Traffic prediction for agent route planning

Abstract

This paper describes a methodology and initial results of predicting traffic by autonomous agents within a vehicle route planning system. The traffic predictions are made using AQ21, a natural induction system that learns and applies attributional rules. The presented methodology is implemented and experimentally evaluated within a multiagent-based simulation system. Initial results obtained by simulation indicate advantage of agents using AQ21 predictions when compared to naïve agents that make no predictions and agents that use only weather-related information. © 2008 Springer-Verlag Berlin Heidelberg.