Public bicycle system regulation and control method based on Markov model

Abstract

The invention discloses a public bicycle system regulation and control method based on a Markov model. The public bicycle system regulation and control method comprises the steps of: (1) acquiring bicycle lending and returning statistics through a public bicycle information center; (2) dividing the acquired bicycle lending and returning statistics into a prior group and a posterior group, and calculating the statistics of the prior group by using a first-order Markov model parameterize method to obtain prior information used for subsequent inference; (3) combining the prior information with data of the posterior group, and utilizing Bayesian reasoning combined with the Markov model to obtain a Markov transition probability matrix; (4) inferring a public bicycle system OD matrix on the basis of the Markov transition probability matrix; (5) and conducting public bicycle system scheduling by using the OD matrix. The public bicycle system regulation and control method based on the Markov model utilizes the OD matrix statistical inference for conducting public bicycle system scheduling in real time, can obtain data easily, avoids solving the unclosed problem, effectively utilizes the prior information and improves accuracy of the obtained data.

Description

technical field [0001] The invention belongs to the field of public transportation in transportation planning and management, and in particular relates to a public bicycle system regulation method based on a Markov model. Background technique [0002] The OD matrix (origin-destination traffic volume, that is, the traffic volume between the starting point and the end point of a traffic trip) provides basic information on the movement of vehicles or pedestrians from a specific geographical area to another area. It plays a key role in traffic and transport management. Facing the increasingly serious problem of urban traffic congestion, giving priority to the development of public transportation has become a social consensus, and public bicycles have also become an effective solution to the problem of public transportation and rail transit connections. Accurate statistical inferences can be made to plan and arrange more reasonably the setting of public bicycle service stat...

AI Technical Summary

Problems solved by technology

However, due to economic reasons, the number of selected relevant routes is relatively small in practical applications, so there are a large number of unknown parameters (occurrence and attraction) but few observations are available, which leads to the problem of high omission; in order to solve the problem of high omission, Van Zuylen and Willumsen proposed the maximum entropy method, this method determines an OD matrix, which makes the choice travel matrix add as little data information as possible to the pre-selected routes in each transportation network

None of the above methods effectively utilize the prior information of OD, the Bayesian method proposed by Maher and Hazelton, and the maximum entropy method proposed by Van Zuylen and Willumsen combine the prior information and current information of traffic flow, so that the prior information can be obtained Effective utilization, but computationally cumbersome due to the large number of parameters

Images