CN102289932A - Dynamic OD (Origin Destination) matrix estimating method based on AVI (Automatic Vehicle Identification) device - Google Patents
Dynamic OD (Origin Destination) matrix estimating method based on AVI (Automatic Vehicle Identification) device Download PDFInfo
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Abstract
The invention belongs to the field of traffic plan and management, in particular relating to a dynamic OD (Origin Destination) matrix estimating method based on an AVI (Automatic Vehicle Identification) device. The method comprises the following steps of: introducing a part of path information detected through AVI, dynamic travel time information and detector measurability criterion; performing lost path range determination and selection probability modification on the path-lost vehicle information according to a Bayes estimation algorithm firstly; simulating any vehicle by utilizing monte carlo random simulation and selecting lost paths so as to obtain an initially modified OD matrix based on a part of path of an individual vehicle; and finally correcting the initially modified OD matrix by utilizing the flow information of the AVI detection to obtain a final OD matrix estimation value. Through the method disclosed by the invention, the defects that the dependency on the priori information is high and aspects, such as detection precision and the like, are never considered because the road section flow and the travel time information are only considered in the traditional method can be overcome.
Description
Technical field
The invention belongs to traffic programme and management domain, be specifically related to a kind of Dynamic OD Matrix Estimation method based on automatic vehicle identification equipment.
Background technology
Vehicle driving OD matrix is the key foundation information of traffic system planning, design and operational management.The precision of OD information directly influences urban traffic control person, and traffic programme engineering technical personnel the accurate judgement of situation may occur for current situation of traffic and future transportation, and therefore can directly influence the validity of traffic management measure, the rationality of traffic programme.Therefore accurate OD matrix no matter for urban traffic control personnel and Urban Traffic Planning personnel's final decision with according to being vital.
Vehicle driving OD matrix is to describe one group of math matrix that any vehicle is reached home from starting point in the transportation network, and it has directly reacted set out between the different districts vehicle number and arrival vehicle number.Yet the OD matrix obtain all be traffic administration all the time with control in a difficult point problem.The acquisition methods of traditional OD matrix normally carries out large-scale vehicle driving sample survey, yet there is huge, the shortcoming such as long that expends time in of technique for investigation difficulty, funds cost in this method, and the traffic data that obtains exists post-processed loaded down with trivial details and can not be applied to the dynamic management of urban transportation.Therefore the problem that exists in traditional OD matrix acquisition methods, from beginning in 1978, the vehicle flow data that Van Zuylen and Willumsen utilize the magnetic test coil that is laid in the road to obtain were carried out the research of OD matrix estimation method.Up to now, the method for estimation of OD matrix has mainly comprised types such as least square method, state-space method, information theory model.Yet these methods are subjected to the restriction and the restriction of detection means, the main analysis data of the highway section vehicle flow data in the detecting device have only been utilized as research, therefore using traditional " section type " detecting device carries out the OD Matrix Estimation to be subjected to artificial assumed condition too much, the serious restriction of conditions such as detection information is few, and the checkout equipment precision is low.
In recent years, along with automatic vehicle identification (Automatic Vehicle Identification, AVI) technology and equipment are in the popularization and the application in China one line city, and the traffic information collection technology turns to " wide area type " detection technique from traditional " section type " detection technique rapidly.The core of automatic vehicle identification technology is to detect vehicle ID(license plate number), by time and vehicle position information.Based on the existing literature data is studied, the method for carrying out the OD Matrix Estimation based on the automatic vehicle identification technology all only is a kind of improvement of traditional OD matrix estimation method at present.
These classic methods exist following problem and challenge:
(1) estimates that for the Dynamic OD under the AVI environment present method remains by classical OD estimation model is improved, and adds new AVI detection information and improves the OD estimated accuracy.In fact, the routing information of AVI detection is most important to the precision that OD estimates.
(2) OD estimates not only and network topology, link flow is relevant, and also the precision with prior imformation has confidential relation, and research in the past supposes all that usually having obtained reliable prior imformation calculates OD, and long at interval owing to OD survey in the reality, priori OD information often precision is not high.
(3) in the OD that detects information based on AVI estimates, do not consider AVI accuracy of detection problem.
Summary of the invention
The objective of the invention is to the deficiency that exists in the research and technology at conventional dynamic OD matrix estimation method, particularly at AVI information excavating degree of depth deficiency, the problem that the prior imformation dependence is strong has excessively proposed a kind of new Dynamic OD Matrix Estimation method based on automatic vehicle identification equipment.
