Method and system for time-delay path planning

Abstract

Relating to the technical field of intelligent traffic, the invention provides a method and system for time-delay path planning. The method includes: acquiring the personalized information recorded by each client in real time; converting the personalized information recorded by each client into semantic information, and extracting mobile pre-judgement information of users according to the semantic information; forecasting the traffic information of each road section in each period of time according to the mobile pre-judgement information of all the users; planning an initial path to a destination in a specific period of time by a current client according to the traffic information of each road section in each period of time and the individualized information of itself; and in the driving process, adjusting the initial path in real time according to the traffic information obtained by continuous forecast. The method and the system provided by the invention has the characteristics of large data sample size, wide data range and data timeliness, can reduce the prediction result deviation, and can more comprehensively, more accurately and more timely forecast the driving road section with good traffic conditions within certain period of time in the future for users, thus enhancing the accuracy of path forecast and adjustment.

Description

technical field [0001] The present application relates to the technical field of intelligent transportation, in particular to a method and system for delay path planning. Background technique [0002] Road route navigation is a typical application in the technical field of GPS, and it has been widely used at present. In the existing technology, path planning is often based on the topological relationship of the road network, considering the basic attributes of the road network, such as the grade of the road, the number of lanes, the number of intersections, the number of traffic lights and other basic attributes, and using the topology algorithm to give the starting point and Path schemes such as the shortest total distance between the destinations or the shortest total time-consuming. [0003] There are generally two navigation methods in the prior art: [0004] 1. Vehicle navigation based on the basic attributes of the road: According to the basic attributes of the road,...

AI Technical Summary

Problems solved by technology

[0006] However, this scheme only considers the historical road conditions. In fact, the road conditions are not fixed, but change in real time. Especially for some long-distance road sections, it is often encountered that it is currently smooth but becomes congested when it arrives.

The data information of this scheme is sparse and limited in scope: only a small number of taxis and buses are equipped with speed recorders, so the obtained historical data is only valid for the main road, and the scope of action is limited, and all samples are not evenly distributed among all vehicles , the samples in the historical data are not uniform enough, which may also lead to inaccurate results

In addition, it is not enough information to predict future road conditions only through the historical traffic conditions of the road, especially for road conditions that are not regularly changed.

Since the road conditions of each road section on which path planning is based will change continuously during the current vehicle's driving process, when the current vehicle travels to a certain road section for path planning before, the road conditions of this road section are likely to change from good to bad. Make users likely to drive on roads that are predicted to be in good condition but actually become congested, increasing travel time

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