CN110047283A - A method of the evaluation and test of road Dynamic Programming data and vehicle shunting based on crowdsourcing - Google Patents
A method of the evaluation and test of road Dynamic Programming data and vehicle shunting based on crowdsourcing Download PDFInfo
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- CN110047283A CN110047283A CN201910297466.5A CN201910297466A CN110047283A CN 110047283 A CN110047283 A CN 110047283A CN 201910297466 A CN201910297466 A CN 201910297466A CN 110047283 A CN110047283 A CN 110047283A
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- 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
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- 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
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
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Abstract
The method of the invention discloses a kind of road Dynamic Programming data evaluation and test and vehicle shunting based on crowdsourcing, which is characterized in that road Dynamic Programming data test process and vehicle shunting process including crowdsourcing.This method can obtain judging the dynamic standard of crowdsourcing data accuracy, filter out more accurate crowdsourcing data, and then reach in navigation with field of traffic control and shunt in advance, the effect of pre- congestion prevention, provide more unobstructed route for public trip.
Description
Technical field
The present invention relates to the technology of crowdsourcing data processing and traffic administration, specifically a kind of road dynamic based on crowdsourcing is advised
The method for drawing data evaluation and test and vehicle shunting.
Background technique
Nowadays, the smart phone for carrying android system is prevailing, crowdsourcing mechanism also gradually penetrates into each of people's life
A aspect.For navigation platform, acquiring road condition information in real time, and feed back that provide the user with respective service particularly important.?
Beneficial to the network system of modern society's prosperity, current major navigation platform has carried out data acquisition using public network, it may be assumed that will
Data acquisition session is contracted out to public network, obtains a large amount of data with lower cost.
The main difficult point of the prior art includes: user's complicated composition of 1. offer data；2. data are various, parameter is big.It is right
In how can be divided into two parts to the problem of data progress secondary treatment, first part is the number that take what kind of user
According to how second part copes with the data in dynamic change daily.
There are most Voting Models about the existing research of this problem, most Voting Models are according to currently having obtained
How much the data of same condition of road surface complete the prediction of present road situation.The essence of this model is each user's mark
The error probability of road condition information is identical.On the majority voting method on this basis, there are many more innovatory algorithms, judge mark
How standard has formulated further research space.
Masses lack professional domain knowledge, and the data provided need to carry out secondary treatment and then obtain judgment criteria.And with
The number of users of automobile increase, transport need continues to increase, if the processing of road situation occurred is not in time, is easy to cause traffic
The generation of accident.When accident occurs, traditional solution is artificially commanded by traffic police, is maintained order, but such efficiency
It is low and more passive.When accident occurs, know that situation is the masses on the scene first, therefore if passing through masses' upload information,
It allows people to go and find out what's going on rapidly after treatment information, and is shunted to the masses that will be gone on a journey, thing can be alleviated significantly
Therefore traffic police's bring pressure is given when occurring.
Crowdsourcing is a kind of distributed Resolving probiems and production model.What one company or mechanism were executed the past by employee
Task is contracted out to unspecific (and being usually large-scale) public volunteer in freely voluntary form.
The credit worthiness for quantifying crowdsourcing user obtains judging crowdsourcing by secondary treatment to crowdsourcing data and in-depth analysis
The dynamic standard of data accuracy, filters out more accurate crowdsourcing data, and then in navigation and field of traffic control, using point
The correlation technique of flow model reaches the effect of shunting in advance, pre- congestion prevention, provides more unobstructed route for public trip.
Summary of the invention
The purpose of the present invention is in view of the deficiencies of the prior art, and provide a kind of road Dynamic Programming data based on crowdsourcing
The method of evaluation and test and vehicle shunting.This method can obtain judging the dynamic standard of crowdsourcing data accuracy, filter out more quasi-
True crowdsourcing data, and then reach in navigation with field of traffic control and shunt in advance, the effect of pre- congestion prevention, it is mentioned for public trip
For more unobstructed route.
