Grid path recommending method and system based on internet of vehicle

Abstract

The invention discloses a grid path recommending method based on internet of vehicle and belongs to the field of information retrieval. The grid division method is adopted, a grid OD matrix is built, a search grid of a sudoku is divided with an initial site and a destination input by a target user as a search center, the individual movement feature and the static feature and dynamic feature of a grid path are considered only in the search grid, a path overhead function is linearly combined, the path nearest neighbor set of the target user is obtained according to the path overhead function, a more accurate neighbor set of the target user is provided in an iterative search mode for the search radius, and the accuracy of a recommendation result is improved. The method reduces the time complexity for computing the grid, solves the problem that a traditional path feature is simplex in computation, and redefines constitutes of the path feature and the computation rule. The grid path recommending method based on internet of vehicle can be widely applied to related areas such as traffic and social networking services.

Description

technical field [0001] The invention belongs to the field of data mining and information retrieval, in particular to a route recommendation method based on Internet of Vehicles. Background technique [0002] With the rapid development of intelligent transportation systems, the Internet of Vehicles has brought opportunities for the effective collection of GPS driving data of urban vehicles. Taxis in many cities are equipped with GPS chips. The chip is mainly used to provide call service and supervision for the dispatch and management departments of taxi companies. These GPS chips will regularly upload data such as vehicle identification, trigger events, operating status, GPS time, GPS latitude and longitude, and GPS speed to the service center, thereby converging large-scale taxi movement trajectory data. In front of these data, there are various problems in reality: flaws in urban traffic planning lead to congestion; some taxi drivers have insufficient driving experience s...

AI Technical Summary

Problems solved by technology

In front of these data, there are various problems in reality: flaws in urban traffic planning lead to congestion; some taxi drivers have insufficient driving experience so that they will take aimless random roaming behaviors when looking for passengers, etc.

However, there are still two deficiencies in this layered approach: first, the obtained layered road network is static and cannot reflect the actual road condition information well according to different time periods; second, it does not fully consider each road section Vehicle mobility, crowd activity and other characteristics

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