Short-term traffic flow prediction method based on generative confrontation network

A short-term traffic flow and prediction method technology, applied in the field of short-term traffic flow prediction, can solve problems such as the inability to effectively use spatial correlation, and achieve the effect of improving accuracy

Inactive Publication Date: 2019-03-19
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0011] The technical problem to be solved by the present invention is to provide a short-term traffic flow prediction method based on a

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  • Short-term traffic flow prediction method based on generative confrontation network
  • Short-term traffic flow prediction method based on generative confrontation network
  • Short-term traffic flow prediction method based on generative confrontation network

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[0048] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0049] The overall process of the short-term traffic flow prediction method based on the generative confrontation network is as follows: figure 1 shown. The modeled traffic flow data is fed into a generative adversarial network model to generate predictions of future short-term traffic flows. Existing studies have shown that periodicity is a prominent feature of traffic flow data. Therefore, the input data includes not only the road network state matrix sequence at several time points before the prediction time, but also the road network state matrix sequence in the past few days or weeks. Specifically, the present invention constructs three sets of data as input:

[0050] f n : n moments t before the forecast time point 1 , t 2 ,...,t n traffic flow data.

[0051] f d : This sequence models the daily periodicity of traffic flow. where...

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Abstract

The invention discloses a short-term traffic flow prediction method based on a generative confrontation network. The prediction method can be applied in an urban traffic road network, wherein historical traffic data is modeled into a matrix sequence, a depth model is used for learning spatial-temporal correlativity in the data, a generator and discriminator in the network are iteratively trained,the trained model can predict the traffic conditions of all road segments in the road network in a short time in the future. The short-term traffic flow prediction method based on the generative confrontation network fully utilizes the spatial-temporal characteristics of the traffic data, the predicted object is extended from a single road segment to the entire traffic network, and the accuracy ofthe prediction is significantly improved.

Description

technical field [0001] The invention discloses a short-term traffic flow prediction method based on a generative confrontation network, and relates to the technical field of intelligent traffic systems. Background technique [0002] With the acceleration of my country's urbanization process, the contradiction between the growing urban population and limited space resources has become increasingly serious, resulting in traffic congestion and becoming a major problem hindering urban development. Effectively alleviating the problem of traffic congestion has important practical significance for reducing environmental pollution, improving people's living standards, and promoting the sustainable development of my country's social economy. [0003] Since the 1960s, countries around the world have conducted research on urban traffic planning and urban traffic control. However, with the continuous expansion of cities and the increasingly complex traffic conditions, it is no longer po...

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Application Information

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IPC IPC(8): G08G1/01G08G1/065
CPCG08G1/0133G08G1/065
Inventor 陈兵张宇轩王森章
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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