Realizing the technical solution of the object of the invention is:
A method of the evaluation and test of road Dynamic Programming data and vehicle shunting, points unlike the prior art based on crowdsourcing exist
In including the following steps:
S1: quantization user's credit worthiness:
S1-1: setting user adopts value: crowdsourcing user, which adopts value and is added by user base value with user contribution value, to be configured to
The credit worthiness of quantization user adopts value to user setting user, and then be to assess on user when user register in systems after
The accuracy for passing data provides foundation, and the traffic information that so-called " adopting " refers to that crowdsourcing user uploads is judged as really being adopted；
S1-2: setting user base value: the prestige for constituting simultaneously initialising subscriber in order to further refine user's credit worthiness
Degree, for the new user for just registering to use system, user base value is set as 60 points；For having registered and having logged in use
Be the period with one month for the old user of system, monthly first day user base value by user's every day last month use
The average value that value is adopted at family is given, and second day user base value is adopted value by first day user and given, and is iterated to calculate with this；
S1-3: setting user contribution value: in order to quantify the contribution that user makes abundant abnormal point numerical collection, user is set
Set user contribution value: No. 1 in every month is zeroed out the contribution margin of all users, if the user uploads traffic information quilt
It is judged to very, then adopting value raising, conversely, then reducing, " the data ballot system " in judgment basis step S3 is provided, user's contribution
Value grade according to locating for user base value is given the increase of corresponding score value or is reduced, it may be assumed that backward gradient positive or negative points mechanism；
S2: it setting " backward gradient positive or negative points mechanism ": sets user and adopts value full marks as 100 points, be divided into 5 points
Several sections, it is followed successively by 0-60,60-70,70-80,80-90,90-100, the bonus point increment of each corresponding score section is successively are as follows: 2
Point, 1 point, 0.5 point, 0.2 point, 0.1 point；Deduction is reduced successively are as follows: 0.1 point, 0.2 point, 0.5 point, 1,2 point, it may be assumed that bonus point and deduction
The effect that gradient and backward is presented, is more in line with and is just distributed very much, such as: it is 65 points that the user of certain user, which adopts value, if on user
Communication breath is judged as true by S3 " data ballot system ", then contribution margin adds 1 point, if upload information is judged as vacation, this user's
Contribution margin subtracts 0.2 point, and corresponding user's adopts value also and will increase 1 point or reduce 0.2 point；
S3: setting " data ballot system ":
Define 1: it is a chronomere that actually active turnout, which is setting one day, one month, 1 year, in a time
In unit, in the traffic information of all uploads, the ratio that correct information accounts for all information is actually active turnout；
It defines 2: being that the method for " data ballot system " is passed through according to previous actually active turnout with reference to effective turnout
The effective turnout of the same day being calculated for reference is to refer to effective turnout；It can will refer to effective turnout and certain position
The upload number for setting certain road conditions is multiplied to obtain with reference to effective votes standard, uploads certain traffic information of some position when this day
Number up to or over it is calculated with reference to effective votes standard when, the traffic information of this position is judged as very；
S3-1: in the first month that system comes into operation, if daily all reporting of user information actually has in this month
The average value for imitating turnout is L1, and so on, the average value of the actually active turnout of every month is L backward2、L3、L4…Ln；
S3-2: set by last month be calculated next month first day reporting of user information the effective turnout of reference as
R1, and so on, the effective turnout of the reference being calculated daily backward is R2、R3、R4…Rn, wherein RnIt is every for last month
The average value L of it actually active turnoutn-1；
S3-3: the actually active turnout of first day every month of reporting of user information is set as E1, and so on, daily backward
Actually active turnout be E2、E3、E4…En.Wherein, E1、E2、E3、E4…EnCorrect information in information is reported for daily
The shared ratio in all information；
S3-4: it is identified in the first month that system comes into operation with majority person in the information that reports, it may be assumed that certain position
Certain traffic information upload number it is most be exactly the most accurate, such as: share 10 people and upload sunlight primary school doorway under
The traffic information of noon 5:00, wherein 7 people report traffic information for traffic congestion, and 3 people report information to collapse, then in this case,
Determine the traffic information of traffic congestion be it is true, still, after the primary data after acquisition system comes into operation, behind monthly daily reality
The effective turnout in border and the effective turnout of reference can all be moved according to previous actually active turnout and with reference to effective turnout
State planning obtains, is no longer with most persons it is determined that true, i.e., the second month beginning of the month after the first month calculates first
The average value L of the actually active turnout of the moon1, with L1The effective turnout R of reference in first day as second month1, in this day
In report in information with R1Information is adopted, if than or equal to R1That calculates adopts number, then information mark
Note is true；This day actually active turnout E is calculated at the end of first day1, then the effective turnout of reference in second day is R2
=(E1+R1)/2, third day and so on calculate all actually active throwings reported in information of second month at the third beginning of the month
The average value L of ticket rate2, with L2As the trimestral effective turnout R of reference in first day1... later every month is with this
Analogize；
Thus, it is possible to the effective turnout of reference daily in n-th month be obtained, if having within n-th month m days, then at n-th month
In have:
Ln-1=(R1+R2+R3+…+Rm)/m
R1=Ln-1
R2=(E1+R1)/2
R3=(E2+R2)/2
…
Rn=(En-1+Rn-1)/2
Ln=(R1+R2+R3+…Rm)/m；
S4: on stretch occur an abnormal point when, by the front and back of the abnormal point and apart from abnormal point itself it is nearest two
A approach point is respectively defined as first point and tail point, calculates early warning route according to first point and tail point；
S4-1: first with tail point for new terminal, first point is new starting point, and the longitude of first point adds 0 to 30 meters, and 5 meters are incremental
Amplitude, they are submitted to network server as ground zero and terminal each time, such as Baidu's server；
S4-2: network server can return to a new route, be returned in the form of route point set, and point, which is concentrated, to be had a little
