CN116153069B - 交通流模型与数据融合驱动的交通状态估计方法及装置 - Google Patents
交通流模型与数据融合驱动的交通状态估计方法及装置 Download PDFInfo
- Publication number
- CN116153069B CN116153069B CN202310091773.4A CN202310091773A CN116153069B CN 116153069 B CN116153069 B CN 116153069B CN 202310091773 A CN202310091773 A CN 202310091773A CN 116153069 B CN116153069 B CN 116153069B
- Authority
- CN
- China
- Prior art keywords
- space
- traffic
- model
- neural network
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 230000004927 fusion Effects 0.000 title claims abstract description 19
- 238000013528 artificial neural network Methods 0.000 claims abstract description 34
- 238000003062 neural network model Methods 0.000 claims abstract description 29
- 238000011160 research Methods 0.000 claims abstract description 25
- 238000007781 pre-processing Methods 0.000 claims abstract description 13
- 230000008569 process Effects 0.000 claims abstract description 8
- 238000012549 training Methods 0.000 claims abstract description 8
- 238000010586 diagram Methods 0.000 claims description 7
- 238000005070 sampling Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 4
- 239000000523 sample Substances 0.000 claims description 4
- 230000004913 activation Effects 0.000 claims description 3
- 238000011835 investigation Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 210000002569 neuron Anatomy 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 claims description 2
- 230000006870 function Effects 0.000 description 11
- 238000004422 calculation algorithm Methods 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 5
- 238000013499 data model Methods 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000002123 temporal effect Effects 0.000 description 2
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000006866 deterioration Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Classifications
-
- 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
- G08G1/0125—Traffic data processing
- G08G1/0133—Traffic data processing for classifying traffic situation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- 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
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Mathematical Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Software Systems (AREA)
- General Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Analytical Chemistry (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Molecular Biology (AREA)
- Evolutionary Biology (AREA)
- Chemical & Material Sciences (AREA)
- Biophysics (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Biomedical Technology (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Traffic Control Systems (AREA)
Abstract
Description
评价指标 | 本发明 | NN |
Err | 0.1739 | 0.5489 |
RMSE | 1.9027 | 6.0043 |
MAE | 3.6172 | 36.0523 |
Claims (4)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310091773.4A CN116153069B (zh) | 2023-02-09 | 2023-02-09 | 交通流模型与数据融合驱动的交通状态估计方法及装置 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202310091773.4A CN116153069B (zh) | 2023-02-09 | 2023-02-09 | 交通流模型与数据融合驱动的交通状态估计方法及装置 |
Publications (2)
Publication Number | Publication Date |
---|---|
CN116153069A CN116153069A (zh) | 2023-05-23 |
CN116153069B true CN116153069B (zh) | 2024-01-30 |
Family
ID=86353879
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202310091773.4A Active CN116153069B (zh) | 2023-02-09 | 2023-02-09 | 交通流模型与数据融合驱动的交通状态估计方法及装置 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN116153069B (zh) |
Citations (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109243172A (zh) * | 2018-07-25 | 2019-01-18 | 华南理工大学 | 基于遗传算法优化lstm神经网络的交通流预测方法 |
CN109871876A (zh) * | 2019-01-22 | 2019-06-11 | 东南大学 | 一种基于浮动车数据的高速公路路况识别与预测方法 |
WO2020010717A1 (zh) * | 2018-07-13 | 2020-01-16 | 南京理工大学 | 一种基于时空相关性的短时交通流预测方法 |
CN112100163A (zh) * | 2020-08-19 | 2020-12-18 | 北京航空航天大学 | 一种基于三维卷积神经网络的路网状态时空预测方法 |
CN112257934A (zh) * | 2020-10-26 | 2021-01-22 | 辽宁工程技术大学 | 一种基于时空动态神经网络的城市人流预测方法 |
