Urban traffic flow prediction method with regional vitality integrated

Abstract

The invention discloses an urban traffic flow prediction method with regional vitality integrated. The method comprises steps: S1, regional division is carried out on an urban road network, and the traffic flow in each region is calculated; S2, a regional vitality model is designed: by using distribution of urban points of interest, holidays and weather information, a 3D convolution neural network(3D CNN) is used to study the dynamic changes of the vitality in each urban region; S3, a flow prediction model is designed: the regional vitality and the traffic flow are integrated, and a convolution long short-term memory network (ConvLSTM) is used for flow prediction; and S4, according to the historical data, the regional vitality model and the flow prediction model are trained at the same time, and the well-trained models are used for predicting the traffic flow in each region in real time. Through integrating the regional vitality and considering influences from a driving force behind crowd activities and external factors, high prediction precision can be acquired.

Description

technical field [0001] The invention relates to the cross-technical application field of deep learning and traffic flow forecasting, in particular to an urban traffic flow forecasting method integrating regional vitality. Background technique [0002] With the acceleration of the urbanization process, a large number of people are pouring into the city. While the economy is booming, the hidden dangers of traffic safety are becoming increasingly prominent. For example, when holidays come, there will be a large number of people gathering and serious traffic jams near the mall, which will bring great safety hazards. Therefore, the prediction of traffic flow is of great significance to urban safety and is a global concern. If the traffic flow in various areas of the city can be accurately predicted, the traffic control department can conduct traffic diversion in a timely manner, and the public can selectively bypass congested areas, thereby reducing safety hazards. [0003] Giv...

AI Technical Summary

Problems solved by technology

[0004] Defects of existing methods or inventions: 1) Most of the existing methods only focus on the traffic flow prediction of one or several road sections, and lack the measurement of the degree of crowd gathering in each area of ​​the city; 2) Most of the existing methods only focus on the traffic flow data itself , and these traffic data lack semantic information and cannot reflect the driving force behind the movement of people; 3) The existing methods do not deal with the impact of changes in external factors on people's activity patterns, such as heavy rains have little impact on commuting behavior, but can have a significant impact on tourism

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