CN110264719A - A kind of motor vehicle Dynamic OD Matrix Estimation method based on multi-source data - Google Patents
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Abstract
The present invention proposes a kind of motor vehicle Dynamic OD Matrix Estimation method based on multi-source data, makes full use of the multi-source datas such as population, employment, economic data, bayonet data, floating car data, proposes based on the dynamic OD estimation method for splitting the factor.Specific steps are as follows: step 1: motor vehicle static state OD matrix is released according to traffic four-phase model；Step 2: calculating motor vehicle OD matrix at times using bayonet data and floating car data；Step 3: splitting the factor according to bayonet OD data and static state OD total amount building time-varying；Step 4: cutting being carried out to static OD, obtains Dynamic OD Matrix.This method practicability is high, implementation is strong, the not only Dynamic OD Matrix of available current generation, also the planning year static state OD matrix that the fractionation factor cutting currently obtained calculates can be used, and then obtain the Dynamic OD Matrix in planning year, to plan in year, microscopic simulation provides input data.
Description
Technical field
It is dynamic that the invention belongs to the technical fields more particularly to a kind of motor vehicle based on multi-source data of traffic programme and management
State OD matrix estimation method.
Background technique
OD matrix (or OD table) is gone out between all starting points (Origin) and terminal (Destination) in transportation network
The matrix of the row volume of traffic, it reflects the basic transport need of city dweller.OD matrix is Urban Traffic Planning and urban transportation
The basic data of operation and the basic input data of Dynamic Traffic Assignment Model and some common microcosmic traffic simulation systems.
Traffic zone be according to the population of subregions different in survey region, land used, economy, etc. essential characteristics survey region is divided and
The multiple subregions come.Rambling individual travelling OD is converted the OD information between traffic zone by traffic zone, more
Facilitate the traffic movement on the intuitive observation space of Urban Planner.
Traditional OD acquisition methods, such as roadside investigation, household interview survey, aviation is taken pictures, follow the bus is investigated, and needs to expend big
The manpower and material resources of amount carry out data acquisition, and are possible to influence normal traffic circulation and precision is not high.
With the raising of scientific and technological level and the investment of various automatic checkout equipments, the acquisition of traffic trip data becomes more
It is easy to add.Dynamic OD Matrix acquiring technology is having made great progress over the past decades, and many methods are put forward one after another, these
Method includes Bi-level Programming Models method, Kalman Filter Estimation method, entropy maximization method etc..But based on mathematical model
Dynamic OD matrix estimation method often solves difficult and is difficult to the problems such as reflecting the variation of traveler optimizing paths, in city road network
It is in the practical application of dynamic OD estimation and undesirable.
Although there is scholar to propose based on AVI (referred to as autoelectrinic tag recognition) data or based on the shifting of GPS in recent years
The dynamic OD estimation method of dynamic detection data, but this method is also immature at present, because of the reliability of mobile source data, sample
The problems such as amount, occupation rate of market and spatial coverage, also needs further to study.In addition to this, since these methods need largely
Real-time section flow information, so can not the Dynamic OD Matrix in planning year be estimated and be predicted.
Summary of the invention
The technical problem to be solved by the invention is to solve the above problems providing a kind of based on multi-source data
Motor vehicle Dynamic OD Matrix Estimation method has comprehensively considered the advantage of multi-source data combination and the maturation of existing static state OD estimation
Property, it introduces time-varying and splits the factor, obtain dynamic estimation information.
The technical proposal adopted by the invention to solve the above technical problems is that: a kind of motor vehicle dynamic based on multi-source data
OD matrix estimation method, which comprises the steps of:
S1 motor vehicle static state OD matrix) is released according to traffic four-phase model；
S2) motor vehicle OD matrix at times is calculated using bayonet data and floating car data；
S3) factor is split according to bayonet OD data and static state OD matrix building time-varying；
S4 factor pair motor vehicle static state OD) is split with time-varying and carries out cutting, obtains motor vehicle Dynamic OD Matrix.
According to the above scheme, step S1) include following content: population, employment and motor vehicle are protected in acquisition survey region first
There are quantity space distribution and socio-economic activity information, is then generated using the trip in traffic Four-stage Method, trip distribution, friendship
Logical three steps of model split extrapolate motor vehicle static state OD matrix.
