CN109147325A - Road condition prediction method and device, storage medium and processor - Google Patents
Road condition prediction method and device, storage medium and processor Download PDFInfo
- Publication number
- CN109147325A CN109147325A CN201811027234.XA CN201811027234A CN109147325A CN 109147325 A CN109147325 A CN 109147325A CN 201811027234 A CN201811027234 A CN 201811027234A CN 109147325 A CN109147325 A CN 109147325A
- Authority
- CN
- China
- Prior art keywords
- moment
- state
- frequency
- occurrences
- transition probability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 230000007704 transition Effects 0.000 claims abstract description 146
- 239000011159 matrix material Substances 0.000 claims abstract description 100
- 238000012546 transfer Methods 0.000 claims description 24
- 238000012545 processing Methods 0.000 claims description 17
- 238000005516 engineering process Methods 0.000 abstract description 9
- 230000015654 memory Effects 0.000 description 12
- 238000010586 diagram Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 230000002354 daily effect Effects 0.000 description 4
- 238000005457 optimization Methods 0.000 description 4
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 230000005389 magnetism Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000003449 preventive effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000005309 stochastic process Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- 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
Landscapes
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a road condition prediction method and device, a storage medium and a processor. Wherein, the method comprises the following steps: acquiring historical road condition information of a target road section in a preset time period, wherein the historical road condition information comprises road states corresponding to different moments; calculating the state occurrence frequency of each moment according to the road states corresponding to different moments, wherein the state occurrence frequency is used for indicating the frequency of the road states occurring in a preset time period; obtaining a transition probability matrix of each moment according to the state occurrence frequency of each moment, wherein the transition probability matrix comprises the probability of state transition from the previous moment to the later moment; and determining the road condition probability of the target road section at the moment to be predicted based on the transition probability matrix, wherein the road condition probability is used for indicating the probability of the road state occurring at the moment to be predicted. The invention solves the technical problems that the traffic jam is aggravated and the travel time is increased due to the fact that the road condition cannot be predicted in the related technology.
Description
Technical field
The present invention relates to traffic information process fields, are situated between in particular to a kind of road condition predicting method and device, storage
Matter, processor.
Background technique
With the continuous propulsion of urban construction, traffic trip becomes emphasis concerned by people, wherein being the most urgently timely
Predicting road conditions, which are given, to shunt and takes preventive measures, and reduces traffic accident and reduces the travel time.For citizen, it can shift to an earlier date
Precognition road conditions simultaneously reasonably select trip mode, can also alleviate traffic jam on the whole.
It is on the one hand the statistical data based on traffic department currently for the analysis and judgement of traffic, such as iron man
Stream statistics；On the other hand the data of traffic application software generation, such as navigation map are derived from.
However, in the analysis of existing traffic, there is no the predictions to road conditions, in fact it could happen that when setting out will by
Section is unimpeded, but the case where being congestion still there may be traffic jam is aggravated increases asking for travel time when reaching
Topic.
For above-mentioned problem, currently no effective solution has been proposed.
Summary of the invention
The embodiment of the invention provides a kind of road condition predicting method and device, storage medium, processor, at least solve by
Can still exist caused by it can not predict in the related technology road conditions and aggravate traffic jam, the technology for increasing the travel time is asked
Topic.
According to an aspect of an embodiment of the present invention, a kind of road condition predicting method is provided, comprising: obtain target road section and exist
History traffic information in preset time period, history traffic information include different moments corresponding road condition；According to it is different when
Corresponding road condition is carved, the state frequency of occurrences at each moment is calculated, the state frequency of occurrences is used to indicate road condition pre-
If the frequency occurred in the period；According to the state frequency of occurrences at each moment, the transition probability matrix at each moment is obtained, is turned
Moving probability matrix includes the probability for being carved into the transfer of later moment in time state for the moment in the past；Based on transition probability matrix, target road is determined
Road conditions probability of the section at the moment to be predicted, road conditions probability are used to indicate the probability that road condition occurs at the moment to be predicted.
Optionally, it is based on transition probability matrix, determines target road section in the road conditions probability at moment to be predicted, comprising: is obtained
The history road conditions probability of the previous moment at moment to be predicted；According to history road conditions probability and transition probability matrix, calculate to be predicted
The road conditions probability at moment.
Optionally, according to history road conditions probability and transition probability matrix, the road conditions probability at moment to be predicted is calculated, comprising:
It sums to history road conditions probability and transition probability matrix, obtains the road conditions probability at moment to be predicted.
Optionally, according to the state frequency of occurrences at each moment, the transition probability matrix for obtaining each moment includes: to obtain
The state frequency of occurrences of t moment and the state frequency of occurrences at t+1 moment；When according to the state frequency of occurrences and t+1 of t moment
The state frequency of occurrences at quarter is calculated from t moment to the state transition probability at t+1 moment；According to from t moment to the shape at t+1 moment
State transition probability obtains the transition probability matrix of t moment.
Optionally, road condition includes unimpeded, slow and congestion；State transfer includes unimpeded to unimpeded, slow to slow
Slowly, congestion to congestion, it is unimpeded to slowly, slowly arrive congestion, congestion to it is slow and slowly arrive it is unimpeded.
Optionally, according to the state frequency of occurrences at each moment, the transition probability matrix for obtaining each moment includes: to obtain
t1The state frequency of occurrences and t at moment2The state frequency of occurrences at moment, wherein t1The state frequency of occurrences at moment includesAndIndicate the unimpeded frequency occurred within a preset period of time,Expression slowly goes out within a preset period of time
Existing frequency,Indicate the frequency that congestion occurs within a preset period of time；Solve ternary once linear equation:
Obtain p11、p22、p33、p12、p23、p32And p21, wherein p11It indicates from t1
Moment is to t2Moment is unimpeded to smooth state transition probability, p22It indicates from t1Moment is to t2Moment slowly arrives slow state and turns
Move probability, p33It indicates from t1Moment is to t2State transition probability of the moment congestion to congestion, p12It indicates from t1Moment is to t2Moment is smooth
Lead to slow state transition probability, p23It indicates from t1Moment is to t2Moment slowly arrives the state transition probability of congestion, p32It indicates
From t1Moment is to t2Moment congestion is to slow state transition probability, p21It indicates from t1Moment is to t2Moment slowly arrives smooth shape
State transition probability, p13=p31=0；According to obtained p11、p22、p33、p12、p23、p32And p21, determine t1The transfer at moment is general
Rate matrix isSuccessively calculate t2Moment is to t1440The transition probability matrix at moment.
Optionally, according to different moments corresponding road condition, the state frequency of occurrences at each moment is calculated, comprising: logical
Cross formulaCalculate the state frequency of occurrences of t moment, wherein ftIndicate the state frequency of occurrences of t moment,
count(st) indicate that the number that road condition occurs in preset time period, T indicate the number of days of preset time period.
