A Thesis Submitted to the Faculty of Graduate Studies in Partial Fulfilment of the Requirements for the Degree of Master of Science

Over the last decade, the rapidly growing population of mobile data terminals, integrating GIS and GPS, has given rise to a new type of real-time spatiotemporal service called Location-Based Service (LBS). Routing is an important function of LBS and Geographic Information Systems for Transportation (GIST). It is used in many land-based transportation applications, such as the Intelligent Vehicles Navigation System (IVNS). There are two common types of queries for navigation service. The first query deals with finding an optimal route from the current location to the desired destination. The other query allows users to locate the closest facility of a certain category, such as the nearest hotel, hospital or gas station, without knowing the destination in advance. In this case, the best destination and an associated optimal route need to be found based on network distance. The challenge lies in the fact that traffic condition always changes such that the optimal route has to be recomputed from time to time in order to adapt to the dynamic environment. Since both traffic condition and the current positions of mobile users change over time, existing shortest path algorithms are either incapable of solving this problem, or are too complicated and time consuming. To address the above challenge, two incremental shortest path algorithms have been proposed to efficiently deal with the two types of queries. For the first query type, an incremental A* algorithm is designed to adaptively derive the optimal path to the desired destination by making use of previous search results. For the second query type, a shortest path based dynamic network Voronoi diagram is devised to implement a service area for each facility. The corresponding shortest path is derived and maintained dynamically using the incremental approach. The experimental results demonstrate that the proposed incremental search approach considerably outperforms the traditional method which recomputes the shortest path from scratch each time without utilization of the previous search results.