Urban rail transit station simulation system and method based on dynamic driving

Abstract

The invention provides an urban rail transit station simulation system and method based on dynamic driving. The system comprises a data access and verification module, a basic model and knowledge base module, a behavior modeling and adaptive optimization module, a dynamic driving simulation control module and a dynamic simulation decision optimization module. The data access and verification module is used for acquiring passenger flow arrival and departure, get-on and get-off data and station service personnel on-duty and distribution data required by modeling and simulation online and offline, and transmitting the data to the corresponding modules as required after cleaning and verification; the basic model and knowledge base module stores related historical data and empirical models according to business themes, and constructs corresponding resource knowledge bases and empirical data model bases; the behavior modeling and adaptive optimization module is used for establishing a behavior control model; the dynamic driving simulation control module is used for injecting data of the data access and verification module and adaptively correcting a simulation data model; the dynamic simulation decision optimization module is used for simulation deduction, analysis, prediction and evaluation, and provides quantitative index support for station operation management.

Description

technical field [0001] The invention relates to a station simulation method, in particular to a dynamic drive-based urban rail transit station simulation system and method. Background technique [0002] With the rapid development of my country's urban rail transit, more and more cities have entered the stage of network operation, and the scale of passenger flow is increasing day by day, and gradually showing the characteristics of network, large passenger flow and high density, which has great impact on the operation organization and station facilities. The carrying capacity of equipment has brought huge challenges; the increasing demand for passenger travel experience also challenges the level of operational services and transportation efficiency; and due to the relatively small space of rail transit stations, large crowds can easily lead to serious accidents. [0003] At present, station operation management is still based on experience, and there is a lack of refined manag...

AI Technical Summary

Problems solved by technology

[0005] (1) Most of the input data of existing station simulation systems use survey or AFC data. Manual surveys generally select a representative time period on weekdays and weekends. Although the weekly passenger flow distribution has certain regularity, there are still differences. Use There is a large error in the station simulation of the survey data; at the same time, the AFC system collects the data at a granularity of 15 minutes, and the data has a large delay. Using AFC data for simulation lacks the ability of accurate real-time prediction

[0006] (2) The existing station simulation models lack adaptive adjustment capabilities. Usually, the simulation parameters are configured at one time before the simulation runs, which cannot be dynamically adjusted during the simulation process, and the simulation data model cannot be adaptively corrected according to changes in the passenger flow distribution in the station. Under these conditions, these parameters will change with time, and the error between the two will lead to the deviation between the simulation results and the actual results, resulting in poor timeliness of the model

[0007] (3) The existing simulation method only simulates the behavior of passengers, lacking the behavior of operators and the interaction between passengers and operators. In the actual operation process, operators have the ability to guide and guide passengers to balance passenger flow and control equipment status to affect passenger flow. Functions such as flow limitation to adjust the rate of passenger flow entering the station, and emergency handling will affect the distribution of passenger flow in the station. Therefore, it is necessary to further strengthen the simulation technology of station operator behavior and passenger interaction on the basis of the existing simulation technology.

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