CN107248317A - A kind of empty vehicle parking position of parking lot number Forecasting Methodology based on Vehicle License Plate Recognition System - Google Patents
A kind of empty vehicle parking position of parking lot number Forecasting Methodology based on Vehicle License Plate Recognition System Download PDFInfo
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- CN107248317A CN107248317A CN201710621642.7A CN201710621642A CN107248317A CN 107248317 A CN107248317 A CN 107248317A CN 201710621642 A CN201710621642 A CN 201710621642A CN 107248317 A CN107248317 A CN 107248317A
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- vehicle
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
Abstract
The invention discloses a kind of parking position Forecasting Methodology based on Vehicle License Plate Recognition System, including：Step 1, the accurate history data for obtaining parking lot vehicle number on the scene：Obtain parking lot gateway data set, the valid data of outlet data set are extracted based on outlet data collection, valid data based on outlet data collection extract the valid data of entry data collection, and the valid data based on gateway data set obtain the accurate history data of parking lot vehicle number on the scene；Step 2, the accurate history data prediction empty vehicle parking position of parking lot number obtained according to step 1.The present invention eliminates the interference to parking lot historical data such as manual intervention, complex environment, obtains the accurate historical data in parking lot.
Description
Technical field
The present invention relates to parking stall electric powder prediction, more particularly to a kind of parking position based on Vehicle License Plate Recognition System are pre-
Survey method.
Background technology
Obtaining the method for traditional empty vehicle parking position of parking lot number mainly has：1st, pacify on each (or multiple) parking stall in parking lot
A detection device (such as video camera, earth inductor, optoelectronic switch) is filled, vacant parking stall is obtained by these detection devices
Number；2nd, swiped the card according to the gateway in parking lot and record the vacant parking stall number for obtaining parking lot.Both approaches can relatively be defined
The vacant parking stall number in true acquisition parking lot, but purchase of equipment is required for, increase operation cost.
For both not installing detection device on parking stall, card-punching system is not installed yet in gateway, only in gateway
For the intelligent parking lot for installing Vehicle License Plate Recognition System, vehicle number typically has two methods in statistics parking lot：1st, artificial statistics：
At regular intervals by manually counting the quantity of vehicle or empty parking space in parking lot by certain mode, now parking lot is obtained
Interior vacant parking stall number；2nd, the information of vehicles recorded by the Vehicle License Plate Recognition System of parking lot gateway obtains to add and subtract calculating
Field vehicle number, thus obtains vacant parking stall number in parking lot.When parking stall is less in parking lot, first method has certain
Feasibility, the method can obtain the accurate history parking stall number in parking lot, but when parking stall is more in parking lot, this method work
Make quantitative change big, make the reduction of its feasibility, and the longer parking stall number on the scene that can not be obtained all the time of the method time interval, because
This historical data that accurate residue parking stall number in parking lot is obtained with this method is extremely difficult.Second method can be obtained
All the time parking stall number on the scene, but be due to the complex environment of parking lot gateway, and manual intervention etc. can be to obtaining standard
True vehicle number on the scene is interfered.
The content of the invention
Weak point present in regarding to the issue above, it is empty that the present invention provides a kind of parking lot based on Vehicle License Plate Recognition System
Remaining parking stall number Forecasting Methodology.
To achieve the above object, the present invention provides a kind of parking position Forecasting Methodology based on Vehicle License Plate Recognition System, bag
Include：
Step 1, the accurate history data for obtaining parking lot vehicle number on the scene：Step 11, based at gateway car plate know
Other system obtains Entrance data set and outlet data collection；
Step 12, the invalid data in the non-recording information of vehicles of outlet data concentration deletion, obtain having for outlet data collection
Imitate data；The valid data of the outlet data collection include information of vehicles and Outlet time；
Step 13, the valid data based on outlet data collection obtain the valid data of entry data collection, the entry data
The valid data of collection include information of vehicles and entry time：
The valid data of outlet data collection are matched with entry data collection, if successful matching, according to successful matching
The entry time and Outlet time of vehicle obtain parking duration, and when obtaining the parking of successful matching vehicle using least square method
Long distribution curve；If pairing failure, according to the Outlet time of pairing failure vehicle and the parking duration point of successful matching vehicle
Cloth curve obtains the entry time of pairing failure vehicle；
Step 14, the valid data based on gateway data set obtain the accurate history data of parking lot vehicle number on the scene；
Step 2, the accurate history data prediction empty vehicle parking position of parking lot number obtained according to step 1：
The vehicle number on the scene in certain parking lot is first predicted with neutral net according to the accurate history data, then by total at moment
The vehicle number on the scene that parking stall number subtracts this moment is just now carved vacant parking stall number.
