CN116187602B - Parking space occupation prediction method for parking lot - Google Patents

Parking space occupation prediction method for parking lot Download PDF

Info

Publication number
CN116187602B
CN116187602B CN202310490841.4A CN202310490841A CN116187602B CN 116187602 B CN116187602 B CN 116187602B CN 202310490841 A CN202310490841 A CN 202310490841A CN 116187602 B CN116187602 B CN 116187602B
Authority
CN
China
Prior art keywords
parking
data
parking lot
space occupation
parking space
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
Application number
CN202310490841.4A
Other languages
Chinese (zh)
Other versions
CN116187602A (en
Inventor
刘寒松
王永
王国强
刘瑞
谭连盛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sonli Holdings Group Co Ltd
Original Assignee
Sonli Holdings Group Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Sonli Holdings Group Co Ltd filed Critical Sonli Holdings Group Co Ltd
Priority to CN202310490841.4A priority Critical patent/CN116187602B/en
Publication of CN116187602A publication Critical patent/CN116187602A/en
Application granted granted Critical
Publication of CN116187602B publication Critical patent/CN116187602B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06Q50/40—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G06V20/586—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of parking space
    • 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

Abstract

The invention belongs to the technical field of traffic, and relates to a parking space occupation prediction method for a parking lot, which comprises the steps of firstly collecting parking data of the parking lot; training a parking space occupation prediction model according to the collected parking data; finally, inputting the current parking space data into a trained parking space occupation prediction model to obtain a parking space occupation prediction interval, returning the result to a user through a corresponding APP, modeling the parking space structure data, and capturing potential relations between different areas through a GCN and an attention mechanism; and the parking space occupation prediction is carried out through the improved TFT, the relation between different types of inputs is captured, the final prediction interval is output for a user to make a decision, the improved TFT can accept and distinguish various heterogeneous inputs, meanwhile, the inputs are comprehensively utilized, long-distance dependence can be captured, and a vehicle owner can judge the prediction confidence according to the prediction interval, so that whether the vehicle owner goes to a target parking lot or not is determined.

