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Online car-hailing order demand prediction method based on space-time context attention network

A space-time context and demand forecasting technology, applied in biological neural network models, data processing applications, instruments, etc., can solve problems such as the inability to accurately predict the demand for online car-hailing orders

Pending Publication Date: 2020-12-29
WUHAN UNIV
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AI Technical Summary

Problems solved by technology

[0003] The present invention proposes a network-based car-hailing order demand prediction method based on spatio-temporal context attention network, which is used to solve or at least partly solve the technical problem that existing methods cannot accurately predict network car-hailing order demand

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Embodiment Construction

[0055] An embodiment of the present invention provides a method for forecasting online car-hailing order demand based on a spatio-temporal contextual attention network, which is used to improve the technical problem of being unable to accurately predict online car-hailing order demand.

[0056] Main inventive idea of ​​the present invention is as follows:

[0057] Based on the relevant knowledge in the field of deep learning and transportation planning, considering the spatial relationship between urban ride-hailing areas and the time dependence of historical orders, a demand prediction method for online car-hailing orders based on spatio-temporal contextual attention networks is proposed. This method fully considers the spatial location of the urban region itself, the relationship between different regions, and the time dependence of historical orders in multiple time periods on the demand for online car-hailing orders, so as to improve the accuracy of urban regional online ca...

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Abstract

In order to more reasonably allocate online car-hailing resources, reduce the empty driving rate of online car-hailing and improve the operation efficiency of online car-hailing, the invention provides an online car-hailing order demand prediction method based on a space-time context attention network, a network model constructed in the method comprises stacked space-time blocks and an output block, and each space-time block comprises TRELLIS-GRU and GAT, the TRELLIS-GRU is a gated cyclic grid network, the GAT is a graph attention network, the TRELLIS-GRU layer is used for fusing spatial and temporal features, and the GAT layer is used for mining spatial dependence in different time slices. According to the invention, influence of the spatial position of the urban area, the mutual relationship between different areas and the time dependence of historical orders in multiple time periods on the online car-hailing order demand is fully considered, so that the accuracy of the online car-hailing order demand in the urban area is improved.

Description

technical field [0001] The invention relates to the interdisciplinary technical fields of urban transportation planning and deep learning, and in particular to a demand prediction method for online car-hailing orders based on a spatio-temporal context attention network. Background technique [0002] With the rapid economic development and the rise of emerging travel methods such as Didi Chuxing and Dongfeng Travel, online car-hailing has become an important way for the public to travel. As of 2019, the proportion of online car-hailing passenger traffic to the total taxi passenger traffic It has reached 36.3%. The current online car-hailing service still has some problems of inefficient operation, such as long waiting time for passengers and high number of empty-loaded vehicles. Contents of the invention [0003] The present invention proposes a network-based car-hailing order demand prediction method based on a spatio-temporal context attention network, which is used to s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q30/02G06Q50/30G06N3/04
CPCG06Q30/0205G06N3/048G06N3/044G06N3/045G06Q50/40
Inventor 乐鹏颜哲人黄立刘广超姜良存
Owner WUHAN UNIV
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