Deep learning-based space-time long-short-term urban pedestrian volume prediction method

Abstract

The invention discloses a deep learning-based space-time long-short-term urban pedestrian volume prediction method. The method is used for pedestrian volume prediction. According to the method, when the spatial correlation is extracted, and the local convolution is carried out on the correlation of the adjacent regions. The correlation of the remote regions is extracted by using graph convolution,so that the integrity of the spatial correlation is considered while parameters and calculated amount are reduced. According to the method, short-term time dependence and long-term time dependence are captured at the same time, so that a prediction result is more accurate in a time dimension. The regional semantic information distribution is considered. Each type of region of interest (POI) in each region is endowed with a corresponding proportion weight, so that the influence of regional semantics on urban pedestrian flow is utilized more accurately.

Description

technical field [0001] The present invention relates to the field of human flow forecasting, in particular to a spatio-temporal long-term and short-term urban human flow forecasting method based on deep learning. Background technique [0002] With the advancement of urbanization, developed urban roads and diverse modes of transportation have brought great convenience to people's travel. However, if the traffic data cannot be used reasonably, the effect will often be counterproductive, followed by problems such as traffic congestion, waste of resources, and potential safety hazards. [0003] For example, road congestion in big cities, frequent traffic accidents, and the New Year's Eve stampede on the Bund in Shanghai have sounded the alarm for urban traffic flow control. The meaning of urban traffic flow prediction is to predict the traffic flow at the current moment or the next moment based on historical traffic flow data. Its significance is that, in view of the predicted...

AI Technical Summary

Problems solved by technology

The existing technology considers the dynamic correlation of inter-regional human flow and the periodic drift in time series, and solves the long-term time dependence problem, but ignores the long-distance spatial dependence, and only considers one or several influencing factors. To predict people flow information well, it is necessary to comprehensively consider various influencing factors, including spatio-temporal features, especially the long-distance spatial dependence and long-term time dependence, regional semantic features and additional features.

The existing technology is still blank for the research of comprehensive consideration of various influencing factors

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