Latitude and longitude information, the longitude and latitude of longitude and latitude and abnormal point that new route point is concentrated compares, and step is jumped to if occurring being overlapped
Rapid S4-10 does not find early warning section if be not overlapped, jumps to next step S4-3；
S4-3: first with tail point for new terminal, first point is new starting point, and the latitude of first point adds 0 to 30 meters, and 5 meters are incremental
Amplitude, each time ground zero and terminal submit to network server；
S4-4: network server can return to a new route, be to be returned in the form of route point set, then by new road
The longitude and latitude of longitude and latitude and abnormal point that line point is concentrated compares, and S4-10 is jumped to if occurring being overlapped, if not being overlapped does not have
Early warning section is found, next step S4-5 is jumped to；
S4-5: first with first point for new starting point, tail point is new terminal, and the longitude of first point adds 0 to 30 meters, and 5 meters are incremental
Amplitude, each time ground zero and terminal submit to network server；
S4-6: network server can return to a new route, be to be returned in the form of route point set, then by new road
The longitude and latitude of longitude and latitude and abnormal point that line point is concentrated compares, and S4-10 is jumped to if occurring being overlapped, if not being overlapped does not have
Early warning section is found, next step S4-7 is jumped to；
S4-7: first with first point for new starting point, tail point is new terminal, and the latitude of first point adds 0 to 30 meters, and 5 meters are incremental
Amplitude, each time ground zero and terminal submit to network server；
S4-8: network server can return to a new route, be to be returned in the form of route point set, then by new road
The longitude and latitude of longitude and latitude and abnormal point that line point is concentrated compares, and S4-10 is jumped to if occurring being overlapped, if not being overlapped does not have
Early warning section is found, next step S4-9 is jumped to；
S4-9: looking for nearest approach point in current approach point outward, as new first point and tail point, jumps to S4-1；
S4-10: the route returned using current network service device is as early warning section, the first point and tail for submitting to server
Origin And Destination of the point as early warning section；
S5: when occurring Multiple outliers on stretch, according to whole abnormal points longitude and latitude and original route starting point,
The longitude and latitude of terminal compares, and finds two abnormal points nearest from beginning and end, looks for again near two abnormal points recently
Two approach points, one is used as first point, and one is used as tail point, and first point to the section between tail point is abnormal section, according on the way
The plus-minus of the longitude and latitude of diameter point obtains new beginning and end, submits to network server and generates new route, after eligible
Early warning route is generated to user:
S5-1: first with tail point for new terminal, first point is new starting point, and the longitude of first point adds 0 to 30 meters, and 5 meters are incremental
Amplitude, each time ground zero and terminal submit to network server；
S5-2: network server can return to a new route, be to be returned in the form of route point set, then by new road
The longitude and latitude of longitude and latitude and all abnormal points that line point is concentrated compares, and S5-10 is jumped to if occurring being overlapped, if be not overlapped
Early warning section is not found, jumps to next step S5-3；
S5-3: first with tail point for new terminal, first point is new starting point, and the latitude of first point adds 0 to 30 meters, and 5 meters are incremental
Amplitude, each time ground zero and terminal submit to network server；
S5-4: network server can return to a new route, be to be returned in the form of route point set, then by new road
The longitude and latitude of longitude and latitude and all abnormal points that line point is concentrated compares, and S5-10 is jumped to if occurring being overlapped, if be not overlapped
Early warning section is not found, jumps to next step S5-5；
S5-5: first with first point for new starting point, tail point is new terminal, and the longitude of first point adds 0 to 30 meters, and 5 meters are incremental
Amplitude, each time ground zero and terminal submit to network server；
S5-6: network server can return to a new route, be to be returned in the form of route point set, then by new road
The longitude and latitude of longitude and latitude and all abnormal points that line point is concentrated compares, and S5-10 is jumped to if occurring being overlapped, if be not overlapped
Early warning section is not found, jumps to next step S5-7；
S5-7: first with first point for new starting point, tail point is new terminal, and the latitude of first point adds 0 to 30 meters, and 5 meters are incremental
Amplitude, each time ground zero and terminal submit to network server；
S5-8: network server can return to a new route, be to be returned in the form of route point set, then by new road
The longitude and latitude of longitude and latitude and all abnormal points that line point is concentrated compares, and S5-10 is jumped to if occurring being overlapped, if be not overlapped
Early warning section is not found, jumps to next step S5-9；
S5-9: looking for nearest approach point in current approach point outward, as new first point and tail point, jumps to S5-1；
S5-10: the route returned using current network service device is as early warning section, the first point and tail for submitting to server
Origin And Destination of the point as early warning section.
Complete above-mentioned steps after origination data to early warning section starting point to early warning section terminal between original equipment manufacturer
Just there is a new travel route, has reached and shunted for abnormal section, evaded the effect of abnormal point.
The system is that the quick road conditions based on Android of Guilin Electronic Science and Technology Univ.'s exploitation mark and assisting navigation system
System, [referred to as: vitroad], V1.0, registration number are as follows: 2019SR0002786, certificate number are as follows: soft work steps on word the 3423543rd, should
System has the function of that user quickly uploads position traffic information and obtains unimpeded navigation routine, can pass through Dynamic Programming
The method of crowdsourcing data evaluating standard come judge user upload traffic information accuracy, and then through judge data it
On, it is that other users cook up more smooth traffic path by shunting model.
" abnormal point numerical collection " refers to the traffic information that user uploads, mainly roadblock information in the technical program, such as: gathering around
It blocks up, repair the roads, blocking up point, restricted driving, landslide, broken road, greasy weather, Ramp bridge etc., the title (such as: congestion) of these traffic informations, road conditions place
Position longitude and latitude (such as: longitude: 110.31, latitude: 25.26), uplink time (such as: 25 minutes 36 13 points of on November 13rd, 2018
Second), uploader (such as: Zhang) collectively constitute abnormal point numerical collection.
