects may be of substantial importance in the drafting of transport policies. For example, they suggest that tailoring policies according to the personalities of agents can improve the effectiveness of certain mobility measures. They also indicate that adaptation of an agent's behaviour involves both syntactic and semantic components. Thus, greater attention should be paid to information-sensitive policy measures.