Intelligent Route Finding: Combining Knowledge and Cases and an Efficient Search Algorithm

One of the major road transportation problems is route finding. With the massive and complicated road network of a modern city (or country), finding a good route to travel from one place to another is not a simple task. In Network Theory, this is a shortest path problem. Shortest path algorithms are often used to solve the problem. However, these algorithms are wasteful in terms of computation when applied to the route finding task. They may also produce routes that are not suitable for human drivers. Furthermore, finding a route only solves part of the route finding problem. Accepting user’s feedback and learning from past experiences are also crucial to the success of a route finding system. In this paper, we present an integrated approach of using knowledge about the road network, past cases and an efficient search algorithm for route finding. This technique not only substantially reduces the time and space required in computation, but also produces humanoriented and personalized solutions. Based on this approach, a system has been implemented for route finding in Singapore.