Value, basic value, the credit worthiness of contribution margin quantization user are adopted to user setting in the technical program, user, which passes through, is
System uploads traffic information, and system evaluates crowdsourcing data by " data dynamic ballot is made ", filters out accurate road conditions letter
Breath, and then abnormal point numerical collection is obtained, then on the basis of abnormal point numerical collection, use " shunting model " to go out other users
Professional etiquette marks more smooth traffic path.
Using " user adopts value and gradient positive or negative points mechanism " by user's credit worthiness quantitative management in the technical program, then
It realizes that dynamic judges crowdsourcing using the strategy of " actually active turnout and the effective turnout of reference are iterated calculating " to obtain
The accuracy of data also drives the floating that user adopts value, effectively improves and judges crowdsourcing user credit worthiness and upload number
According to the flexibility of accuracy.
In the technical scheme, the task that real-time road condition informations all in actual life are collected is distributed to all using system
The user of system, numerous users upload traffic information, at the same time, the Dynamic Programming crowdsourcing data judgement standard of system using system
Method judge the accuracy for uploading traffic information, and then the real-time collecting of traffic information is improved, to use shunting model to be
Other users plan that smooth traffic path provides data supporting.
This method can obtain judging the dynamic standard of crowdsourcing data accuracy, filter out more accurate crowdsourcing data,
And then reach in navigation with field of traffic control and shunt in advance, the effect of pre- congestion prevention, it is provided for public trip more unobstructed
Route.
Detailed description of the invention:
Fig. 1 is the composition schematic diagram that user adopts value；
Fig. 2 is backward gradient addition and subtraction schematic diagram of mechanism；
Fig. 3 is Dynamic Programming crowdsourcing data evaluating standard method example schematic；
Fig. 4 is the shunting model schematic diagram of an abnormal point；
Fig. 5 is the shunting model schematic diagram of Multiple outliers.
In figure, 1. starting point 3. the first first point of 4. first section in the starting point of the first original route of 2. the first early warning of section
Abnormal point 5. the first tail of point 6. terminal 7. first original route of in the first early warning of section terminal 8. the first original route of
9. the abnormal section first 10. the first early warning of sectionThe starting point of second original routeThe starting point in the second early warning section
Second first pointThe abnormal point 1 in the second sectionThe abnormal point 2 in the second sectionThe abnormal point 3 in the second sectionSecond
Tail pointThe terminal in the second early warning sectionThe terminal of second original routeSecond original routeSecond abnormal sectionSecond early warning section.
Specific embodiment
The contents of the present invention are further elaborated with reference to the accompanying drawings and examples, but are not to limit of the invention
It is fixed.
Embodiment:
A method of the evaluation and test of road Dynamic Programming data and vehicle shunting based on crowdsourcing include the following steps:
S1: quantization user's credit worthiness:
S1-1: setting user adopts value: as shown in Figure 1, setting current user's first as new user, second is old user, and first is adopted
Value of receiving is 60, and the value of adopting of second is 71；
S1-2: setting user base value: setting the user base value of current first is 60, and the user base value of second is 70；
S1-3: setting user contribution value: setting the user contribution value of current first is 0, and the user contribution value of second is 1；
S2: setting " backward gradient positive or negative points mechanism ": as shown in Fig. 2, setting user adopts value full marks as 100 points, by it
5 score sections are divided into, 0-60,60-70,70-80,80-90,90-100 are followed successively by, the bonus point of each corresponding score section increases
Amount is successively are as follows: 2 points, 1 point, 0.5 point, 0.2 point, 0.1 point；Deduction is reduced successively are as follows: and 0.1 point, 0.2 point, 0.5 point, 1,2 point, this
In example, it is 60 points that the user of user's first, which adopts value, if user's upload information is judged as true by S3 " data ballot system ", contribution margin
Add 2 points, if upload information is judged as vacation, the contribution margin of this user subtracts 0.1 point, and corresponding user's adopts value and can also increase
Add 2 points or 0.1 point of reduction；
S3: setting " data ballot system ":
Define 1: it is a chronomere that actually active turnout, which is setting one day, one month, 1 year, in a time
In unit, in the traffic information of all uploads, the ratio that correct information accounts for all information is actually active turnout；
It defines 2: being that the method for " data ballot system " is passed through according to previous actually active turnout with reference to effective turnout
The effective turnout of the same day being calculated for reference is to refer to effective turnout；It can will refer to effective turnout and certain position
The upload number for setting certain road conditions is multiplied to obtain with reference to effective votes standard, uploads certain traffic information of some position when this day
Number up to or over it is calculated with reference to effective votes standard when, the traffic information of this position is judged as very；
S3-1: in the first month that system comes into operation, if daily all reporting of user information actually has in this month
The average value for imitating turnout is L1, and so on, the average value of the actually active turnout of every month is L backward2、L3、L4…Ln,
In this example: October 1, totally 10 people mark certain point jointly, and the traffic information for having 8 people to describe is consistent, then actually active ballot in this day
Rate are as follows: 8/10=0.8, finally, calculates the actually active turnout of the entire moon in October by No. 2, No. 3 ... No. .31 and so on
