Urban area crowd flow prediction method and system

Abstract

The invention provides an urban area crowd flow prediction method and system, and the method comprises the steps: downloading a data set which comprises trajectory data and external influence factor data; preprocessing all data, including crowd flow calculation, external influence factor data one-hot coding and normalization processing; constructing a network structure based on ResNet and LSTM, wherein the network structure comprises a ResNet sub-network for simulating urban area crowd flow spatial features, an LSTM sub-network for simulating urban area crowd flow time features, an external factor neural network for simulating influence of external factors on crowd flow, and a fusion module; constructing a training data set and a test data set, taking a network structure based on ResNet and LSTM after network training and test as a model of urban regional crowd flow prediction, and inputting the preprocessed crowd flow state of each region of a target city and external influence factors into the network structure, and finally, obtaining a crowd flow prediction result of each region of the city in a certain time period in the future.

Description

technical field [0001] The invention belongs to the technical field of mobile positioning information, in particular to a method and system for predicting crowd flow in urban areas. Background technique [0002] In recent years, due to the development of mobile positioning technology, the trajectory data of a large number of moving objects has been quantitatively collected. These spatio-temporal trajectory data contain rich spatio-temporal feature information of moving objects, which attracts a large number of researchers to dig out various valuable information and apply them. In particular, city-wide crowd flow prediction based on spatio-temporal trajectory data has attracted the attention of researchers, because crowd flow prediction has important research value and practical significance for many aspects such as network optimization, traffic management, public safety, and disease control. . For example, if the flow of urban people can be predicted in advance, the deploy...

AI Technical Summary

Problems solved by technology

However, the problem of crowd flow forecasting is affected by many factors, such as time factors, space factors, external factors, etc. How to build a suitable deep learning framework in the forecasting process and take into account the influence of these factors, so as to make more Accurate predictions remain a major challenge for researchers

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