Candidate bus route calculating method and system and electronic device

Abstract

The invention relates to the technical field of intelligent public transport and especially relates to a candidate bus route calculating method and system and an electronic device. The method comprises the following steps: a) screening a candidate bus stop set according to screening rules, and constructing an initial candidate bus route map according to the screened candidate bus stops; b) carrying out pruning on the initial candidate bus route map to obtain a final candidate bus route map; and c) based on a bus route generation algorithm, calculating a bus route reaching maximum passenger flow within the maximum driving time in the final candidate bus route map to obtain an optimal candidate bus route. The method realizes design of city candidate bus routes based on heuristic search, canquickly find the bus route reaching the maximum passenger flow under the constraint condition of the maximum driving time, fully considers travel requirements of passengers and economic benefit of a bus operation company, and is simple in algorithm parameters, simple in optimization process and high in execution efficiency.

Description

technical field [0001] The invention relates to the technical field of intelligent public transport, in particular to a method, system and electronic equipment for calculating candidate bus lines. Background technique [0002] In the process of urbanization in our country, with the increasing urban population scale, more and more cities have become "cities that never sleep". What follows is that traditional traffic resources and traffic services can no longer meet people's current night traffic travel needs. In many cities, due to the imperfect public transport services at night, urban residents choose taxis, black cars or private cars to meet their night travel needs. It also brings a series of urban problems that cannot be ignored, such as high transportation costs, high energy consumption that is not conducive to energy conservation and environmental protection, and hidden dangers to personal safety in illegal cars, etc. In order to better protect people's safe, economi...

AI Technical Summary

Problems solved by technology

[0006] 1) Genetic algorithm has many parameters, such as population size, crossover rate and mutation rate, etc. The solution to the problem depends on the values ​​of these parameters. However, the value of parameters is difficult to determine at present, and usually depends on experience;

[0007] 2) The ant colony optimization algorithm is likely to be limited to the local optimal solution in the later stage of the search process, making it difficult to search forward

In addition, like the genetic algorithm, the ant colony optimization algorithm has many parameters, such as the size of the ant colony, the relative weight of the residual information, the relative weight of the heuristic information, and the pheromone volatilization parameters. If the parameters are not set properly, it is easy to cause the algorithm to converge slowly and obtain Poor quality solutions, but the current parameter values ​​also need to rely on experience

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