Average value L10: (0.8+0.7+ ...+0.8)/31=0.6；
S3-2: set by last month be calculated next month first day reporting of user information the effective turnout of reference as
R1, and so on, the effective turnout of the reference being calculated daily backward is R2、R3、R4…Rn, wherein RnIt is every for last month
The average value L of it actually active turnoutn-1, in this example: the effective turnout R of No. 1 reference at the beginning of 11 months1It is exactly upper one month
Actually active turnout average value L10: 0.6；
S3-3: the actually active turnout of first day every month of reporting of user information is set as E1, and so on, daily backward
Actually active turnout be E2、E3、E4…En, wherein E1、E2、E3、E4…EnCorrect information in information is reported for daily
The shared ratio in all information, in this example: November 1, totally 10 people mark certain point jointly, terminate November 1, compare real
Number and the numerical value through being calculated with reference to effective turnout are adopted in border, are actually had 8 people to be in the crowd actually adopted,
The then actually active turnout E on November 11Are as follows: 8/10=0.8；
S3-4: after the primary data after acquisition system comes into operation, behind monthly daily actually active turnout with
And can all be obtained according to previous actually active turnout and with reference to effective turnout Dynamic Programming with reference to effective turnout, no longer
It is with most persons it is determined that true, it may be assumed that the second month beginning of the month after the first month calculates the actually active throwing of first month
The average value L of ticket rate1, with L1The effective turnout R of reference in first day as second month1, reported in information in this day
With R1Information is adopted, if than or equal to R1That calculates adopts number, then be information labeling it is true,
This day actually active turnout E is calculated at the end of first day1, then the effective turnout of reference in second day be
R2=(E1+R1)/2, third day and so on, at the third beginning of the month, calculate second month it is all report it is actually active in information
The average value L of turnout2, with L2As the trimestral effective turnout R of reference in first day1... later every month with
This analogizes,
This example as shown in figure 3, due to November 1 actually active turnout E1It is 0.8, with reference to effective turnout R1For
0.6, then the effective turnout of reference for being computed November 2 is (E1+R1)/2=(0.8+0.6)/2=0.7, if November 2 is shared
10 people upload certain two kinds of road conditions of certain position, wherein 8 people's upload informations are consistent, in addition 2 people's upload informations are consistent, because of November 2
Number the effective turnout of reference be 0.7, then 0.7*10=7, it may be assumed that need to upload certain position jointly up to or over 7 people
Same traffic information can determine that certain traffic information of certain position is very, under this setting, to have 8 people to upload jointly
The same traffic information of certain position, because 8 > 7,8 people upload traffic information jointly and are adopted, and the upper communication of another 2 people
Breath is judged as vacation,
If user's first is in the crowd of this road conditions upload being adopted, then represents user's first and upload true road
Condition information, and because it is 60 that the user of first, which adopts value, backward gradient addition and subtraction mechanism is matched, then is increased for the user contribution value of first
2 points, at the same time, the user of first adopts value and is also increased by 2 points；If second is in the unadopted crowd of this road conditions upload
In, then it represents user's second and uploads false traffic information, and because it is 71 that the user of second, which adopts value, matching backward gradient adds
Subtraction mechanism then reduces 1 point for the user contribution value of second, and at the same time, the user of second adopts value and is also increased by 1 point；
S4: Fig. 4 is the shunting model schematic diagram for having an abnormal point, is expressed as one section and has been judged an abnormal point
Section, branching process generate early warning route thus, this part be the first original route 8. on there is first abnormal point
4. this example is that Baidu's server finds two nearest approach points of abnormal point front and back, and one is first then to network server
First point 3., a first tail point 5., first point first to tail point first 5. between section be the first abnormal section 9., branching process
The plus-minus of longitude and latitude according to approach point obtains new beginning and end, submits to Baidu's server and generates new route, meets
Early warning route is generated to user after condition；
5. S4-1: being first new terminal with the first tail point, 3. the first first point is new starting point, the longitude of the first first point 3. adds
0 to 30 meters, 5 meters be incremented by, each time ground zero and terminal submit to Baidu's server；
S4-2: Baidu's server can return to a new route, be to be returned in the form of route point set, model is then tapped off
The longitude and latitude comparison of the longitude and latitude and the first abnormal point that new route point is concentrated 4., jumps to S4-10 if occurring being overlapped, if
It is not overlapped and does not find early warning section, jump to next step S4-3；
5. S4-3: being first new terminal with the first tail point, 3. the first first point is new starting point, the latitude of the first first point 3. adds
0 to 30 meters, 5 meters be incremented by, each time ground zero and terminal submit to Baidu's server；
S4-4: Baidu's server can return to a new route, be to be returned in the form of route point set, model is then tapped off
The longitude and latitude comparison of the longitude and latitude and the first abnormal point that new route point is concentrated 4., jumps to S4-10 if occurring being overlapped, if
It is not overlapped and does not find early warning section, jump to next step S4-5；
3. S4-5: being first new starting point with the first first point, 5. the first tail point is new terminal, the longitude of the first first point 3. adds
