LU500511B1 - Method for short-term traffic flow prediction based on cnn-lstm-at neural network - Google Patents

Method for short-term traffic flow prediction based on cnn-lstm-at neural network

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Publication number
LU500511B1
LU500511B1 LU500511A LU500511A LU500511B1 LU 500511 B1 LU500511 B1 LU 500511B1 LU 500511 A LU500511 A LU 500511A LU 500511 A LU500511 A LU 500511A LU 500511 B1 LU500511 B1 LU 500511B1
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Luxembourg
Prior art keywords
traffic flow
cnn
short
lstm
neural network
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LU500511A
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Yun Zhao
Chengxing Liu
Xing Xu
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Univ Zhejiang Sience & Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0633—Workflow analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
    • G06Q30/0635—Processing of requisition or of purchase orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0645—Rental transactions; Leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/048—Activation functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

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  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Strategic Management (AREA)
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  • Human Resources & Organizations (AREA)
  • General Health & Medical Sciences (AREA)
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  • Evolutionary Computation (AREA)
  • Biomedical Technology (AREA)
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  • Software Systems (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • Data Mining & Analysis (AREA)
  • Development Economics (AREA)
  • Computational Linguistics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Biophysics (AREA)
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  • Entrepreneurship & Innovation (AREA)
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  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
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  • Primary Health Care (AREA)
  • Educational Administration (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The present invention provides a method for short-term traffic flow prediction based on a convolutional neural network (CNN)-long-short-term memory (LSTM)-attention (At) neural network. By taking taxi data in a region as a representative of a traffic flow, the method includes: dividing the region into grids, and defining a traffic flow in a time span as a quantity of orders of taxis arriving at and/or leaving the grids in the time span, where an order of a taxi is represented by using a matrix; extracting an adjacent matrix by using a sliding window, to constitute a data set, and performing a convolution operation by using a CNN; and inputting an output result of the convolution operation into an LSTM network, adding an At mechanism to an output result, finding a most desired time step, and performing full sample connection and integer transform to generate a prediction result matrix.
LU500511A 2021-05-08 2021-08-05 Method for short-term traffic flow prediction based on cnn-lstm-at neural network LU500511B1 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110498124.7A CN113222239B (en) 2021-05-08 2021-05-08 Short-time traffic flow prediction method based on CNN-LSTM-At neural network

Publications (1)

Publication Number Publication Date
LU500511B1 true LU500511B1 (en) 2022-02-07

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LU500511A LU500511B1 (en) 2021-05-08 2021-08-05 Method for short-term traffic flow prediction based on cnn-lstm-at neural network

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LU (1) LU500511B1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115442253A (en) * 2022-09-15 2022-12-06 西安电子科技大学 Network flow prediction method using attention mechanism

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113643066A (en) * 2021-08-16 2021-11-12 京东城市(北京)数字科技有限公司 Passenger flow inference model training method and passenger flow inference method and device
CN115240418B (en) * 2022-07-20 2023-07-25 浙江科技学院 Short-time traffic flow prediction method based on causal gate control-low-pass graph convolution network
CN116110234B (en) * 2023-04-11 2023-07-14 城云科技（中国）有限公司 Traffic flow prediction method and device based on artificial intelligence and application of traffic flow prediction method and device
CN118116200B (en) * 2024-03-27 2024-09-17 山东高速集团有限公司 Traffic flow prediction method and system based on CNN-LSTM-At

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7228151B2 (en) * 2018-03-26 2023-02-24 東日本高速道路株式会社 Traffic congestion prediction system, traffic congestion prediction method, learning device, prediction device, program, and learned model
CN109242140A (en) * 2018-07-24 2019-01-18 浙江工业大学 A kind of traffic flow forecasting method based on LSTM_Attention network
GB2593180A (en) * 2020-03-17 2021-09-22 Univ Court Univ Of Edinburgh A distributed network traffic data decomposition method
CN112532439B (en) * 2020-11-24 2022-08-23 山东科技大学 Network flow prediction method based on attention multi-component space-time cross-domain neural network model
CN112668694A (en) * 2020-12-21 2021-04-16 山东大学 Regional flow prediction method based on deep learning

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115442253A (en) * 2022-09-15 2022-12-06 西安电子科技大学 Network flow prediction method using attention mechanism
CN115442253B (en) * 2022-09-15 2023-07-18 西安电子科技大学 Network flow prediction method utilizing attention mechanism

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Publication number Publication date
CN113222239A (en) 2021-08-06
CN113222239B (en) 2022-07-01

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