Urban public transport route selection method

Abstract

The invention provides an urban public transport route selection method. The method solves urban traffic congestion by fully utilizing an computational intelligence advantage, closely associates various interactive units of a urban intelligent transportation system, solves correlative problems as viewed from a system perspective, accurately predicts road condition information, reasonably plans public transport routes according to a traffic condition, enlarges the coverage of public transport, shortens the waiting time of passengers and the running periods of the public transport, and adjusts traffic flow to be in an optimal state. The method comprises establishing a multi-object model and using the average travel time of a passenger and the total length of a route as two measurement indexes; and planning an optimal public transport route by means of a heuristic algorithm in order to achieve wider coverage and better connectivity. The method may dynamically treat a urban public transport route selection problem and is better in adaptability and wider in application range.

Description

technical field [0001] The invention relates to a method for selecting an urban bus route, which mainly uses an evolutionary calculation strategy to solve the problem of vehicle scheduling in an urban traffic system, and belongs to the cross-technical application field of machine learning, computer and software theory. Background technique [0002] The urban intelligent transportation system UITS is a multi-dimensional complex system that integrates computing, network and physical environment. Through the organic integration and deep cooperation of 3C technology, real-time perception, dynamic control and information service of large-scale engineering systems are realized. Modern UITS realizes the integrated design of computing, communication, and physical systems, which can make the system more reliable, efficient, and real-time collaborative, and has important and extensive application prospects. [0003] UITS combines ubiquitous environmental perception, embedded computing...

AI Technical Summary

Problems solved by technology

It was proposed by Marco Dorigo in his doctoral dissertation in 1992. It was inspired by the behavior of ants finding paths in the process of searching for food. The algorithm has strong robustness and parallel computing capabilities, but the population evolution rate is slow. and easy to fall into local optimal solution

Particle swarm algorithm is an evolutionary algorithm that simulates the behavior of birds foraging. It was proposed by Dr. Eberhart and Dr. Kennedy. This algorithm does not have the crossover and mutation used by genetic algorithm, but the particles follow the optimal particle in the solution space to search. The particles in the algorithm have the ability to self-summarize and learn from the optimal individual in the group, which makes the algorithm search fast and easy to implement, but it is easy to fall into local optimal solutions and cause premature phenomena

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