Method for dynamically generating bus timetables on basis of Bayesian network models

Abstract

The invention relates to a method for dynamically generating bus timetables on the basis of Bayesian network models. The method includes screening microscopic and macroscopic factors affecting dynamic generation of the bus timetables; building the double-layer microscopic and macroscopic Bayesian network models for dynamically generating the bus timetables, and in other words, building the Bayesian network models for forecasting dynamic variation of bus environments and the Bayesian network models for dynamically generating the bus timetables; predicting transport capacity and transport volume occurrence probabilities of various routes under the condition of random disturbance and reasons for unbalance of the transport capacity and the transport volumes of the various routes; combining scheduling policies with one another and generating possible timetable schemes around the target for timely evacuating passengers; computing various indexes for evaluating the quality of the timetables from the points of governments, enterprises and the passengers and evaluating the quality of the timetables. The method has the advantages that a function of dynamically adjusting the timetables according to variation of the bus environments can be implemented, and accordingly technical support can be provided for daily bus operation management.

Description

technical field [0001] The invention relates to the technical field of public transport informationization, in particular to a method for dynamically generating a public transport schedule based on a Bayesian network model. Background technique [0002] The compilation of bus timetable is one of the core tasks of the daily operation of public transport. According to the spatial and temporal distribution characteristics of residents’ travel, the frequency and type of departures in each time period are reasonably organized and arranged, and the problem of the maximum matching of transport capacity and traffic volume is mainly solved. When random factors in reality cause changes in bus passenger flow or travel time, this will cause an imbalance in bus transport capacity and volume, and thus the bus dispatching scheme will fail. Therefore, it is of great theoretical value and practical significance to dynamically adjust the timetable according to the dynamic changes of the bus e...

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 schedule caused by random interference. We should start from the whole and reveal how changes in the external environment cause changes in passenger flow or travel

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