CN102289932B - 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 matrixby 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 operation management.The precision of OD information directly affects 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 affect 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 in transportation network, vehicle is reached home from starting point arbitrarily, and it has directly reacted between different districts set out vehicle number and arrival vehicle number.Yet obtaining of OD matrix is all a difficulties in traffic administration and control all the time.The acquisition methods of traditional OD matrix normally carries out large-scale vehicle driving sample survey, yet this method exists, technique for investigation difficulty, funds cost are huge, the shortcoming such as long that expends time in, and the traffic data obtained exists post-processed loaded down with trivial details and can not be applied to the dynamic management of urban transportation.Therefore for fear of the problem existed in traditional OD matrix acquisition methods, from 1978 s, Van Zuylen and Willumsen utilize the vehicle flow data of the magnetic test coil acquisition be laid in road to carry out the research of OD matrix estimation method.Up to now, the method for estimation of OD matrix has mainly comprised the types such as least square method, state-space method, Information Theory Model.Yet these methods are subject to restriction and the restriction of detection means, the Main Analysis data of the highway section vehicle flow data in the detecting device as research have only been utilized, therefore applying traditional " section type " detecting device carries out the OD Matrix Estimation to be subject to artificial assumed condition too much, the serious restriction of the 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 at the propagation and employment 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 existing documents and materials are studied, the method for at present based on the automatic vehicle identification technology, carrying out the OD Matrix Estimation is only all a kind of improvement of traditional OD matrix estimation method.
These classic methods exist following problem and challenge:
(1) for the dynamic OD estimation under the AVI environment, current method remains by classical OD estimation model is improved, and adds new AVI detection information to improve the OD estimated accuracy.In fact, the precision that the routing information that AVI detects is estimated OD is most important.
(2) OD estimates not only and network topology, link flow is relevant, also with the precision of prior imformation, close relationship is arranged, and research in the past all supposes that having obtained reliable prior imformation calculates OD usually, and long due to the OD survey interval in 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 object of the invention is to the deficiency existed in the research and technology for conventional dynamic OD matrix estimation method, particularly for AVI information excavating degree of depth deficiency, the problem that the prior imformation dependence is excessively strong, 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 relied 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, solve the dependence of classic method to prior imformation, can under random prior imformation, extrapolate the OD of degree of precision.
For reaching above target, the present invention proposes the Dynamic OD Matrix Estimation method based on automatic vehicle identification equipment.At first each is estimated to the vehicle license data that obtain in each checkout equipment in the period, vehicle due in data, the detecting device numbering is extracted; Then will extract the data that obtain and take the vehicle license data as according to carrying out Data classification, and convert the vehicle sections routing information to according to vehicle license data and detecting device numbering, and carry out Data classification according to the building form of vehicle sections routing information; Then take priori OD information as basis, according to the installation position of detecting device, based on detecting device measurability criterion, reduce the scope of optional residual paths and the selection probability of residual paths is revised simultaneously; The process that further Actual path of the method simulating vehicle of employing random simulation is selected, to obtain the fullpath of vehicle; And the initial OD matrix of complete rear acquisition is repaired in the path that gathers all vehicles that obtain, the link flow finally obtained in conjunction with the AVI detection carries out the initial OD matrix and revises.
AVI measurability (Measurability) concept definition that said method is mentioned is: the characteristics that lack based on AVI detection information data and the dynamic travel time information that has detected vehicle, analyze the possibility of judgement vehicle through a certain path.According to the result of measurability criterion, can greatly dwindle the range of choice of vehicle residual paths, the reliability that raising vehicle residual paths judges and then the precision that significantly improves dynamic OD estimation.Concrete steps are as follows:
(1) extraction of AVI information data and classification
The information that AVI is obtained is usingd the vehicle license data as index data, by vehicle license information, identical data are mated combination, and according to the quantity of information of arbitrary same vehicle licence plate, information of vehicles is divided into to following 3 large classes: 1, comprise the vehicle starting point, the data of all path nodes of terminal and process are called path omniscient type vehicle data; 2, the data of two path nodes that at least comprise the process of vehicle are called path and partly know the type vehicle data; 3, the data that only comprise a path node of vehicle process are called path and singly know the type vehicle data.
(2) the expansion sample of prior imformation
The vehicle number that the AVI of take catches is basis, and prior imformation is expanded to the sample processing according to the vehicle number of actual acquisition.
(3) filtration path omniscient type vehicle data
Convert the vehicle data of path omniscient type to the OD matrix information, and this OD matrix information is subtracted each other with the prior imformation after the expansion sample (if in prior imformation, corresponding OD is less than 0 to flow, give a minimum flow 1), obtain the priori OD information after filtering.
(4) estimate the path journey time in the period
According to the laying information of different AVI, the journey time of the vehicle of two groups of AVI processes of arbitrary neighborhood is added up and obtained its average travel time; According to the different vehicle journey time, distribute in addition, the principle of Based on Probability opinion, set up AVI journey time probability distribution function, and by the possibility of all the other these two groups of AVI detecting devices of vehicles faileds process of this Functional Analysis; Then pass through highway section-path journey time relation function, the journey time of way to acquire, and using this as judging whether vehicle is estimating the criterion that enters road network in the period.
