Method and device for route recommendation

Abstract

The embodiment of the present invention discloses a path recommendation method and device. Based on the acquired historical trajectory data, a directed graph corresponding to the determined destination point is constructed; any two adjacent nodes contained in the directed graph The corresponding line segments have their corresponding turning probability values; according to the maximum probability pathfinding algorithm, a path with the highest probability value from the starting node to the destination point is selected from the directed graph as the recommended path. This technical solution is no longer limited to using prior knowledge to build a large number of models to solve complex and changeable road conditions. Compared with the trajectory planning generated by algorithm models and strategy calculations in current mainstream navigation applications, this technical solution analyzes historical trajectories Using data to mine user travel patterns is more in line with the complex changes in road conditions in the real environment. Based on the constructed directed graph, the most popular route, the recommended route, can be mined to better meet the travel requirements of users.

Description

technical field [0001] The present invention relates to the technical field of path planning, in particular to a path recommendation method and device. Background technique [0002] With the widespread use of mobile communication devices such as smart phones and the rapid development of mobile communication technology, the mobile Internet and its applications have shown a blowout trend in recent years. According to authoritative data, the number of mobile Internet users in China reached 759 million in January 2016, surpassing the total number of traditional PC users and forming a huge market with a trillion-scale output value. Subsequently, location-based services (Location based Service) represented by various map applications have become popular tools, profoundly changing people's work and lifestyle. When using these services, a large number of records are persisted to the enterprise server. These records contain information such as location, time, direction, speed, etc.,...

AI Technical Summary

Problems solved by technology

However, complex models lead to high maintenance costs. With the complexity of application scenarios, it is difficult to define the applicable boundary of the model when using mechanized models. For example, 8:00 am to 10:00 am is defined as the morning peak period, and the morning peak model is used, 7:59 Just use the non-morning peak model, obviously this kind of artificial division of the model is not reasonable

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