Distributed Learning Automata is automata based modelling approach for solving stochastic shortest path problems. The DLA can be applied to road networks to find shortest path that provides a spatial approach to bottom-up modelling of complex geographic systems that are comprised of infrastructure and human objects. Route finding is a popular Geographical Information System (GIS) application under Intelligent Transportation Systems (ITS). TheITS reduce traffic congestion and improve road network performance. They provide real time traffic information and route recommendation to road users, to increase their ability to choose the best alternative path. In recent years, DLA models for urban growth simulation are gaining popularity because of their ability to incorporate the spatial and temporal dimensions of the processes. The usage of DLA in route finding has not been explored to its fullest. In this work, a routing finding algorithm is proposed on road networks using DLA. The Distributed Learning Automata based Route Planning (DLARP) algorithm based on two dimensional automata is proposed. Transition rule of automata are proposed in such a way that at each step time the user cell is exchanged with best goal directing cell. DLARP algorithm achieves a proper solution to route finding in spatial road networks.

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Distributed Learning Automata Cellular Automata Multi-agent Systems Route Planner Intelligent Transportation Systems Spatial Databases Gis Nearest Neighbor Gps Location Based Services Path Planning.