0 to 30 meters, 5 meters be incremented by, each time ground zero and terminal submit to Baidu's server；
S4-6: Baidu's server can return to a new route, be to be returned in the form of route point set, model is then tapped off
The longitude and latitude comparison of the longitude and latitude and the first abnormal point that new route point is concentrated 4., jumps to S4-10 if occurring being overlapped, if
It is not overlapped and does not find early warning section, jump to next step S4-7；
3. S4-7: being first new starting point with the first first point, 5. the first tail point is new terminal, the latitude of the first first point 3. adds
0 to 30 meters, 5 meters be incremented by, each time ground zero and terminal submit to Baidu's server；
S4-8: Baidu's server can return to a new route, be to be returned in the form of route point set, model is then tapped off
The longitude and latitude comparison of the longitude and latitude and the first abnormal point that new route point is concentrated 4., jumps to S4-10 if occurring being overlapped, if
It is not overlapped and does not find early warning section, jump to next step S4-9；
S4-9: looking for nearest approach point in current approach point outward, as the first new first point 3. with the first tail point 5.,
Jump to S4-1；
S4-10: the route returned using current Baidu's server as the first early warning section 10., submitting to the of server
One first point be 3. 5. used as with the first tail point first starting point in early warning section 2. with First terminal point 6.；
S5: Fig. 5 is the shunting model schematic diagram for having Multiple outliers, is expressed as one section and has been judged Multiple outliers
Section, branching process generate early warning route thus, this part is in the second original routeOn there are Multiple outliers, this
Assume to be three abnormal points in example, be done by the longitude and latitude of the starting point of the longitude and latitude of whole abnormal points and original route, terminal
Two second abnormal points nearest from beginning and end are found in comparisonWithIt is looked for again near two abnormal points nearest
Two approach points, one is used as the second first pointOne is used as the second tail pointSecond first pointTo the second tail pointIt
Between section be the second abnormal sectionBranching process obtains new beginning and end according to the plus-minus of the longitude and latitude of approach point,
It submits to Baidu's server and generates new route, give user's generation early warning route after eligible,
S5-1: first with the second tail pointFor new terminal, the second first pointFor new starting point, the second first pointLongitude
Add 0 to 30 meters, 5 meters are incremented by, each time ground zero and terminal submit to Baidu's server；
S5-2: Baidu's server can return to a new route, be to be returned in the form of route point set, model is then tapped off
The abnormal point 1 of longitude and latitude and the second section that new route point is concentratedThe abnormal point 2 in the second sectionWith the second section
Abnormal point 3Longitude and latitude comparison, S5-10 is jumped to if occurring being overlapped, does not find early warning road if be not overlapped
Section, jumps to next step S5-3；
S5-3: first with the second tail pointFor new terminal, the second first pointFor new starting point, the second first pointLatitude
Add 0 to 30 meters, 5 meters are incremented by, each time ground zero and terminal submit to Baidu's server；
S5-4: Baidu's server can return to a new route, be to be returned in the form of route point set, model is then tapped off
The abnormal point 1 of longitude and latitude and the second section that new route point is concentratedThe abnormal point 2 in the second sectionWith the second section
Abnormal point 3Longitude and latitude comparison, S5-10 is jumped to if occurring being overlapped, does not find early warning road if be not overlapped
Section, jumps to next step S5-5；
S5-5: first with the second first pointFor new starting point, the second tail pointFor new terminal, the second first pointLongitude
Add 0 to 30 meters, 5 meters are incremented by, each time ground zero and terminal submit to Baidu's server；
S5-6: Baidu's server can return to a new route, be to be returned in the form of route point set, model is then tapped off
The abnormal point 1 of longitude and latitude and the second section that new route point is concentratedThe abnormal point 2 in the second sectionWith the second section
Abnormal point 3Longitude and latitude comparison, S5-10 is jumped to if occurring being overlapped, does not find early warning section if be not overlapped,
Jump to next step S5-7；
S5-7: first with the second first pointFor new starting point, the second tail pointFor new terminal, the second first pointLatitude
Add 0 to 30 meters, 5 meters are incremented by, each time ground zero and terminal submit to Baidu's server；
S5-8: Baidu's server can return to a new route, be to be returned in the form of route point set, model is then tapped off
The abnormal point 1 of longitude and latitude and the second section that new route point is concentratedThe abnormal point 2 in the second sectionWith the second section
Abnormal point 3Longitude and latitude comparison, S5-10 is jumped to if occurring being overlapped, does not find early warning road if be not overlapped
Section, jumps to next step S5-9；
S5-9: nearest approach point is looked for outward in current approach point, as the second new first pointWith the second tail pointJump to S5-1；
S5-10: using the route that current Baidu's server returns as the second early warning sectionSubmitting to the of server
Two first pointsWith the second tail pointThe second starting point as early warning sectionWith the second terminal
1. or the second origination data origination data is completed after above-mentioned steps after firstTo the first early warning section starting point 2.
Or second early warning section starting pointTo the first early warning section terminal 6. or the terminal in the second early warning section is to the first original end
7. or the second original equipment manufacturer pointBetween just have a new travel route, reached and shunted for abnormal section, evaded abnormal point
Effect.
