CN106327871B - A kind of crowded prediction technique of highway of fusion historical data and reservation data - Google Patents
A kind of crowded prediction technique of highway of fusion historical data and reservation data Download PDFInfo
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Abstract
The invention discloses a kind of crowded prediction techniques of highway of fusion historical data and reservation data, including step：1）The highway of selected research, obtains basic data, includes the data of four aspects in total：Highway user reservation data, freeway network basic data, freeway traffic flow historical data, expressway tol lcollection policy data；2）According to road network basic data and user's reservation data, space-time is carried out to freeway network and divides the prediction period for handling and determining each section；3）The volume of traffic in each section of freeway network in prediction period is predicted；4）Predict the crowding in each section；5）Realize that the visualization of crowded prediction result exports using programming language.The present invention can solve traditional crowded prediction technique and not merge reservation data, cannot reflect that the problems such as the variation of the regularity of distribution, more reliable foundation is provided for induced travel and freeway management work in freeway network for the current volume of traffic brought of reservation.
Description
Technical field
The present invention relates to the technical field of the crowded prediction of highway, a kind of fusion historical data and reservation number are referred in particular to
According to the crowded prediction technique of highway.
Background technology
With being continuously increased for volume of transport, the congestion problems of China's highway (especially in festivals or holidays peak period)
It is more serious.The first day on National Day in 2012, the whole nation had 26 highways in 16 provinces congestion occur；And National Day in 2015
The first day, only Guangdong Province just have 31 highways congestion occur.In order to solve congestion problems, freeway management department can gather around
It is stifled occur after take and certain dredge measure.Although these measures can reach certain effect, the congestion occurred still to
Traveler and manager cause loss.Therefore, some scholars propose the imagination that congestion dispersal is carried out before congestion occurs.
Gradually ripe development of Mobile Internet technology makes it possible this imagination in recent years：By mobile Internet and highway operation
Management combines, and realizes that the active of congestion is dredged by preengaging current and induced travel.
Realize such target, it is premise to carry out accurately crowded prediction to highway.Domestic and foreign scholars are right
The crowded prediction of highway has carried out a large amount of research.Developed country starts from the middle of last century to the research of crowded prediction model.
Research of the China in terms of road traffic congestion prediction start late and most of research be all by the short-term volume of traffic or other
Index carrys out evaluation path traffic congestion.Main method having time series model, the card in terms of crowded prediction are applied at present
Kalman Filtering model, neural network model, supporting vector machine model etc..These methods have his own strong points, but all there are one common
Point --- it is predicted on the basis of historical data.This makes the reservation that existing method is difficult to cope with highway pass through as band
The challenge come：1. reservation data will provide new data source for crowded prediction work, reservation number is considered during prediction
According to the fusion with historical data.2. the behavior of reservation will change distribution of the volume of traffic in freeway network.If crowded predicted
These variations are not accounted in journey and simply apply mechanically existing method, it will leads to the decline of crowded precision of prediction.
Therefore, the present invention proposes a kind of fusion reservation data and historical data carries out the crowded prediction technique of highway,
More reliable foundation is provided for induced travel and freeway management work.
Invention content
The shortcomings that it is an object of the invention to overcome the prior art and deficiency provide a kind of fusion historical data and reservation number
According to the crowded prediction technique of highway, break through the conventional crowded prediction technique of highway do not consider user's reservation data, can not
The defects of volume of traffic redistributes in road network after reservation is passed through, is carried out in reflection, realizes under the conditions of highway preengages current
Crowded prediction, precision of prediction can be effectively improved, laid the foundation for the current realization of highway reservation.
To achieve the above object, technical solution provided by the present invention is：A kind of fusion historical data and reservation data
The crowded prediction technique of highway, includes the following steps：
1) highway for selecting research, obtains basic data, includes the data of four aspects in total：Highway user is pre-
About data, freeway network basic data, freeway traffic flow historical data, expressway tol lcollection policy data.
2) according to highway user reservation data and freeway network basic data, when being carried out to freeway network
Sky divides the prediction period for handling and determining each section.
3) volume of traffic in each section of freeway network in prediction period is predicted：First, historical data pair is utilized
Traffic total amount is predicted；Then, total wheel traffic is allocated and with reservation data according to reservation amount and non-reservation amount to non-
Reservation amount is modified；Finally reservation amount after distribution is added to obtain the volume of traffic in each section with non-reservation amount.