This method has following four characteristics: one, broken through and only rely on the main information that link flow and journey time are estimated as OD in the classic method; Two, propose the measurability criterion of detecting device, be specially adapted to the accuracy of detection problem of AVI checkout facility outwardness; Three, do not require for the road network topology structure, go for any type of open road network; Four, solved the dependence of classic method, promptly can under prior imformation at random, extrapolate the OD of degree of precision prior imformation.
For reaching above target, the present invention proposes Dynamic OD Matrix Estimation method based on automatic vehicle identification equipment.At first each is estimated the vehicle license data that obtain in each checkout equipment in period, vehicle due in data, the detecting device numbering is extracted; Then will extract the data that obtain serves as according to carrying out data qualification, converting the vehicle sections routing information to according to vehicle license data and detecting device numbering, and carry out data qualification according to the composition mode of vehicle sections routing information with the vehicle license data; Then based on priori OD information,, reduce the scope of optional residual paths and the selection probability of residual paths is revised based on detecting device measurability criterion simultaneously according to the installation position of detecting device; The process that the Actual path of the method simulating vehicle of further employing random simulation is selected is to obtain the fullpath of vehicle; And complete back is repaired in the path of all vehicles of gather obtaining obtain initial OD matrix, detect the link flow that obtains in conjunction with AVI at last and carry out the initial OD matrix and revise again.
AVI measurability (Measurability) concept definition that said method is mentioned is: detect the characteristics of information data disappearance and detected the dynamic travel time information of vehicle based on AVI, the analysis and judgement vehicle passes through the possibility in a certain path.According to the result of measurability criterion, can dwindle the range of choice of vehicle residual paths greatly, improve the reliability of vehicle residual paths judgement and then significantly improve the precision that Dynamic OD is estimated.Concrete steps are as follows:
(1) extraction of AVI information data and classification
The information that AVI is obtained with the vehicle license data as index data, the data that vehicle license information is identical are mated combination, and according to the quantity of information of arbitrary same vehicle licence plate information of vehicles is divided into following 3 big classes: 1, comprise the vehicle starting point, the data of all path nodes of terminal point and process are called path omniscient type vehicle data; 2, the data of two path nodes that comprise the process of vehicle at least are called the path and partly know the type vehicle data; 3, the data that only comprise a path node of vehicle process are called the path and singly know the type vehicle data.
(2) the expansion sample of prior imformation
The vehicle number of catching based on AVI expands sample with prior imformation according to the vehicle number of actual acquisition and handles.
(3) filtration path omniscient type vehicle data
Convert the vehicle data of path omniscient type to the OD matrix information, and with this OD matrix information with expand sample after prior imformation subtract each other (if in the prior imformation corresponding OD to flow less than 0, then give a minimum flow 1), the priori OD information after obtaining to filter.
(4) estimate the path journey time that the period is interior
According to the laying information of different AVI, the journey time of the vehicle of any two adjacent groups AVI process is added up its average stroke time of acquisition; Distribute according to the different vehicle journey time in addition,, set up AVI journey time probability distribution function based on the principle of theory of probability, and the possibility of not passing through these two groups of AVI detecting devices by all the other vehicles of this Functional Analysis; Then, obtain the journey time in path by highway section-path journey time relation function, and with this as the criterion of judging whether vehicle enters road network in the estimation period.
(5) path estimation of type vehicle is partly known in the path
The part path of the path partly being known type vehicle disappearance is divided into upstream miss path and downstream miss path by direction of traffic.According to different miss path,,, dwindle the optional miss path of any vehicle hunting zone by the Bayesian Estimation algorithm with reference to detecting device measurability foundation.And on the basis of filtering priori OD information, the selection probability of possible path is revised; By the Monte Carlo random simulation any vehicle is made routing in its feasible path at last, and carry out the path with this and repair.The routing information of type vehicle is partly known in acquired any one group of path, can pass through the path journey time, judges that vehicle enters the time and the time of leaving road network of road network.
(6) secondary filtration of priori OD information
Path after repairing is known that partly the type data-switching becomes the OD matrix information, and with this OD matrix information with expand sample after prior imformation subtract each other (as if OD corresponding in the prior imformation to flow less than 0, then give a minimum flow 1).Then know singly that according to remaining path the type vehicle number carries out secondary and expands sample, finally obtain the priori OD information behind the secondary filtration.