The system used in this example for Guilin Electronic Science and Technology Univ. develop based on Android quick road conditions label with it is auxiliary
Navigation system is helped, [referred to as: vitroad], V1.0, registration number are as follows: 2019SR0002786, certificate number are as follows: soft work steps on word the
No. 3423543.
Claims (2)
1. a kind of method of road Dynamic Programming data evaluation and test and vehicle shunting based on crowdsourcing, which is characterized in that including as follows
Step:
S1: quantization user's credit worthiness:
S1-1: setting user adopts value: crowdsourcing user, which adopts value and is added by user base value with user contribution value, to be constituted, and user is worked as
After registering in systems, value is adopted to user setting user, and then provide foundation for the accuracy that assessment user uploads data, institute
Meaning " adopting " refers to that the traffic information that crowdsourcing user uploads is judged as true and is used；
S1-2: setting user base value: for the new user for just registering to use system, user base value is set as 60 points；
It was the period with one month for having registered and having logged in for the old user using system, monthly first day user base value
It is given by the average value that the user of user every day last month adopts value, user base value was adopted by first day user within second day
Value is given, is iterated to calculate with this；
S1-3: setting user contribution value: to user setting user contribution value: No. 1 contribution margin to all users in every month
It is zeroed out, if the user uploads traffic information and is judged as very, value raising being adopted, conversely, then reducing, judgment basis step
" data ballot system " in S3 provide, user contribution value grade according to locating for user base value give corresponding score value increase or
It reduces, it may be assumed that backward gradient positive or negative points mechanism；
S2: it setting " backward gradient positive or negative points mechanism ": sets user and adopts value full marks as 100 points, be divided into 5 scores
Section is followed successively by 0-60,60-70,70-80,80-90,90-100, and the bonus point increment of each corresponding score section is successively are as follows: and 2 points, 1
Point, 0.5 point, 0.2 point, 0.1 point；Deduction is reduced successively are as follows: 0.1 point, 0.2 point, 0.5 point, 1,2 point, it may be assumed that bonus point and deduction are presented
The effect of gradient and backward is more in line with and is just distributed very much；
S3: setting " data ballot system ":
Define 1: it is a chronomere that actually active turnout, which is setting one day, one month, 1 year, in a chronomere
In, in the traffic information of all uploads, the ratio that correct information accounts for all information is actually active turnout；
It defines 2: being to be calculated according to previous actually active turnout by the method for " data ballot system " with reference to effective turnout
The effective turnout of the obtained same day for reference is to refer to effective turnout；It can will refer to effective turnout and certain position
The upload number of road conditions is multiplied to obtain with reference to effective votes standard, uploads the people of certain traffic information of some position when this day
When number is up to or over calculated reference effective votes standard, the traffic information of this position is judged as very；
S3-1: in the first month that system comes into operation, if in this month daily all reporting of user information actually active throwing
The average value of ticket rate is L1, and so on, the average value of the actually active turnout of every month is L backward2、L3、L4…Ln；
S3-2: set by last month be calculated next month first day reporting of user information the effective turnout of reference as R1, with
This analogizes, and the effective turnout of the reference being calculated daily backward is R2、R3、R4…Rn, wherein RnFor the reality that last month is daily
The average value L of the effective turnout in bordern-1；
S3-3: the actually active turnout of first day every month of reporting of user information is set as E1, and so on, backward daily reality
The effective turnout in border is E2、E3、E4…En.Wherein, E1、E2、E3、E4…EnReport in information correct information in institute to be daily
There is ratio shared in information；
S3-4: it is identified in the first month that system comes into operation with majority person in the information that reports, it may be assumed that certain of certain position
Kind of traffic information upload number it is most be exactly it is the most accurate, still, after the primary data after acquisition system comes into operation, after
Face monthly daily actually active turnout and all can be according to previous actually active turnout and ginseng with reference to effective turnout
It examines effective turnout Dynamic Programming to obtain, is no longer with most persons it is determined that true, i.e., second month after the first month
The beginning of the month calculates the average value L of the actually active turnout of first month1, with L1Reference in first day as second month is effective
Turnout R1, report in information in this day with R1Information is adopted, if than or equal to R1That calculates adopts
Number is then true information labeling；This day actually active turnout E is calculated at the end of first day1, then second day ginseng
Examining effective turnout is R2=(E1+R1)/2, third day and so on, at the third beginning of the month, calculate second month it is all on notify
The average value L of actually active turnout in breath2, with L2As the trimestral effective turnout R of reference in first day1……
Later every month and so on；
Thus, it is possible to the effective turnout of reference daily in n-th month be obtained, if having within n-th month m days, then in n-th month
Have:
Ln-1=(R1+R2+R3+…+Rm)/m
R1=Ln-1
R2=(E1+R1)/2
R3=(E2+R2)/2
…
Rn=(En-1+Rn-1)/2
Ln=(R1+R2+R3+…Rm)/m；
S4: when occurring an abnormal point on stretch, by the front and back of the abnormal point and two ways nearest apart from abnormal point itself
Diameter point is respectively defined as first point and tail point, calculates early warning route according to first point and tail point；
S4-1: first with tail point for new terminal, first point is new starting point, and the longitude of first point adds 0 to 30 meters, and 5 meters are incremental amplitude,