Description

Parking space occupation prediction method for parking lot
Technical Field
The invention belongs to the technical field of traffic, and relates to a parking lot parking space occupation prediction method, in particular to a parking lot parking space occupation prediction method based on Temporal Fusion Transformer (a transform-based deep neural network, called TFT for short) and GCN (graph convolution neural network).
Background
With the development of economy and the increase of urban population, vehicle data in cities are rapidly increased, and with the rapid increase of demand for parking spaces, people hope to find free parking spaces accurately and rapidly. However, the current parking data of the current parking lot is not opened to the public, and the parking space occupation information has timeliness, so that even if an idle parking space exists currently, the idle parking space still exists after the vehicle arrives, and the time is wasted; in addition, because the parking space occupation information is not easy to obtain and has uncertainty, partial idle parking spaces cannot be well utilized.
For the above problems, parking space occupation prediction is a mainstream solution. For a parking lot, the prediction data of the parking space occupation condition of each area of the parking lot in a certain time period (such as 15 minutes) are provided for a user in real time, the user can orderly arrange the travel and determine the parking position, so that the time for the user to find the parking space is reduced, and the utilization rate of the parking space of the parking lot is improved. A good parking space occupation prediction method can provide great convenience for vehicle owners and parking lots. Parking space occupation algorithms generally predict the occupation situation of a parking lot within a certain period of time by modeling information such as historical parking data, current parking data (current occupation situation, surrounding vehicle situation of the parking lot) and other auxiliary information (such as current date).
The existing parking space occupation prediction methods mainly comprise two types, namely a statistical-based method, the method generally adopts a regression idea, a parking space occupation curve is fitted by modeling historical data, so that the prediction of future data is realized, but the prediction step length of the method is limited, long-time dependency cannot be captured, and meanwhile, when the number of variables increases, the prediction precision of the methods is poor; the other type is a deep learning-based method, which generally uses a deep neural network as a main network (such as an LSTM) and uses historical parking data as input to predict the parking space occupation situation in a certain step length, but cannot model the data well, and directly splice the historical data as input, so that the obtained prediction model cannot capture the deep dependency relationship among various types of data, and meanwhile, the effect is further improved when facing long-distance dependence.
Disclosure of Invention
In order to solve the problems, the invention provides a novel parking space occupation prediction method of a parking lot, and the GCN is used for modeling the dependency relationship between each floor and each region of the parking lot, so that deep connection among different types of data is captured, and prediction of each floor and each region is facilitated; temporal Fusion Transformer is adopted to accept different types of input information, meanwhile, the output is a range value, and in practical application, one range value shows the current parking space condition to a user, so that the user can conveniently schedule.
In order to achieve the above purpose, the specific process of the invention for realizing the parking space occupation prediction is as follows:
s1, collecting parking data of a parking lot;
s2, training a parking space occupation prediction model according to the parking data collected in the step S1;
s3, inputting the current parking space data into a trained parking space occupation prediction model to obtain a parking space occupation prediction interval, and returning the result to the user through the corresponding APP.
As a further technical scheme of the present invention, the parking data in step S1 includes a parking lot structure, historical parking data, current parking data, road conditions around the parking lot and auxiliary information; wherein the parking lot structure comprises a parking lot, an adjacent relation among parking buildings and an adjacent relation among parking areas; the historical parking data contains the number of parks in each area at a certain moment in the history; the current parking data is the parking quantity of each area in the current time period; the historical parking data and the current parking data form parking data of a parking lot; the road vehicles around the parking lot are data of vehicles nearby the parking lot; the auxiliary information includes date information and holiday information.
