GPS-Based Smart System for Enhancing Driving Directions for Finding Fastest Route using Driver’s Intelligence

Traveling is a part of every person's day-today life. 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. The knowledge of the actual current state of the road traffic and its short-term and dynamic path evolution for the entire road network is a basic component of ATIS (Advanced Traveler Information Systems) and ATMS Advanced Traffic Management System) applications. In this view the use of real-time Taxi Data (TD), based on traces of GPS positions to gather accurate travel times/speeds in a road network and to improve short-term predictions of travel conditions. GPS-equipped taxis can be regarded as traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. We mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. The essential components that will be discussed are a Web-servicesbased data collection approach then, a Variance-EntropyBased Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a twostage routing algorithm to compute the practically fastest route. In our existing system static (Dynamic)-path and not update the rout.