Using Cellular Automata to Reduce Congestion for Tourist Navigation Systems in Mobile Environments

Abstract

Tourist navigation systems have become an important area of research because they help people increase the quality of their travel. This work proposes an adaptive recommendation mechanism that relies on a congestion-aware scheduling method for multiple groups of travelers on multi-destination trips. The recommendation scheme uses the cell (number of groups) mechanism of the cellular automata model for group system distribution. To reduce congestion while visiting multiple destinations, we present a tour group with adaptive recommendations from a system to yield a high quality tour experience. When faced with congestion, the system proposes a path by which the group visits a secondary destination first and then visits the primary destination. Simulation results reveal the strengths of the proposed "adaptive recommendation mechanism" model in terms of decreasing the average wait time, congestion, and the ratio of congestion avoidance to the number of groups.