Method for discovering congestion time period based on composite network

Abstract

The invention belongs to the field of traffic condition prediction, and relates to a method for discovering a congestion time period based on a composite network. According to the method, a compositenetwork of an intersection network and a sensor network is constructed based on a complex network theory, and a congestion time period in the traffic network can be accurately judged. The method is implemented specifically according to the following steps: firstly, a congestion index Ti, a congestion time period and congestion level quantization are defined, then the congestion time period is calculated through establishing a mapping relation between the intersection network and the sensor network and performing congestion time period analysis, and finally frequent congestion time period analysis is performed. The model established according to the method is simple, the calculation data amount is small, the processing process is concise and clear, the operability and the practicability aregood, and effective implementation is facilitated. Meanwhile, the overall design is ingenious in conception, the required infrastructure are easy to obtain, the prediction process is simple, the application environment is friendly, and the market prospect is broad.

Description

Technical field: [0001] The invention belongs to the field of traffic condition prediction, and relates to a method for predicting traffic congestion conditions, in particular to a method for discovering congestion periods based on a composite network. Background technique: [0002] With the rapid economic development, the number of private cars has greatly increased, and the problem of urban road congestion has become increasingly severe. This has brought huge challenges to the smooth operation of urban traffic and has also had a great impact on people's lives and travel. The traffic control management center relies on the Intelligent Transportation System (ITS) to handle accidents in a timely manner to ensure smooth roads. ITS obtains traffic data by monitoring the state of roads and vehicles, and provides analysis results for the traffic department, effectively alleviating the problem of road congestion. Among them, through the analysis of historical data, obtaining congestio...

AI Technical Summary

Problems solved by technology

When the congestion time and duration of different intersections can be analyzed, relevant departments can improve road driving rules based on the analysis results. However, this method lacks a reliable traffic congestion propagation model, and it is difficult to achieve large-scale and precise traffic congestion analysis. It also cannot support high-efficiency traffic congestion prediction, and can only realize congestion prediction analysis in a small area. In the prior art, Chinese patent No. 201610976252.7 discloses a traffic congestion prediction method and system based on a traffic congestion propagation model , calculate the passing speed of the vehicle passing through the first road section through the historical trajectory of the vehicle: calculate the vehicle driving threshold according to the passing speed, if the current instantaneous driving speed of the vehicle is less than the vehicle driving threshold, it is determined that traffic congestion occurs, and the monthly traffic A road section with a congestion frequency greater than a certain number of times is determined to be a road section with frequent traffic congestion, and a congestion submap is generated according to the road section with frequent traffic congestion, and the traffic congestion probability is generated after calibrating the congestion submap according to the probability of simultaneous traffic congestion in all connected road sections Predicting the traffic congestion after the graphical model can realize the extraction of accurate road traffic status based on multi-source trajectory big data, so as to complete the analysis of urban traffic congestion propagation, but this method needs to establish an extremely complex model, and the calculation and processing process is cumbersome, and It needs to be based on the acquisition of a large amount of traffic data, which not only includes the precise form trajectory data and driving speed data of each vehicle, so the operability and practicability are poor, and it is difficult to implement effectively. Therefore, the present invention proposes a method based on a composite A network congestion period discovery method, which decomposes the urban road network into an intersection network and a sensor network

Images