4) crowding in each section is predicted：Using saturation degree as crowded evaluation index, pass through each section of threshold decision
Congestion state.
5) programming language is utilized to realize that the visualization of crowded prediction result exports.
In step 1), the highway user reservation data includes departure place input by user, destination, when entering the station
The occupation rate of market of section, vehicle, user app or wechat public platform is obtained by the backstages user app or wechat public platform backstage
It takes.The road network basic data of the highway include the length of each road, number of track-lines, lane width, design capacity, from
By the time of vehicle operation under the conditions of flowing, by Operation and Management of Expressway, department obtains.The freeway traffic flow history
Data include flow and vehicle speed data, and by Operation and Management of Expressway, department obtains.The expressway tol lcollection policy data
It is worth including basic rate, the margin of preference and hourage, by Operation and Management of Expressway, department obtains.
In step 2), according to freeway network basic data and user's reservation data, freeway network is carried out
Space-time divides the prediction period for handling and determining each section, specifically includes following steps：
2.1) division of time：When using the unit consistent with the Time segments division that enters the station on user app or wechat public platform
Between interval of delta t, 10min, 20min, 30min, 60min can be sought according to Operation and Management of Expressway department, one day 24
Hour is divided into m timeslice.
In formula：T --- timeslice set；
ti--- tiA timeslice；
M --- timeslice number, it is a；
Δ t --- unit interval, min.
2.2) division in space：Phase one is divided using the section for acquiring traffic flow data with Operation and Management of Expressway department
The unit space interval delta s of cause can will seek 1km, 2km, 5km, 10km, by road network according to Operation and Management of Expressway department
Interior all highways are divided intoA section.
In formula：Gather in S --- section；
--- the kth of j-th strip highwayjA section；
kj--- the section number of j-th strip highway, it is a；
Lj--- the length of j-th strip highway, km；
Δ s --- unit space interval, km；
N --- highway item number, item.
2.3) prediction period in each section is determined：The time that user reaches each road segment end is calculated by following formula：
In formula：--- user reaches the kth of j-th strip highwayjThe time of a road segment end；
--- user reaches the time of the entrance of j-th strip highway；
vj--- the average speed of operation of j-th strip highway；
ta--- the starting point of the period of entering the station of user's reservation；
tb--- the terminal of the period of entering the station of user's reservation.
In this manner it is possible to judge sectionPrediction period：IfThen sectionPrediction period be
ti。
In step 3), the volume of traffic in each section of prediction period freeway network is predicted, is specifically included following
Step：
3.1) total wheel traffic that each section is predicted according to historical data, is predicted using BP neural network, can directly be existed
Included function is called to realize in matlab.
3.2) reservation traffic total amount is calculatedWith non-reservation traffic total amountIts calculation formula is：
In formula：--- prediction period tiWhen road network total wheel traffic；
--- prediction period tiWhen road network reservation traffic total amount；
--- prediction period tiWhen road network non-reservation traffic total amount；
--- prediction period tiWhen j-th strip highway kthjThe volume of traffic in a section；
α --- the occupation rate of market of user app or wechat public platform.
Remaining each parameter meaning is as previously described.
3.3) according to the historical rethinking rule of vehicle flowrate, non-reservation traffic total amount is allocated within the scope of road network, is obtained
To the non-reservation amount in each section, calculation formula is：
In formula：--- prediction period tiWhen j-th strip highway kthjThe non-reservation traffic in a section
Amount.
3.4) it uses user equilibrium model to be allocated reservation traffic total amount within the scope of road network, obtains the pre- of each section
About measure.
In formula：--- prediction period tiWhenjThe flow of highway.
--- Impedance Function of the subscriber in road network, concrete form are：
In formula：to--- the running time under the conditions of free flow, min；
--- thejThe kth of highwayjThe highway layout traffic capacity in a section, pcu/h；
co--- the current expense in basis, member；
γ --- the margin of preference, %；
α, β --- parameter to be calibrated；
τ --- hourage is worth, member/h.
3.5) the reservation amount in each section is modified using practical reservation amount, modification rule is as follows：
If 1.ThenIt is constant.
If 2.Then
In formula：--- prediction period tiWhenjThe kth of highwayjThe practical reservation in a section is handed over
Logical total amount；
3.6) each section reservation amount and non-reservation amount are overlapped, obtain the volume of traffic in each section.