As a further technical scheme of the invention, the specific process of the step S2 is as follows:
step S21, pair S1 collect
Figure SMS_1
Preprocessing parking data of a parking lot, vehicle data nearby the parking lot, date and holiday data at each moment to obtain each timeTime->
Figure SMS_2
Feature vector +.>
Figure SMS_3
，/>
Figure SMS_4
As the dimension of the feature vector,
Figure SMS_5
；
step S22, embedding the parking lot structure data through the GCN to obtain each moment
Figure SMS_6
Parking lot structure embedded feature->
Figure SMS_7
；
Step S23, each time is set
Figure SMS_8
Parking lot structure embedded feature->
Figure SMS_9
Along with the feature vector->
Figure SMS_10
And training a parking space occupation prediction model as input data. As a further technical scheme of the invention, the specific process of the step S22 is as follows:
taking one floor of the parking building as a basic unit, wherein the outdoor parking lot is treated as one floor and is shared by the parking buildings
Figure SMS_25
Layer, time->
Figure SMS_13
The parking area structure of each layer can be modeled as a graph structure +.>
Figure SMS_19
，/>
Figure SMS_23
Wherein->
Figure SMS_27
Represents a set of parking areas, let->
Figure SMS_26
Layer commonness->
Figure SMS_29
Each region is defined as +.>
Figure SMS_16
Is a vertex of (1)
Figure SMS_28
Wherein->
Figure SMS_11
Represents the parking lot->
Figure SMS_17
All areas of the layer;>
Figure SMS_14
representation->
Figure SMS_18
Positional relationship between individual parking areas at the moment, wherein ∈ ->
Figure SMS_15
Is area->
Figure SMS_20
The weights between the two are initialized according to the following rules: if->
Figure SMS_12
And->
Figure SMS_22
Adjacent, then->
Figure SMS_21
On the contrary, the->
Figure SMS_24
；
By passing through
Figure SMS_34
Calculating the relation weight between each parking area according to the parking quantity of each area at the moment, and setting +.>
Figure SMS_30
Time parking area->
Figure SMS_38
Is +.>
Figure SMS_32
Definitions->
Figure SMS_36
Difference vector from other region->
Figure SMS_41
In the formula +.>
Figure SMS_44
Represents->
Figure SMS_43
Number of layer areas>
Figure SMS_46
Using disparity vector->
Figure SMS_33
Derived from self-attention mechanisms
Figure SMS_37
Weight between other nodes->
Figure SMS_35
Update by the weight +.>
Figure SMS_39
Middle->
Figure SMS_42
Weights between regions
Figure SMS_45
Wherein->
Figure SMS_31
Is->
Figure SMS_40
Weights in between;
map after updating
Figure SMS_47
Input into GCN to get->
Figure SMS_48
Time->
Figure SMS_49
Embedded features of layers->
Figure SMS_50
Calculating attention weights between different layers using a self-attention mechanism>
Figure SMS_51
Obtain->
Figure SMS_52
Parking lot structure embedded feature->
Figure SMS_53
。
As a further technical scheme of the invention, the specific process of the step S23 is as follows:
will each time instant
Figure SMS_54
Feature vector +.>
Figure SMS_55
Embedding features with corresponding parking structures>
Figure SMS_56
Integration to obtain input feature vector->
Figure SMS_57
；
The parking space occupation prediction model adopts a deep neural network based on a transducer as a main network, and inputs characteristic vectors
Figure SMS_58
Inputting a parking space occupation prediction model, and obtaining a feature map ++using 1×1 convolution on the features passing through the transform decoder>
Figure SMS_59
Wherein->
Figure SMS_60
Representing the total number of parking floors>
Figure SMS_61
And->
Figure SMS_62
Height and width of the feature map; for->
Figure SMS_63
And (3) flattening the characteristics of the channel, obtaining a final prediction interval through a linear layer, and finally iterating 100 times by using the MSE loss function as a loss function training model.
Compared with the prior art, the invention has the following advantages:
(1) Modeling the parking lot structure data, and capturing potential links between different areas through the GCN and an attention mechanism;
(2) The parking space occupation prediction is carried out through the improved TFT, the relation among different types of inputs is captured, the final prediction interval is output for a user to make a decision, the improved TFT can accept and distinguish various heterogeneous inputs, meanwhile, the inputs are comprehensively utilized, long-distance dependence can be captured, and a vehicle owner can judge the prediction confidence according to the prediction interval, so that whether the vehicle owner goes to a target parking lot or not is determined.
Drawings
FIG. 1 is a block diagram of a workflow for implementing parking space occupancy prediction in accordance with the present invention.
Fig. 2 is a flow chart of the parking lot structure embedding according to the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings.
Examples:
as shown in fig. 1 and 2, the parking space occupation prediction in this embodiment specifically includes the following steps:
