Multiagent based interpolation system for traffic condition by estimation/learning

We propose a multiagent based interpolation system for traffic conditions that includes estimation and learning agents. These agents are allocated to all the road links. The Normalized Velocity (NV) is used in this system. Estimation agents renew the NV for each road link, and learning agents renew the weight values for estimation. The weight values can be calculated by multivariate analysis. Estimation and learning agents alternately calculate the results to improve the interpolation accuracy. The Coefficient of Determination (CD) and Mean Square Error (MSE) are used to evaluate the interpolation accuracy. Vehicle Information and Communication System (VICS) data and Probe Car Data (PCD) are usually used for traffic information systems, but we have confirmed that the estimation accuracy without VICS data (only PCD) is higher than with VICS data. The standard deviation of the estimated NV error can be improved to 0.1312, and the standard deviation of the estimated velocity error is 6.56 km/h in the mid velocity region. It was possible to improve the CD and MSE by repeated estimation and learning.