As a further improvement on the present invention, in step 2：
The neutral net is radial base neural net.
As a further improvement on the present invention, in addition to：
Step 3, the number for calculating " corpse car " in parking lot：
" corpse car " to park in parking lot, not going out the vehicle in parking lot always, at obtained parking lot each moment
After vehicle number on the scene, maximum vehicle number on the scene is calculated, the maximum vehicle number on the scene is the maximum effectively parking stall number in parking lot；If
The maximum effectively parking stall number in the parking lot is equal with total space number, then the parking lot does not have " corpse car "；If the parking lot is most
Big effectively parking stall number, which is less than when total space number and the parking lot vehicle number are maximum effectively parking stall number, reaches saturation, then the parking lot
Total space number subtract the parking lot maximum effectively parking stall number be " corpse car " in parking lot number.
Compared with prior art, beneficial effects of the present invention are：
The present invention eliminates the interference to parking lot historical data such as manual intervention, complex environment, obtains parking lot accurate
Historical data, on this basis vacant parking stall number is predicted can obtain and accurately predicted the outcome.
Brief description of the drawings
Fig. 1 is the flow of the parking position Forecasting Methodology based on Vehicle License Plate Recognition System disclosed in an embodiment of the present invention
Figure；
Fig. 2 is the partial history data for the accurate vehicle number of certain community parking field that the Forecasting Methodology based on the present invention is obtained
Figure；
Fig. 3 is one week training pattern figure of certain community parking field.
Embodiment
To make the purpose, technical scheme and advantage of the embodiment of the present invention clearer, below in conjunction with the embodiment of the present invention
In accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is
A part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, ordinary skill people
The every other embodiment that member is obtained on the premise of creative work is not made, belongs to the scope of protection of the invention.
The present invention is described in further detail below in conjunction with the accompanying drawings：
As shown in figure 1, the present invention provides a kind of parking position Forecasting Methodology based on Vehicle License Plate Recognition System, including：
S1, the accurate history data for obtaining parking lot vehicle number on the scene；
S2, the accurate history data prediction empty vehicle parking position of parking lot number obtained according to S1.
Wherein：S1 is specifically included：
S11, Entrance data set and outlet data collection, entrance number obtained based on the Vehicle License Plate Recognition System at gateway
Include information of vehicles, entry time and other invalid datas according to collection, outlet data collection includes information of vehicles, Outlet time and other
Invalid data：
The vehicle data for being recorded Entrance based on the Vehicle License Plate Recognition System present invention at gateway is designated as parking lot
Entry data collection, EXIT data set is designated as by the vehicle data that EXIT is recorded；In Entrance data set
There are a variety of situations, including：(a) vehicle snapshot is marched into the arena, and (b) vehicle snapshot is not marched into the arena, and (c) manually lets pass, and (d) non-vehicle is captured
Deng；EXIT data set includes：(a) vehicle for the first time capture appear on the scene, (b) repeat capture (for the first time recognize mistake or its
His reason does not appear on the scene take pictures again after appear on the scene), (c) car plate None- identified or identification mistake, knowledge of (d) vehicle in entrance and exit
Other result is inconsistent or entrance leakage is clapped, (e) non-vehicle capture (capture such as artificial triggering, it is mobile it is on duty take pictures, such data
The information of vehicles such as car plate, vehicle characteristics are not recorded, are invalid data) etc..