(7) path estimation of type vehicle is singly known in the path
The path knows that singly type vehicle route estimation approach and path know that partly type vehicle route estimation approach is similar, specifically can partly know type vehicle route method of estimation referring to the path, and singly be known the OD matrix information of type information of vehicles with this.
(8) initially repairing the OD matrix obtains
The OD matrix information that front 3 classes are obtained adds up to obtain initially to repair the OD matrix.
(9) initially repair the OD matrix information based on the flow adjustment
The data based highway section of link flow-path flow flow function of the OD matrix information initially repaired and AVI information interception concerned dynamically stroll match.When path flow and highway section measured discharge relative error are 5%, stop to stroll match, and as final OD matrix information.
Among the present invention, the path estimation of type vehicle and the path estimation that the type vehicle is singly known in the middle path of step (7) are partly known in path described in the step (5), are specially:
Arrive next highway section detecting device when a certain vehicle has grace time, but the routing information of vehicle is not when comprising next highway section detecting device, has in the following situation any:
(1), vehicle passes through next highway section, but is not recorded owing to detect error;
(2), vehicle through laying the highway section of detecting device, does not directly arrive the highway section that sensorless is laid;
To the possible residual paths of vehicle, set up initial routing probability set according to prior imformation,
,Analyze vehicle according to following formula then and can in this period, pass through next highway section, and dwindle range of choice residual paths with this;
（1.2）
Wherein
: represent the journey time between any two AVI;
: represent the distance between any two adjacent AVI detecting devices;
: the stochastic error during the expression journey time is calculated.
: h is in the period in expression, is numbered the journey time of the vehicle of j by two AVI detecting devices;
S: expression random chance numerical value;
N: express time burst length, i.e. unit interval length;
For the residual paths that can select, revise the selection probability of residual paths again according to following formula, obtain new routing probability set;
: the length of period is estimated in expression.
Compared with prior art, the present invention has broken through the defective of estimating main information foundation on the traditional sense with link flow as Dynamic OD, repair the main means of estimating as Dynamic OD by microcosmic path with vehicle, with link flow information is auxiliary correction means, makes AVI information be fully used.This method is any open road network of the extraordinary adaptation of energy simultaneously, and can improve the lower OD prior imformation of precision under low AVI coverage rate condition.This has considerable realistic meaning for not high actual of domestic OD survey information deficiency, precision, in China's traffic administration and control reality boundless application space is arranged.
Description of drawings
The Dynamic OD Matrix Estimation method flow diagram that Fig. 1 proposes for the present invention.
Fig. 2 repairs synoptic diagram for the path based on the open road network of measurability criterion that the present invention proposes.
Fig. 3 is the road network figure that the embodiment of the invention 1 adopts.
Fig. 4 is the OD Matrix Estimation result schematic diagram under lower accuracy coverage rate and prior imformation prerequisite in the embodiment of the invention 1.
Fig. 5 is to the improved effect synoptic diagram of the lower initial OD of precision in the embodiment of the invention 1.
Embodiment
Elaborate below in conjunction with 3 pairs of embodiments of the invention of accompanying drawing: present embodiment is being to implement under the prerequisite with the technical solution of the present invention, provided detailed embodiment and concrete operating process, but protection scope of the present invention is not limited to following embodiment.
Embodiment 1: this method of estimation is used for certain ground velocity road system shown in Figure 3, this through street system has the AVI equipment of 17 gateways and 9 sections, be 216 groups wherein, account for all OD 74.74% of number by the right number of the actual detectable OD of covering of AVI facility.The equipment input that needs: lay the AVI video detector at road section or ring road gateway.The acquisition input information that requires: the vehicle license information after the identification, vehicle due in, AVI detecting device numbering.
After obtaining above-mentioned input information, know partly that with certain path repair in the path of type vehicle data is example, repair the driving path of any vehicle according to Fig. 2, step is as follows:
(1) when the known vehicle licence plate be that the vehicle of A is respectively by being numbered AVI
1And AVI
4The time, can obtain vehicle A respectively at the T time of arrival of these two groups of detecting devices
1And T
4And, analyze the optional residual paths of vehicle A and it is demarcated with the prior imformation in different paths according to the topological structure of road network
(2) arrive AVI by path journey time function calculation vehicle A
3Journey time.