s1, collecting parking data of a parking lot, wherein the parking data comprise a parking lot structure, historical parking data, current parking data, road vehicle conditions around the parking lot and auxiliary information; wherein the parking lot structure comprises a parking lot, an adjacent relation among parking buildings and an adjacent relation among parking areas; the historical parking data contains the number of parks in each area at a certain moment in the history; the current parking data is the parking quantity of each area in the current time period (such as 15 minutes); the historical parking data and the current parking data form parking data of a parking lot; the road vehicles around the parking lot are data of vehicles nearby the parking lot; the auxiliary information comprises date information and holiday information;
s2, training a parking space occupation prediction model according to the parking data collected in the step S1; the specific process is as follows:
step S21, pair S1 collect
Figure SMS_64
Parking data at each moment (including +/each moment>
Figure SMS_65
Parking data of the parking lot, the number of vehicles nearby the parking lot, and date and holiday) to obtain feature vector +.>
Figure SMS_66
，/>
Figure SMS_67
For the dimension of the feature vector, +.>
Figure SMS_68
；
Step S22, embedding the parking lot structure data through the GCN to obtain each moment
Figure SMS_69
Parking lot structure embedded feature->
Figure SMS_70
；
Taking one floor of the parking building as a basic unit, wherein the outdoor parking lot is treated as one floor and is shared by the parking buildings
Figure SMS_86
Layer, time->
Figure SMS_72
The parking area structure of each layer can be modeled as a graph structure +.>
Figure SMS_78
，/>
Figure SMS_84
Wherein->
Figure SMS_89
Represents a set of parking areas, let->
Figure SMS_85
Layer commonness->
Figure SMS_90
Each region is defined as +.>
Figure SMS_83
Is a vertex of (1)
Figure SMS_88
Wherein->
Figure SMS_76
Represents the parking lot->
Figure SMS_81
All areas of the layer;>
Figure SMS_73
representation->
Figure SMS_77
Positional relationship between individual parking areas at the moment, wherein ∈ ->
Figure SMS_75
Is area->
Figure SMS_79
The weights between the two are initialized according to the following rules: if->
Figure SMS_74
And->
Figure SMS_82
Adjacent, then->
Figure SMS_87
On the contrary, the->
Figure SMS_91
. In the next step, the method will go through +.>
Figure SMS_71
Time parking data update ∈>
Figure SMS_80
；
By passing through
Figure SMS_104
Calculating the relation weight between each parking area according to the parking quantity of each area at the moment, and setting +.>
Figure SMS_93
Time parking area->
Figure SMS_99
Is +.>
Figure SMS_97
Definitions->
Figure SMS_101
Difference vector from other region->
Figure SMS_105
In the formula +.>
Figure SMS_108
Represents->
Figure SMS_96
Number of layer areas>
Figure SMS_98
Using disparity vector->
Figure SMS_92
Derived from self-attention mechanisms
Figure SMS_103
Weight between other nodes->
Figure SMS_94
Update by the weight +.>
Figure SMS_100
Middle->
Figure SMS_106
Weights between regions
Figure SMS_107
Wherein->
Figure SMS_95
Is->
Figure SMS_102
Weight of the two, inInitializing according to a certain rule in the last step;
map after updating
Figure SMS_109
Input into GCN to get->
Figure SMS_110
Time->
Figure SMS_111
Embedded features of layers->
Figure SMS_112
Calculating attention weights between different layers using a self-attention mechanism>
Figure SMS_113
Obtain->
Figure SMS_114
Parking lot structure embedded feature->
Figure SMS_115
；
S23, using a depth neural network based on a transducer as a backbone network to input feature vectors in a parking space occupation prediction model
Figure SMS_116
Inputting a parking space occupation prediction model, and obtaining a feature map ++using 1×1 convolution on the features passing through the transform decoder>
Figure SMS_117
Wherein->
Figure SMS_118
Representing the total number of parking floors>
Figure SMS_119
And->
Figure SMS_120
Height and width of the feature map; for->
Figure SMS_121
The characteristics of each channel (representing the characteristics of each floor) of the channel are flattened, a final prediction interval is obtained through a linear layer, and finally an MSE loss function (mean square loss function) is used as a loss function training model to iterate 100 times, so that a trained parking space occupation prediction model is obtained;
s3, inputting the current parking space data into a trained parking space occupation prediction model to obtain a parking space occupation prediction interval, and returning the result to the user through the corresponding APP.
Algorithms, network structures, and computational processes not described in detail herein are all general techniques in the art.
It should be noted that the purpose of the disclosed embodiments is to aid further understanding of the present invention, but those skilled in the art will appreciate that: various alternatives and modifications are possible without departing from the spirit and scope of the invention and the appended claims, and therefore the invention should not be limited to the embodiments disclosed, but rather the scope of the invention is defined by the appended claims.