Optionally, the above method further include: the road condition of acquisition target road section, the state for updating each moment go out in real time
Existing frequency, updates transition probability matrix.
According to another aspect of an embodiment of the present invention, a kind of road condition predicting device is additionally provided, comprising: acquiring unit is used
In obtaining the history traffic information of target road section within a preset period of time, history traffic information includes different moments corresponding road
State；Computing unit, for calculating the state frequency of occurrences at each moment, state according to different moments corresponding road condition
The frequency of occurrences is used to indicate the frequency that road condition occurs within a preset period of time；Processing unit, for according to each moment
The state frequency of occurrences, obtains the transition probability matrix at each moment, and transition probability matrix includes being carved into later moment in time for the moment in the past
The probability of state transfer；Determination unit determines that target road section is general in the road conditions at moment to be predicted for being based on transition probability matrix
Rate, road conditions probability are used to indicate the probability that road condition occurs at the moment to be predicted.
Optionally it is determined that unit is based on transition probability matrix for executing following steps, determine target road section to be predicted
The road conditions probability at moment: the history road conditions probability of the previous moment at moment to be predicted is obtained；According to history road conditions probability and transfer
Probability matrix calculates the road conditions probability at moment to be predicted.
Optionally, processing unit is for executing following steps according to the state frequency of occurrences at each moment, when obtaining each
The transition probability matrix at quarter: the state frequency of occurrences of t moment and the state frequency of occurrences at t+1 moment are obtained；According to t moment
The state frequency of occurrences and the state frequency of occurrences at t+1 moment are calculated from t moment to the state transition probability at t+1 moment；According to
From t moment to the state transition probability at t+1 moment, the transition probability matrix of t moment is obtained.
Optionally, computing unit is for executing following steps according to different moments corresponding road condition, when calculating each
The state frequency of occurrences at quarter: pass through formulaCalculate the state frequency of occurrences of t moment, wherein ftIndicate t moment
The state frequency of occurrences, count (st) indicate that the number that road condition occurs in preset time period, T indicate preset time period
Number of days.
According to another aspect of an embodiment of the present invention, a kind of storage medium is additionally provided, storage medium includes the journey of storage
Sequence, wherein equipment where control storage medium executes above-mentioned road condition predicting method in program operation.
According to another aspect of an embodiment of the present invention, a kind of processor is additionally provided, processor is used to run program,
In, program executes above-mentioned road condition predicting method when running.
In embodiments of the present invention, the history traffic information using acquisition target road section within a preset period of time, history road
Condition information includes different moments corresponding road condition；According to different moments corresponding road condition, the shape at each moment is calculated
The state frequency of occurrences, the state frequency of occurrences are used to indicate the frequency that road condition occurs within a preset period of time；According to each moment
The state frequency of occurrences, obtain the transition probability matrix at each moment, transition probability matrix includes when being carved into latter for the moment in the past
The probability of quarter state transfer；Based on transition probability matrix, determine target road section in the road conditions probability at moment to be predicted, road conditions probability
It is used to indicate the mode for the probability that road condition occurred at the moment to be predicted, by acquiring the history traffic information of target road section,
The state frequency of occurrences at each moment is calculated, and then obtains the transition probability predicted for the following road conditions to target road section
Matrix has reached and has predicted section road conditions, and for the purpose made reference of going on a journey, traffic jam is effectively relieved to realize,
The technical effect of travel time is reduced, and then solves and can still be deposited as caused by can not predict in the related technology road conditions
The technical issues of aggravating traffic jam, increasing the travel time.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes part of this application, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is one kind according to an embodiment of the present invention optionally for realizing the hard of the terminal of road condition predicting method
Part structural block diagram；
Fig. 2 is a kind of flow diagram of optional road condition predicting method according to an embodiment of the present invention；
Fig. 3 (a) is a kind of schematic diagram of the state relation of optional state space S according to an embodiment of the present invention；
Fig. 3 (b) is one kind according to an embodiment of the present invention optionally from tiMoment is to ti+1The state transition probability at moment
Schematic diagram；
Fig. 4 be it is according to an embodiment of the present invention one kind optionally from t moment to the signal of the transition probability matrix at t+1 moment
Figure；
Fig. 5 is a kind of structural schematic diagram of optional road condition predicting device according to an embodiment of the present invention.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only
The embodiment of a part of the invention, instead of all the embodiments.Based on the embodiments of the present invention, ordinary skill people
The model that the present invention protects all should belong in member's every other embodiment obtained without making creative work
It encloses.
It should be noted that description and claims of this specification and term " first " in above-mentioned attached drawing, "
Two " etc. be to be used to distinguish similar objects, without being used to describe a particular order or precedence order.It should be understood that using in this way
Data be interchangeable under appropriate circumstances, so as to the embodiment of the present invention described herein can in addition to illustrating herein or
Sequence other than those of description is implemented.In addition, term " includes " and " having " and their any deformation, it is intended that cover
Cover it is non-exclusive include, for example, the process, method, system, product or equipment for containing a series of steps or units are not necessarily limited to
Step or unit those of is clearly listed, but may include be not clearly listed or for these process, methods, product
Or other step or units that equipment is intrinsic.
Firstly, the part noun or term that occur during the embodiment of the present application is described are suitable for following solution
It releases:
Markov chain (Markov Chain): being the discrete time stochastic process in mathematics with Markov property.
During being somebody's turn to do, in the case where given current instruction or information, the past is unrelated to prediction future.It is stochastic variable X_1,
A quantity of X_2, X_3 ....The range of these variables, the i.e. set of their all possible values, referred to as " state is empty
Between ", and the value of X_n is then the state in time n.
State space: refer to the ordered set of the variable of the minimal amount of decision systems state in systems.
Transition probability: from any one state, by any primary transfer, necessarily out present condition 1, in 2 ..., m
One, the transfer between this state is known as transition probability.
Embodiment 1
According to embodiments of the present invention, a kind of embodiment of the method for road condition predicting method is additionally provided, it should be noted that
The step of process of attached drawing illustrates can execute in a computer system such as a set of computer executable instructions, also,
It, in some cases, can be to be different from shown in sequence execution herein although logical order is shown in flow charts
The step of out or describing.
Embodiment of the method provided by the embodiment of the present application one can be in mobile terminal, terminal or similar fortune
It calculates and is executed in device.Fig. 1 shows a kind of hardware of terminal (or mobile device) for realizing road condition predicting method
Structural block diagram.As shown in Figure 1, terminal 10 (or mobile device 10) may include it is one or more (in figure using 102a,
102b ... ..., 102n are shown) (processor 102 can include but is not limited to Micro-processor MCV or programmable patrols processor 102
The processing unit of volume device FPGA etc.), memory 104 for storing data and the transmission module for communication function.It removes
It can also include: that display, input/output interface (I/O interface), the port universal serial bus (USB) (can be made other than this
Included for a port in the port of I/O interface), network interface, power supply and/or camera.Those of ordinary skill in the art
It is appreciated that structure shown in FIG. 1 is only to illustrate, the structure of above-mentioned electronic device is not caused to limit.For example, computer
Terminal 10 may also include than shown in Fig. 1 more perhaps less component or with the configuration different from shown in Fig. 1.
