Multidirectional traffic flow prediction method based on interest point space-time residual neural network

A neural network and traffic flow technology, which is applied in the multi-directional traffic flow prediction field based on the spatiotemporal residual neural network of points of interest, can solve problems such as the increase of observation values, and achieve the effect of improving accuracy and strengthening geographical features.

Pending Publication Date: 2022-03-08
杭州电子科技大学上虞科学与工程研究院有限公司 +1
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Problems solved by technology

[0005] 2. Temporal correlation: Observations obtained at consecutive time intervals are highly correlated, i.e., if an upward trend is detect...

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  • Multidirectional traffic flow prediction method based on interest point space-time residual neural network
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  • Multidirectional traffic flow prediction method based on interest point space-time residual neural network

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Embodiment

[0118] The original data used in this example is the real battery car trajectory data set in Zhengzhou. Through preprocessing, each battery car track record retains five fields: vehicle number, longitude, latitude, direction and time. So far, many pieces of data σ= have been formed, and the time unit is minute. Map each σ to the map of each time according to time. Rasterize the map with a size of 400m*400m per square. Count the sum of all vehicles in the same driving state in each grid, and then count the sum of interest points in the same group in each grid, generate traffic flow matrix and interest point signals respectively, and then process the time to be predicted into time signals. Finally, the traffic flow matrix, interest point signal and time signal are input into the model for training according to the above method, and the trained model is used for result prediction. As shown in Figure 1, the multi-directional traffic flow prediction method based on the spatial-t...

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Abstract

The invention discloses a multidirectional traffic flow prediction direction based on a point-of-interest space-time residual neural network. According to the method, based on short-time vehicle trajectory data, time signals and interest point signals are added to enhance spatial-temporal features, 3D CNN is utilized to extract spatial-temporal features of traffic flow changes along with time, a residual neural network is combined to avoid model overfitting, finally, weighted compression is performed on information with the spatial-temporal features, and a traffic flow distribution matrix with a moving state is output. And the prediction of the regional traffic flow is realized. According to the method, the characteristic that the space distribution of the traffic flow changes along with time is well associated, the relation between the time signals and the interest point signals is integrated, the method has the advantages of being high in precision, high in practicability and the like, and decision support can be provided for public facility deployment, traffic diversion, land use planning and the like.

Description

technical field [0001] The invention relates to the field of traffic flow forecasting, in particular to a multi-directional traffic flow forecasting method based on interest point space-time residual neural network. Background technique [0002] Traffic data, as a ubiquitous spatiotemporal data type, exhibits correlations in both temporal and spatial dimensions. In addition, traffic forecasting research in the past few decades has mainly focused on taxis, private cars, and shared bicycles, while less research has been done on electric bicycles. In addition, due to the short driving time and short driving distance of electric bicycles, the periodicity of electric bicycle trajectory data is not obvious. Various reasons make the prediction of electric bicycles more challenging than traditional traffic prediction. In recent years, electric bicycles have gradually become an indispensable means of transportation in people's daily life due to their low price and small size. Accor...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/30G06N3/04G06N3/08G08G1/01
CPCG06Q10/04G06Q50/30G06N3/08G08G1/0125G06N3/045
Inventor 何宏王欣峰孙笑笑俞东进
Owner 杭州电子科技大学上虞科学与工程研究院有限公司
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