Artificial bee colony algorithm-based commuting private car sharing matching method

Abstract

The invention relates to a commuting private car sharing matching method based on an artificial bee colony algorithm, and belongs to the technical field of data algorithm processing, in particular to the technical field of commuting private car sharing matching methods. According to the method, an artificial bee colony optimization algorithm is improved, a nectar source and a fitness rule are redesigned, the departure time of each commuting bus is used as each dimension of the nectar source, the interval of the departure time is used as the change interval of the nectar source, and the length of a path saved after car pooling is used as the fitness. And the method has stronger search capability and development capability. According to the method, the commuting private car travel itinerary and the commuting private car travel itinerary subsequence are obtained through statistics according to the vehicle RFID electronic license plate identification data, the experiment efficiency can be improved, the complexity can be reduced, the method has few parameters, the efficiency is high, the effect is good, compared with other methods, more journeys can be saved, more private cars can be matched, and the solution is better.

Description

technical field [0001] The invention belongs to the technical field of data algorithm processing, in particular to the technical field of carpooling matching methods for commuter private cars, and relates to a carpooling matching method for commuter private cars based on an artificial bee colony algorithm. Background technique [0002] In recent years, with the rapid development of the economy, the number of private cars has increased. Especially in the morning and evening peak hours, a large number of commuting private cars not only cause urban traffic congestion, but also cause environmental pollution. Although ordinary private cars have a carrying capacity of at least 5 people, during morning and evening peak hours, the vacancy rate of commuter private cars is relatively high, and the phenomenon of one person per car is common. Commuter private cars with a large number and high occupancy rate lead to increased traffic flow on urban roads, waste of traffic resources, incr...

AI Technical Summary

Problems solved by technology

However, this method cannot obtain the global optimal solution, and this method takes a long time to search once, with high time complexity and low efficiency

Swarm intelligence optimization algorithm is also commonly used to solve carpooling matching methods, such as genetic algorithm, particle swarm algorithm, hill climbing algorithm, etc. These algorithms obtain the optimal solution by initializing the population and evolving the population, not only have fewer parameters, but also have high efficiency and comprehensive search. The global optimal solution is obtained, but the above method also has the problem of insufficient search ability and low efficiency

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