They are submitted to network server as ground zero and terminal each time；
S4-2: network server can return to a new route, be to be returned in the form of route point set, point concentrates the warp having a little
The longitude and latitude of latitude information, longitude and latitude and abnormal point that new route point is concentrated compares, and step S4- is jumped to if occurring being overlapped
10, early warning section is not found if be not overlapped, and jumps to next step S4-3；
S4-3: first with tail point for new terminal, first point is new starting point, and the latitude of first point adds 0 to 30 meters, and 5 meters are incremental amplitude,
Each time ground zero and terminal submit to network server；
S4-4: network server can return to a new route, be to be returned in the form of route point set, then by new route point
The longitude and latitude of concentration and the longitude and latitude of abnormal point compare, and S4-10 is jumped to if occurring being overlapped, is not found if be not overlapped
Next step S4-5 is jumped in early warning section；
S4-5: first with first point for new starting point, tail point is new terminal, and the longitude of first point adds 0 to 30 meters, and 5 meters are incremental amplitude,
Each time ground zero and terminal submit to network server；
S4-6: network server can return to a new route, be to be returned in the form of route point set, then by new route point
The longitude and latitude of concentration and the longitude and latitude of abnormal point compare, and S4-10 is jumped to if occurring being overlapped, is not found if be not overlapped
Next step S4-7 is jumped in early warning section；
S4-7: first with first point for new starting point, tail point is new terminal, and the latitude of first point adds 0 to 30 meters, and 5 meters are incremental amplitude,
Each time ground zero and terminal submit to network server；
S4-8: network server can return to a new route, be to be returned in the form of route point set, then by new route point
The longitude and latitude of concentration and the longitude and latitude of abnormal point compare, and S4-10 is jumped to if occurring being overlapped, is not found if be not overlapped
Next step S4-9 is jumped in early warning section；
S4-9: looking for nearest approach point in current approach point outward, as new first point and tail point, jumps to S4-1；
S4-10: using the route that current network service device returns as early warning section, the first point and tail point of submitting to server are made
For the Origin And Destination in early warning section；
S5: when occurring Multiple outliers on stretch, according to the longitude and latitude of whole abnormal points and starting point, the terminal of original route
Longitude and latitude compare, find two abnormal points nearest from beginning and end, look for nearest two again near two abnormal points
A approach point, one is used as first point, and one is used as tail point, and first point to the section between tail point is abnormal section, according to approach point
The plus-minus of longitude and latitude obtain new beginning and end, submit to network server and generate new route, it is eligible after to using
Family generates early warning route:
S5-1: first with tail point for new terminal, first point is new starting point, and the longitude of first point adds 0 to 30 meters, and 5 meters are incremental amplitude,
Each time ground zero and terminal submit to network server；
S5-2: network server can return to a new route, be to be returned in the form of route point set, then by new route point
The longitude and latitude of concentration and the longitude and latitude of all abnormal points compare, and S5-10 is jumped to if occurring being overlapped, is not had if be not overlapped
Early warning section is found, next step S5-3 is jumped to；
S5-3: first with tail point for new terminal, first point is new starting point, and the latitude of first point adds 0 to 30 meters, and 5 meters are incremental amplitude,
Each time ground zero and terminal submit to network server；
S5-4: network server can return to a new route, be to be returned in the form of route point set, then by new route point
The longitude and latitude of concentration and the longitude and latitude of all abnormal points compare, and S5-10 is jumped to if occurring being overlapped, is not had if be not overlapped
Early warning section is found, next step S5-5 is jumped to；
S5-5: first with first point for new starting point, tail point is new terminal, and the longitude of first point adds 0 to 30 meters, and 5 meters are incremental amplitude,
Each time ground zero and terminal submit to network server；
S5-6: network server can return to a new route, be to be returned in the form of route point set, then by new route point
The longitude and latitude of concentration and the longitude and latitude of all abnormal points compare, and S5-10 is jumped to if occurring being overlapped, is not had if be not overlapped
Early warning section is found, next step S5-7 is jumped to；
S5-7: first with first point for new starting point, tail point is new terminal, and the latitude of first point adds 0 to 30 meters, and 5 meters are incremental amplitude,
Each time ground zero and terminal submit to network server；
S5-8: network server can return to a new route, be to be returned in the form of route point set, then by new route point
The longitude and latitude of concentration and the longitude and latitude of all abnormal points compare, and S5-10 is jumped to if occurring being overlapped, is not had if be not overlapped
Early warning section is found, next step S5-9 is jumped to；
S5-9: looking for nearest approach point in current approach point outward, as new first point and tail point, jumps to S5-1；
S5-10: using the route that current network service device returns as early warning section, the first point and tail point of submitting to server are made
For the Origin And Destination in early warning section.
2. the method for road Dynamic Programming the data evaluation and test and vehicle shunting according to claim 1 based on crowdsourcing, special
Sign is that the system is that the quick road conditions based on Android of Guilin Electronic Science and Technology Univ.'s exploitation mark and assisting navigation system
System, [referred to as: vitroad], V1.0, registration number are as follows: 2019SR0002786, certificate number are as follows: soft work steps on word the 3423543rd.
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