S12, the valid data based on outlet data collection extraction outlet data set：Concentrate to delete in outlet data and do not record car
The invalid data of information is the valid data of outlet data collection：
In statistics parking lot during history vehicle number on the scene, by no record car plate, vehicle in Entrance and outlet
The invalid data of the information of vehicles such as feature is deleted.Then the gateway data set for deleting invalid data is classified, entrance number
It can be divided into parking lot vehicle data according to collection and be introduced into the class of parking lot vehicle data two, but be difficult to distinguish which car is
Into parking lot, with uncertainty；Outlet data collection only has the class of appearance vehicle data one, and this is having for EXIT
Data set is imitated, because the vehicle that EXIT valid data are concentrated all is to enter parking lot from Entrance, therefore is stopped
Outlet valid data in parking lot concentrate the vehicle data included to be effective vehicle data in Entrance data set.Parking lot
The valid data collection of outlet only have recorded all history vehicles and go out the time in parking lot, therefore only need to concentrate from entry data again
The time for finding out the entrance parking lot corresponding with these vehicles can be obtained by Entrance valid data collection, by gateway
Valid data concentrate the gateway time to count parking lot vehicle number on the scene all the time.
S13, the valid data based on outlet data collection extract the valid data of entry data collection：
On the basis of all vehicles in the valid data of EXIT data set, all cars concentrated with entry data
Matched, as a result for：(a) successful matching, i.e., concentrate and all remember in the valid data and outlet valid data of entry data collection
The information of same car is recorded；(b) pairing failure, i.e., have recorded the information of certain car in the valid data of outlet data collection, but
The information without the car is concentrated in entry data.
Successful matching vehicle data includes：(a) vehicle is captured appear on the scene for the first time；(b) repeat to capture.Repeat the vehicle captured
Data are exactly the same entry time of multiple Outlet time correspondences in pairing, then go out last Outlet time as it
The mouth time.The vehicle data of successful matching (is referred to as Part I for the Part I valid data of parking lot vehicle number on the scene
Valid data), stopping for vehicle on the scene in parking lot is obtained with Outlet time according to the entry time of all successful matching vehicles
Car duration, then obtained with least square fitting parking lot vehicle number on the scene parking duration distribution curve (transverse axis for parking
Duration, the longitudinal axis is vehicle number on the scene).
The vehicle data of pairing failure includes：(a) car plate None- identified or identification mistake；(b) vehicle is in entrance and exit
Recognition result is inconsistent or entrance leakage is clapped.This Some vehicles data is parking lot vehicle number Part II valid data on the scene
(referred to as Part II valid data).
Part I valid data and the total valid data (letter of Part II valid data composition parking lot vehicle number on the scene
Referred to as total valid data), total valid data are accurate history vehicle number, and wherein Part I valid data are in total valid data
Middle proportion is much larger than Part II valid data proportion in total valid data.
S14, the entry time for calculating the effective vehicle data of entry data concentration：
Because the gateway time of the vehicle in Part II valid data and parking duration be not by human intervention,
The gateway time of each vehicle and parking duration are independent, and Part II valid data can be regarded as from total significant figure
The low volume data randomly selected in, therefore Part I valid data and Part II valid data are independent identically distributed.
It can be counted respectively in the gateway time of vehicle in counting parking lot number total valid data in parking stall on the scene and its parking duration
The gateway time of vehicle and its parking duration, wherein Part I in Part I valid data and Part II valid data
The gateway time of vehicle and parking duration have been calculated in S13 and obtained in valid data, vehicle in Part II valid data
Outlet time, it is known that in Part II valid data the parking duration distribution of vehicle stop with vehicle in Part I valid data
The distribution of car duration is identical, and all vehicles in Part II valid data are stopped according to vehicle in Part I valid data
Car duration distribution curve distribution parking duration, obtains the parking duration of each car in Part II valid data, then with second
In point valid data the Outlet time of vehicle subtract the car parking duration can be obtained by the car into parking lot entrance when
Between.
The gateway time of vehicle closes in S15, Part I valid data obtained above and Part II valid data
And just obtain the gateway time of vehicle in total valid data, then can be obtained by the number of parking lot each moment vehicle on the scene
Amount, you can obtain the accurate history data of vehicle on the scene in parking lot.So far just complete by going out with deterministic parking lot
Mouth valid data collection obtains the process with probabilistic Entrance valid data collection.
S2 is specifically included：
S21, parking lot history accurate data is pre-processed, i.e., data are marked, can be according to data volume
Size, parking lot size, purposes etc. are marked, and data volume is big to be marked in detail, and data volume is small to be carried out
Short-hand notations, for example：Monday to Sunday, rain or shine festivals or holidays, sleet sky etc., in detail mark can obtain more accurately predicted value；
S22, according to mark content, data are carried out smooth, remove some noises；
S23, obtain after the accurate history data of vehicle number on the scene in parking lot, according to this accurate history data radial direction base
Neutral net first predicts the vehicle number on the scene in certain parking lot at moment, and the vehicle number for then subtracting this moment by total space number is just obtained
This moment vacant parking stall number.