CN112289034A (zh) * | 2020-12-29 | 2021-01-29 | 四川高路交通信息工程有限公司 | 基于多模态时空数据的深度神经网络鲁棒交通预测方法 |
CN112669606A (zh) * | 2020-12-24 | 2021-04-16 | 西安电子科技大学 | 利用动态时空图训练卷积神经网络的交通流预测方法 |
CN112950924A (zh) * | 2019-12-10 | 2021-06-11 | 东北大学秦皇岛分校 | 一种基于深度学习的复杂交通路网交通速度预测方法 |
CN113590971A (zh) * | 2021-08-13 | 2021-11-02 | 浙江大学 | 一种基于类脑时空感知表征的兴趣点推荐方法及系统 |
CN114141029A (zh) * | 2021-11-25 | 2022-03-04 | 东南大学 | 基于线下强化学习与宏观模型的匝道控制方法 |
CN114822033A (zh) * | 2022-04-24 | 2022-07-29 | 山东交通学院 | 基于特征金字塔网络的路网交通流量数据修复方法及系统 |
CN115314925A (zh) * | 2022-08-04 | 2022-11-08 | 南京智联达科技有限公司 | 一种基于gnn和lstm的智能电网融合网络流量预测方法 |
Family Cites Families (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8103435B2 (en) * | 2007-07-27 | 2012-01-24 | George Mason Intellectual Properties, Inc. | Near real-time traffic routing |
-
2023
- 2023-02-09 CN CN202310091773.4A patent/CN116153069B/zh active Active
Patent Citations (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2020010717A1 (zh) * | 2018-07-13 | 2020-01-16 | 南京理工大学 | 一种基于时空相关性的短时交通流预测方法 |
CN109243172A (zh) * | 2018-07-25 | 2019-01-18 | 华南理工大学 | 基于遗传算法优化lstm神经网络的交通流预测方法 |
CN109871876A (zh) * | 2019-01-22 | 2019-06-11 | 东南大学 | 一种基于浮动车数据的高速公路路况识别与预测方法 |
CN112950924A (zh) * | 2019-12-10 | 2021-06-11 | 东北大学秦皇岛分校 | 一种基于深度学习的复杂交通路网交通速度预测方法 |
CN112100163A (zh) * | 2020-08-19 | 2020-12-18 | 北京航空航天大学 | 一种基于三维卷积神经网络的路网状态时空预测方法 |
CN112257934A (zh) * | 2020-10-26 | 2021-01-22 | 辽宁工程技术大学 | 一种基于时空动态神经网络的城市人流预测方法 |
CN112669606A (zh) * | 2020-12-24 | 2021-04-16 | 西安电子科技大学 | 利用动态时空图训练卷积神经网络的交通流预测方法 |
CN112289034A (zh) * | 2020-12-29 | 2021-01-29 | 四川高路交通信息工程有限公司 | 基于多模态时空数据的深度神经网络鲁棒交通预测方法 |
CN113590971A (zh) * | 2021-08-13 | 2021-11-02 | 浙江大学 | 一种基于类脑时空感知表征的兴趣点推荐方法及系统 |
CN114141029A (zh) * | 2021-11-25 | 2022-03-04 | 东南大学 | 基于线下强化学习与宏观模型的匝道控制方法 |
CN114822033A (zh) * | 2022-04-24 | 2022-07-29 | 山东交通学院 | 基于特征金字塔网络的路网交通流量数据修复方法及系统 |
CN115314925A (zh) * | 2022-08-04 | 2022-11-08 | 南京智联达科技有限公司 | 一种基于gnn和lstm的智能电网融合网络流量预测方法 |
Non-Patent Citations (1)
Title |
---|
基于时空特性的交通自由流短时预测状态空间模型;董春娇;邵春福;诸葛承祥;孟梦;;土木工程学报(08);全文 * |
Also Published As
Publication number | Publication date |
---|---|
CN116153069A (zh) | 2023-05-23 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111798051B (zh) | 基于长短期记忆神经网络的空气质量时空预测方法 | |
CN109490814B (zh) | 基于深度学习和支持向量数据描述的计量自动化终端故障诊断方法 | |
CN113096388B (zh) | 一种基于梯度提升决策树的短时交通流量预测方法 | |
CN113554466B (zh) | 一种短期用电量预测模型构建方法、预测方法和装置 | |
CN114220271A (zh) | 基于动态时空图卷积循环网络的交通流预测方法、设备及存储介质 | |
CN117494034A (zh) | 基于交通拥堵指数和多源数据融合的空气质量预测方法 | |
CN111815806A (zh) | 一种基于野值剔除和特征提取的飞参数据预处理方法 | |
CN115827335B (zh) | 基于模态交叉方法的时序数据缺失插补系统以及插补方法 | |
CN113435658B (zh) | 一种基于时空融合相关性和注意力机制的交通流预测方法 | |
CN104599500A (zh) | 基于灰熵分析和改进贝叶斯融合的交通流预测方法 | |
CN114596726B (zh) | 基于可解释时空注意力机制的停车泊位预测方法 | |
CN115392554A (zh) | 基于深度图神经网络和环境融合的轨道客流预测方法 | |
CN114912666A (zh) | 一种基于ceemdan算法和注意力机制的短时客流量预测方法 | |
CN114510778A (zh) | 基于混合智能优化lstm的轨道不平顺预测方法 | |
CN118277770A (zh) | 一种障碍物感知方法、装置、电子设备及存储介质 | |
CN114611814A (zh) | 聚合多尺度时空相似信息的城市交通流预测方法 | |
CN116913088A (zh) | 一种用于高速公路的智能流量预测方法 | |
CN113947904A (zh) | 基于s-g滤波和深信度网络的多尺度短时交通流预测方法 | |
CN118116194A (zh) | 一种基于自适应动态时空图卷积网络的交通状态预测方法 | |
CN116153069B (zh) | 交通流模型与数据融合驱动的交通状态估计方法及装置 | |
CN115348182B (zh) | 一种基于深度堆栈自编码器的长期频谱预测方法 | |
CN113408191A (zh) | 一种基于图自监督学习的pm2.5预测方法及存储介质 | |
CN117584792B (zh) | 一种电动汽车充电站充电功率在线预测方法及系统 | |
CN117456738B (zh) | 一种基于etc门架数据的高速公路交通量预测方法 | |
CN114758311B (zh) | 一种基于异质特征融合的交通流量预测方法及系统 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
CB03 | Change of inventor or designer information | ||
CB03 | Change of inventor or designer information |
Inventor after: Ding Fan Inventor after: Li Jinyu Inventor after: Tan Huachun Inventor after: Peng Jiankun Inventor after: Han Yu Inventor after: Yang Yang Inventor before: Li Jinyu Inventor before: Ding Fan Inventor before: Tan Huachun Inventor before: Peng Jiankun Inventor before: Han Yu Inventor before: Yang Yang |
|
GR01 | Patent grant | ||
GR01 | Patent grant |