It is to be noted that said one or multiple processors 102 and/or other data processing circuits lead to herein
Can often " data processing circuit " be referred to as.The data processing circuit all or part of can be presented as software, hardware, firmware
Or any other combination.In addition, data processing circuit for single independent processing module or all or part of can be integrated to meter
In any one in other elements in calculation machine terminal 10 (or mobile device).As involved in the embodiment of the present application,
The data processing circuit controls (such as the selection for the variable resistance end path connecting with interface) as a kind of processor.
Memory 104 can be used for storing the software program and module of application software, such as (the road conditions in the embodiment of the present invention
Corresponding program instruction/the data storage device of prediction technique, the software that processor 102 is stored in memory 104 by operation
Program and module realize the road condition predicting of above-mentioned application program thereby executing various function application and data processing
Method.Memory 104 may include high speed random access memory, may also include nonvolatile memory, such as one or more magnetism
Storage device, flash memory or other non-volatile solid state memories.In some instances, memory 104 can further comprise phase
The memory remotely located for processor 102, these remote memories can pass through network connection to terminal 10.On
The example for stating network includes but is not limited to internet, intranet, local area network, mobile radio communication and combinations thereof.
Transmitting device is used to that data to be received or sent via a network.Above-mentioned network specific example may include calculating
The wireless network that the communication providers of machine terminal 10 provide.In an example, transmitting device includes a network adapter
(Network Interface Controller, NIC), can be connected by base station with other network equipments so as to interconnection
Net is communicated.In an example, transmitting device can be radio frequency (Radio Frequency, RF) module, be used to pass through
Wireless mode is communicated with internet.
Display can such as touch-screen type liquid crystal display (LCD), the liquid crystal display aloow user with
The user interface of terminal 10 (or mobile device) interacts.
Under above-mentioned running environment, this application provides road condition predicting methods as shown in Figure 2.Fig. 2 is according to the present invention
The flow chart of the road condition predicting method of embodiment one.
Step S202 obtains the history traffic information of target road section within a preset period of time.
In the application above-mentioned steps S202, history traffic information includes different moments corresponding road condition；Road condition
Including unimpeded, slow and congestion.
The road condition predicting method of the present embodiment, for constructing transition probability matrix, the problem of core, is how knowing
The state space probability at t+1 moment is speculated under the state space probability scenarios of road t moment, and solving this key problem will be according to
Rely historical data, the probability of state transfer is found from historical statistics.Therefore, it is necessary first to acquire target road section in preset time
History traffic information in section, for example, acquisition target road section past three year every point of road conditions daily, this data is from navigation map
Or government statistics data are available.
Step S204 calculates the state frequency of occurrences at each moment according to different moments corresponding road condition.
In the application above-mentioned steps S204, the state frequency of occurrences is used to indicate what road condition occurred within a preset period of time
Frequency.That is, in getting history traffic information after different moments corresponding road condition, when can be according to the difference
It carves corresponding road condition and counts the frequency that each three kinds of states of moment (i.e. unimpeded, slow and congestion) occur.
Optionally, according to different moments corresponding road condition, the state frequency of occurrences at each moment is calculated, comprising: logical
Cross formulaCalculate the state frequency of occurrences of t moment, wherein ftIndicate the state frequency of occurrences of t moment,
count(st) indicate that the number that road condition occurs in preset time period, T indicate the number of days of preset time period.
By taking preset time period is 3 years (1095 days) in the past as an example, the frequency that three kinds of states of t moment occur is are as follows:
Three kinds of states are respectively indicated in the space t moment S in 3 years
The frequency occurred in 1095 days, wherein(i=1,2,3) indicate i-th of state frequency of occurrence in 1095 times.
Step S206 obtains the transition probability matrix at each moment according to the state frequency of occurrences at each moment.
It, can be each according to this after calculating the state probability of occurrence at each moment in the application above-mentioned steps S206
The state probability of occurrence at moment, obtains the transition probability matrix at each moment.Wherein, transition probability matrix includes from previous moment
The probability shifted to later moment in time state, i.e., from t moment to the state transition probability at t+1 moment.
Optionally, according to the state frequency of occurrences at each moment, the transition probability matrix for obtaining each moment includes: to obtain
The state frequency of occurrences of t moment and the state frequency of occurrences at t+1 moment；When according to the state frequency of occurrences and t+1 of t moment
The state frequency of occurrences at quarter is calculated from t moment to the state transition probability at t+1 moment；According to from t moment to the shape at t+1 moment
State transition probability obtains the transition probability matrix of t moment.
In the present embodiment, state transfer may include it is unimpeded to it is unimpeded, slowly to slowly, congestion to congestion, it is unimpeded to delay
Slowly, slowly arrive congestion, congestion to slowly and slowly arrive it is unimpeded.Wherein, for from it is unimpeded directly to congestion and from congestion it is direct
It is excluded to unimpeded both of these case.
Still optionally further, according to the state frequency of occurrences at each moment, the transition probability matrix packet at each moment is obtained
It includes: obtaining t1The state frequency of occurrences and t at moment2The state frequency of occurrences at moment, wherein t1The state frequency of occurrences at moment
IncludingAndIndicate the unimpeded frequency occurred within a preset period of time,It indicates slowly in preset time period
The frequency of interior appearance,Indicate the frequency that congestion occurs within a preset period of time；Solve ternary once linear equation:
Obtain p11、p22、p33、p12、p23、p32And p21, wherein p11Indicate from
t1Moment is to t2Moment is unimpeded to smooth state transition probability, p22It indicates from t1Moment is to t2Moment slowly arrives slow state
Transition probability, p33It indicates from t1Moment is to t2State transition probability of the moment congestion to congestion, p12It indicates from t1Moment is to t2Moment
It is unimpeded to arrive slow state transition probability, p23It indicates from t1Moment is to t2Moment slowly arrives the state transition probability of congestion, p32Table
Show from t1Moment is to t2Moment congestion is to slow state transition probability, p21It indicates from t1Moment is to t2Moment slowly arrives smooth
State transition probability, p13=p31=0；According to obtained p11、p22、p33、p12、p23、p32And p21, determine t1The transfer at moment
Probability matrix isSuccessively calculate t2Moment is to t1440The transition probability matrix at moment.
Step S208 is based on transition probability matrix, determines target road section in the road conditions probability at moment to be predicted.
In the application above-mentioned steps S208, road conditions probability is used to indicate the probability that road condition occurs at the moment to be predicted.
The present embodiment can predict any time road conditions based on above-mentioned transition probability matrix.
