Bayesian network model based public transit environment dynamic change forecasting method

Abstract

The invention relates to a Bayesian network model based public transit environment dynamic change forecasting method. The Bayesian network model based public transit environment dynamic change forecasting method comprises the following steps of screening out various factors affecting public transit passenger flow fluctuation or travel time change; abstracting random jamming conditions of exterior environments and passenger flow or travelling time decision variables into nodes of a Bayesian network, determining a station set and the value range of the station set, and performing discretization on the historical information data of the station set and the value range of the station set; analyzing the influence relation between exterior environment jamming input nodes and passenger flow or travelling time decision nodes and establishing a Bayesian network structural diagram for public transit dynamic environment forecasting; determining a conditional probability table between determinant conditions and the decision nodes; computing the posterior probability when certain public transit passenger flow or travelling time occurs, and accordingly, achieving forecasting of public transit environment dynamic change. Combined with public transit incident detection under the environment of an Internet of vehicles, the Bayesian network model based public transit environment dynamic change forecasting method achieves a dynamic passenger flow time and space change forecasting function and provides data support for daily public transit operation and management.

Description

technical field [0001] The invention relates to the technical field of public transport informationization, in particular to a method for forecasting dynamic changes of the public transport environment based on a Bayesian network model. Background technique [0002] The bus passenger flow and travel time are the data basis for the formulation of the bus operation plan. When the random factors in reality cause the bus passenger flow or travel time to change, this will cause the bus capacity and volume to be unbalanced, and the bus dispatching plan will fail. Therefore, early warning of dynamic changes in the bus environment and providing a reliable and prepared data basis for dynamic bus dispatching are of great theoretical value and practical significance. [0003] There are many and complex factors affecting bus passenger flow or travel time, such as weather changes, traffic congestion, large-scale events, etc., and they are interrelated. At present, many domestic and fore...

AI Technical Summary

Problems solved by technology

[0006] It can be seen from the above that the existing research methods cannot solve the chain reaction process of the dynamic change of the bus environment caused by random interference. It should be based on the whole to reveal how the influen...

Images