Heuristic path culculating method for treating large scale floating vehicle data

Abstract

The invention relates to heuristic path presuming method used to process large-scale floating car data. It includes the following steps: building road net data structure includes node, road section, and road chain, road net topological structure which is the connected relation of the each road chain according to road geography information data in city navigation electric map; processing floating car data by heuristic path presuming method; computing traveling path according to recorded time and position for each floating car. The invention uses road net connectivity topological structure and directed line segment heuristics caused by vehicle position point to search the most possible next traveling path, processes project matching for the selected road to effectively increase processing efficiency; in addition, it combines consecutive many GPS location data to presume, increases algorithm accuracy by vehicle path traveling continuity.

Description

technical field [0001] The invention relates to a path estimation method in a floating vehicle real-time traffic information processing system in the field of intelligent transportation systems (ITS), in particular to a heuristic path estimation method for processing large-scale floating vehicle data. Background technique [0002] In the field of intelligent transportation, real-time and dynamic traffic information can provide effective traffic guidance and travel planning information for vehicle travel and transportation, so as to save travel time and reduce exhaust emissions. [0003] Float Car Data technology, also known as "Probe car", is one of the technical means to obtain road traffic information in the International Intelligent Transportation System (ITS). The system using floating car technology is called floating car system. The basic principle of its operation is: according to the vehicle equipped with the vehicle-mounted global positioning system (GPS), that is, ...

AI Technical Summary

Problems solved by technology

Such an algorithm has a large search range and low efficiency, and cannot well meet the real-time requirements when dealing with large-scale floating vehicle trajectory points

At the same time, this method of only using two points in the GPS positioning point set has fewer sampling points, and it is impossible to refer back to the previous speculation and subsequent positioning points to improve accuracy. It is also necessary to perform independent matching on each point. Reduced operating efficiency of the system

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