Optionally, it is based on transition probability matrix, determines target road section in the road conditions probability at moment to be predicted, comprising: is obtained
The history road conditions probability of the previous moment at moment to be predicted；According to history road conditions probability and transition probability matrix, calculate to be predicted
The road conditions probability at moment.
Optionally, according to history road conditions probability and transition probability matrix, the road conditions probability at moment to be predicted is calculated, comprising:
It sums to history road conditions probability and transition probability matrix, obtains the road conditions probability at moment to be predicted.
In the following, the road condition predicting method to the present embodiment is described in detail:
Under scene for that need to provide road conditions trend, the road condition predicting method of the present embodiment is based on Markov chain
The analysis method of (Markov Chain) calculates transition probability square in conjunction with the frequency that the various states of section road conditions in history occur
Battle array, the following road conditions for current road segment are prejudged, are made reference for trip.
Road conditions trend is analyzed with Markov chain, be based on two hypothesis: 1) state space of road conditions is to meet Ma Er
Can husband's process timing；2) transition probability of road condition can be found by historical data.
Firstly, making in order to facilitate subsequent descriptions as given a definition:
Define the state space S={ s of road conditions1,s2,s3, wherein s1Indicate unimpeded, s2Indicate slow, s3Indicate congestion.
The timing of definition status transfer, T={ t1,t2,...,tn(n=1440,24 hours one day, per hour 60 points), it
Calculate the state transition probability that t assigns to t+1 points.
Define initial state probabilities, P1={ p1,p2,p3, wherein p1Indicate probability, the p of congestion2Indicate slow probability,
p3Indicate smooth probability, this can be obtained according to historical data (i.e. above-mentioned history traffic information), be shifted by state
Probability can obtain P2To Pn(n=1440) probability.
The road condition predicting method of the present embodiment, can be to the prediction of each moment road conditions in each section every day, specifically
Steps are as follows:
Step 1: acquisition historical data
For constructing transition probability matrix, most crucial problem is how in the state space probability feelings for knowing t moment
The state space probability at t+1 moment is speculated under condition, history traffic information will be relied on by solving this key problem, from history road conditions
The probability of state transfer is found in information.
Acquire training data: every point of road condition daily of main acquisition target road section past three year, this data is from leading
Boat map or government statistics data are available.According to totally 1095 days annual 3 years 365 days, it is 1440 points daily, share
1576800 rows record, every row include: day, divide, road condition, and day can be numbered from 1 to 1095, divide and number from 1 to 1440, state
Take three values in the space S.It is as shown in table 1:
Table 1
| It | Point | Road condition |
| Codomain [1-1095] | Codomain [1-1440] | S={ s1,s2,s3} |
Step 2: the state frequency of occurrences at each moment is counted
(1) firstly, calculating the frequency that current three kinds of states of t moment occur:
Three kinds of states are respectively indicated in the space t moment S in 3 years
The frequency occurred in 1095 days, wherein(i=1,2,3) indicates i-th of state frequency of occurrence in 1095 times.
(2) based on number statistics and accounting, one day 1440 moment (i.e. t is generated1To t1440) timing, each moment includes
The frequency of three states, timing are as follows:
……
……
Step 3: the state relation definition of state space S
In conjunction with shown in 3 (a), transfer has 7 kinds between state, it may be assumed that
1:s1→s1；
2:s2→s2；
3:s3→s3；
4:s1→s2；
5:s2→s3；
6:s3→s2；
7:s2→s1。
Wherein 1: unimpeded to unimpeded；2: slowly to slow；3: congestion to congestion；4: unimpeded to slow；5: slowly arriving congestion；
6: congestion is to slowly；7: slowly arriving unimpeded.For directly directly being done from unimpeded to unimpeded both of these case to congestion and from congestion
It excludes.
On the timing of step 2, need to obtain as shown in Fig. 3 (b) from tiMoment is to ti+1The state transfer at moment is general
Rate, specific method subsequent step are described in detail.
Step 4: transition probability matrix is calculated
Transition probability matrix is as shown in figure 4, Fig. 4 indicates the probability converted from t moment to t+1 moment state, according to above
The state relation of definition, it is evident that p13And p31It is equal to 0, there is no shifting, seeks to calculate 7 kinds of states turn
The probability changed.
(1) it calculates from t1Moment is to t2The transition probability matrix at moment
t1Moment and t2Frequency (the alternatively referred to as t that every kind of state at moment occurs in 1095 days1The state at moment occurs
Frequency and t2The state frequency of occurrences at moment), it may be assumed that
It is now to calculate from t1Moment is to t27 kinds of state transition probabilities at moment:
p11=p (1:s1→s1)
p22=p (2:s2→s2)
p33=p (3:s3→s3)
p12=p (4:s1→s2)
p23=p (5:s2→s3)
p32=p (6:s3→s2)
p21=p (7:s2→s1)
It is as follows to construct ternary once linear equation:
f1 1×p11+f2 1×p21+f3 1×p31=f1 2
f1 1×p12+f2 1×p22+f3 1×p32=f2 2
f1 1×p13+f2 1×p23+f3 1×p33=f3 2
Wherein p13=p31=0, the equation is solved, other 7 values can be obtained.
(2) t is calculated according to method identical in (1)2Moment is to t1440The transition probability matrix at moment.
Step 5: any time road conditions are predicted based on transition probability matrix
Assuming that the road conditions probability (i.e. above-mentioned history road conditions probability) of t moment is:
T moment to the t+1 moment transition probability matrix P as shown in figure 4, then calculating: pt+1=(pt× P), it obtains:
Thus, there is three kinds of shape probability of states of road conditions (i.e. above-mentioned road conditions probability) in the t+1 moment.
According to the method for above-mentioned steps one to step 5, it is assumed that three kinds of shape probability of states of target road section 8:00 point are
(0.1,0.4,0.5), according to transition probability matrix, successively calculates 30 acquisitions if need to speculate 8:30 points of road condition probability
Three kinds of shape probability of states of 8:30 point are (0.1,0.2,0.7), it is seen that a possibility that target road section 70% is congestion.
Step 6: optimization transition probability matrix is updated
Road conditions are not actually independent, depending on ambient enviroment variation and the road conditions and various traffic in neighbouring section
Probability matrix that is abnormal, counting and calculate according to historical data, needs to constantly update optimization, is just able to maintain current time trend point
The accuracy of analysis.
The strategy for updating optimization, exactly acquires the road conditions at each moment in daily section (uninterrupted), and more new state occurs
Frequency keep the timely tracking to the section road conditions to optimize matrix probability.
It should be noted that target road section section adjacent thereto can have correlative relationship, and this correlation is closed
System can be embodied by updating optimization, to guarantee completeness and forecasting accuracy.
It should be added that the present embodiment during calculating transition probability matrix, can distinguish festivals or holidays and
Working day realizes the prediction of congestion road conditions occur in festivals or holidays to establish corresponding transition probability matrix, and it is accurate to improve prediction
Property.