Claims (1)

1. A parking space occupation prediction method for a parking lot is characterized by comprising the following specific steps:
s1, collecting parking data of a parking lot; the parking data comprise a parking lot structure, historical parking data, current parking data, road vehicle conditions around a parking lot and auxiliary information; wherein the parking lot structure comprises a parking lot, an adjacent relation among parking buildings and an adjacent relation among parking areas; the historical parking data contains the number of parks in each area at a certain moment in the history; the current parking data is the parking quantity of each area in the current time period; the historical parking data and the current parking data form parking data of a parking lot; the road vehicles around the parking lot are data of vehicles nearby the parking lot; the auxiliary information comprises date information and holiday information;
s2, training a parking space occupation prediction model according to the parking data collected in the step S1, wherein the parking space occupation prediction model specifically comprises the following steps:
step S21, pair S1 collect
Figure QLYQS_1
Preprocessing parking data of a parking lot, vehicle data nearby the parking lot, date and holiday data at each moment to obtain +.>
Figure QLYQS_2
Feature vector +.>
Figure QLYQS_3
，/>
Figure QLYQS_4
For the dimension of the feature vector, +.>
Figure QLYQS_5
；
Step S22, embedding the parking lot structure data through the GCN to obtain each moment
Figure QLYQS_6
Parking lot structure embedded features of (a)
Figure QLYQS_7
The method specifically comprises the following steps:
taking one floor of the parking building as a basic unit, wherein the outdoor parking lot is treated as one floor and is shared by the parking buildings
Figure QLYQS_13
Layer, time->
Figure QLYQS_9
The parking area structure of each layer can be modeled as a graph structure +.>
Figure QLYQS_15
，/>
Figure QLYQS_11
Wherein->
Figure QLYQS_17
Represents a set of parking areas, let->
Figure QLYQS_22
Layer commonness->
Figure QLYQS_26
Each region is defined as +.>
Figure QLYQS_20
Is->
Figure QLYQS_24
Wherein->
Figure QLYQS_8
Represents the parking lot->
Figure QLYQS_14
All areas of the layer;>
Figure QLYQS_18
representation->
Figure QLYQS_23
Positional relationship between individual parking areas at the moment, wherein ∈ ->
Figure QLYQS_21
Is area->
Figure QLYQS_25
The weights between the two are initialized according to the following rules: if->
Figure QLYQS_10
And->
Figure QLYQS_19
Adjacent, then->
Figure QLYQS_12
On the contrary, the->
Figure QLYQS_16
；
By passing through
Figure QLYQS_39
Calculating the relation weight between each parking area according to the parking quantity of each area at the moment, and setting +.>
Figure QLYQS_28
Time parking area->
Figure QLYQS_34
Is +.>
Figure QLYQS_31
Definitions->
Figure QLYQS_37
Difference vector from other region->
Figure QLYQS_32
In the formula +.>
Figure QLYQS_38
Represents->
Figure QLYQS_41
Number of layer areas>
Figure QLYQS_43
Using disparity vector->
Figure QLYQS_27
Is associated with self-attention mechanism>
Figure QLYQS_36
Weight between other nodes->
Figure QLYQS_30
Update by the weight +.>
Figure QLYQS_35
Middle->
Figure QLYQS_40
Weight between regions->
Figure QLYQS_42
Wherein->
Figure QLYQS_29
Is->
Figure QLYQS_33
Weights in between;
map after updating
Figure QLYQS_44
Input into GCN to get->
Figure QLYQS_45
Time->
Figure QLYQS_46
Embedded features of layers->
Figure QLYQS_47
Calculating attention weights between different layers using a self-attention mechanism>
Figure QLYQS_48
Obtain->
Figure QLYQS_49
Parking lot structure embedded feature->
Figure QLYQS_50
；
Step S23, each time is set
Figure QLYQS_51
Parking lot structure embedded feature->
Figure QLYQS_52
Along with the feature vector->
Figure QLYQS_53
Training a parking space occupation prediction model as input data; the method comprises the following steps:
will each time instant
Figure QLYQS_54
Feature vector +.>
Figure QLYQS_55
Embedding features with corresponding parking structures>
Figure QLYQS_56
Integration to obtain input feature vector->
Figure QLYQS_57
；
The parking space occupation prediction model adopts a deep neural network based on a transducer as a main network, and inputs characteristic vectors
Figure QLYQS_58
Inputting a parking space occupation prediction model, and obtaining a feature map ++using 1×1 convolution on the features passing through the transform decoder>
Figure QLYQS_59
Wherein->
Figure QLYQS_60
Representing the total number of parking floors>
Figure QLYQS_61
And->
Figure QLYQS_62
Height and width of the feature map; for->
Figure QLYQS_63
After flattening the characteristics of the channel, obtaining a final prediction interval through a linear layer, and finally iterating 100 times by using an MSE loss function as a loss function training model;
s3, inputting the current parking space data into a trained parking space occupation prediction model to obtain a parking space occupation prediction interval, and returning the result to the user through the corresponding APP.
CN202310490841.4A 2023-05-05 2023-05-05 Parking space occupation prediction method for parking lot Active CN116187602B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202310490841.4A CN116187602B (en) 2023-05-05 2023-05-05 Parking space occupation prediction method for parking lot

