Abstract
Along with the development of Intelligent Transportation System, traffic detectors collect numerous transportation state data in information databases and accumulate. Such data is greatly meaningful to the vehicle navigation. In this paper, we propose a noble two-stage algorithm about vehicle navigation by using data mining methods on the historical and current transportation dataset. This algorithm begins with picking sensitive data about start and end point in an urban traffic network, and data from related (or nearest) road fragments. Referring to current time and season, the algorithm gives an evaluation to every related road fragments and outputs a most reasonable route between start and end point. The experimental and theoretical analyzes show that this algorithm can form an efficient and effective route in reasonable time.
Highlights
Urban traffic navigation algorithms are greatly based on traffic information datasets, which can be get from detectors of ITS (Intelligent Transformation System)
When a road segment between start and end point is used frequently, it might have highly related with the new route
Transportation network can be translated into graph data, which vertices are road segments and edges are road crosses. This translation keeps the traffic signals and road conditions more greatly than other methods. It is a part of an urban traffic network a), and it is translated in the way above c)
Summary
Urban traffic navigation algorithms are greatly based on traffic information datasets, which can be get from detectors of ITS (Intelligent Transformation System). After the ITS occurred, a huge number of detectors were spread into traffic network to monitor the partial or total transportation system situation In such situation today, mass data is formed more and more quickly than before, on which introduces new problem into traffic information analysis. Mass data is formed more and more quickly than before, on which introduces new problem into traffic information analysis According to such situation, analysis methods cannot just be the regular ones, such as association rules mining, data fusion, and data classification, in order to simulate the nearest future about traffic condition. Analysis methods cannot just be the regular ones, such as association rules mining, data fusion, and data classification, in order to simulate the nearest future about traffic condition These methods have inherently functionally insufficient problem. This segment happens to have nothing to do with the new route when it is a one-way street, or a recently broken one
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