Wherein
: represent the journey time between any two AVI;
: h is in the period in expression, is numbered the journey time of the vehicle of j by two AVI detecting devices;
: h is in the period in expression, detects the vehicle average stroke time according to two AVI detecting devices;
S: expression random chance numerical value;
N: express time burst length, i.e. unit interval length;
(3), arrive next highway section detecting device AVI3 when vehicle A has grace time, but the routing information of vehicle A does not comprise detecting device AVI according to detecting device measurability criterion
3The time, there are two kinds of sights:
Sight 1: vehicle A passes through AVI
5, but be not recorded owing to detect error.
Sight 2: vehicle is not through laying AVI
5The highway section, and be short to most and reach D based on walking line time
6
[0036]Therefore can revise the probability of these optional residual paths
(4) revised routing probability is approached the process that actual vehicle route is selected by the method maximum of Monte Carlo random simulation, obtain the fullpath of vehicle.
[0038](5) the incomplete vehicle data in all the other paths can carry out the reparation in path and convert the OD matrix of initial reparation to by above-mentioned method, and repairs initial matrix by highway section-path flow flow function relation.
Wherein
: be illustrated in the revised routing probability of h period n bar miss path;
All the other alphabetical meanings are identical with following formula.
Wherein Fig. 4 represent under low coverage rate condition, estimate the relative error of OD and actual OD, Fig. 5 represents is synoptic diagram to the improved effect of priori OD information.Even working as the AVI coverage rate is 60%, priori OD precision is 40% o'clock, and its relative error also only is 21.8%.
Claims (2)
1. Dynamic OD Matrix Estimation algorithm based on the AVI checkout equipment is characterized in that concrete steps are as follows:
(1) extraction of AVI information data and classification
As main index data, the data that vehicle license information is identical are mated combination to the information that AVI is obtained, and according to the quantity of information of arbitrary same vehicle licence plate information of vehicles are divided into following 3 big classes with the vehicle license data:
1., comprise the vehicle starting point, the data of all path nodes of terminal point and process are called path omniscient type vehicle data;
2., the data of two path nodes that comprise the process of vehicle at least are called the path and partly know the type vehicle data;
3., the data that only comprise a path node of vehicle process are called the path and singly know the type vehicle data;
(2) the expansion sample of prior imformation
The vehicle number of catching based on AVI expands sample with prior imformation according to the vehicle number of actual acquisition and handles;
(3) filtration path omniscient type vehicle data
Convert path omniscient type vehicle data to the OD matrix information, and this OD matrix information and the prior imformation that expands behind the sample subtracted each other, if the OD of correspondence less than 0, then gives a minimum flow 1 to flow in the prior imformation, and then the priori OD information after obtaining to filter;
(4) estimate the path journey time that the period is interior
According to the laying information of different AVI, the journey time of the vehicle of any two adjacent groups AVI process is added up its average stroke time of acquisition; Distribute according to the different vehicle journey time,, set up AVI journey time probability distribution function based on the theory of probability model, and the possibility of not passing through these two groups of AVI detecting devices by all the other vehicles of this Functional Analysis; Then, obtain the journey time in path, and this is judged that as one whether vehicle is in the criterion of estimating to enter road network in the period by highway section-path journey time relation function;
(5) path estimation of type vehicle is partly known in the path
The part path of the path partly being known type vehicle disappearance is divided into upstream miss path and downstream miss path by direction of traffic; According to different miss path, with reference to detecting device measurability foundation,, the optional miss path of any vehicle is dwindled the hunting zone rapidly, and the selection probability of possible path is revised on the basis of filtering priori OD information by the Bayesian Estimation algorithm; By the Monte Carlo random simulation any vehicle is made routing in its feasible path at last, and carry out the path with this and repair; The routing information of type vehicle is partly known in acquired any one group of path, by the path journey time, judges that vehicle enters the time and the time of leaving road network of road network;
(6) secondary filtration of priori OD information
Path after repairing is known that partly the type data-switching becomes the OD matrix information, and this OD matrix information and the prior imformation that expands behind the sample subtracted each other, if in the prior imformation corresponding OD to flow less than 0, then give a minimum flow 1, simultaneously know singly that according to remaining the type vehicle number carries out secondary and expands sample, finally obtain the OD information behind the secondary filtration;
(7) path estimation of type vehicle is singly known in the path
The path knows that singly type vehicle route estimation approach and path know that partly the method for path estimation of type vehicle is similar, partly knows type vehicle route method of estimation according to the path of step (5), and is singly known the OD matrix information of type information of vehicles with this;
(8) initially repairing the OD matrix obtains
The OD matrix information that front 3 classes are obtained adds up to obtain initially to repair the OD matrix;
(9) initially repair the OD matrix information based on the flow adjustment
The OD matrix information of initially reparation and the link flow information of AVI detection are dynamically strolled match according to highway section-path flow flow function relation; When path flow and highway section measured discharge relative error are 5%, stop to stroll match, and as final OD matrix information.