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310490841.4A CN116187602B (en) 2023-05-05 2023-05-05 Parking space occupation prediction method for parking lot

Publications (2)

Publication Number Publication Date
CN116187602A CN116187602A (en) 2023-05-30
CN116187602B true CN116187602B (en) 2023-07-14

Family

ID=86436872

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202310490841.4A Active CN116187602B (en) 2023-05-05 2023-05-05 Parking space occupation prediction method for parking lot

Country Status (1)

Country Link
CN (1) CN116187602B (en)

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115223132B (en) * 2021-11-10 2023-10-27 广州汽车集团股份有限公司 Empty space recognition method and system and computer readable storage medium
CN114802296A (en) * 2022-03-17 2022-07-29 浙江工业大学 Vehicle track prediction method based on dynamic interaction graph convolution
CN115223019B (en) * 2022-07-14 2023-05-19 清华大学 All-day parking space detection method based on fusion of camera and laser radar
CN115643285A (en) * 2022-10-14 2023-01-24 成都秦川物联网科技股份有限公司 Smart city parking lot recommendation method, internet of things system, device and storage medium
CN115985132A (en) * 2022-12-29 2023-04-18 浙大城市学院 Roadside parking availability prediction method based on graph neural network

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于卷积神经网络的环视车位检测研究;杨科;张效宇;徐维庆;;时代汽车(04);第1-7页 *

Also Published As

Publication number Publication date
CN116187602A (en) 2023-05-30

Similar Documents

Publication Publication Date Title
CN110991713B (en) Irregular area flow prediction method based on multi-graph convolution sum GRU
CN113724504B (en) Urban area traffic prediction system and method oriented to vehicle track big data
CN110047291B (en) Short-term traffic flow prediction method considering diffusion process
CN111311122B (en) Charging station construction method for smart city
US20240054321A1 (en) Traffic prediction
CN111242292B (en) OD data prediction method and system based on deep space-time network
CN106017496A (en) Real-time navigation method based on road condition
CN112863182B (en) Cross-modal data prediction method based on transfer learning
CN111242395B (en) Method and device for constructing prediction model for OD (origin-destination) data
JP2021182408A (en) Method for predicting water level in sewage pipe sewer
US20230057564A1 (en) Methods for managing cleaning routes in smart cities, internet of things systems, and storage mediums
CN109543895A (en) A kind of transit network planning method out based on taxi passenger flow conversion
CN116913088A (en) Intelligent flow prediction method for expressway
CN116187602B (en) Parking space occupation prediction method for parking lot
WO2023276420A1 (en) Distributed multi-task machine learning for traffic prediction
CN113837148A (en) Pedestrian trajectory prediction method based on self-adjusting sparse graph transform
CN116089744B (en) Hospital parking lot recommendation method based on transform dynamic time-space association
CN113327417A (en) Traffic flow prediction method based on 3D dynamic space-time residual convolution associated network
CN115762147B (en) Traffic flow prediction method based on self-adaptive graph meaning neural network
Ara et al. Ride Hailing Service Demand Forecast by Integrating Convolutional and Recurrent Neural Networks.
CN115660157A (en) Smart city charging pile construction planning method, internet of things system, device and medium
CN114139773A (en) Public traffic flow prediction method based on space-time graph convolutional network
CN117252404B (en) Method and system for measuring and calculating population and post scale of city updating unit
CN111242394B (en) Method and system for extracting spatial correlation characteristics
Ye et al. Urban Traffic Dynamic OD Prediction Based on Multi-Source Data

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
GR01 Patent grant
GR01 Patent grant