In formula：--- prediction period tiWhenjThe kth of highwayjThe reservation volume of traffic in a section.
In step 4), predicts the crowding in each section, specifically include following steps：
4.1) saturation degree in each section is calculated, calculation formula is：
In formula：--- prediction period tiWhenjThe kth of highwayjThe saturation degree in a section.
4.2) crowding is divided into different grades by the division of crowding grade by saturation degree threshold value.It can basis
The traffic survey data in each city carrys out threshold value.In the case of no survey data, it is proposed that be classified using following table.
Table 1
In step 5), realize that the visualization of crowded prediction result exports using programming language.It will be different crowded
It spends grade and different colors is corresponding, corresponding color code is assigned to each section when writing code.Run code, you can defeated
Go out crowded prediction result.
Compared with prior art, the present invention having the following advantages that and advantageous effect：
1, development of Mobile Internet technology is applied in freeway management, is obtained by mobile phone app or wechat public platform
Reservation data, to realize that the reservation passage of highway provides basis.
2, user's reservation data is fused in prediction algorithm, the result to being carried out crowded prediction using historical data is carried out
It corrects, the precision of prediction work can be improved.
3, algorithm is easier, and the process of modeling is simple, and a large amount of learning sample, prediction essence are not needed during training
Degree is higher.
4, it is convenient for Operation and Management of Expressway department by lever of price, traffic dispersion is carried out using reservation trip mode,
The user equilibrium for promoting highway network, avoids the formation of congestion, also allows for user and avoid crowded section of highway and congestion period as possible
Staggered shifts.
5, be conducive to freeway management department and grasp road grid traffic stream information and jam situation in time, to the following possible shape
At congestion carry out early warning, take necessary discongest measure in time.
Description of the drawings
Fig. 1 is the logic flow schematic diagram of the present invention.
Fig. 2 is the road network schematic diagram of the embodiment of the present invention.
Fig. 3 is the crowded prediction visualization output result of the embodiment of the present invention.
Specific implementation mode
With reference to specific embodiment, the present invention is described further.
As shown in Figure 1, the crowded prediction technique of highway of the fusion historical data and reservation data described in the present embodiment,
Include the following steps：
1) highway for selecting research, obtains basic data, includes the data of four aspects in total：Highway user is pre-
About data, freeway network basic data, freeway traffic flow historical data, highway preengage cost data.Wherein,
The highway user reservation data includes departure place input by user, destination, period of entering the station, vehicle, user app or micro-
The occupation rate of market for believing public platform is obtained by the backstages user app or wechat public platform backstage.The road network of the highway
Basic data includes the vehicle traveling under the conditions of the length of each road, number of track-lines, lane width, design capacity, free flow
Time, by Operation and Management of Expressway, department obtains.The freeway traffic flow historical data includes flow and speed number
According to by Operation and Management of Expressway, department obtains.The expressway tol lcollection policy data includes basic rate, the margin of preference
It is worth with hourage, by Operation and Management of Expressway, department obtains.
Freeway network used by the present embodiment is as shown in Figure 2.It is assumed that obtaining certain user input from the backstages user app
Departure place be A, destination B, period of entering the station are 8：00–9:00, vehicle is a kind of vehicle；The occupation rate of market of user app is
12%.
It according to the departure place of certain above-mentioned user reservation, destination and enters the station the period, it is as follows to be collected into remaining data：At a high speed
Highway network basic data is as shown in table 1, the flow histories data of highway 1,2 and current subscriber total amount be respectively such as table
2, (since length is limited, only listing the historical data of prediction period), expressway tol lcollection policy data such as table 4 shown in table 3
Shown, the average speed of operation of two highways is 80km/h, and the time of vehicle operation under the conditions of free flow is respectively 1.1h
And 1.5h.