According to the above scheme, step S2) include following content:
S21) bayonet data and floating car data are cleaned and pre-processed；
S22) by bayonet data according to license plate number classification and according to time-sequencing；
S23) first bayonet that record vehicle passes through, this bayonet are that the motor vehicle first segment goes out beginning-of-line；
S24 the time by next bayonet) is successively searched, compared with the previous bayonet time, time interval is less than
Threshold value does not record this data then, and continuation is searched backward；If time interval is more than threshold value, it is considered as one section of trip and ties during this period
Beam records this two data, and previous item is the last period travel destination, and latter item is next section and goes out beginning-of-line；
S25) circulation step 4, until motor vehicle the last item data, the last one bayonet that motor vehicle passes through is last
One section of travel destination；
S26) judge whether that all vehicle datas have been processed, if it is not, return step S23), if handled
Finish, then the OD result come out is mapped to corresponding traffic zone, obtains the OD matrix based on traffic zone.
According to the above scheme, step S24) described in threshold value be not definite value, it is by longest path in active path between bayonet
Length divided by Floating Car in the average speed of respective path and corresponding period and multiplied by obtained by a delay factor, such as following formula:
In formula: the length of L longest path in active path between bayonet；V be Floating Car the section to it is corresponding when
The average overall travel speed of section；α is delay factor, can take 1~1.2 according to traffic condition.
According to the above scheme, step S3) include following content:
Within the same research period, static OD total amount is equal to the sum of Dynamic OD total amount, is shown below:
In formula: TijFor the static OD trip total amount from traffic zone i to traffic zone j；TijIt (k) is kth
Period is from traffic zone i to the travel amount of traffic zone j；K is segment number when setting out, and k=1,2 ... k-1, k, k can be according to reality
Border situation takes different value；
Internal relation is based on floating car data and bayonet OD data between reflection static state OD total amount and Dynamic OD amount
Time-varying splits the factor, it refers to that the travel amount between some OD pairs of different periods accounts for the OD of whole periods to the ratio of trip total amount
Example, is shown below:
In formula: Sij(k) --- the fractionation factor of the kth time period from traffic zone i to traffic zone j,
Static state OD total amount T in the formulaijIt is obtained by traffic four-phase model, day part OD volume of traffic Tij(k) by bayonet number
It is obtained according to floating car data.
According to the above scheme, step S4) include following content: cutting is carried out to static OD, such as following formula:
Tij(k)=Sij(k)×Tij, motor vehicle can be obtained multiplied by motor vehicle static state OD total amount with the time-varying fractionation factor and move
State OD matrix.
The beneficial effects of the present invention are: making full use of population, employment, economic data, bayonet data, floating car data etc. more
Source data is proposed based on the dynamic OD estimation method for splitting the factor, for practical application angle, bayonet data and Floating Car
Data are more easier to obtain at present, therefore this method has very high practicability and feasibility；On the other hand, it is obtained in this method
Time-varying split the factor be applicable not only to the current generation, can also to planning year prediction static OD matrix carry out cutting, so
This method can not only provide reliable Dynamic OD data for Current traffic administrative decision, can also mention for the traffic simulation in planning year
For reliable input data.
Detailed description of the invention
Fig. 1 is the flow chart of one embodiment of the invention.
Fig. 2 is one embodiment of the invention based on the matrix of OD at times of bayonet and floating car data acquisition flow chart.
Specific embodiment
For a better understanding of the invention, with reference to the accompanying drawings and examples to further description of the present invention.
As shown in Figure 1 and Figure 2, motor vehicle static state OD matrix is released according to traffic four-phase model, using floating car data and
Bayonet data calculate extension set motor-car period OD matrix, split the factor according to bayonet OD data and static state OD matrix building time-varying, cut
Static state OD matrix is divided to obtain four steps of motor vehicle Dynamic OD Matrix.
Step 1: population, employment and vehicle guaranteeding organic quantity spatial distribution and social economy are living in acquisition survey region first
Dynamic data, then using in traffic Four-stage Method trip generation and trip distribution model extrapolate full mode travelling OD matrix,
Model finally, which is drawn, using mode of transportation extrapolates motor vehicle static state OD matrix.
Step 2: calculating OD matrix at times using bayonet data and floating car data, the specific steps are as follows:
1) bayonet data are cleaned and are pre-processed:
Bayonet data are the high-definition camera systems by bayonet each in city road network installation to all vehicles by bayonet
It is recorded in real time, then carries out the data that identification is got using video or image of the video identification technology to vehicle.It obtains
Content mainly include vehicle license, body color, bayonet number, cross vehicle time, running speed, type of vehicle, lane number
Etc. essential informations.Floating car data be according to equipment vehicle-bone global positioning system Floating Car in its driving process periodic logging
Vehicle location, direction and velocity information, handled using map match, path culculating scheduling algorithm, available vehicle row
Sail the traffic informations such as speed and driving trace.Due to the influence of the factors such as detection device itself and ambient enviroment, may cause
There is a certain error and defect for initial data, such as path missing data, font identification mistake and path invalid data etc..
In order to improve the quality of initial data, reduces the difficulty of data processing, need to carry out initial data to clean and pre-
Processing, including wrong data amendment, Reduction for Redundant Data, loss Data-parallel language, and unnecessary data is deleted, reservation license plate number,
Bayonet number crosses the information such as vehicle time, bayonet longitude and latitude, turn direction, and city road network information pair is finally combined in ArcGIS
Bayonet position carries out spatialization.
2) by bayonet data according to license plate number classification and according to time-sequencing:
It will be according to the trip matrix of bayonet data acquisition motor vehicle, it is necessary first to know the individual trip letter of single motor vehicle
Breath.The motor vehicle travelled on road network would generally be by multiple bayonets, can by the position and time of each bayonet by motor vehicle
To obtain the trip information sequence of motor vehicle, it is possible thereby to reflect the OD point of Vehicle emission.So in the database will first
Pretreated data are grouped according to license plate number, and according to time-sequencing, thus obtain the trip sequence of each vehicle
{T1, T2, T3... Tn}。
3) first bayonet that record vehicle passes through is that the motor vehicle first segment goes out beginning-of-line, i.e. T with this bayonet1Place
Bayonet position is O1。
4) single trip identification
It successively searches and spends the vehicle time by next bayonet, compared with spending the vehicle time with previous bayonet, time interval is not
This data is not recorded then more than threshold value, and continuation is searched backward；If time interval is more than threshold value T, it is considered as one section of trip in this phase
Between terminate, record this two data, previous item is the last period travel destination, and latter item is next section and goes out beginning-of-line；
The threshold value is not definite value, it by between bayonet in active path the length of longest path divided by Floating Car in respective path
Obtained by average speed with the period.Such as following formula:
In formula:
The length of L longest path in active path between bayonet, this embodiment active path is using classical active path
Searching algorithm graph traversal algorithm solves.
V corresponds to the average overall travel speed of period in the section for all Floating Cars；
α is delay factor, 1-1.2 can be taken according to traffic condition, the implementation case α=1.1.
5) circulation step 4, until motor vehicle the last item data, the last one bayonet that motor vehicle passes through is last
Section travel destination, i.e. TnPlace bayonet position is Dn。
6) judge whether that all vehicle datas have been processed, if it is not, the 3) step is returned, if be disposed,
The OD result come out is then organized into matrix form.Then the bayonet data information to contain in the traffic zone that is divided
For the data information of the traffic zone, the OD matrix based on bayonet is being mapped to corresponding traffic zone, is being obtained small based on traffic
The OD matrix in area.Trip proportion for being located at the bayonet of two traffic zone intersections, according to Floating Car in the traffic zone
The OD information of the bayonet is assigned to corresponding traffic zone, finally arranges OD matrix for OD matrix T at times according to the timeij
(k)。
Step 3: the time-varying between constructing each OD pairs according to bayonet OD data and static state OD matrix splits the factor, specific method
It is as follows:
Within the same research period, static OD total amount is equal to the sum of Dynamic OD total amount, is shown below:
In formula:
TijFor the static OD trip total amount from traffic zone i to traffic zone j；
TijIt (k) is kth time period from traffic zone i to the travel amount of traffic zone j；
K is segment number when setting out, k=1,2 ... k-1, k, and k can take different value according to the actual situation, and the present embodiment takes often
A length of 15 minutes when a period.
Internal relation is based on floating car data and bayonet OD between reflection motor vehicle static state OD total amount and Dynamic OD amount
The time-varying of data splits the factor, it refers to goes on a journey always between the travel amount between some OD pairs of different periods accounts for this OD pairs of whole periods
The ratio of amount, is shown below:
In formula:
Sij(k) --- the fractionation factor of the kth time period from traffic zone i to traffic zone j.
Motor vehicle static state OD total amount T in the formulaijIt is generated by the trip in traffic four-phase model, trip is distributed and is handed over
Logical model split obtains, day part OD volume of traffic Tij(k) it is obtained by bayonet data.
Step 4: factor pair static state OD, which is split, using time-varying carries out cutting, such as following formula:
Tij(k)=Sij(k)×Tij
Splitting the factor with time-varying can be obtained motor vehicle Dynamic OD Matrix, T in formula multiplied by motor vehicle static state OD total amountijBoth may be used
To be the motor vehicle static state OD matrix of current generation, it is also possible to the planning year motor vehicle static state OD matrix of prediction.
The above described is only a preferred embodiment of the present invention, being not the limit for making any other form to the present invention
System, and made any modification or equivalent variations according to the technical essence of the invention, still fall within present invention model claimed
It encloses.
Claims (6)
1. a kind of motor vehicle Dynamic OD Matrix Estimation method based on multi-source data, which comprises the steps of:
S1 motor vehicle static state OD matrix) is released according to traffic four-phase model；
S2) motor vehicle OD matrix at times is calculated using bayonet data and floating car data；
S3) factor is split according to bayonet OD data and static state OD matrix building time-varying；
S4 factor pair motor vehicle static state OD) is split with time-varying and carries out cutting, obtains motor vehicle Dynamic OD Matrix.
2. a kind of motor vehicle Dynamic OD Matrix Estimation method based on multi-source data according to claim 1, feature exist
In step S1) include following content: first acquisition survey region in population, employment and vehicle guaranteeding organic quantity spatial distribution and
Then socio-economic activity information is generated, trip distribution, traffic modal splitting three steps using the trip in traffic Four-stage Method
Suddenly motor vehicle static state OD matrix is extrapolated.
3. a kind of motor vehicle Dynamic OD Matrix Estimation method based on multi-source data according to claim 1 or 2, feature
It is, step S2) include following content:
S21) bayonet data and floating car data are cleaned and pre-processed；
S22) by bayonet data according to license plate number classification and according to time-sequencing；
S23) first bayonet that record vehicle passes through, this bayonet are that the motor vehicle first segment goes out beginning-of-line；
S24 the time by next bayonet) is successively searched, compared with the previous bayonet time, time interval is less than threshold value
This data is not recorded then, continuation is searched backward；If time interval is more than threshold value, be considered as one section of trip terminates during this period, note
This two data is recorded, previous item is the last period travel destination, and latter item is next section and goes out beginning-of-line；
S25) circulation step 4, until motor vehicle the last item data, the last one bayonet that motor vehicle passes through is final stage
Travel destination；
S26) judge whether that all vehicle datas have been processed, if it is not, return step S23), if be disposed,
The OD result come out is then mapped to corresponding traffic zone, obtains the OD matrix based on traffic zone.
4. a kind of motor vehicle Dynamic OD Matrix Estimation method based on multi-source data according to claim 3, feature exist
The threshold value described in step S24) is not definite value, it is by the length of longest path exists divided by Floating Car in active path between bayonet
The average speed of respective path and corresponding period and multiplied by obtained by a delay factor, such as following formula:
In formula: the length of L longest path in active path between bayonet；V corresponds to the period in the section for Floating Car
Average overall travel speed；α is delay factor, can take 1~1.2 according to traffic condition.
5. a kind of motor vehicle Dynamic OD Matrix Estimation method based on multi-source data according to claim 3, feature exist
In step S3) include following content:
Within the same research period, static OD total amount is equal to the sum of Dynamic OD total amount, is shown below:
In formula: TijFor the static OD trip total amount from traffic zone i to traffic zone j；TijIt (k) is kth time period
From traffic zone i to the travel amount of traffic zone j；K is segment number when setting out, and k=1,2 ... k-1, k, k can be according to practical feelings
Condition takes different value；
Internal relation is the time-varying based on floating car data and bayonet OD data between reflection static state OD total amount and Dynamic OD amount
The factor is split, it refers to that the travel amount between some OD pairs of different periods accounts for the OD of whole periods to the ratio of trip total amount, such as
Shown in following formula:
In formula: Sij(k) --- the fractionation factor of the kth time period from traffic zone i to traffic zone j,
Static state OD total amount T in the formulaijIt is obtained by traffic four-phase model, day part OD volume of traffic Tij(k) by bayonet data and
Floating car data obtains.
6. a kind of motor vehicle Dynamic OD Matrix Estimation method based on multi-source data according to claim 5, feature exist
In step S4) include following content: to static OD carry out cutting, such as following formula:
Tij(k)=Sij(k)×Tij, splitting the factor with time-varying can be obtained motor vehicle Dynamic OD multiplied by motor vehicle static state OD total amount
Matrix.
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