Traffic bottleneck identification method in urban traffic network

Abstract

The invention discloses a traffic bottleneck identification method in a complicated traffic environment, mainly solving the problem of serous traffic congestion because the prior art cannot satisfy the current requirement for traffic. The implementation steps of the traffic bottleneck identification method are 1) using an undirected graph with weight to represent a complex network which abstracts a practical traffic network; 2) utilizing a tolerance flow allocation algorithm to perform balanced flow allocation for a user on the abstract network topological graph; 3) distributing a weight for each road segment in the network, wherein the weight is a zero flow impedance on each road segment; 4) according to the network after the weights are distributed, searching for a minimal spanning tree for the zero flow impedance; and 5) performing priority ranking for the degree of importance of the bottlenecks in the minimal spanning tree obtained from the step 5). The traffic bottleneck identification method in an urban traffic network can provide quantitative description for the degree of importance of the bottlenecks for a traffic management department to enable the traffic management department to accurately grasp the distribution characteristics of the traffic bottlenecks, reasonably plan the traffic network topology and match the traffic demand for each road segment of a road network with the traffic capacity, and can be applied to an integrated traffic dispersion system.

Description

technical field [0001] The invention belongs to the field of traffic information control, and mainly relates to a traffic bottleneck identification method in a complex traffic environment, which can be used to support the formation of an integrated traffic guidance system. Background technique [0002] Since the famous mathematicians Erdos and Renyi proposed the ER random graph model in the 1960s, the research path of complex network theory has been opened. In the following decades, the study of complex networks has attracted more and more attention from researchers in various fields, and related work has been carried out in many fields including biology, physics, communication networks and computer networks. During this period, the ER random graph model has been the basic model for studying complex networks. On June 4, 1998, Nature published a paper on the network by two young physicists D.J.Watts and S.H.Strogatz, which explained the small-world effect of the network and ...

AI Technical Summary

Problems solved by technology

In scenarios such as highways and railways, there is no need to consider complex issues such as intricate nodes and roads, and the interaction between nodes and nodes. These research methods and research results are not suitable for complex urban traffic environments.

In the identification of bottlenecks in urban environments, foreign scholars mostly focus on the micro-traffic flow characteristics analysis of bottleneck road sections and the simulation analysis of traffic bottlenecks; the research work of domestic scholars mainly identifies traffic bottlenecks in the road network from a qualitative perspective and gives corresponding solutions. Countermeasures, the lack of quantitative road network fixed bottleneck identification method to support road system planning and improvement of traffic facilities, resulting in the result of "headaches and foot pains", failing to solve the improvement of road network traffic bottlenecks at the overall level

Some research results are based on the results of traffic distribution to calculate saturation and service level. Only relying on saturation indicators to identify traffic bottlenecks cannot reflect the actual traffic flow characteristics of the road network.

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