1 freeway network basic data of table
The flow histories data and current subscriber total amount of 2 highway 1 of table
| 8/1 | 8/8 | 8/15 | 8/22 | 8/29 | 9/5 | 9/12 | 9/19 | Reservation | |
| 8：00—9：00 | 4513 | 4351 | 4315 | 4415 | 4483 | 4335 | 4214 | 4377 | 29 |
| 9：00—10：00 | 4296 | 4021 | 4141 | 4113 | 4291 | 4061 | 4109 | 4121 | 14 |
The flow histories data and current subscriber total amount of 3 highway 2 of table
| 8/1 | 8/8 | 8/15 | 8/22 | 8/29 | 9/5 | 9/12 | 9/19 | Reservation | |
| 8：00—9：00 | 1193 | 1210 | 1189 | 1198 | 1170 | 1175 | 1161 | 1191 | 686 |
| 9：00—10：00 | 1121 | 1144 | 1123 | 1174 | 1098 | 1124 | 1113 | 1113 | 570 |
4 expressway tol lcollection policy data of table
2) according to freeway network basic data and user's reservation data, freeway network is carried out at space-time division
The prediction period for managing and determining each section, specifically includes following steps：
2.1) division of time：When using the unit consistent with the Time segments division that enters the station on user app or wechat public platform
Between interval of delta t, 10min, 20min, 30min, 60min can be sought according to Operation and Management of Expressway department, one day 24
Hour is divided into m timeslice.
In formula：T --- timeslice set；
ti--- tiA timeslice；
M --- timeslice number, it is a；
Δ t --- unit interval, min.
In the present embodiment, unit interval Δ t=60min is divided into m=24 timeslice in 24 hours one day, then T=
{t1,…,t12,…,t24}。
2.2) division in space：Phase one is divided using the section for acquiring traffic flow data with Operation and Management of Expressway department
The unit space interval delta s of cause can will seek 1km, 2km, 5km, 10km, by road network according to Operation and Management of Expressway department
Interior all highways are divided intoA section.
In formula：Gather in S --- section；
--- the kth of j-th strip highwayjA section；
kj--- the section number of j-th strip highway, it is a；
Lj--- the length of j-th strip highway, km；
Δ s --- unit space interval, km；
N --- highway item number, item.
In the present embodiment, every highway only carries out a flow collection job, therefore every highway is 1
Section.The length of each highway is unit space interval, then S={ s11, s21}。
2.3) prediction period in each section is determined：The time that user reaches each road segment end is calculated by following formula：
In formula：--- user reaches the kth of j-th strip highwayjThe time of a road segment end；
--- user reaches the time of the entrance of j-th strip highway；
vj--- the average speed of operation of j-th strip highway；
ta--- the starting point of the period of entering the station of user's reservation；
tb--- the terminal of the period of entering the station of user's reservation.
In this manner it is possible to judge sectionPrediction period：IfThen sectionPrediction period be
ti。
In the present embodiment, time of entering the station is(indicating at 8 points 30 minutes, similarly hereinafter).It is each that user reaches highway 1
The terminal time in section is 9.5, therefore the prediction period of highway 1 is t9(8:00-9:And t 00)10(9:00-10:00).User
The terminal time for reaching 2 each section of highway is 10.00.Therefore the prediction period of highway 2 is t9(8:00-9:And t 00)10
(9:00-10:00)。
3) volume of traffic in each section of prediction period freeway network is predicted, specifically includes following steps：
3.1) total wheel traffic that each section is predicted according to historical data, is predicted using BP neural network, can directly be existed
Included function is called to realize in matlab.
In the present embodiment, prediction result is as shown in table 5.
5 freeway total wheel traffic predicted value of table
3.2) reservation traffic total amount is calculatedWith non-reservation traffic total amountIts calculation formula is：
In formula：--- prediction period tiWhen road network total wheel traffic；
--- prediction period tiWhen road network reservation traffic total amount；
--- prediction period tiWhen road network non-reservation traffic total amount；
--- prediction period tiWhen j-th strip highway kthjThe volume of traffic in a section；
α --- the occupation rate of market of user app or wechat public platform.
Remaining each parameter meaning is as previously described.
Result of calculation is as shown in table 6.
Table 6 preengages the volume of traffic and non-reservation volume of traffic result of calculation
3.3) according to the historical rethinking rule of vehicle flowrate, non-reservation traffic total amount is allocated within the scope of road network, is obtained
To the non-reservation amount in each section, calculation formula is：
In formula：--- prediction period tiWhen j-th strip highway kthjThe non-reservation traffic in a section
Amount.
Result of calculation is as shown in table 7.
7 non-reservation amount allocation result of table
3.4) it uses user equilibrium model to be allocated reservation traffic total amount within the scope of road network, obtains the pre- of each section
About measure.