In the present embodiment, by the history traffic information of acquisition target road section, the state frequency of occurrences at each moment is calculated,
And then obtain the transition probability matrix predicted for the following road conditions to target road section, reach and section road conditions have been carried out in advance
It surveys, for the purpose made reference of going on a journey, traffic jam is effectively relieved to realize, reduces the technical effect of travel time, in turn
It solves still exist as caused by the related technology can not predict road conditions and aggravates traffic jam, increase the travel time
The technical issues of.
Optionally, the road condition predicting method of the present embodiment, further includes: the road condition of acquisition target road section in real time updates
The state frequency of occurrences at each moment updates transition probability matrix.
Road condition predicting method provided in this embodiment, the historical data based on section count the frequency of three kinds of states of road conditions,
And the state transition probability matrix of Markov chain is established accordingly, so as at a time, thus it is speculated that go out the following a certain moment
Road condition probability provides trend reference for trip, reasonably selects travel plan, congestion is avoided to save the time.
It should be noted that for the various method embodiments described above, for simple description, therefore, it is stated as a series of
Combination of actions, but those skilled in the art should understand that, the present invention is not limited by the sequence of acts described because
According to the present invention, some steps may be performed in other sequences or simultaneously.Secondly, those skilled in the art should also know
It knows, the embodiments described in the specification are all preferred embodiments, and related actions and modules is not necessarily of the invention
It is necessary.
Through the above description of the embodiments, those skilled in the art can be understood that according to above-mentioned implementation
The method of example can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but it is very much
In the case of the former be more preferably embodiment.Based on this understanding, technical solution of the present invention is substantially in other words to existing
The part that technology contributes can be embodied in the form of software products, which is stored in a storage
In medium (such as ROM/RAM, magnetic disk, CD), including some instructions are used so that a terminal device (can be mobile phone, calculate
Machine, server or network equipment etc.) method that executes each embodiment of the present invention.
Embodiment 2
According to embodiments of the present invention, additionally provide it is a kind of for implementing the road condition predicting device of above-mentioned road condition predicting method,
As shown in figure 5, the device includes: acquiring unit 50, computing unit 52, processing unit 54 and determination unit 56.
Acquiring unit 50, for obtaining the history traffic information of target road section within a preset period of time, history traffic information
Including different moments corresponding road condition；Computing unit 52, for calculating each according to different moments corresponding road condition
The state frequency of occurrences at moment, the state frequency of occurrences are used to indicate the frequency that road condition occurs within a preset period of time；Processing
Unit 54 obtains the transition probability matrix at each moment, transition probability matrix for the state frequency of occurrences according to each moment
Probability including being carved into the transfer of later moment in time state for the moment in the past；Determination unit 56 determines mesh for being based on transition probability matrix
Road conditions probability of the section at the moment to be predicted is marked, road conditions probability is used to indicate the probability that road condition occurs at the moment to be predicted.
Herein it should be noted that above-mentioned acquiring unit 50, computing unit 52, processing unit 54 and determination unit 56 are right
Should be in the step S202 to step S208 in embodiment 1, example and application scenarios that four modules and corresponding step are realized
It is identical, but it is not limited to the above embodiments a disclosure of that.It should be noted that above-mentioned module can as a part of device
To operate in the terminal 10 of the offer of embodiment one.
Optionally it is determined that unit 56 is based on transition probability matrix for executing following steps, determine target road section to pre-
It surveys the road conditions probability at moment: obtaining the history road conditions probability of the previous moment at moment to be predicted；According to history road conditions probability and turn
Probability matrix is moved, the road conditions probability at moment to be predicted is calculated.
Optionally it is determined that unit 56 is calculated for executing following steps according to history road conditions probability and transition probability matrix
The road conditions probability at moment to be predicted: summing to history road conditions probability and transition probability matrix, and the road conditions for obtaining the moment to be predicted are general
Rate.
Optionally, processing unit 54 obtains each for executing following steps according to the state frequency of occurrences at each moment
The transition probability matrix at moment: the state frequency of occurrences of t moment and the state frequency of occurrences at t+1 moment are obtained；According to t moment
The state frequency of occurrences and the state frequency of occurrences at t+1 moment, calculate from t moment to the state transition probability at t+1 moment；Root
, to the state transition probability at t+1 moment, the transition probability matrix of t moment is obtained according to from t moment.
Optionally, road condition includes unimpeded, slow and congestion；State transfer includes unimpeded to unimpeded, slow to slow
Slowly, congestion to congestion, it is unimpeded to slowly, slowly arrive congestion, congestion to it is slow and slowly arrive it is unimpeded.
Optionally, processing unit 54 obtains each for executing following steps according to the state frequency of occurrences at each moment
The transition probability matrix at moment: t is obtained1The state frequency of occurrences and t at moment2The state frequency of occurrences at moment, wherein t1When
The state frequency of occurrences at quarter includesAndIndicate the unimpeded frequency occurred within a preset period of time,Indicate slow
Slowly the frequency occurred within a preset period of time,Indicate the frequency that congestion occurs within a preset period of time；Solve ternary primary line
Property equation:Obtain p11、p22、p33、p12、p23、p32And p21, wherein p11It indicates
From t1Moment is to t2Moment is unimpeded to smooth state transition probability, p22It indicates from t1Moment is to t2Moment slowly arrives slow shape
State transition probability, p33It indicates from t1Moment is to t2State transition probability of the moment congestion to congestion, p12It indicates from t1Moment is to t2When
It carves unimpeded to slow state transition probability, p23It indicates from t1Moment is to t2Moment slowly arrives the state transition probability of congestion, p32
It indicates from t1Moment is to t2Moment congestion is to slow state transition probability, p21It indicates from t1Moment is to t2Moment slowly arrives unimpeded
State transition probability, p13=p31=0；According to obtained p11、p22、p33、p12、p23、p32And p21, determine t1Moment turns
Moving probability matrix isSuccessively calculate t2Moment is to t1440The transition probability matrix at moment.
Optionally, computing unit 52 calculates each for executing following steps according to different moments corresponding road condition
The state frequency of occurrences at moment: pass through formulaCalculate the state frequency of occurrences of t moment, wherein ftWhen indicating t
The state frequency of occurrences at quarter, count (st) indicate that the number that road condition occurs in preset time period, T indicate preset time
The number of days of section.
Optionally, above-mentioned road condition predicting device, further includes: updating unit, for acquiring the road like of target road section in real time
State updates the state frequency of occurrences at each moment, updates transition probability matrix.
In the present embodiment, by the history traffic information of acquisition target road section, the state frequency of occurrences at each moment is calculated,
And then obtain the transition probability matrix predicted for the following road conditions to target road section, reach and section road conditions have been carried out in advance
It surveys, for the purpose made reference of going on a journey, traffic jam is effectively relieved to realize, reduces the technical effect of travel time, in turn
It solves still exist as caused by the related technology can not predict road conditions and aggravates traffic jam, increase the travel time
The technical issues of.