The present invention can also calculate in parking lot the number of " corpse car "：
" corpse car " to park in parking lot, not going out the vehicle in parking lot always, at obtained parking lot each moment
After vehicle number on the scene, and then maximum vehicle number on the scene is calculated, the maximum vehicle number on the scene is the maximum effectively parking stall in parking lot
Number；If the maximum effectively parking stall number in the parking lot is equal with total space number, the parking lot does not have " corpse car "；If the parking lot
Maximum effectively parking stall number be less than total space number and reach saturation when the parking lot vehicle number is maximum effectively parking stall number, then this stops
The maximum effectively parking stall number that the total space number in parking lot subtracts the parking lot is the number of " corpse car " in parking lot.
With the Forecasting Methodology of the present invention, partial history data such as Fig. 2 institutes of the accurate vehicle number of certain community parking field of acquisition
Show, training pattern is as shown in Figure 3 within one week.
A kind of parking position Forecasting Methodology based on Vehicle License Plate Recognition System that the present invention is provided, eliminate manual intervention,
The interference to parking lot historical data such as complex environment, obtains the accurate historical data in parking lot, on this basis to vacant car
Digit is predicted can obtain and accurately predicted the outcome.
The preferred embodiments of the present invention are these are only, are not intended to limit the invention, for those skilled in the art
For member, the present invention can have various modifications and variations.Any modification within the spirit and principles of the invention, being made,
Equivalent substitution, improvement etc., should be included in the scope of the protection.
Claims (3)
1. a kind of parking position Forecasting Methodology based on Vehicle License Plate Recognition System, it is characterised in that including：
Step 1, the accurate history data for obtaining parking lot vehicle number on the scene：Step 11, based on the Car license recognition system at gateway
System obtains Entrance data set and outlet data collection；
Step 12, the invalid data in the non-recording information of vehicles of outlet data concentration deletion, obtain the significant figure of outlet data collection
According to；The valid data of the outlet data collection include information of vehicles and Outlet time；
Step 13, the valid data based on outlet data collection obtain the valid data of entry data collection, the entry data collection
Valid data include information of vehicles and entry time：
The valid data of outlet data collection are matched with entry data collection, if successful matching, according to successful matching vehicle
Entry time and Outlet time obtain parking duration, and obtain using least square method the parking duration point of successful matching vehicle
Cloth curve；If pairing failure, it is distributed according to the parking duration of the Outlet time of pairing failure vehicle and successful matching vehicle bent
Line obtains the entry time of pairing failure vehicle；
Step 14, the valid data based on gateway data set obtain the accurate history data of parking lot vehicle number on the scene；
Step 2, the accurate history data prediction empty vehicle parking position of parking lot number obtained according to step 1：
The vehicle number on the scene in certain parking lot is first predicted with neutral net according to the accurate history data, then by total space at moment
Number subtracts the vehicle number on the scene at this moment and is just now carved vacant parking stall number.
2. the parking position Forecasting Methodology as claimed in claim 1 based on Vehicle License Plate Recognition System, it is characterised in that in step
In 2：
The neutral net is radial base neural net.
