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Combination Forecasting Method of Traffic Congestion Based on Markov Chain and Neural Network

A Markov chain, traffic congestion technology, applied in biological neural network models, traffic flow detection, traffic control systems for road vehicles, etc., can solve problems such as predicting congestion status

Inactive Publication Date: 2017-01-25
TONGJI UNIV
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Problems solved by technology

However, these studies mainly focus on the prediction of traffic flow parameters (such as flow rate, speed and occupancy rate, etc.). Can predict the congestion status of the entire road network from a macro perspective

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  • Combination Forecasting Method of Traffic Congestion Based on Markov Chain and Neural Network
  • Combination Forecasting Method of Traffic Congestion Based on Markov Chain and Neural Network
  • Combination Forecasting Method of Traffic Congestion Based on Markov Chain and Neural Network

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[0054] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0055] Such as figure 1 As shown, the present embodiment provides a combined prediction method of traffic congestion state based on Markov chain and neural network, comprising the following steps:

[0056] 1) adopt the Markov chain method similar to PageRank to carry out traffic congestion state prediction, obtain the first prediction result;

[0057] 2) The BP neural network method optimized by the quantum multi-agent algorithm is used to predict the traffic congestion state, and obtain the second prediction result;

[0058] 3) Obtaining the weights of the first prediction result a...

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Abstract

The invention relates to a Markov chain and neural network based traffic congestion state combined prediction method. The Markov chain and neural network based traffic congestion state combined prediction method comprises the following steps of 1 adopting a similar-PageRank Markov chain method to perform traffic congestion state prediction so as to obtain a first prediction result, 2 adopting a quantum multi-agent algorithm optimized back-propagating (BP) neural network method to perform traffic congestion state prediction so as to obtain a second prediction result, 3 obtaining the weight of the first prediction result and the weight of the second prediction result based on information entropy, 4 obtaining a final prediction result according to the first prediction result, the second prediction result and the corresponding weights. Compared with the prior art, the Markov chain and neural network based traffic congestion state combined prediction method has the advantages of being good in prediction real-timeliness, high in accuracy, good in extension and the like.

Description

technical field [0001] The invention relates to the field of traffic state prediction, in particular to a combined prediction method of traffic congestion state based on Markov chain and neural network. Background technique [0002] There are many reasons for road traffic congestion, but the root cause can be attributed to the imbalance between traffic demand and traffic supply. Solving the problem of traffic congestion is nothing more than taking measures from two aspects of supply and demand: on the supply side, improving the overall traffic capacity of the road network by strengthening infrastructure construction, and optimizing the spatio-temporal distribution of travel behaviors on the demand side. Considering the feasibility and economy, starting from the latter, maximizing the utilization of the existing road network has gradually become the focus of traffic researchers and managers. Traffic state prediction is to estimate the future state by comprehensively analyzin...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G08G1/01G06N3/02
CPCG06N3/02G08G1/0125
Inventor 刘敏吴薇章锋李玲刘清
Owner TONGJI UNIV
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