Performance of pheromone model for predicting traffic congestion

Social insects perform complex tasks without top-down-style control, by sensing and depositing chemical markers called "pheromone". We have examined applications of this pheromone paradigm towards realizing intelligent transportation systems (ITS). Many of the current traffic management approaches require central processing with the usual risks of overload, bottlenecks and delay. Our work points towards a more decentralized approach that may overcome those risks. We use new category of the ITS infrastructure called the probe-car system. The probe-car system is an emerging data collection method, in which a number of vehicles are used as moving sensors to detect actual traffic situations. In this paper, a car is regarded as a social insect that deposits multi-semantics of (digital) pheromone on the basis of sensed traffic information. We have developed a basic model for predicting traffic congestion in the immediate future using pheromone. In the course of our experimentation, we have identified the need to properly tune the model to achieve acceptable performance. Therefore, we refined the model for practical use. We evaluate our method using real-world traffic data and results indicate applicability to prediction. Furthermore, we describe the practical implications of this method in the real world.