Seepage-analysis-based method for determining elastic critical point of transportation system

Abstract

The invention provides a seepage-analysis-based method for determining an elastic critical point of a transportation system. The method comprises the following steps: step one, constructing a dynamicurban traffic network; step two, calculating system steady states of the dynamic urban traffic network under different congestion rates; and step three, determining an elastic mode and an elastic critical point of the transportation system. Therefore, the elastic mode and the elastic critical point of the urban traffic network system are analyzed effectively and whether the evolution direction ofthe urban transportation network system is close to the elastic critical point of the system can be predicted; the urban traffic administrator is assisted in evaluating and predicting the state changeof the urban traffic network, so that an early warning effect can be provided for the urban traffic congestion; a problem of the predicting indicator of the system's elastic critical point can be solved; the theoretical and technical guidance can be provided for the urban traffic system management and control; and thus current hot-spot study problems like urban traffic congestion solution and smart city building can be solved.

Description

technical field [0001] The invention proposes a method for determining the elastic critical point of a traffic system based on seepage analysis, which relates to a method for determining the elastic critical point of a traffic system based on seepage analysis and a prediction index, and belongs to the interdisciplinary technical field of traffic reliability and network science. Background technique [0002] The operational reliability of urban transportation system is an important branch emerging in the field of reliability. It mainly studies the ability of the transportation system to meet certain traffic demands within a specified time and under specified conditions. With the continuous improvement of the management level of the industry managers themselves, the research on the reliability of the operation of the urban transportation system, people not only stay at the level of understanding whether it will fail, but also hope to design a system that can be used when the sy...

AI Technical Summary

Problems solved by technology

First of all, the existing indicators are all indicators based on the time series data of the system state, and they need to rely on high-quality, long-term time series data to obtain more accurate results; secondly, although the existing indicators can measure the changing trend of the system state, However, it cannot reveal the influence of system topology on system state, so it is difficult to determine the structural factors that affect system changes

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