Urban road network path planning method for non-global information

Abstract

The invention discloses an urban road network path planning method for the non-global information and improves efficiency of the urban transportation network through balancing the traffic network flow. A path planning method in the prior art can only calculate the best path for a single vehicle or requires more information and assumptions. In response to the above problems, through periodically monitoring state changes of road segments, intelligently learning and evaluating current road segment congestion indexes and constructing a PST model, the A*R algorithm is utilized for path selection, so the road network state is made to be in a flow balancing state. The method is advantaged in that a large number of experiments show that the method can more effectively balance the traffic network flow and alleviate traffic congestion in the scene with relatively a large number of vehicles and has a good performance in reducing the average driving time and the average driving distance, in addition, the method can significantly improve traffic congestion even when relatively lots of vehicles in the road network do not accept path planning navigation.

Description

technical field [0001] The invention belongs to the technical fields of computer science, intelligent transportation and machine learning, and relates to a non-global information-oriented urban road network path planning method. Background technique [0002] In order to improve the resource utilization of urban road sections, alleviate road congestion and reduce vehicle travel delay time, scholars at home and abroad have proposed many methods. The current research on path optimization mainly includes: A * Algorithm, Dijkstra shortest path algorithm, SPFA algorithm, dynamic programming algorithm, etc. These algorithms belong to the deterministic algorithm, that is, the only path can be calculated for a given road network state; the corresponding to the deterministic algorithm is the uncertain algorithm, such as : PSO algorithm, genetic algorithm, ant colony optimization algorithm, neural network algorithm, etc. These algorithms are also called intelligent algorithms, which c...

AI Technical Summary

Problems solved by technology

The above method solves the problem of route optimization for a single vehicle. The problem is that it can only calculate the best route for a single vehicle, without taking into account the competition of other vehicles for road resources, and ignoring the high dynamics of modern urban traffic networks. , if congestion occurs, giving multiple vehicles the same alternate route at the same time may lead to new congestion

However, in these methods, either the agents can only learn and make decisions in isolation from each other, and cannot realize the overall coordination and cooperation of the agents; or although the coordination and cooperation of the agents is realized, it causes a high space-time cost and reduces the convergence speed of the algorithm.

[0004] In summary, most existing algorithms can only find the optimal path for a single vehicle based on the current road network state; or the application scenarios of the algorithm require multiple assumptions as a premise; or perform poorly when the vehicle density is high

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