A method and an apparatus for predicting inter-regional travel demand

A technology of travel demand and prediction method, which is applied in the fields of security monitoring, deep learning, and urban traffic management. It can solve the problems of ignoring relevant and global information, difficulty in capturing data change trends, and less work for travel demand, so as to improve accuracy. Effect

Active Publication Date: 2019-01-11
SUN YAT SEN UNIV
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Problems solved by technology

[0003] In academic research, scholars have put forward a lot of work on the space-time modeling of regional travel demand, but there is relatively little work on inter-regional travel demand, because the demand between different regional pairs is not only different in quantity, but also changing. There are also significant differences in the rules
Existing research screens out the demand of high-frequency regional pairs for research, which is insufficient for the overall research on inter-regi

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[0044] The implementation of the present invention is described below through specific examples and in conjunction with the accompanying drawings, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific examples, and various modifications and changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention.

[0045] figure 1 It is a flow chart of the steps of a method for forecasting inter-regional travel demand in the present invention. Such as figure 1 As shown, a method for forecasting interregional travel demand of the present invention comprises the following steps:

[0046] Step S1, constructing a deep model for extracting multiple contextual information, that is, a Contextualized Spatia...

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Abstract

A method and an apparatus for predicting inter-area travel demand are disclosed. The method comprises the steps of: step S1, constructing a deep model of multi-context information extraction, utilizing a traffic demand matrix sequence of a plurality of historical time periods as an input and an actual traffic demand matrix of a corresponding time period as a target output, and utilizing a back propagation algorithm of a neural network to train the deep model; step S2, constructing a traffic travel demand matrix sequence rich in context information; step S3, taking the deep model parameters andthe deep model trained in step S1 as the final predictor together, inputting the continuous traffic trip demand matrix sequence, and predicting the unknown traffic trip demand matrix of the next timeperiod. The invention can improve the accuracy of the inter-area trip demand prediction.

Description

technical field [0001] The present invention relates to security monitoring, urban traffic management, deep learning and other technical fields, in particular to a method and device for inter-regional travel demand prediction based on a deep learning-based multi-temporal context information fusion mechanism. Background technique [0002] Interregional travel demand forecasting is an important task, and it has important applications in issues such as urban traffic intelligent management and traffic resource scheduling in advance. Regional travel demand analysis obtains the quantity of travel demand in different regions by analyzing the passenger information and GPS location information of vehicles at historical moments, so as to predict the travel demand at the next moment. The forecast of inter-regional traffic demand further refines the regional travel demand and predicts the travel demand from one region to another. The key to these travel demand forecasting problems is h...

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Application Information

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IPC IPC(8): G06Q10/04G06Q10/06G06Q50/30
CPCG06Q10/04G06Q10/06315G06Q50/30
Inventor 林倞邱志林张雨浓张冬雨王青
Owner SUN YAT SEN UNIV
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