Urban traffic network reliability prediction method based on seepage analysis

A technology of urban traffic and forecasting method, which is applied in traffic flow detection, traffic control system of road vehicles, forecasting, etc. It can solve problems such as the difficulty in distinguishing the relationship between urban traffic jams and explaining the inner mechanism of urban traffic jams

Active Publication Date: 2018-06-01
BEIHANG UNIV
View PDF3 Cites 29 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The urban transportation system is an open and complex giant system. Under the influence of external factors and the interaction of internal elements, its characteristics are changing nonlinearly and uncertainly all the time. Real-time evaluation and prediction of reliability under different conditions, because these methods are difficult to distinguish the correlation between urban traffic congestion sections, and it is also difficult to explain the internal mechanism of urban traffic congestion formation scale

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Urban traffic network reliability prediction method based on seepage analysis
  • Urban traffic network reliability prediction method based on seepage analysis
  • Urban traffic network reliability prediction method based on seepage analysis

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0037] In order to make the technical problem to be solved in the present invention, the technical scheme clearer, the following will be combined with the attached figure 1 The flow chart of the method describes in detail a specific implementation case.

[0038] A method for predicting reliability of urban traffic network based on seepage analysis of the present invention, see figure 1 As shown, it specifically includes the following steps:

[0039] Step 1: Based on the topological structure of the urban traffic network and the urban traffic operation data in the historical period, construct a dynamic network of traffic flow in the historical period;

[0040] (1) The present invention takes a certain city A as an example, and establishes its dynamic traffic network based on historical data. It contains about 50,000 edges and 27,000 nodes. It covers a time span of 17 working days in a certain month of a certain year. In the corresponding time span, there is a measured (co...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides an urban traffic network reliability prediction method based on seepage analysis. The method comprises the following steps that step one: the dynamic network of traffic flow ofhistorical periods is constructed; step two: the seepage threshold of an urban traffic dynamic network is calculated; step three: the anomaly value of the seepage threshold mean of each period is calculated and acts as the basis of distinguishing the level of the urban traffic network reliability of the historical periods; and step four, the evolution law of the congestion subgroups in the high-reliability urban traffic operation state and the low-reliability traffic operation state of the historical periods is compared and the real-time urban traffic network reliability is accordingly predicted. With application of the steps, the evolution characteristics and the evolution mechanism of the traffic network reliability of the historical periods are effectively analyzed, and the high-reliability operation state and the low-reliability operation state of the evolution direction of the real-time urban traffic network reliability can be accordingly predicted so as to provide guidance or early warning for the urban traffic manager; and the method has critical support effect for solving the urban traffic congestion and building the smart city.

Description

technical field [0001] The invention proposes a method for predicting the reliability of an urban traffic network based on seepage analysis, which belongs to the technical field of traffic reliability. Background technique [0002] With the continuous development of the economic level, the field of planning and construction of infrastructure has gradually prospered. The level of a country's infrastructure construction not only reflects its own level of comprehensive strength, but also an important material guarantee for its national economy and people's livelihood. Therefore, how to improve the management and service level of infrastructure is a crucial issue. From the perspective of the engineering field, it is how to improve the reliability of infrastructure to meet certain usage requirements. Among them, key infrastructure such as communications, electricity, energy, transportation, water supply and other fields are the most important infrastructure systems related to t...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06G08G1/0129
Inventor 李大庆曾冠文
Owner BEIHANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products