A ramp control method based on congestion situation classification

Abstract

The present invention relates to a ramp control method based on the classification of congestion situation. The invention uses Gaussian mixture model clustering to redefine the congestion situation through the analysis of historical data, because the critical occupancy rate is usually difficult to obtain, and the inaccurate occupancy rate will directly Affects the performance of the ALINEA algorithm. The present invention takes the traffic flow as the control parameter of the ALINEA algorithm, and self-adaptively selects the control rate according to the congestion situation. In order to avoid the impact of the overflow caused by the excessively long queue on the ramp on the ground traffic, a segmented control method is adopted to increase the queue length constraint, and the queuing If it is too long, transfer the control target to the ramp. Using SUMO simulation software to carry out simulation experiments, the results show that the present invention can optimize ramp queuing and vehicle waiting time in different degrees under the condition of ensuring main line traffic, and improves road traffic efficiency.

Description

technical field [0001] The invention relates to the technical field of intelligent transportation, in particular to a ramp control method based on congestion situation classification. Background technique [0002] With the rapid development of the economy, many cities have built urban expressways to meet the growing traffic demand, but the number of vehicles is increasing year by year, and the problem of traffic congestion is becoming more and more serious. At present, the main control methods that can effectively alleviate the problem of urban expressway traffic congestion include ramp control, variable speed limit and traffic guidance. Ramp control is the most widely used and most effective means of expressway traffic control. The signal lights set up on the ramp control the traffic flow into the main line, so as to achieve the purpose of improving the traffic capacity of the main line. On-ramp single-point control methods mainly include: demand-capacity control, occupanc...

AI Technical Summary

Problems solved by technology

[0003] Although ALINEA is very effective in controlling single-ramp congestion, including ALINEA and many of its extended algorithms, the control objectives are focused on the control of the main line, ignoring the problem that the ramp is prone to overflow, and the selection of the critical occupancy value of ALINEA has a great influence on the control. The impact is great. If the critical occupancy value is set unreasonably or the occupancy value cannot be obtained, the control effect will be greatly reduced

In terms of congestion situation classification, it is more common to use k-means for clustering, and on this basis to optimize the selection of center points and the number of clusters, reduce the number of algorithm iterations, or increase the accuracy of clustering, but k-means is A hard clustering method that assigns each object to the nearest cluster by calculating the distance between the object and the center point of the cluster. For some cases that are difficult to distinguish, the probability of cluster assignment is lacking, so it is difficult to distinguish

Secondly, the boundary of the k-means cluster is circular, which is not effective when fitting some non-circular data.

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