Intelligent rail transit time control method based on target detection technology and aco-bp algorithm

Abstract

The present invention claims to protect an intelligent rail transit time control method based on target detection technology and ACO-BP algorithm. First, the daily traffic flow of each station of Chongqing Rail Transit Line 3 is counted by using the target detection technology, and the number of people waiting and the number of actual vehicles are obtained. The relationship weight of the impact of the departure flow and the arrival time interval on the train carrying capacity; further, the current flow of people in each station is used as the input, and the relationship weight is used as the influencing parameter to be input to the ant colony algorithm to establish a mathematical model, and by constantly changing Departure interval to get the maximum carrying capacity. At the same time, taking the error between the expected and the actual carrying capacity as the input, based on the BP (Backpropagation) neural network learning function, the weights are updated and the results are optimized. The present invention obtains the train time interval with the maximum carrying capacity through real-time collection of the environmental information of the light rail station and continuous update of the algorithm.

Description

technical field [0001] The invention belongs to the field of urban rail transit control, and specifically uses the current human flow and historical data in each station to match an optimal traffic plan through an algorithm. The system is an intelligent rail transit time control system based on target detection technology and ACO-BP algorithm. Background technique [0002] With the continuous expansion of the city, the number of cars has soared, and the roads are very congested during high commuting hours and key areas. Under the circumstance of huge traffic demand, urban rail transit is more and more popular because of its convenient, safe, fast, and high punctuality rate. But now the light rail is facing the same congestion problem. In some business districts, airports, stations and during peak hours, the subway and subway stations have a huge flow of people, which leads to long waiting times for passengers and is very crowded, and it is also prone to safety accidents. ...

AI Technical Summary

Problems solved by technology

Liu Tao [5] and others put forward the concept of "paying for congestion" to reduce passenger flow and increase the ticket price to improve congestion. This method is effective to a certain extent and can make some People who are not in a hurry don’t take the subway during peak hours, but the effect needs to be further improved for school and school time periods; based on this, Wei Huayong [6] proposed an urban rail transit signal control system based on fuzzy control algorithms from a dynamic perspective. Design method, using the variable structure PID fuzzy neural network control method to improve the control rules of urban rail transit signals, and carry out the hardware design of urban rail transit signal control system in the embedded environment; Chen Zhijie [7] takes passenger flow as the main line of research, based on A F C card swiping data, from the estimation of the whereabouts of incoming passengers, the calculation of parameters in the process of passengers entering and leaving the station and transfers, and the calculation of train and station loads, by controlling passenger flow to alleviate congestion, this method is mainly to control passenger flow, which can slow down The congestion of the subway and the subway platform, but it cannot alleviate the congestion of the subway station, and makes the waiting time of passengers longer, which is contrary to the convenience and speed of the subway

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