Subway transfer channel traffic state fuzzy prediction method

Abstract

The invention discloses a subway transfer channel traffic state fuzzy prediction method, which comprises the following steps: forming a collection environment by cameras arranged at an entrance and an exit of a subway transfer channel; calculating average travel time that passengers pass through the subway transfer channel according to the time that the passengers pass through the entrance and the exit and which is recorded by the cameras; identifying and eliminating the abnormal values of data recorded at the average travel time; making up the missing data of the average travel time; calculating the time duty ratio corresponding to the arriving time of each subway at that day; carrying out traffic state fuzzy division on all time duty ratio data recorded at that time and before that time by applying a fuzzy C-mean clustering analyzing method according to the preset traffic state, so as to obtain a fuzzy set; calculating Gaussian subordinate function parameters corresponding to each cluster; selecting a first order Sugeno fuzzy inference system as a prediction model basic frame; establishing a fuzzy prediction model for prediction through learning training samples; and carrying out defuzzification on a prediction result, and finally outputting an accurate value and a fuzzy value of the traffic state prediction result.

Description

technical field [0001] The invention relates to a method for predicting the passage state of a subway transfer channel, which belongs to the field of intelligent analysis and processing of subway information. Background technique [0002] At present, the subway has become one of the main directions of urban transportation development due to its advantages of convenience, safety, punctuality, and environmental protection. However, the efficient subway network also has some safety hazards. Some subway transfer passages are narrow, tortuous and relatively closed, which can easily cause congestion during peak hours with dense crowds, and are also one of the preferred locations for terrorist attacks, directly posing a safety hazard. In addition, the space of the subway transfer channel is relatively closed, and it is difficult to deal with emergencies when the passenger flow is large. Existing methods can only monitor the traffic status of the transfer channel in real time. Sinc...

AI Technical Summary

Problems solved by technology

[0002] At present, the subway has become one of the main directions of urban transportation development due to its advantages of convenience, safety, punctuality, and environmental protection. However, the efficient subway network also has some safety hazards

Some subway transfer passages are narrow, tortuous and relatively closed, which can easily cause congestion during peak hours of dense crowds, and are also one of the preferred locations for terrorist attacks, directly posing a safety hazard

In addition, the space of the subway transfer channel is relatively closed, and it is difficult to deal with emergencies when the passenger flow is large

The existing method can only monitor the traffic status of the transfer channel in real time. Since the real-time information cannot explain the development trend of the traffic status, it cannot play a good preventive role

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