Traffic network simplifying model and navigating method based on same

Abstract

The invention discloses a traffic network simplifying model and a navigating method based on same, belonging to buildings method of urban traffic network models. As for the model, road crossings are abstracted into nodes; a one-way street between every two nodes is abstracted into a directed arc; a two-way street between every two nodes is abstracted into two directed arcs, time spending weights are weighted on the directed arcs, and the data representing the time spending weights in a database is obtained by an external GPS (global position system) apparatus; and in the navigating method, the traditional Dijkstra algorithm is improved, and a time spending weight function cost (i, j, pre) is introduced. By the traffic network simplifying model and the navigating method disclosed by the invention, the navigation result of a path on which the vehicle spends minimum time can be obtained rapidly and accurately.

Description

technical field [0001] The invention relates to a construction method of an urban traffic road network model, in particular to a simplified traffic road network model and a navigation method based on the model. Background technique [0002] The road network model is the information used to describe the road network topology and road network entities. It is an important basis for the intelligent transportation system (ITS) and vehicle navigation system (VNS), and also plays a very important role in the traffic simulation system. In ITS and VNS, through the combination of road network model and electronic map, users are prompted with road information or planned travel routes, and visual dynamic traffic navigation is provided during driving. Traffic simulation is also based on a road network model. Through in-depth analysis of the characteristics of vehicles, pedestrians, and roads, various traffic phenomena such as traffic flow and traffic accidents can be reproduced, and traf...

AI Technical Summary

Problems solved by technology

Although this model makes up for the shortcomings of the "point-arc" to a certain extent (such as the inability to accurately describe road attributes), this model greatly increases data storage, which may be dozens or even more than the "point-arc" model. Hundreds of times; and this model still cannot effectively solve the intersection delay time

[0006] To sum up, in the traditional optimal path navigation algorithm, there is only one path weight of model 1 (the first type), which can represent the length or time spent of this path, and then use the shortest path algorithm to solve The static optimal path or the optimal path is planned through the dynamic optimal path algorithm. However, as mentioned above, it is difficult to take the intersection delay into account in the shortest path calculated by model 1, which causes the entire planning result to be different from the actual situation. There is a large deviation, or although the calculated dynamic delay time of the intersection is relatively accurate, it needs to use more traffic parameters and use complex mathematical formulas to calculate

If you use model 2 (the second type), because there may be multiple arcs between every two points, there may be multiple weights between the two points, but these multiple weights are the vehicles passing through different lanes. The time or lane length has little significance for actual path planning, because vehicles in reality may overtake the lane

Similarly, the path planning algorithm does not take into account the intersection delay when using the model 2 path with multiple weights

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