Traffic State Estimation Method for Expressway Sections Based on Dynamic Bayesian Network

A dynamic Bayesian and traffic state technology, applied in traffic flow detection, calculation, instruments, etc., can solve the problems of GPS data limit, travel time uncertainty, traffic state uncertainty, etc., to achieve good results and reliability effect

CN104809879BActive Publication Date: 2017-05-03重庆科知源科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Publication Date
2017-05-03

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Abstract

The invention belongs to the technical field of road traffic detection and particularly discloses an expressway road traffic state estimation method based on a dynamic Bayesian network; the method comprises the following steps: (1) extracting relevant parameters of the road traffic state as nodes; (2) determining an interrelationship among the nodes and establishing the dynamic Bayesian network; (3) carrying out a fuzzy classification on data of the observable nodes, analyzing the historical data to obtain a clustering center of each classification and determining a membership degree of the data of the observable data, belonging to each classification; 4) for a target node selected in the dynamic Bayesian network, acquiring a corresponding conditional probability and a transition probability and establishing each moment characteristic table of the selected target node; 5) inputting road traffic flow parameters of the current moment to the dynamic Bayesian network and triggering to reason a target of each moment to obtain a traffic state estimation result. According to the expressway road traffic state estimation method disclosed by the invention, the uncertainty in a single parameter estimation state is solved and simultaneously the relevance in the traffic state is considered, so that better effect and reliability when the road traffic state is estimated are achieved.
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Description

technical field

[0001] The invention belongs to the technical field of road traffic detection, and in particular relates to a method for estimating a traffic state of an expressway section. Background technique

[0002] With the increasing importance of expressway in my country's transportation, traffic congestion, traffic accidents, environmental pollution and other problems are becoming more and more serious. Whether it is traffic managers or travelers, the demand for traffic information management is gradually increasing. Therefore, how to use the existing detection equipment to realize the estimation of expressway traffic status as effectively and accurately as possible, and grasp the real-time and accurate traffic conditions of the current road section. Traffic conditions are the premise of efficient management and service, and have important theoretical and practical research significance.

[0003] Various devices for traffic data acquisition, such as fixed detectors,...

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Embodiment Construction

[0042] In order to make the purpose, technical solution and advantages of the present invention clearer, the specific implementation manners of the present invention will be further described in detail below.

[0043] see figure 1 , 2 , 3, the highway section traffic state estimation method based on dynamic Bayesian network of the present embodiment, comprises the following steps:

[0044] 1) Determine variable nodes: Extract variables related to the traffic state of the road section as nodes, including observable nodes and hidden nodes; the observable nodes include the average travel time of the road section and the relative density of the road section, and the hidden nodes include the traffic state of the road section .

[0045] For the average travel time of the road section, the statistics of the toll stations of the vehicles passing through the road section within a certain period of time are used:

[0046] ①The actual travel time of the bicycle:

[0047] The actual t...