Embodiment 3
The embodiments of the present invention also provide a kind of storage mediums.Optionally, in the present embodiment, above-mentioned storage medium can
For saving program code performed by road condition predicting method provided by above-described embodiment one.
Optionally, in the present embodiment, above-mentioned storage medium can be located in computer network in computer terminal group
In any one terminal, or in any one mobile terminal in mobile terminal group.
Optionally, in the present embodiment, storage medium is arranged to store the program code for executing following steps: obtaining
The history traffic information of target road section within a preset period of time is taken, history traffic information includes different moments corresponding road like
State；According to different moments corresponding road condition, the state frequency of occurrences at each moment is calculated, the state frequency of occurrences is used to indicate
The frequency that road condition occurs within a preset period of time；According to the state frequency of occurrences at each moment, turning for each moment is obtained
Probability matrix is moved, transition probability matrix includes the probability for being carved into the transfer of later moment in time state for the moment in the past；Based on transition probability square
Battle array, determines target road section in the road conditions probability at moment to be predicted, road conditions probability is used to indicate road condition and carves when to be predicted
Existing probability.
Optionally, in the present embodiment, storage medium is also configured to store the program code for executing following steps:
Obtain the history road conditions probability of the previous moment at moment to be predicted；According to history road conditions probability and transition probability matrix, calculate to
The road conditions probability of prediction time.
Optionally, in the present embodiment, storage medium is also configured to store the program code for executing following steps:
It sums to history road conditions probability and transition probability matrix, obtains the road conditions probability at moment to be predicted.
Optionally, in the present embodiment, storage medium is also configured to store the program code for executing following steps:
Obtain the state frequency of occurrences and the state frequency of occurrences at t+1 moment of t moment；According to the state frequency of occurrences and t of t moment
The state frequency of occurrences at+1 moment is calculated from t moment to the state transition probability at t+1 moment；According to from t moment to the t+1 moment
State transition probability, obtain the transition probability matrix of t moment.
Optionally, in the present embodiment, storage medium is also configured to store the program code for executing following steps:
Obtain t1The state frequency of occurrences and t at moment2The state frequency of occurrences at moment, wherein t1The state frequency of occurrences at moment includesAndIndicate the unimpeded frequency occurred within a preset period of time,Expression slowly goes out within a preset period of time
Existing frequency,Indicate the frequency that congestion occurs within a preset period of time；Solve ternary once linear equation:
Obtain p11、p22、p33、p12、p23、p32And p21, wherein p11Indicate from
t1Moment is to t2Moment is unimpeded to smooth state transition probability, p22It indicates from t1Moment is to t2Moment slowly arrives slow state
Transition probability, p33It indicates from t1Moment is to t2State transition probability of the moment congestion to congestion, p12It indicates from t1Moment is to t2Moment
It is unimpeded to arrive slow state transition probability, p23It indicates from t1Moment is to t2Moment slowly arrives the state transition probability of congestion, p32Table
Show from t1Moment is to t2Moment congestion is to slow state transition probability, p21It indicates from t1Moment is to t2Moment slowly arrives smooth
State transition probability, p13=p31=0；According to obtained p11、p22、p33、p12、p23、p32And p21, determine t1The transfer at moment
Probability matrix isSuccessively calculate t2Moment is to t1440The transition probability matrix at moment.
Optionally, in the present embodiment, storage medium is also configured to store the program code for executing following steps:
Pass through formulaCalculate the state frequency of occurrences of t moment, wherein ftIndicate the state frequency of occurrences of t moment,
count(st) indicate that the number that road condition occurs in preset time period, T indicate the number of days of preset time period.
Optionally, in the present embodiment, storage medium is also configured to store the program code for executing following steps:
The road condition of acquisition target road section in real time, updates the state frequency of occurrences at each moment, updates transition probability matrix.
The embodiments of the present invention also provide a kind of processors.Optionally, in the present embodiment, above-mentioned processor is for transporting
Line program, wherein program executes program code performed by road condition predicting method provided by above-described embodiment one when running.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
In the above embodiment of the invention, it all emphasizes particularly on different fields to the description of each embodiment, does not have in some embodiment
The part of detailed description, reference can be made to the related descriptions of other embodiments.
In several embodiments provided herein, it should be understood that disclosed technology contents can pass through others
Mode is realized.Wherein, the apparatus embodiments described above are merely exemplary, such as the division of unit, only a kind of
Logical function partition, there may be another division manner in actual implementation, such as multiple units or components can combine or can
To be integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed is mutual
Coupling, direct-coupling or communication connection can be through some interfaces, the indirect coupling or communication connection of unit or module,
It can be electrical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can for personal computer, server or network equipment etc.) execute each embodiment the method for the present invention whole or
Part steps.And storage medium above-mentioned includes: that USB flash disk, read-only memory (ROM, Read-Only Memory), arbitrary access are deposited
Reservoir (RAM, Random Access Memory), mobile hard disk, magnetic or disk etc. be various to can store program code
Medium.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (14)
1. a kind of road condition predicting method characterized by comprising
The history traffic information of target road section within a preset period of time is obtained, the history traffic information includes corresponding to different moments
Road condition；
According to the different moments corresponding road condition, the state frequency of occurrences at each moment is calculated, frequency occurs in the state
Rate is used to indicate the frequency that the road condition occurs in the preset time period；
According to the state frequency of occurrences at each moment, the transition probability matrix at each moment is obtained, the transfer is general
Rate matrix includes the probability for being carved into the transfer of later moment in time state for the moment in the past；
Based on the transition probability matrix, determine the target road section in the road conditions probability at moment to be predicted, the road conditions probability
It is used to indicate the probability that the road condition occurs at the moment to be predicted.
2. determining the mesh the method according to claim 1, wherein described be based on the transition probability matrix
Section is marked in the road conditions probability at moment to be predicted, comprising:
Obtain the history road conditions probability of the previous moment at the moment to be predicted；
According to the history road conditions probability and the transition probability matrix, the road conditions probability at the moment to be predicted is calculated.
3. according to the method described in claim 2, it is characterized in that, described according to the history road conditions probability and the transfer is general
Rate matrix calculates the road conditions probability at the moment to be predicted, comprising:
It sums to the history road conditions probability and the transition probability matrix, obtains the road conditions probability at the moment to be predicted.
4. the method according to claim 1, wherein the state frequency of occurrences according to each moment,
The transition probability matrix for obtaining each moment includes:
Obtain the state frequency of occurrences and the state frequency of occurrences at t+1 moment of t moment；
According to the state frequency of occurrences of the t moment and the state frequency of occurrences at the t+1 moment, calculate from the t moment
To the state transition probability at the t+1 moment；
, to the state transition probability at the t+1 moment, the transition probability matrix of the t moment is obtained according to from the t moment.