2. the Dynamic OD Matrix Estimation method based on the AVI checkout equipment according to claim 1 is characterized in that path described in the step (5) knows that partly path in the path estimation of type vehicle and the step (7) singly knows the path estimation of type vehicle, is specially:
Arrive next highway section detecting device when a certain vehicle has grace time, but the routing information of vehicle is not when comprising next highway section detecting device, has in the following situation any:
(1), vehicle passes through next highway section, but is not recorded owing to detect error;
(2), vehicle through laying the highway section of detecting device, does not directly arrive the highway section that sensorless is laid;
To the possible residual paths of vehicle, set up initial routing probability set according to prior imformation,
, analyze vehicle according to following formula then and can in this period, pass through next highway section, and dwindle range of choice residual paths with this;
Wherein
: represent the journey time between any two AVI;
: expression h in the period, the selection probability in i journey time interval by two AVI detecting devices;
S: expression random chance numerical value;
N: express time burst length, i.e. unit interval length;
For the residual paths that can select, revise the selection probability of residual paths again according to following formula, obtain new routing probability set;
: the prior imformation of n group miss path in the travel direction of expression downstream;
: the length of period is estimated in expression.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103440764A (en) * | 2013-08-19 | 2013-12-11 | 同济大学 | Urban road network vehicle travel path reconstruction method based on vehicle automatic identification data |
| CN103646187A (en) * | 2013-12-27 | 2014-03-19 | 中国科学院自动化研究所 | Method for obtaining vehicle travel path and OD (Origin-Destination) matrix in statistic period |
| CN103903437A (en) * | 2014-02-27 | 2014-07-02 | 中国科学院自动化研究所 | Motor vehicle out-driving OD matrix obtaining method based on video traffic detection data |
| CN104915731A (en) * | 2015-06-11 | 2015-09-16 | 同济大学 | Vehicle travel path reconstruction macro/micro integrated new method based on automatic vehicle identification data |
| CN105631945A (en) * | 2014-10-30 | 2016-06-01 | 国际商业机器公司 | Method and device for identifying starting-point/end-point pairing |
| CN105679037A (en) * | 2016-03-29 | 2016-06-15 | 重庆云途交通科技有限公司 | Dynamic path planning method based on user travel habit |
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| CN109461307A (en) * | 2018-11-16 | 2019-03-12 | 中电科新型智慧城市研究院有限公司 | A method of estimating road-section average vehicle flow and OD demand |
| CN110245423A (en) * | 2019-06-14 | 2019-09-17 | 重庆大学 | Discharge relation analysis method between a kind of freeway toll station |
| CN110491157A (en) * | 2019-07-23 | 2019-11-22 | 中山大学 | A kind of vehicle correlating method based on parking data and bayonet data |
| CN110910647A (en) * | 2019-12-17 | 2020-03-24 | 江苏智城慧宁交通科技有限公司 | Electronic police based traffic travel OD matrix estimation system |
| CN110956808A (en) * | 2019-12-23 | 2020-04-03 | 北京交通大学 | Heavy truck traffic flow prediction method based on non-full-sample positioning data |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0902404A2 (en) * | 1997-09-11 | 1999-03-17 | Siemens Aktiengesellschaft | Method for transmitting traffic information |
| CN101739824A (en) * | 2009-11-12 | 2010-06-16 | 上海第二工业大学 | Data fusion technology-based traffic condition estimation method |
-
2011
- 2011-06-17 CN CN 201110163206 patent/CN102289932B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP0902404A2 (en) * | 1997-09-11 | 1999-03-17 | Siemens Aktiengesellschaft | Method for transmitting traffic information |
| CN101739824A (en) * | 2009-11-12 | 2010-06-16 | 上海第二工业大学 | Data fusion technology-based traffic condition estimation method |
Non-Patent Citations (2)
| Title |
|---|
| 孙剑等: "OD矩阵估计中的AVI设施布局优化", 《公路交通科技》 * |
| 孙剑等: "基于视频牌照识别的动态交通OD估计仿真优化", 《公路交通科技》 * |
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| US10346235B2 (en) | 2014-10-30 | 2019-07-09 | International Business Machines Corporation | Identifying origin and destination pairs |
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