Kouji Kagawa

Traffic congestion forecasting based on pheromone communication model for intelligent transport systems

The basic framework of next generation intelligent transport systems (ITSs) is discussed. We propose a new congestion forecast system, which reacts to dynamically changing traffic conditions based on a coordination mechanism using pheromone communication models. We evaluate and verify the basic effectiveness of this method using simple simulations.

Interpolation System of Traffic Condition by Estimation/Learning Agents

Interpolation system of traffic condition is proposed, which consists of estimation and learning agents. To evaluate the interpolation accuracy, coefficient of determination (CD) and mean square error (MSE) are used. The interpolation accuracy can be improved by the alternate use of estimation and learning agents, and the iterative uses of the same probe data. The standard deviation of the normalized velocity can be improved to 0.1353, and that of the velocity is 6.77 km/h in the mid velocity region. Furthermore, the CD and MSE could be improved by the additional repetition of estimation and learning.