5. according to the method described in claim 4, it is characterized in that, the road condition includes unimpeded, slow and congestion；Institute
State state transfer include it is unimpeded to it is unimpeded, slowly to slowly, congestion to congestion, it is unimpeded to slowly, slowly arrive congestion, congestion to delay
Slowly it arrives and slowly unimpeded.
6. according to the method described in claim 5, it is characterized in that, the state frequency of occurrences according to each moment,
The transition probability matrix for obtaining each moment includes:
Obtain t1The state frequency of occurrences and t at moment2The state frequency of occurrences at moment, wherein the t1The state at moment occurs
Frequency includes f1 1、Andf1 1Indicate the unimpeded frequency occurred in the preset time period,It indicates slowly described
The frequency occurred in preset time period,Indicate the frequency that congestion occurs in the preset time period；
Solve ternary once linear equation:Obtain p11、p22、p33、p12、p23、p32With
And p21, wherein p11It indicates from t1Moment is to t2Moment is unimpeded to smooth state transition probability, p22It indicates from t1Moment is to t2When
Quarter slowly arrives slow state transition probability, p33It indicates from t1Moment is to t2State transition probability of the moment congestion to congestion, p12
It indicates from t1Moment is to t2Moment is unimpeded to slow state transition probability, p23It indicates from t1Moment is to t2Moment slowly arrives congestion
State transition probability, p32It indicates from t1Moment is to t2Moment congestion is to slow state transition probability, p21It indicates from t1Moment
To t2Moment slowly arrives smooth state transition probability, p13=p31=0；
According to obtained p11、p22、p33、p12、p23、p32And p21, determine t1The transition probability matrix at moment is
Successively calculate t2Moment is to t1440The transition probability matrix at moment.
7. the method according to claim 1, wherein described according to the different moments corresponding road condition,
Calculate the state frequency of occurrences at each moment, comprising:
Pass through formulaCalculate the state frequency of occurrences of t moment, wherein ftIndicate that frequency occurs in the state of t moment
Rate, count (st) indicate that the number that the road condition occurs in the preset time period, T indicate the preset time period
Number of days.
8. method according to any one of claim 1 to 7, which is characterized in that further include:
The road condition of the target road section is acquired in real time, updates the state frequency of occurrences at each moment, updates described turn
Move probability matrix.
9. a kind of road condition predicting device characterized by comprising
Acquiring unit, for obtaining the history traffic information of target road section within a preset period of time, the history traffic information packet
Include different moments corresponding road condition；
Computing unit, for calculating the state frequency of occurrences at each moment, institute according to the different moments corresponding road condition
The state frequency of occurrences of stating is used to indicate the frequency that the road condition occurs in the preset time period；
Processing unit obtains the transition probability square at each moment for the state frequency of occurrences according to each moment
Battle array, the transition probability matrix include the probability for being carved into the transfer of later moment in time state for the moment in the past；
Determination unit, for being based on the transition probability matrix, determine the target road section in the road conditions probability at moment to be predicted,
The road conditions probability is used to indicate the probability that the road condition occurs at the moment to be predicted.
10. device according to claim 9, which is characterized in that the determination unit is based on institute for executing following steps
Transition probability matrix is stated, determines the target road section in the road conditions probability at moment to be predicted:
Obtain the history road conditions probability of the previous moment at the moment to be predicted；
According to the history road conditions probability and the transition probability matrix, the road conditions probability at the moment to be predicted is calculated.
11. device according to claim 9, which is characterized in that the processing unit is for executing following steps according to institute
The state frequency of occurrences for stating each moment obtains the transition probability matrix at each moment:
Obtain the state frequency of occurrences and the state frequency of occurrences at t+1 moment of t moment；
According to the state frequency of occurrences of the t moment and the state frequency of occurrences at the t+1 moment, calculate from the t moment
To the state transition probability at the t+1 moment；
, to the state transition probability at the t+1 moment, the transition probability matrix of the t moment is obtained according to from the t moment.
12. device according to claim 9, which is characterized in that the computing unit is for executing following steps according to institute
Different moments corresponding road condition is stated, the state frequency of occurrences at each moment is calculated:
Pass through formulaCalculate the state frequency of occurrences of t moment, wherein ftIndicate that frequency occurs in the state of t moment
Rate, count (st) indicate that the number that the road condition occurs in the preset time period, T indicate the preset time period
Number of days.
13. a kind of storage medium, which is characterized in that the storage medium includes the program of storage, wherein run in described program
When control the storage medium where equipment perform claim require any one of 1 to 8 described in road condition predicting method.
14. a kind of processor, which is characterized in that the processor is for running program, wherein right of execution when described program is run
Benefit require any one of 1 to 8 described in road condition predicting method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811027234.XA CN109147325B (en) | 2018-09-04 | 2018-09-04 | Road condition prediction method and device, storage medium and processor |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811027234.XA CN109147325B (en) | 2018-09-04 | 2018-09-04 | Road condition prediction method and device, storage medium and processor |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109147325A true CN109147325A (en) | 2019-01-04 |
| CN109147325B CN109147325B (en) | 2022-01-28 |
Family
ID=64826792
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811027234.XA Active CN109147325B (en) | 2018-09-04 | 2018-09-04 | Road condition prediction method and device, storage medium and processor |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109147325B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110264711A (en) * | 2019-05-29 | 2019-09-20 | 北京世纪高通科技有限公司 | A kind of traffic accident method of determining probability and device |
| CN110428629A (en) * | 2019-09-03 | 2019-11-08 | 浙江创研信息技术有限公司 | A kind of condition of road surface detection method based on intelligent road measuring device |
| CN111582030A (en) * | 2020-04-02 | 2020-08-25 | 北京百度网讯科技有限公司 | Traffic light identification method and device, electronic equipment and computer storage medium |
| CN111693060A (en) * | 2020-06-08 | 2020-09-22 | 西安电子科技大学 | Path planning method based on congestion level prediction analysis |