In formula：--- prediction period tiWhenjThe flow of highway.
--- Impedance Function of the subscriber in road network, concrete form are：
In formula：to--- the running time under the conditions of free flow, min；
--- thejThe kth of highwayjThe highway layout traffic capacity in a section, pcu/h；
co--- the current expense in basis, member；
γ --- the margin of preference, %；
α, β --- parameter to be calibrated；
τ --- hourage is worth, member/h.
In the present embodiment, parameter value is α=0.5, β=1.Think that the user for walking highway 2 is staggered shifts, high speed
The non-staggered shifts of user of highway 1.Therefore the impedance function of two highways is respectively：
Solve linear equation in two unknowns groupObtain reservation amount
Allocation result is as shown in table 8.
8 reservation amount allocation result of table
3.5) the reservation amount in each section is modified using practical reservation amount, modification rule is as follows：
If 1.ThenIt is constant.
If 2.Then
In formula：--- prediction period tiWhenjThe kth of highwayjThe practical reservation in a section is handed over
Logical total amount；
It is revised that the results are shown in Table 9：
The correction result of 9 reservation amount of table distribution
3.6) each section reservation amount and non-reservation amount are overlapped, obtain the volume of traffic in each section.
In formula：--- prediction period tiWhenjThe of article highwaykjThe reservation volume of traffic in a section.
Final prediction result is as shown in table 10：
The final prediction result of 10 link counting of table
4) crowding in each section is predicted：Using saturation degree as crowded evaluation index, pass through each section of threshold decision
Congestion state.Specifically include following steps：
4.1) saturation degree in each section is calculated, calculation formula is：
In formula：--- user selects trip period tiWhenjThe kth of highwayjThe saturation degree in a section.
Saturation computation result is as shown in table 11.
11 saturation computation result of table
4.2) crowding is divided into different grades by the division of crowding grade by saturation degree threshold value.It can basis
The traffic survey data in each city carrys out threshold value.In the case of no survey data, it is proposed that be classified using table 12.
Table 22
5) programming language is utilized to realize that the visualization of crowded prediction result exports.By different crowding grades and not
Same color corresponds to, and corresponding color code is assigned to each section when writing code.Run code, you can export crowded prediction
As a result.The results are shown in Figure 3 for output.
In conclusion after using above scheme, the present invention provides new method for the crowded prediction of highway, can
The current new change brought of highway reservation is adapted to, the congested conditions of highway is effectively predicted in a new condition, is
The induced travel and management work of highway provide more reliable basis, effectively push the development of China's highway, have
Actual promotional value is worthy to be popularized.
Embodiment described above is only the preferred embodiments of the invention, and but not intended to limit the scope of the present invention, therefore
Change made by all shapes according to the present invention, principle, should all cover within the scope of the present invention.
Claims (3)
1. a kind of crowded prediction technique of highway of fusion historical data and reservation data, which is characterized in that including following step
Suddenly：
1) highway for selecting research, obtains basic data, includes the data of four aspects in total：Highway user reservation number
According to, freeway network basic data, freeway traffic flow historical data, expressway tol lcollection policy data；
2) according to highway user reservation data and freeway network basic data, space-time is carried out to freeway network and is drawn
Divide the prediction period for handling and determining each section；
Specifically include following steps：
2.1) division of time：Using between the unit interval consistent with the Time segments division that enters the station on user app or wechat public platform
Every Δ t, 10min, 20min, 30min, 60min to be sought according to Operation and Management of Expressway department, one day 24 small time-division
At m timeslice；
In formula：T --- timeslice set；
ti--- tiA timeslice；
M --- timeslice number；
Δ t --- unit interval, chronomere min；
2.2) division in space：It is divided using the section for acquiring traffic flow data with Operation and Management of Expressway department consistent
Unit space interval delta s will seek 1km, 2km, 5km, 10km according to Operation and Management of Expressway department, by the institute in road network
There is highway to be divided intoA section；
In formula：Gather in S --- section；
--- the kth of j-th strip highwayjA section；
kj--- the section number of j-th strip highway；
Lj--- the length of j-th strip highway, unit km；
Δ s --- unit space interval, unit km；
N --- highway item number；
2.3) prediction period in each section is determined：The time that user reaches each road segment end is calculated by following formula：
In formula：--- user reaches the kth of j-th strip highwayjThe time of a road segment end；
