One challenge to researchers dealing with traffic management is to find efficient ways to model and predict traffic flow. Due to the social nature of traffic, most of the decisions are not independent. Thus, in traffic systems the interdependence of actions leads to a high frequency of implicit co-ordination decisions. Although there are already systems designed to assist drivers in these tasks (broadcast, Internet, etc.), such systems do not consider or even have a model of the way drivers decide. Our research goal is the study of commuting scenarios, drivers' decision-making, its influence on the system as a whole, and how simulation can be used to understand complex traffic systems. The present paper addresses two key issues: simulation of driver decision-making, and the role of a traffic forecast component. The former is realised by a naive model for the route choice adaptation, where commuters behaviour is based on heuristics they evolve. The second issue is realised via a traffic control system which perceives drivers' decisions and returns a forecast, thus allowing drivers to decide the actual route selection. For validation, we use empirical data from real experiments and show that the heuristics drivers evolve lead to a situation similar to that obtained in the real experiments. As for the forecast scenario, our results confirm that a traffic system in which a large share of drivers reacts to the forecast will not develop into equilibrium. However, a more stable situation arises by introducing some individual tolerance to sub-optimal forecasts. (Author/publisher)
Abstract