| CN112241806A (en) * | 2020-07-31 | 2021-01-19 | 深圳市综合交通运行指挥中心 | Road damage probability prediction method, device terminal equipment and readable storage medium |
| CN112989278A (en) * | 2019-12-12 | 2021-06-18 | 北京沃东天骏信息技术有限公司 | Method and device for determining state data |
| CN113643535A (en) * | 2021-08-02 | 2021-11-12 | 宝方云科技(浙江)有限公司 | Road traffic prediction method, device, equipment and medium based on smart city |
| CN114154711A (en) * | 2021-11-30 | 2022-03-08 | 北京世纪新运交通运输科技应用研究所 | Traffic information recommendation method and device, electronic equipment and computer storage medium |
| CN118155425A (en) * | 2024-05-11 | 2024-06-07 | 四川智能交通***管理有限责任公司 | Clustering-based real-time traffic congestion prediction method and device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040246147A1 (en) * | 2000-12-08 | 2004-12-09 | Von Grabe J. B. | Real time vehicular routing and traffic guidance system |
| WO2011023247A1 (en) * | 2009-08-25 | 2011-03-03 | Tele Atlas B.V. | Generating raster image representing road existence probability based on probe measurements |
| CN103413443A (en) * | 2013-07-03 | 2013-11-27 | 太原理工大学 | Short-term traffic flow forecasting method based on hidden Markov model |
| US20140032089A1 (en) * | 2012-07-30 | 2014-01-30 | Massachusetts Institute Of Technology | System and method for providing driver behavior classification at intersections and validation on large naturalistic data sets |
| CN104346926A (en) * | 2013-07-31 | 2015-02-11 | 国际商业机器公司 | Travelling time prediction method and apparatus, and correlative terminal device |
| CN104616498A (en) * | 2015-02-02 | 2015-05-13 | 同济大学 | Markov chain and neural network based traffic congestion state combined prediction method |
| CN107610464A (en) * | 2017-08-11 | 2018-01-19 | 河海大学 | A kind of trajectory predictions method based on Gaussian Mixture time series models |
-
2018
- 2018-09-04 CN CN201811027234.XA patent/CN109147325B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040246147A1 (en) * | 2000-12-08 | 2004-12-09 | Von Grabe J. B. | Real time vehicular routing and traffic guidance system |
| WO2011023247A1 (en) * | 2009-08-25 | 2011-03-03 | Tele Atlas B.V. | Generating raster image representing road existence probability based on probe measurements |
| US20140032089A1 (en) * | 2012-07-30 | 2014-01-30 | Massachusetts Institute Of Technology | System and method for providing driver behavior classification at intersections and validation on large naturalistic data sets |
| CN103413443A (en) * | 2013-07-03 | 2013-11-27 | 太原理工大学 | Short-term traffic flow forecasting method based on hidden Markov model |
| CN104346926A (en) * | 2013-07-31 | 2015-02-11 | 国际商业机器公司 | Travelling time prediction method and apparatus, and correlative terminal device |
| CN104616498A (en) * | 2015-02-02 | 2015-05-13 | 同济大学 | Markov chain and neural network based traffic congestion state combined prediction method |
| CN107610464A (en) * | 2017-08-11 | 2018-01-19 | 河海大学 | A kind of trajectory predictions method based on Gaussian Mixture time series models |
Non-Patent Citations (3)
| Title |
|---|
| 刘张等: "基于复杂城市道路网络的交通拥堵预测模型", 《电子科技大学学报》 * |
| 李军怀: "基于指数平滑和马尔可夫链的短时交通流量预测", 《计算机***应用》 * |
| 王龙健: "基于出租车GPS数据的区域路网交通流状态演化识别方法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110264711A (en) * | 2019-05-29 | 2019-09-20 | 北京世纪高通科技有限公司 | A kind of traffic accident method of determining probability and device |
| CN110428629A (en) * | 2019-09-03 | 2019-11-08 | 浙江创研信息技术有限公司 | A kind of condition of road surface detection method based on intelligent road measuring device |
| CN112989278A (en) * | 2019-12-12 | 2021-06-18 | 北京沃东天骏信息技术有限公司 | Method and device for determining state data |
| CN111582030A (en) * | 2020-04-02 | 2020-08-25 | 北京百度网讯科技有限公司 | Traffic light identification method and device, electronic equipment and computer storage medium |
| CN111582030B (en) * | 2020-04-02 | 2023-08-29 | 北京百度网讯科技有限公司 | Traffic light identification method and device, electronic equipment and computer storage medium |
| CN111693060A (en) * | 2020-06-08 | 2020-09-22 | 西安电子科技大学 | Path planning method based on congestion level prediction analysis |
| CN112241806A (en) * | 2020-07-31 | 2021-01-19 | 深圳市综合交通运行指挥中心 | Road damage probability prediction method, device terminal equipment and readable storage medium |
| CN112241806B (en) * | 2020-07-31 | 2021-06-22 | 深圳市综合交通运行指挥中心 | Road damage probability prediction method, device terminal equipment and readable storage medium |
| CN113643535A (en) * | 2021-08-02 | 2021-11-12 | 宝方云科技(浙江)有限公司 | Road traffic prediction method, device, equipment and medium based on smart city |
| CN113643535B (en) * | 2021-08-02 | 2023-02-21 | 宝方云科技(浙江)有限公司 | Road traffic prediction method, device, equipment and medium based on smart city |
| CN114154711A (en) * | 2021-11-30 | 2022-03-08 | 北京世纪新运交通运输科技应用研究所 | Traffic information recommendation method and device, electronic equipment and computer storage medium |
| CN118155425A (en) * | 2024-05-11 | 2024-06-07 | 四川智能交通***管理有限责任公司 | Clustering-based real-time traffic congestion prediction method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109147325B (en) | 2022-01-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109147325A (en) | Road condition prediction method and device, storage medium and processor | |
| CN109598368B (en) | Path decision evaluation and order distribution method and device, electronic equipment and medium | |
| CN109816315A (en) | Path planning method and device, electronic equipment and readable storage medium | |
| CN109873712A (en) | A kind of network flow prediction method and device | |
| CN103347259B (en) | A kind of flow prompt method and system | |
| CN107480028B (en) | Method and device for acquiring usable residual time of disk | |
| CN110163368A (en) | Deep learning model training method, apparatus and system based on mixed-precision | |
| CN109636166A (en) | To the method that the net about vehicle in predetermined area is scheduled, system and storage medium | |
| CN110009429A (en) | A kind of method, apparatus and computer equipment of predicted flow rate data | |
| Rivero et al. | Energy associated tuning method for short-term series forecasting by complete and incomplete datasets | |
| CN107292455A (en) | Electricity demand forecasting method, device, readable storage medium storing program for executing and computer equipment | |
| Vargas et al. | Automobile spare-parts forecasting: A comparative study of time series methods | |
| CN109829015A (en) | Finance data storage method, device and storage medium based on HBase | |
| CN109583612A (en) | For determining the method, system and storage medium of share-car driver | |
| CN110853349A (en) | Vehicle scheduling method, device and equipment | |
| CN110019420A (en) | A kind of data sequence prediction technique and calculate equipment | |
| CN109743385A (en) | Method for Calculate Mileage, device and storage medium | |
| CN110032437A (en) | A kind of calculating task processing method and processing device based on information timeliness | |
| CN116055406B (en) | Training method and device for congestion window prediction model | |
| CN117561517A (en) | Computer-implemented apparatus and method for predicting traffic conditions in a route planning application | |
| CN110135636A (en) | A kind of acquisition methods, the apparatus and system of workshop operation status prediction information | |
| CN112819226A (en) | Parking lot recommendation method and device, storage medium and terminal | |
| CN108334079A (en) | Pilotless automobile method for obtaining road condition information in real time | |
| CN113205227B (en) | Vehicle line optimization method and device | |
| CN110060089A (en) | A kind of user draws a portrait construction method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |