CN109686090A - A kind of virtual traffic method of calculating flux based on multisource data fusion - Google Patents

A kind of virtual traffic method of calculating flux based on multisource data fusion Download PDF

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CN109686090A
CN109686090A CN201910042487.2A CN201910042487A CN109686090A CN 109686090 A CN109686090 A CN 109686090A CN 201910042487 A CN201910042487 A CN 201910042487A CN 109686090 A CN109686090 A CN 109686090A
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time window
trajectories
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CN109686090B (en
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王璞
赖积宇
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Central South University
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
    • G08G1/127—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station
    • G08G1/13—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station the indicator being in the form of a map

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Abstract

本发明公开了一种基于多源数据融合的虚拟交通流量计算方法，包括以下步骤：步骤一、获取浮动车行驶轨迹；步骤二、对浮动车行驶轨迹进行单元化处理后进行聚类，并获取每类中的热点轨迹；步骤三、利用卡口记录得到卡口对应路段在不同时间窗内的车辆观测流量数据；步骤四、对于任一时间窗T，基于该时间窗内具有车辆观测流量数据的路段，建立最优化目标函数，以求解该时间窗内各热点轨迹的扩样系数；步骤五、法求解最优化目标函数；步骤六、对于时间窗T内不具有车辆观测流量数据的路段，将该时间窗内所有经过其的轨迹产生的路段流量利用相应的扩样系数进行扩样后叠加，作为该路段的虚拟交通流量。本发明能获取具有精度和广度的交通流量数据。

The invention discloses a virtual traffic flow calculation method based on multi-source data fusion, comprising the following steps: step 1, acquiring the traveling trajectory of a floating vehicle; step 2, performing clustering on the traveling trajectory of the floating vehicle after unit processing, and obtaining the The hotspot track in each category; Step 3, use the bayonet record to obtain the vehicle observation flow data in different time windows of the road section corresponding to the bayonet; Step 4, for any time window T, based on the vehicle observation flow data in the time window In the road section of T, establish an optimization objective function to solve the sample expansion coefficient of each hot spot trajectory in the time window; step 5, method to solve the optimization objective function; step 6, for the road section without vehicle observation flow data in the time window T, The traffic flow of all the road sections generated by the trajectories passing through it in the time window is expanded and superimposed by using the corresponding sample expansion coefficient, which is regarded as the virtual traffic flow of the road section. The present invention can acquire traffic flow data with precision and breadth.

Description

A kind of virtual traffic method of calculating flux based on multisource data fusion
Technical field
The present invention relates to a kind of virtual traffic method of calculating flux based on multisource data fusion.
Background technique
In urban highway traffic status analysis, road traffic flow is an important index, can be urban congestion etc. Analysis, the early warning of situation provide important data basis, while can also provide for the planning, control, induction of urban highway traffic Data are supported.The acquisition of traditional cities road traffic flow is mainly by ring coil detector or video detector come real It is existing, have the advantages that precision is higher, but due to input cost is high, approval process is complicated, use environment is severe etc., cause The data volume of acquisition is smaller, and many roads can not cover in city road network.Floating Car GPS data is that a kind of coverage area is wider City dweller's crawler behavior data, can preferably represent the feature of Urban Residential Trip, have the advantages that range is higher, but Since Floating Car is only a part in motor vehicle, thus its flow cannot be carried out directly as traffic flow data using.It is comprehensive Upper described, the acquisition methods of Forecast of Urban Traffic Flow exist at present or data coverage is smaller, it is difficult to the high data of range are obtained, And it is limited simultaneously by input cost, hardware facility etc. condition；Or data integrity degree itself is inadequate, it is difficult to as height The defect that accuracy data uses.
There are certain requirement, magnitude of traffic flow number to the precision and range of traffic flow data in urban highway traffic research According to precision and range affect the result of traffic decision-making.Therefore, urgent need provides one kind and has both higher precision and range Traffic flow data acquisition methods.
Summary of the invention
Technical problem solved by the invention is, for the deficiency of available data, to propose a kind of based on multisource data fusion Virtual traffic method of calculating flux, by fusion Floating Car GPS data and bayonet data, to calculate in city road network without vehicle The virtual traffic flow in the section of observed volume data, data acquisition is convenient, has real-time and higher precision and range, fits Formulation for urban highway traffic research and successive policy.
A kind of virtual traffic method of calculating flux based on multisource data fusion, comprising the following steps:
Step 1: Floating Car GPS data is distributed by map-matching method to city road network, the row of Floating Car is obtained Sail trace information；
Step 2: carrying out blocking processing to floating vehicle travelling track, to guarantee the track between two bayonets without it His bayonet；Divide time window to carry out the cluster based on time-space matrix the track obtained after cell processing, and obtains in every class Hot spot track；
Step 3: vehicle of the bayonet corresponding road section in time windows is obtained using the traffic flow data that bayonet records Observed volume data；
Step 4: definition expands spline coefficient and carries out expansion sample come the link flow generated to known trajectory；For each time window T constructs an expansion spline coefficient set A={ α respectivelyk| k=1,2 ..., K }, size K, K are full track data in the time Hot spot track number in window.Any time window T is built based on the section in the time window with vehicle observed volume data Vertical optimization objective function, for solving the corresponding expansion spline coefficient set A of the time window:
F=Minimize Z
VE(k)=αkVT(k)
Wherein, f is target function value, and Z is fitness, and i and j are the roads in time window T with vehicle observed volume data Section, N are the sum in the section in time window T with vehicle observed volume data, VR(i) be section i in time window T vehicle see Measurement of discharge, PiJ is the hot spot track set in time window T from section i to section j, VTIt (k) is that hot spot track k is produced in time window T Raw link flow, value are equal to the number that hot spot track k occurs in time window T, VEIt (k) is VT(k) expand the flow after sample, αk It is the expansion spline coefficient of hot spot track k；
Step 5: solving optimization objective function using genetic algorithm, obtains the corresponding expansion spline coefficient set of time window T Global optimum is as a result, be denoted as A*；
Step 6: for not having the section of vehicle observed volume data in time window T, by all processes in the time window The link flow that its track generates is superimposed using corresponding expand after spline coefficient carries out expansion sample, the virtual traffic stream as the section Amount；Specifically, for the hot spot track by section l, expand the link flow that spline coefficient generates it using it and carry out expansion sample；It is right In the non-thermal locus of points by section l, it is first determined with its maximally related hot spot track, determine method are as follows: it is non-hot to obtain this Common segment of each hot spot track, calculates separately the length of each common segment, by longest common segment pair in track and cluster belonging to it The hot spot track answered is considered as its maximally related hot spot track；Then it utilizes with the expansion spline coefficient of its maximally related hot spot track to it The link flow of generation carries out expansion sample.
Further, in the step one, to guarantee that the trip of Floating Car has Urban Residential Trip feature Trip, filters out the Floating Car GPS data of passenger carrying status, and wherein each GPS point is a three-dimensional vector, and the 1st component is World concordant time, the 2nd component are the longitude of Floating Car position, and the 3rd component is the latitude of Floating Car position； GPS point is matched on city road network using ST-Matching map-matching algorithm, obtains the driving trace information of Floating Car, Wherein each tracing point is a three-dimensional vector, and the 1st component is that section is passed through the time, and the 2nd component is track number, the 3rd A component is to number by section；
Further, in the step two, Floating Car GPS data is after the processing by map-matching algorithm, GPS Point is matched on section, and the expression-form of track is changed by tracing point Point-A, Point-B...Point-C, Point-D For section Road-U, Road-V ..., Road-Y, Road-Z；In order to guarantee the track between two bayonets without other bayonets, In order to during subsequent assignment of traffic will not duplicate allocation flow, to track carry out blocking processing, specific steps are such as Under:
All tracks are numbered, item identifies the bayonet section number that every track is passed through one by one, if certain track is passed through Bayonet section number M≤1, then abandon the track；If the bayonet section number M=2 that certain track is passed through, which is passed through Track between two bayonet sections retains by way of section as new track；If bayonet section number M >=3 that certain track is passed through, Then this track is split, since first bayonet section that the track is passed through, every two phase which is passed through Track between adjacent bayonet section retains by way of the section track new as one.
Further, in the step two, carrying out the cluster based on time-space matrix to track is that similarity is high Track integration, and using the hot spot track in a kind of track as such representative track, to subsequent calculating step Simplified.Specific step is as follows:
2.1) untreated state is set by all tracks, and sets hot spot track collection and is combined into empty set；
2.2) a track Traj is randomly selected, track collection identical as its origin and destination and in same time window is obtained It closes, i.e., the space-time of track Traj is neighbouring set C (Traj)；
2.3) classify to the track in set C (Traj), wherein kth class track is denoted as CTk, CTkIn any two rails The overlap length ratio q of mark is greater than the minimum overlay length ratio Minq (empirical value) of setting, and adds the hot spot in every one kind Track to hot spot track set；Wherein overlap length ratio q possesses road divided by this two tracks for two tracks overlapping section numbers The mean value of number of segment, hot spot track (HT, Hot Trajectory) are that all tracks in a kind of track are arranged by frequency of occurrence descending After column, take account for n% before the sum of all track frequency of occurrence in such track (value range of n be [0,100], be experience Value)；Tag set C (TrajiIn track be processed state；
2.4) judge whether that all tracks are all marked as processed state, if so, terminating, otherwise with untreated state Track based on, return step 2.2).
Further, in the step three, using DBSCAN clustering algorithm to the same card of time window same in more days The traffic flow data of mouth record is clustered, and removes exception stream magnitude, wherein cluster the point for including in the maximum cluster of acquisition It is considered as normal stream magnitude, remaining point is considered as exception stream magnitude；Meanwhile if being less than record flow comprising point number in maximum cluster The 50% of number of days is then considered as the bayonet hardware (bayonet test device) and is abnormal, and removes the traffic flow data of bayonet record； Bayonet is matched on the section of its detection, each bayonet match information is a bivector, and the 1st component is bayonet volume Number, the 2nd component is section number；The traffic flow data that remaining bayonet is recorded is as the bayonet corresponding road section (with this The matched section of bayonet) vehicle observed volume data.
Further, in the step five, the solution of optimization objective function is carried out by genetic algorithm, it is specific to walk It is rapid as follows:
5.1) initialize: setting evolutionary generation counter g=0, n individual of random generation are used as initial population, each Individual is an expansion spline coefficient set A={ αk| k=1,2 ..., K }, wherein element αkValue given birth at random in the range of [0,1] At；Setting genetic algebra is Ng, convergence precision Q；
5.2) individual choice: the probability that q-th of individual is selected in population in g generation is calculatedWherein Zg(q)Indicate that g, i.e., will be in the individual for the fitness of q-th of individual in population Element substitutes into optimization objective function, obtained fitness value；The probability being selected according to Different Individual is continuously to g for population In individual repeat n wheel and select, obtain the new individuals of n；
5.3) individual intersection and variation:
The n individual that step 5.2) is obtained carries out random pair two-by-two, takes fixed crossover probability pcCarry out crossover operation； If certain group individual need is intersected, a crosspoint is randomly generated in all elements of group individual, by group individual Element after crosspoint is exchanged with each other, and generates two new individuals；If certain group individual does not need to be intersected, group individual It remains unchanged；
Take fixed mutation probability pmMutation operation is carried out, n individual after successively selecting crossover operation, to each individual All elements traversed, if some element needs to make a variation, change the value of the element at random in the range of [0,1]；
It results in through n individual after heredity, constitutes g+1 for population；
Wherein crossover probability pcWith mutation probability pm, it is calculated respectively according to following formula:
Wherein, pc1It is the crossover probability for the individual that g is less than average fitness value for fitness value in population, pc2It indicates Crossover probability of the g for the individual in population with maximum adaptation angle value, FmaxIt is g for the maximum adaptation of all individuals of population Angle value, FaveIt is g for the average fitness value of all individuals of population, F*It is biggish adaptation in two individuals intersected Angle value；pm1It is the mutation probability for the individual that g is less than average fitness value for fitness value in population, pm2It is g in population The mutation probability of individual with maximum adaptation angle value, F ' are the fitness values of the individual to make a variation；pc1、pc2、pm1、pm2For Empirical parameter (can be adjusted by many experiments and be obtained)；
5.4) g+1 is calculated for the fitness of individual each in population, and the maximum for obtaining g+1 for all individuals of population is suitable Response, i.e. g+1 are for the corresponding target function value f of population；
5.5) algorithm termination condition judges；
Convergence judgement: in g+1 generation and g, are compared for target function value f corresponding to population, if difference is less than Precision Q, then it is assumed that meet convergence, algorithm terminates, using g+1 for the individual in population with maximum adaptation degree as the complete of A Office's optimal result；
Genetic algebra judgement: if current genetic algebra g=Ng, then algorithm terminates, by this NgFor in population have maximum adaptation Global optimum result of the individual of degree as A；
If being unsatisfactory for above-mentioned algorithm termination condition, genetic algebra g=g+1 is enabled, is grasped back to step 5.2) circulation Make.
Since hot spot track is the representative of full track mark, and the lower section of some category of roads is not by hot spot track institute Covering, but covered by the remaining non-thermal locus of points is covered and do not covered by hot spot track by the non-thermal locus of points to fill up these The flow in the section of lid, the invention proposes a kind of calculations based on hot spot track and non-hot Track association in trajectory clustering Method, the supplement of Lai Jinhang data on flows.According to trajectory clustering in step 3, similar track space-time with higher is similar Degree, hot spot track therein and the non-thermal locus of points have certain relevance, therefore utilize relevance by the expansion sample system of hot spot track Number is transmitted on the similar non-thermal locus of points, the specific steps are as follows:
6.1) all non-thermal locus of points in acquisition time window T, and be marked as untreated；
6.2) optional one untreated non-thermal locus of points non-HT determines its affiliated class in trajectory clustering, obtain its with The common segment of each hot spot track in class, calculates separately the length of each common segment, and the corresponding hot spot track of longest common segment is regarded For its maximally related hot spot track；
6.3) using the expansion spline coefficient with its maximally related hot spot track as the expansion spline coefficient of the non-thermal locus of points；Label should The non-thermal locus of points is processed；It repeats 6.2).
Further, in the step six, by the expansion spline coefficient acquired be applied to whole track by way of section On, can calculate according to the following formula single track Traj by way of the expansion sample stream that is generated by the single track of i-th section Amount:
tReal, i=tTraj, i*αi
I=1,2 ..., u
Wherein, tTraj, iFor track Traj by way of the link flow that is generated by track Traj of i-th section, αiFor track The expansion spline coefficient of Traj, tReal, iFor tTraj, iCarry out the flow value obtained after expansion sample, u be track Traj by way of section quantity.
After obtaining the expansion sample flow that each section is generated by single track therethrough, to any section, by institute therethrough The expansion sample flow for having track to generate is overlapped, and can acquire the virtual traffic flow in the section:
Wherein VSum, rFor the flow total amount that section r is obtained after expanding sample, that is, the virtual traffic flow of the section r required, s For by the tracking quantity of section r.
The utility model has the advantages that
The present invention provides a kind of combination multi-source data, the characteristic of different data is made full use of to obtain traffic flow data Method, the traffic flow data of acquisition has precision and range simultaneously, and the present invention is to the traffic programme in intelligent transportation, traffic Tissue, traffic administration and control, traffic safety all have very high real value.
Detailed description of the invention
Fig. 1 is flow diagram of the present invention
Fig. 2 is trajectory unit schematic diagram
Fig. 3 is the virtual flow result figure of the 8:00-9:00 on the 1st of September in 2016
Specific embodiment
Further detailed number of seconds is made to the present invention with specific implementation example with reference to the accompanying drawing, but not as limit of the invention It is fixed.
Floating Car GPS data used in the present embodiment is China Shenzhen Luohu District and Futian District on September 1, to 30 2016 Day data, record include a record point longitude coordinate, latitude coordinate and time tag information, and data record summary is 202,391, 745, Floating Car sum is 8,168；Bayonet data are 1 to 14 data of China Shenzhen September in 2016, are amounted to 14 days. The present invention specific implementation the following steps are included:
Step 1: Floating Car GPS data record sum is 202,391,745, the vehicle 8,168 for having passenger carrying status to record ?.
Step 1: by continuous carrying record as primary trip, decision condition are as follows: continuous 3 or more record, is adjacent The time difference is recorded less than 35 seconds, adjacent record linear distance less than 1500m.
Step 2: filtering out effective trip, all trips are screened with the following conditions: trip radius distance 0.5km < Distance < 32km；Travel time 3min < Time < 60min；Go on a journey radius speed 0.5km/h < Speed < 120km/h. Obtain 420,129 trips altogether by screening.
Step 3:ST-Matching map-matching algorithm is a kind of map-matching algorithm suitable for low sampling rate, is utilized Effective trip GPS point is matched on city road network by the map-matching algorithm, and obtains corresponding trace information；Algorithm specifically walks It is rapid as follows:
1) track trajectory and Shenzhen's road network information that input taxi is once gone on a journey；
2) Shenzhen's road network is divided into the regional scope of 10*10；
3) after road network being divided into zonule, the trip trace information in same zonule is subjected to section matching, it will Tracing point, according to being matched on candidate side, obtains the path of path matching using distance；
Step 2: after the processing by map-matching algorithm, tracing point is matched on section Floating Car GPS data, The expression-form of track is changed into section Road-U, Road- by tracing point Point-A, Point-B ... Point-C, Point-D V ..., Road-Y, Road-Z；In order to guarantee the track between two bayonets without other bayonets, in order in subsequent flow In assigning process will not duplicate allocation flow, to track carry out blocking processing, the specific steps are as follows:
420,129 tracks are numbered, respectively 1,2 ... ..., 420,129, track carries out identification process one by one Bayonet quantity, if bayonet section number M≤1 that certain track is passed through, abandons the track；If the bayonet section that certain track is passed through Number M=2 then retains the track between two bayonet sections by way of section, retains in this, as new track；If certain track is passed through Bayonet section number M >=3, then the track is split, since the track pass through first bayonet section, by the track Track between the adjacent bayonet section of the every two of process retains by way of the section track new as one.
As shown in Fig. 2, A, B, C are respectively 3 bayonets, then it is 2 (AB by the track after blocking track, obtained With BC).
Trajectory clustering based on space-time is the track integration that similarity is high, and utilizes the hot spot in a kind of track Track further simplifies subsequent calculating step as such representative track.The specific steps of trajectory clustering It is as follows:
2.1) untreated state is set by all tracks, and sets hot spot track collection and is combined into empty set；
2.2) a track Traj is randomly selected, track collection identical as its origin and destination and in same time window is obtained It closes, i.e., the space-time of track Traj is neighbouring set C (Traj)；
2.3) classify to the track in set C (Traj), wherein kth class track is denoted as CTk, CTkIn any two rails The overlap length ratio q of mark is greater than the minimum overlay length ratio Minq (0.7 is set as in the present embodiment) of setting, and adds every Hot spot track to hot spot track set in one kind；Wherein overlap length ratio q be two tracks be overlapped section numbers divided by this two Track possesses the mean value of section number, and hot spot track (HT, Hot Trajectory) is that all tracks in a kind of track press out Occurrence number descending arrangement after, take account for n% before the sum of all track frequency of occurrence in such track (value range of n be [0, 100], it is empirical value, is set as 90) in the present embodiment；Tag set C (Traji) in track be processed state；
2.4) judge whether that all tracks are all marked as processed state, if so, terminating, otherwise with untreated state Track based on, return step 2.2)
9,132 track classes and 54,796 hot spot tracks are obtained altogether by step 2.
Step 3: bayonet data have 5,287,649 enumeration datas altogether in detection in 14 days, and definition number of days occurs and is greater than Vehicle equal to 2 days is to commonly use vehicle, totally 287 ten thousand；Bayonet test device is matched with record section, including bayonet test Crossing and direction, in this, as the observed volume of the section in this direction.
Due to bayonet probably due to hardware or other external factor cause failure, thus some abnormal datas are generated, in order to These abnormal datas are removed, DBSCAN method used herein is clustered, to remove abnormal data；Cluster the Clique obtained The point for including in cluster is considered as normal value, remaining point is considered as exceptional value；If being less than record flow comprising point number in maximum cluster The 50% of number of days is then considered as the bayonet test device hardware and is abnormal, the magnitude of traffic flow for not using the bayonet test device to record.
There are two main major parameters needed for DBSCAN clustering algorithm: a parameter is radius (Eps), is indicated with given The range of circle shaped neighborhood region centered on point A (in the present invention, A indicates flow)；Another parameter is the circle centered on point A The quantity (MinAts) at least put in neighborhood.If meet: centered on point A, radius for the point in the neighborhood of Eps number not Less than MinAts, then point P is referred to as core point.
By bayonet, at more days, the data on flows of window record was denoted as data set A={ a (i) i=1 ... n } at the same time, Middle p (i) indicates the bayonet in the flow of i-th day time window；For each point A (i), the son that point A (i) arrives set A is calculated Collect the distance between all the points in B={ a (1), a (2) ..., a (i-1), a (i+1) ..., a (n) }, distance according to from it is small to Big sequence sequence, the distance set after being sorted are D={ d (1), d (2) ..., d (k-1), d (k), d (k+1) ..., d (n) }, wherein d (k) is known as k- distance (it is close apart from kth between all the points other than a (i) point that k- distance is that point a (i) is arrived Distance)；
The value of k in empirically determined k- distance, so that it is determined that the quantity MinAts at least put；K=is taken in the present embodiment 4, then MinAts=4；
It treats each point a (i) in cluster set and calculates k- distance, finally obtain the k- distance set E={ e of all the points (1),e(2),…,e(n)}。
According to the k- distance set E of obtained all the points, k- distance set E ' is obtained after carrying out ascending sort to set E, It is fitted the change curve of k- distance in E ' set, in change curve, x-axis coordinate point is directly using incremental natural number Sequence, each pair of point answer a natural number, and y-axis coordinate point is k- distance in E ' set；Select the most express delivery in change curve Increase point and be used as flow radius Eps, the corresponding k- distance of the two o'clock of maximum slope is averagely steepest incremented point；
According to the value of given MinAts and the value of radius Eps, all core points are calculated；According to obtained core point set The value of conjunction and radius Eps calculate the core point that can be connected to；The each group of core point that will be connected to, and arrive core point Distance is less than the point of radius Eps, all puts together, forms a cluster；Thus cluster obtains one group of cluster；
The present invention utilizes rejecting outliers method of this kind based on density, can effectively resist exceptional value (" noise ") Interference.In the present embodiment, the effective percentage for obtaining bayonet record data is 72%.
Step 4: definition expands spline coefficient and carries out expansion sample come the link flow generated to known trajectory；For each time window T constructs an expansion spline coefficient set A={ α respectivelyk| k=1,2 ..., K }, size K, K are full track data in the time Hot spot track number in window.1 hour is used in this example as a time window, the expansion spline coefficient collective number of acquisition is 24. Optimization objective function is established based on the section in the time window with vehicle observed volume data for any time window T, For solving the corresponding expansion spline coefficient set A of the time window:
F=Minimize Z
VE(k)=αkVT(k)
Wherein, f is target function value, and Z is fitness, and i and j are the roads in time window T with vehicle observed volume data Section, N are the sum in the section in time window T with vehicle observed volume data, VR(i) be section i in time window T vehicle see Measurement of discharge, PijIt is the hot spot track set in time window T from section i to section j, VTIt (k) is that hot spot track k is produced in time window T Raw link flow, value are equal to the number that hot spot track k occurs in time window T, VEIt (k) is VT(k) expand the flow after sample, αk It is the expansion spline coefficient of hot spot track k.
Optimization objective function is solved using genetic algorithm, obtains the corresponding overall situation for expanding spline coefficient set of time window T most Excellent result, the specific steps are as follows:
5.1) initialize: setting evolutionary generation counter g=0, n individual of random generation are used as initial population, each Individual is an expansion spline coefficient set A={ αk| k=1,2 ..., K }, wherein element αkValue given birth at random in the range of [0,1] At；Setting genetic algebra is Ng, convergence precision Q；In this example, Ng=1500；
5.2) individual choice: the probability that q-th of individual is selected in population in g generation is calculatedWherein Zg(q)Indicate that g, i.e., will be in the individual for the fitness of q-th of individual in population Element substitutes into optimization objective function, obtained fitness value；The probability being selected according to Different Individual is continuously to g for population In individual repeat n wheel and select, obtain the new individuals of n；
5.3) individual intersection and variation:
The n individual that step 5.2) is obtained carries out random pair two-by-two, takes fixed crossover probability pcCarry out crossover operation； If certain group individual need is intersected, a crosspoint is randomly generated in all elements of group individual, by group individual Element after crosspoint is exchanged with each other, and generates two new individuals；If certain group individual does not need to be intersected, group individual It remains unchanged；
Take fixed mutation probability pmMutation operation is carried out, n individual after successively selecting crossover operation, to each individual All elements traversed, if some element needs to make a variation, change the value of the element at random in the range of [0,1]；
It results in through n individual after heredity, constitutes g+1 for population；
Wherein crossover probability pcWith mutation probability pm, it is calculated respectively according to following formula:
Wherein, pc1It is the crossover probability for the individual that g is less than average fitness value for fitness value in population, pc2It indicates Crossover probability of the g for the individual in population with maximum adaptation angle value, FmaxIt is g for the maximum adaptation of all individuals of population Angle value, FaveIt is g for the average fitness value of all individuals of population, F*It is biggish adaptation in two individuals intersected Angle value；pmIt is the mutation probability for the individual that g is less than average fitness value for fitness value in population, pm2It is g in population The mutation probability of individual with maximum adaptation angle value, F ' are the fitness values of the individual to make a variation；pc1、pc2、pm1、pm2For Empirical parameter；In the present embodiment, pc1Take 0.9, pm1Take 0.1, pc2And pm2Two class values, p are taken respectivelyc2=0.6 and pm2=0.001 or pc2=0.5 and pm2=0.05；
5.4) g+1 is calculated for the fitness of individual each in population, and the maximum for obtaining g+1 for all individuals of population is suitable Response, i.e. g+1 are for the corresponding target function value f of population；
5.5) algorithm termination condition judges；
Convergence judgement: in g+1 generation and g, are compared for target function value f corresponding to population, if difference is less than Precision Q, then it is assumed that meet convergence, algorithm terminates, using g+1 for the individual in population with maximum adaptation degree as the complete of A Office's optimal result；
Genetic algebra judgement: if current genetic algebra g=Ng, then algorithm terminates, by this NgFor in population have maximum adaptation Global optimum result of the individual of degree as A；
If being unsatisfactory for above-mentioned algorithm termination condition, genetic algebra g=g+1 is enabled, is grasped back to step 5.2) circulation Make.
To every each time window, calculating through the above steps, can obtain the corresponding 24 expansions sample of 24 time windows respectively Coefficient sets.
Step 6: for not having the section of vehicle observed volume data in time window T, by all processes in the time window The link flow that its track generates is superimposed using corresponding expand after spline coefficient carries out expansion sample, the virtual traffic stream as the section Amount；Specifically, for the hot spot track by section l, expand the link flow that spline coefficient generates it using it and carry out expansion sample；It is right In the non-thermal locus of points by section l, it is first determined with its maximally related hot spot track, determine method are as follows: it is non-hot to obtain this Common segment of each hot spot track, calculates separately the length of each common segment, by longest common segment pair in track and cluster belonging to it The hot spot track answered is considered as its maximally related hot spot track；Then it utilizes with the expansion spline coefficient of its maximally related hot spot track to it The link flow of generation carries out expansion sample.
The virtual traffic flow value passed through in certain time window T obtained after calculating in this example is as shown in Figure 3.
It is different with traditional magnitude of traffic flow acquisition modes, will there are bayonet data section and no bayonet by GPS data The association of data section, it is abundant in the present invention by the virtual flow in expansion spline coefficient calculating acquisition Floating Car GPS data covering section The precision of bayonet flow metering number evidence and the high coverage property of Floating Car GPS data is utilized, has the characteristics that implement simple, tool There is real-time, the virtual flow in no bayonet data on flows section can be directly acquired, be conducive to carry out traffic in city road network The work that flow is filled up.

Claims (6)

1.一种基于多源数据融合的虚拟交通流量计算方法，其特征在于，包括以下步骤：1. a virtual traffic flow calculation method based on multi-source data fusion, is characterized in that, comprises the following steps: 步骤一：获取浮动车GPS数据，将浮动车GPS数据通过地图匹配方法分配至城市路网上，获取浮动车行驶轨迹信息；Step 1: Obtain the GPS data of the floating car, distribute the GPS data of the floating car to the urban road network through the map matching method, and obtain the driving track information of the floating car; 步骤二：对浮动车行驶轨迹进行单元化处理，以保证两个卡口间的轨迹不经过其他卡口；对单元化处理后获得的所有轨迹按时间窗进行基于时空距离的聚类，并获取每类中的热点轨迹；Step 2: Unitize the driving trajectory of the floating car to ensure that the trajectory between the two bayonets does not pass through other bayonets; perform clustering based on space-time distance for all trajectories obtained after unitized processing according to the time window, and obtain Hotspot trajectories in each class; 步骤三：利用卡口记录的交通流量数据得到卡口对应路段在不同时间窗内的车辆观测流量数据；Step 3: Use the traffic flow data recorded by the bayonet to obtain the observed traffic flow data of vehicles in different time windows of the road section corresponding to the bayonet; 步骤四：针对每个时间窗T，分别构建一个扩样系数集合A＝{αk|k＝1，2，…，K}，其大小为K，K为全轨迹数据在该时间窗内的热点轨迹数；对于任一时间窗T，基于该时间窗内具有车辆观测流量数据的路段，建立最优化目标函数，用于求解该时间窗对应的扩样系数集合A：Step 4: For each time window T, construct a sample expansion coefficient set A = {α k |k = 1, 2, ..., K}, the size of which is K, where K is the total trajectory data in the time window. The number of hotspot trajectories; for any time window T, an optimization objective function is established based on the road segment with vehicle observation flow data in the time window, which is used to solve the sample expansion coefficient set A corresponding to the time window: f＝Minimize Zf=Minimize Z VE(k)＝αkVT(k)V E (k) = α k V T (k) 其中，f为目标函数值，Z为适应度，i和j是时间窗T内具有车辆观测流量数据的路段，N是时间窗T内具有车辆观测流量数据的路段的总数，VR(i)是时间窗T内路段i的车辆观测流量，Pij是时间窗T内从路段i到路段j的热点轨迹集合，VT(k)是时间窗T内热点轨迹k产生的路段流量，其值等于时间窗T内热点轨迹k出现的次数，VE(k)是VT(k)扩样后的流量，αk是热点轨迹k的扩样系数；Among them, f is the objective function value, Z is the fitness, i and j are the road segments with vehicle observation flow data in time window T, N is the total number of road segments with vehicle observation flow data in time window T, VR ( i) is the observed vehicle flow of road segment i in time window T, P ij is the set of hotspot trajectories from road segment i to road segment j in time window T, V T (k) is the road segment traffic generated by hotspot trajectory k in time window T, and its value is equal to the number of occurrences of the hot spot trajectory k in the time window T, V E (k) is the flow rate after V T (k) sample expansion, α k is the sample expansion coefficient of the hot spot trajectory k; 步骤五：利用遗传算法求解最优化目标函数，得到时间窗T对应的扩样系数集合的全局最优结果；Step 5: use the genetic algorithm to solve the optimization objective function, and obtain the global optimal result of the sample expansion coefficient set corresponding to the time window T; 步骤六：对于时间窗T内不具有车辆观测流量数据的路段，将该时间窗内所有经过其的轨迹产生的路段流量利用相应的扩样系数进行扩样后叠加，作为该路段的虚拟交通流量；具体地，对于经过路段l的热点轨迹，利用其扩样系数对其产生的路段流量进行扩样；对于经过路段l的非热点轨迹，首先确定与其最相关的热点轨迹，确定方法为：获取该非热点轨迹与其所属聚类中各条热点轨迹的公共段，分别计算各公共段的长度，将最长公共段对应的热点轨迹视为其最相关的热点轨迹；然后利用与其最相关的热点轨迹的扩样系数对其产生的路段流量进行扩样。Step 6: For the road section that does not have vehicle observation flow data in the time window T, the road section flow generated by all the trajectories passing through it in the time window is expanded and superimposed with the corresponding sample expansion coefficient, as the virtual traffic flow of the road section. Specifically, for the hot-spot track passing through the road section 1, use its sample expansion coefficient to expand the sample flow of the road section that it produces; For the non-hot-spot track passing through the road section 1, first determine the hot-spot track that is most relevant to it, and the determination method is: obtain The non-hotspot track and the common segments of each hotspot track in the cluster to which it belongs, calculate the length of each common segment, and regard the hotspot track corresponding to the longest common segment as its most relevant hotspot track; then use the most relevant hotspot track. The expansion coefficient of the trajectory is used to expand the traffic of the road segment it generates. 2.根据权利要求1所述的基于多源数据融合的虚拟交通流量计算方法，其特征在于，所述的步骤一中，首先筛选出载客状态的浮动车GPS数据，再利用ST-Matching地图匹配算法将浮动车GPS数据中的GPS点匹配到城市路网上，获取浮动车行驶轨迹信息。2. The virtual traffic flow calculation method based on multi-source data fusion according to claim 1, is characterized in that, in the described step 1, first filter out the GPS data of the floating car in the passenger-carrying state, and then use the ST-Matching map The matching algorithm matches the GPS points in the GPS data of the floating car to the urban road network to obtain the driving track information of the floating car. 3.根据权利要求1所述的基于多源数据融合的虚拟交通流量计算方法，其特征在于，所述步骤二中，对浮动车行驶轨迹进行单元化处理的方法为：3. The virtual traffic flow calculation method based on multi-source data fusion according to claim 1, is characterized in that, in the described step 2, the method for unitized processing to the traveling track of the floating vehicle is: 将所有轨迹进行编号，逐一条识别每条轨迹经过的卡口路段数，若某条轨迹经过的卡口路段数M≤1，则弃用该轨迹；若某条轨迹经过的卡口路段数M＝2，则将该轨迹经过的两个卡口路段之间的轨迹途经路段作为新轨迹保留；若某条轨迹经过的卡口路段数M≥3，则对该条轨迹进行分割，从该轨迹经过的第一个卡口路段开始，将该轨迹经过的每两个相邻卡口路段间的轨迹途经路段作为一条新的轨迹保留。Number all tracks, and identify the number of bayonet segments each track passes through one by one. If the number of bayonet segments passed by a track is less than or equal to 1, the track will be discarded; if the number of bayonet segments passed by a track is M = 2, then the track passing section between the two checkpoint sections that the track passes through is reserved as a new track; if the number of checkpoint sections M ≥ 3 passes through a track, the track will be divided, and the track will be divided from this track. Starting from the first bayonet road segment passed by the track, the track passing segment between every two adjacent bayonet road segments that the track passes through is reserved as a new track. 4.根据权利要求1所述的基于多源数据融合的虚拟交通流量计算方法，其特征在于，所述步骤二中，对所有轨迹按时间窗进行基于时空距离的聚类的具体步骤如下：4. the virtual traffic flow calculation method based on multi-source data fusion according to claim 1, is characterized in that, in described step 2, all tracks are carried out the concrete steps of clustering based on space-time distance by time window as follows: 2.1)将某一时间窗内的所有轨迹设置为未处理状态，并设定热点轨迹集合为空集；2.1) Set all trajectories in a certain time window to the unprocessed state, and set the set of hot trajectories as an empty set; 2.2)随机选取一条轨迹Traj，获得与其起讫点相同且处于同一时间窗内的轨迹集合，即轨迹Traj的时空邻近集合C(Traj)；2.2) Randomly select a trajectory Traj to obtain a trajectory set with the same start and end points and within the same time window, that is, the spatiotemporal adjacent set C(Traj) of the trajectory Traj; 2.3)对集合C(Traj)中的轨迹进行分类，其中第k类轨迹记为CTk，CTk中任意两条轨迹的重叠长度比率q大于设定的最小重叠长度比率Minq，并添加每一类中的热点轨迹至热点轨迹集合；其中重叠长度比率q为两条轨迹重叠路段数除以这两条轨迹拥有路段数的均值，热点轨迹为一类轨迹中的所有轨迹按出现次数降序排列后，取占该类中所有轨迹出现次数之和前n％的轨迹；标记集合C(Traji)中的轨迹为已处理状态；2.3) Classify the trajectories in the set C (Traj), where the k-th trajectory is denoted as CT k , the overlap length ratio q of any two trajectories in CT k is greater than the set minimum overlap length ratio Minq, and add each The hotspot trajectories in the class to the set of hotspot trajectories; where the overlap length ratio q is the number of overlapping sections of the two trajectories divided by the mean of the number of sections that the two trajectories have, and the hotspot trajectories are all trajectories in a class of trajectories sorted in descending order of the number of occurrences , take the trajectory that accounts for the top n% of the total number of occurrences of all trajectories in this class; mark the trajectory in the set C(Traj i ) as the processed state; 2.4)判断该时间窗内的所有轨迹是否都被标记为已处理状态，若是，则结束该时间窗内轨迹的聚类，否则以未处理状态的轨迹为基础，返回步骤2.2)。2.4) Determine whether all trajectories in the time window are marked as processed state, if so, end the clustering of trajectories in this time window, otherwise, based on the unprocessed state trajectories, return to step 2.2). 5.根据权利要求1所述的基于多源数据融合的虚拟交通流量计算方法，其特征在于，在所述的步骤二中，利用DBSCAN聚类算法对多天内同一时间窗同一卡口记录的交通流量数据进行聚类，去除异常流量值，其中，聚类获得的最大团簇中包含的点被视为正常流量值，其余点视为异常流量值；同时，若最大团簇中包含点个数小于记录流量天数的50％，则视为该卡口硬件发生异常，去除该卡口记录的交通流量数据；将剩下的交通流量数据作为卡口对应路段的车辆观测流量数据。5. the virtual traffic flow calculation method based on multi-source data fusion according to claim 1, is characterized in that, in described step 2, utilizes DBSCAN clustering algorithm to the traffic recorded at same checkpoint of same time window in many days The flow data is clustered to remove abnormal flow values. The points contained in the largest cluster obtained by clustering are regarded as normal flow values, and the remaining points are regarded as abnormal flow values; at the same time, if the maximum cluster contains the number of points If it is less than 50% of the number of days of recorded traffic, it is considered that the hardware of the bayonet is abnormal, and the traffic flow data recorded by the bayonet is removed; the remaining traffic flow data is used as the vehicle observation flow data of the corresponding road section of the bayonet. 6.根据权利要求1所述的基于多源数据融合的虚拟交通流量计算方法，其特征在于，所述步骤五中，利用遗传算法求解最优化目标函数，具体步骤如下：6. the virtual traffic flow calculation method based on multi-source data fusion according to claim 1, is characterized in that, in described step 5, utilizes genetic algorithm to solve optimization objective function, and concrete steps are as follows: 5.1)初始化：设置进化代数计数器g＝0，随机生成n个个体作为初始群体，每一个个体为一个扩样系数集合A＝{αk|k＝1，2，…，K}，其中元素αk的值在[0，1]的范围内随机生成；设置遗传代数为Ng，收敛精度为Q；5.1) Initialization: Set the evolutionary algebra counter g=0, randomly generate n individuals as the initial population, each individual is a sample expansion coefficient set A={α k |k=1, 2,..., K}, where the element α The value of k is randomly generated in the range of [0, 1]; the genetic algebra is set to N g , and the convergence accuracy is Q; 5.2)个体选择：计算出第g代中种群中第q个个体被选择的概率其中Zg(q)表示第g代种群中第q个个体的适应度，即将该个体中的元素代入最优化目标函数，得到的适应度值；根据不同个体被选中的概率连续对第g代种群中的个体重复进行n轮挑选，得到n个新的个体；5.2) Individual selection: Calculate the probability that the qth individual in the population in the gth generation is selected Among them, Z g(q) represents the fitness of the qth individual in the gth generation population, that is, the fitness value obtained by substituting the elements in the individual into the optimization objective function; according to the probability of different individuals being selected, the gth generation Individuals in the population are repeatedly selected for n rounds to obtain n new individuals; 5.3)个体的交叉与变异：5.3) Crossover and variation of individuals: 将步骤5.2)得到的n个个体进行两两随机配对，取固定交叉概率pc进行交叉操作；若某组个体需要进行交叉，则在该组个体的所有元素中随机产生一个交叉点，将该组个体交叉点之后的元素相互交换，产生两个新的个体；若某组个体不需要进行交叉，则该组个体保持不变；The n individuals obtained in step 5.2) are randomly paired in pairs, and the fixed crossover probability p c is used to perform the crossover operation; if a group of individuals needs to be crossed, a crossover point is randomly generated in all elements of the group of individuals, and the The elements after the intersection of group individuals are exchanged with each other to generate two new individuals; if a group of individuals does not need to be crossed, the group of individuals remains unchanged; 取固定的变异概率pm进行变异操作，依次选择交叉操作后的n个个体，对每个个体的所有元素进行遍历，若某个元素需要变异，则在[0，1]的范围内随机改变该元素的取值；Take a fixed mutation probability p m for mutation operation, select n individuals after the crossover operation in turn, and traverse all elements of each individual. If an element needs to be mutated, it will be randomly changed in the range of [0, 1] the value of this element; 由此得到了经遗传过后的n个个体，构成第g+1代种群；Thereby, n individuals after inheritance are obtained, which constitute the g+1 generation population; 其中交叉概率pc和变异概率pm，分别按照以下公式计算：The crossover probability p c and the mutation probability p m are calculated according to the following formulas: 其中，pc1是第g代种群中适应度值小于平均适应度值的个体的交叉概率，pc2表示第g代种群中具有最大适应度值的个体的交叉概率，Fmax为第g代种群所有个体的最大适应度值，Fave为第g代种群所有个体的平均适应度值，F*是进行交叉的两个个体中较大的适应度值；pm1是第g代种群中适应度值小于平均适应度值的个体的变异概率，pm2是第g代种群中具有最大适应度值的个体的变异概率，F′是进行变异的个体的适应度值；pc1、pc2、pm1、pm2为经验参数；where p c1 is the crossover probability of individuals whose fitness value is less than the average fitness value in the g-th generation population, p c2 is the crossover probability of the individual with the largest fitness value in the g-th generation population, and F max is the g-th generation population The maximum fitness value of all individuals, F ave is the average fitness value of all individuals in the g-th generation population, F * is the larger fitness value of the two individuals performing crossover; p m1 is the fitness value in the g-th generation population is the mutation probability of the individual whose value is less than the average fitness value, p m2 is the mutation probability of the individual with the largest fitness value in the g-th generation population, F' is the fitness value of the individual undergoing mutation; p c1 , p c2 , p m1 and p m2 are empirical parameters; 5.4)计算第g+1代种群中各个个体的适应度，得到第g+1代种群所有个体的最大适应度，即第g+1代种群对应的目标函数值f；5.4) Calculate the fitness of each individual in the g+1 generation population, and obtain the maximum fitness of all individuals in the g+1 generation population, that is, the objective function value f corresponding to the g+1 generation population; 5.5)算法终止条件判断；5.5) Judgment of algorithm termination condition; 收敛性判断：将第g+1代和第g代种群所对应的目标函数值f进行对比，若差值小于精度Q，则认为满足收敛性，算法终止，将第g+1代种群中具有最大适应度的个体作为A的全局最优结果；Convergence judgment: compare the objective function value f corresponding to the g+1 generation and the g generation population. If the difference is less than the precision Q, it is considered that the convergence is satisfied, and the algorithm terminates, and the g+1 generation population has The individual with the maximum fitness is regarded as the global optimal result of A; 遗传代数判断：若当前遗传代数g＝Ng，则算法终止，将这Ng代种群中具有最大适应度的个体作为A的全局最优结果；Genetic algebra judgment: if the current genetic algebra g=N g , the algorithm is terminated, and the individual with the largest fitness in the N g generation population is taken as the global optimal result of A; 若不满足上述算法终止条件，则令遗传代数g＝g+1，返回到步骤5.2)循环进行操作。If the termination condition of the above algorithm is not satisfied, set the genetic algebra g=g+1, and return to step 5.2) to operate in a loop.
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