Urban Traffic Flow Forecasting Method Integrating Regional Vitality

Abstract

The invention discloses a method for predicting urban traffic flow that integrates regional vitality, including: S1, dividing the urban road network into regions, and calculating the traffic flow of each region; S2, designing a regional vitality model: using the distribution of urban interest points and For holidays and weather information, use 3D convolutional neural network (3D CNN) to learn the dynamic changes in the vitality of various regions in the city; S3, design traffic forecasting model: integrate regional vitality and traffic flow, use convolutional long-term short-term memory network (ConvLSTM) Traffic forecasting; S4. Simultaneously train the regional vitality model and the traffic forecasting model according to the historical data, and then use the trained model to predict the traffic volume of each area in real time. This method can achieve high prediction accuracy by integrating regional vitality and taking into account the driving force behind crowd activities and the influence of external factors.

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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