Method for discovering congestion points, congestion lines and congestion areas based on composite network

Abstract

The invention belongs to the field of traffic condition prediction, and relates to a method for discovering congestion points, congestion lines and congestion areas based on a composite network. The composite network in the method is composed of two or more sub networks and connecting edges between the sub networks. The sub networks are independent networks constituting the composite network. Thecomposite network includes an intersection network and a sensor network. The sensor network takes traffic sensors at intersections as nodes, and the sensors are related by connecting edges. The intersection network takes intersections as nodes, and connecting edges are established according to whether the intersections can be connected. On the basis of the intersection network, the definitions ofa congestion index, a congestion point, a congestion line and a congestion area are given. Calculation is carried out based on the network structure. The method specifically comprises the steps as follows: first, defining a congestion index Ti, a congestion point, a congestion line and a congestion area; then, quantifying the congestion level; and finally, analyzing and calculating congestion points, congestion lines and congestion areas. The method is designed ingeniously, has good operability and practicability, and has a wide market prospect.

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 points, congestion lines and congestion areas based on a composite network. Background technique: [0002] With the rapid development of the economy, the number of private cars has increased greatly, and the problem of urban road congestion has become increasingly serious, which has brought huge challenges to the smooth operation of urban traffic, and has also had a great impact on people's life and travel. The traffic control management center relies on the intelligent transportation system (Intelligent Transportation System, ITS) to deal with accidents in a timely manner and ensure the smooth flow of roads. ITS obtains traffic data by monitoring road and vehicle status, and provides analysis results for the traffic department, effectively alleviating road conges...

AI Technical Summary

Problems solved by technology

Among them, through the analysis of historical data, obtaining the congestion periods of different locations in the city plays an important role in the construction of ITS. When the congestion periods and duration of different intersections can be analyzed, relevant departments can also improve road driving rules based on the analysis results. , but this method lacks a reliable traffic congestion propagation model, it is difficult to achieve large-scale and precise traffic congestion analysis, and it cannot support high-efficiency traffic congestion prediction. It can only realize congestion prediction analysis in a small area. , the Chinese patent No. 201610976252.7 discloses a traffic congestion prediction method and system based on the traffic congestion propagation model, which calculates the traffic speed of the vehicle passing through the first road section through the historical trajectory of the vehicle: calculates the vehicle driving threshold according to the traffic speed , if the instantaneous driving speed of the current vehicle is less than the vehicle driving threshold, it is determined that traffic congestion occurs, and the road section with the number of traffic jams greater than a certain number of times per month is judged as a road section with frequent traffic congestion, and the congestion sub-section is generated according to the road section with frequent traffic congestion Graph, according to the probability of traffic congestion occurring at the same time on all connected road sections, the congestion sub-graph is calibrated and the traffic congestion probability map model is generated to predict the traffic congestion situation, which can extract accurate road traffic status based on multi-source trajectory big data, thereby Complete the urban traffic congestion propagation analysis, but this method needs to establish an extremely complex model, the calculation and processing process is cumbersome, and it needs to be based on a large amount of traffic data, which not only includes the precise form trajectory data and driving speed of each vehicle data, so the operability and practicability are poor, and it is difficult to implement effectively. Therefore, the present invention proposes a method for discovering congestion points, congestion lines, and congestion areas based on a composite network, and decomposes the road network into an intersection network and a sensor network.

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