--- user reaches the time of the entrance of j-th strip highway；
vj--- the average speed of operation of j-th strip highway；
ta--- the starting point of the period of entering the station of user's reservation；
tb--- the terminal of the period of entering the station of user's reservation；
In this way, it is possible to judge sectionPrediction period：IfThen sectionPrediction period be ti；
3) volume of traffic in each section of freeway network in prediction period is predicted
First, traffic total amount is predicted using historical data；Then, by total wheel traffic according to reservation amount and non-reservation measure into
Row is distributed and is modified to non-reservation amount with reservation data；Finally reservation amount after distribution is added to obtain each road with non-reservation amount
The volume of traffic of section；
Specifically include following steps：
3.1) total wheel traffic that each section is predicted according to historical data, is predicted using BP neural network, can directly be existed
Included function is called to realize in matlab；
3.2) reservation traffic total amount is calculatedWith non-reservation traffic total amountIts calculation formula is：
In formula：--- prediction period tiWhen road network total wheel traffic；
--- prediction period tiWhen road network reservation traffic total amount；
--- prediction period tiWhen road network non-reservation traffic total amount；
--- prediction period tiWhen j-th strip highway kthjThe volume of traffic in a section；
α --- the occupation rate of market of user app or wechat public platform；
Remaining each parameter meaning is as previously described；
3.3) according to the historical rethinking rule of vehicle flowrate, non-reservation traffic total amount is allocated within the scope of road network, is obtained each
The non-reservation amount in section, calculation formula are：
In formula：--- prediction period tiWhen j-th strip highway kthjThe non-reservation volume of traffic in a section；
3.4) it uses user equilibrium model to be allocated reservation traffic total amount within the scope of road network, obtains the reservation in each section
Amount；
In formula：--- prediction period tiWhenjThe flow of highway；
--- Impedance Function of the subscriber in road network, concrete form are：
In formula：to--- the running time under the conditions of free flow, unit min；
--- thejThe kth of highwayjThe highway layout traffic capacity in a section, unit pcu/h；
co--- the current expense in basis, unit are member；
γ --- the margin of preference, unit %；
α, β --- parameter to be calibrated；
τ --- hourage is worth, and unit is member/h；
3.5) the reservation amount in each section is modified using practical reservation amount, modification rule is as follows：
If 1.ThenIt is constant；
If 2.Then
In formula：--- prediction period tiWhenjThe kth of highwayjThe practical reservation traffic in a section is total
Amount；
3.6) each section reservation amount and non-reservation amount are overlapped, obtain the volume of traffic in each section；
In formula：--- prediction period tiWhen j-th strip highway kthjThe reservation volume of traffic in a section；
4) crowding in each section is predicted：Using saturation degree as crowded evaluation index, pass through gathering around for each section of threshold decision
The state of squeezing；
Specifically include following steps：
4.1) saturation degree in each section is calculated, calculation formula is：
In formula：--- prediction period tiWhen j-th strip highway kthjThe saturation degree in a section；
4.2) crowding is divided into different grades by the division of crowding grade by saturation degree threshold value, can be according to each city
The traffic survey data in city carrys out threshold value；
5) programming language is utilized to realize that the visualization of crowded prediction result exports.
2. the crowded prediction technique of highway of a kind of fusion historical data and reservation data according to claim 1,
It is characterized in that：In step 1), the highway user reservation data includes departure place input by user, destination, enters the station
Period, vehicle, user app or wechat public platform occupation rate of market, obtained by the backstage user app or wechat public platform backstage
It takes；The freeway network basic data includes length, number of track-lines, lane width, design capacity, the freedom of each road
Time of vehicle operation under the conditions of stream, by Operation and Management of Expressway, department obtains；The freeway traffic flow history number
According to including flow and vehicle speed data, by Operation and Management of Expressway, department obtains；The expressway tol lcollection policy data packet
Basic rate, the margin of preference and hourage value are included, department obtains by Operation and Management of Expressway.
3. the crowded prediction technique of highway of a kind of fusion historical data and reservation data according to claim 1,
It is characterized in that：In step 5), realize that the visualization of crowded prediction result exports using programming language, it will be different crowded
It spends grade and different colors is corresponding, corresponding color code is assigned to each section when writing code, run code, you can defeated
Go out crowded prediction result.
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