3. the parking position Forecasting Methodology as claimed in claim 1 based on Vehicle License Plate Recognition System, it is characterised in that also wrap
Include：
Step 3, the number for calculating " corpse car " in parking lot：
" corpse car " is on the scene at obtained parking lot each moment to park, not going out the vehicle in parking lot in parking lot always
After vehicle number, maximum vehicle number on the scene is calculated, the maximum vehicle number on the scene is the maximum effectively parking stall number in parking lot；If this stops
The maximum effectively parking stall number in parking lot is equal with total space number, then the parking lot does not have " corpse car "；If the maximum in the parking lot has
Effect parking stall number, which is less than when total space number and the parking lot vehicle number are maximum effectively parking stall number, reaches saturation, then the parking lot is total
The maximum effectively parking stall number that parking stall number subtracts the parking lot is the number of " corpse car " in parking lot.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108320582A (en) * | 2018-03-30 | 2018-07-24 | 合肥城市泊车投资管理有限公司 | A kind of parking management system having remaining parking stall statistical function |
| CN109376178A (en) * | 2018-08-17 | 2019-02-22 | 中国电子科技集团公司电子科学研究院 | Space-time big data trajectory analysis platform, method, server and storage medium |
| CN110223533A (en) * | 2019-06-19 | 2019-09-10 | 同济大学 | A kind of resident parking facilities' forecasting method of the night based on survival analysis |
| CN110517503A (en) * | 2019-08-28 | 2019-11-29 | 武汉烽火众智数字技术有限责任公司 | Corpse vehicle analysis and early warning method and device based on big data |
| CN111861187A (en) * | 2020-07-15 | 2020-10-30 | 上海运晓机器人有限公司 | Automatic plate moving control method for entrance and exit of parking lot of vehicle lifting plate type robot |
| CN111882917A (en) * | 2020-06-12 | 2020-11-03 | 深圳市捷顺科技实业股份有限公司 | Vehicle queue-jumping problem processing method and processing device |
| CN112435500A (en) * | 2020-12-01 | 2021-03-02 | 深圳市顺易通信息科技有限公司 | Method and device for counting remaining parking spaces of parking lot and terminal equipment |
| CN112863231A (en) * | 2020-12-31 | 2021-05-28 | 深圳市顺易通信息科技有限公司 | Method, system and device for calibrating remaining parking spaces of parking lot and storage medium |
| CN113257004A (en) * | 2021-06-03 | 2021-08-13 | 成都宜泊信息科技有限公司 | Parking space management method and system, storage medium and electronic equipment |
| CN113299108A (en) * | 2020-02-21 | 2021-08-24 | 浙江宇视科技有限公司 | Parking space information determination method, device, equipment and storage medium |
| CN113570866A (en) * | 2021-09-24 | 2021-10-29 | 成都宜泊信息科技有限公司 | Parking lot management method and system, storage medium and electronic equipment |
| CN114822070A (en) * | 2022-03-28 | 2022-07-29 | 阿里巴巴（中国）有限公司 | Parking lot state determination method and electronic equipment |
| CN115050188A (en) * | 2022-08-15 | 2022-09-13 | 中交一公局第六工程有限公司 | Method for predicting remaining parking spaces of indoor parking lot |
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| CN108320582B (en) * | 2018-03-30 | 2020-01-24 | 合肥城市泊车投资管理有限公司 | Parking management system with remaining parking space counting function |
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| CN111882917A (en) * | 2020-06-12 | 2020-11-03 | 深圳市捷顺科技实业股份有限公司 | Vehicle queue-jumping problem processing method and processing device |
| CN111861187A (en) * | 2020-07-15 | 2020-10-30 | 上海运晓机器人有限公司 | Automatic plate moving control method for entrance and exit of parking lot of vehicle lifting plate type robot |
| CN112435500A (en) * | 2020-12-01 | 2021-03-02 | 深圳市顺易通信息科技有限公司 | Method and device for counting remaining parking spaces of parking lot and terminal equipment |
| CN112863231A (en) * | 2020-12-31 | 2021-05-28 | 深圳市顺易通信息科技有限公司 | Method, system and device for calibrating remaining parking spaces of parking lot and storage medium |
| CN113257004A (en) * | 2021-06-03 | 2021-08-13 | 成都宜泊信息科技有限公司 | Parking space management method and system, storage medium and electronic equipment |
| CN113570866A (en) * | 2021-09-24 | 2021-10-29 | 成都宜泊信息科技有限公司 | Parking lot management method and system, storage medium and electronic equipment |
| CN114822070A (en) * | 2022-03-28 | 2022-07-29 | 阿里巴巴（中国）有限公司 | Parking lot state determination method and electronic equipment |
| CN115050188A (en) * | 2022-08-15 | 2022-09-13 | 中交一公局第六工程有限公司 | Method for predicting remaining parking spaces of indoor parking lot |
| CN115050188B (en) * | 2022-08-15 | 2022-10-28 | 中交一公局第六工程有限公司 | Method for predicting remaining parking spaces of indoor parking lot |
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Application publication date: 20171013 |