Urban fine-grained flow prediction method and system based on limited data resources

A data resource and traffic forecasting technology, applied in the field of smart transportation, can solve problems such as difficult access to training data, overfitting, poor generalization performance, and impact on the accuracy of prediction results, and achieve simple network structure and fewer model parameters , The effect of improving the accuracy and reliability of traffic forecasting

Pending Publication Date: 2022-01-18
SHANDONG UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Most of the existing urban fine-grained traffic forecasting methods rely on huge training data, which is not easy to obtain in practical applications
If limited data resources are used for model training, it is easy to cause overfitting, poor generalization performance, and affect the accuracy of prediction results.

Method used

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  • Urban fine-grained flow prediction method and system based on limited data resources
  • Urban fine-grained flow prediction method and system based on limited data resources
  • Urban fine-grained flow prediction method and system based on limited data resources

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Experimental program
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Effect test

Embodiment 1

[0040] Due to limited training data, previous methods are prone to overfitting and poor generalization performance. This embodiment discloses a fine-grained urban traffic forecasting method based on limited data resources, including the following steps:

[0041] Step 1: Obtain the flow distribution data and corresponding external factor data within a certain period of time in the area to be predicted.

[0042] In this embodiment, the traffic distribution data includes pedestrian distribution data, bicycle distribution data and motor vehicle distribution data in the area, which can be obtained from public websites.

[0043] In real life, traffic forecasting is inseparable from external factors (weather, temperature, holidays, etc.), for example: on rainy days, people tend to be indoors rather than outdoors. In order to make urban fine-grained flow forecasting accuracy higher, this embodiment obtains certain external factors, which include continuous features and discrete featu...

Embodiment 2

[0102] The purpose of this embodiment is to provide a city fine-grained traffic forecasting system based on limited data resources, the system includes:

[0103] The data acquisition module is used to acquire traffic distribution data within a certain period of time in the area to be predicted;

[0104] The coarse-grained flow distribution map acquisition module is used to obtain the fine-grained flow distribution map and the coarse-grained flow distribution map according to the set coarse-grained scaling factor and combined with the flow distribution data; process the external factor data to obtain the external factor flow picture;

[0105] The spatial reasoning encoder pre-training module is used to down-sample the coarse-grained traffic distribution map according to the scaling factor to obtain a down-sampled coarse-grained traffic map; training from the down-sampled coarse-grained traffic map to the coarse-grained traffic map A spatial reasoning encoder for fine-grained t...

Embodiment 3

[0110] The purpose of this embodiment is to provide an electronic device.

[0111] An electronic device includes a memory, a processor, and a computer program stored in the memory and operable on the processor. The processor implements the method described in Embodiment 1 when executing the program.

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Abstract

The invention discloses an urban fine-grained flow prediction method and system based on limited data resources. The method comprises the following steps: acquiring flow distribution data and corresponding external factor data of a to-be-predicted area within a certain time; obtaining a fine granularity flow distribution diagram and a coarse granularity flow distribution diagram according to a set coarse and fine granularity scaling factor; performing down-sampling on the coarse-grained flow distribution diagram to obtain a down-sampled coarse-grained flow diagram; and training a spatial inference encoder from a down-sampling coarse-grained flow diagram to a coarse-grained flow distribution diagram, wherein the spatial inference encoder is used for predicting the fine-grained flow of the region. Under the condition that training resources are limited, an inference network is provided to simplify the urban fine-grained traffic prediction problem, and the traffic prediction precision and reliability of the model are improved.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation, and in particular relates to a method and system for urban fine-grained traffic forecasting based on limited data resources. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] In recent years, with the advent of the era of big data, artificial intelligence technology has been widely applied to all levels of society: machine translation, intelligent customer service and smart cities. Among them, intelligent transportation is an important part of the field of smart cities. It requires urban planners to conduct overall monitoring of urban traffic and issue warnings when traffic congestion or unexpected conditions occur on urban roads. However, in order to obtain global traffic information, it is necessary to deploy a large number of sensing devic...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06N5/04G06Q50/26
CPCG06Q10/04G06Q10/067G06Q50/26G06N5/04
Inventor 宫永顺曲浩陈勐尹义龙
Owner SHANDONG UNIV
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