(5) path estimation of type vehicle is partly known in path
The part path of path partly being known to 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, by the Bayesian Estimation algorithm, dwindle the optional miss path of any vehicle hunting zone.And on the basis of filtering priori OD information, the selection probability of possible path is revised; Finally by the Monte Carlo random simulation, any vehicle is made to routing in its feasible path, and carry out the path reparation with this.The routing information of type vehicle is partly known in acquired any one group of path, can pass through the path journey time, and the judgement vehicle enters the time of road network and leaves the time of road network.
(6) secondary filtration of priori OD information
Path after repairing is partly known to the type data-switching becomes the OD matrix information, and this OD matrix information is subtracted each other to (if in prior imformation, corresponding OD is less than 0 to flow, giving a minimum flow 1) with the prior imformation after the expansion sample.Then according to remaining path, singly know that the type vehicle number carries out secondary and expands sample, finally obtain the priori OD information after secondary filtration.
(7) path estimation of type vehicle is singly known in path
Path singly knows that the method that the type vehicle route is estimated partly knows that with path the method for type vehicle route estimation is similar, specifically can partly know type vehicle route method of estimation referring to path, and obtain singly knowing 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 initial reparation OD matrix.
(9) initially repair the OD matrix information based on the flow adjustment
The data based highway section of link flow of OD matrix information and the AVI information interception of initially reparation-path flow funtcional relationship is dynamically strolled to matching.When path flow and highway section measured discharge relative error are 5%, stop strolling matching, and as final OD matrix information.
In 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 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 while comprising next highway section detecting device, has any in following situation:
(1), vehicle passes through next highway section, but is not recorded owing to detecting 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,
,then analyze vehicle according to following formula and can within this period, pass through next highway section, and dwindle the range of choice to residual paths with this;
Wherein
: mean the journey time between any two AVI;
S: mean random chance numerical value;
N: mean time interval length, i.e. unit interval length;
For the residual paths that can select, according to the selection probability of following formula revised residual paths, obtain new routing probability set;
: the zero hour that means to estimate the period;
: the length that means to estimate the period.
Compared with prior art, the present invention has broken through usings the defect of link flow as dynamic OD estimation main information foundation on the traditional sense, repair the Main Means as dynamic OD estimation by usining the microcosmic path of vehicle, take link flow information as auxiliary correction means, make AVI information be fully used.The method is any open road network of the extraordinary adaptation of energy simultaneously, and can under low AVI coverage rate condition, improve the lower OD prior imformation of precision.This is not enough for domestic OD survey information, precision is not high actually has a considerable realistic meaning, China's traffic administration with control reality in boundless application space is arranged.
The accompanying drawing explanation
Fig. 1 is the Dynamic OD Matrix Estimation method flow diagram that the present invention proposes.
Schematic diagram is repaired in the path that Fig. 2 is the open road network based on the measurability criterion that proposes of the present invention.
Fig. 3 is the road network figure that the embodiment of the present 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 present invention 1.
Fig. 5 be in the embodiment of the present invention 1 to precision the improved effect schematic diagram of lower initial OD.
Embodiment
Below in conjunction with 3 pairs of embodiments of the invention of accompanying drawing, elaborate: the present embodiment is implemented take technical solution of the present invention under prerequisite, 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 for certain the ground velocity road system shown in Fig. 3, this urban expressing system has the AVI equipment of 17 gateways and 9 sections, by the right number of the AVI facility detectable OD of actual covering, be wherein 216 groups, account for all OD to 74.74% of number.The equipment input needed: at road section or ring road gateway, lay the AVI video detector.The acquisition input message that requires: the vehicle license information after identification, vehicle due in, AVI detecting device numbering.
After obtaining above-mentioned input message, take certain path and partly know that 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 vehicle that is A respectively by being numbered AVI
1and AVI
4the time, can obtain respectively vehicle A at the T time of arrival of these two groups of detecting devices
1and T
4.And, according to the topological structure of road network, analyze the optional residual paths of vehicle A and also with the prior imformation in different paths, it is demarcated
.
(2) calculate vehicle A by path journey time function and arrive AVI
3journey time.
（1）
Wherein
: mean the journey time between any two AVI;
: mean that h is in the period, the selection probability in i the journey time interval by two AVI detecting devices;
: mean that h, in the period, detects the vehicle average travel time according to two AVI detecting devices;
S: mean random chance numerical value;
N: mean time interval length, i.e. unit interval length;
(3) according to detecting device measurability criterion, when having grace time, vehicle A arrives next highway section detecting device AVI3, but the routing information of vehicle A inclusion test device AVI not
3the time, there are two kinds of sights:
Sight 1: vehicle A passes through AVI
5, but be not recorded owing to detecting error.
Sight 2: vehicle is not through laying AVI
5highway section, and be short to most and reach D based on travel time
6.
（5）
: the prior imformation that means n group miss path in the travel direction of upstream;
: mean to meet the total j group of the AVI detecting device that can survey condition in the travel direction of n group miss path downstream;
: be illustrated in the moment that vehicle i is detected for the first time;
: the length that means to estimate the period.
Therefore can revise the probability of these optional residual paths
.
(4) revised routing probability is approached to the process that actual vehicle route is selected by the method maximum of Monte Carlo random simulation, obtain the fullpath of vehicle.
(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 funtcional relationship.
Wherein
: be illustrated in the revised routing probability of h period n bar miss path;
: the priori that is illustrated in h period n bar miss path is selected probability;
All the other alphabetical meanings are identical with above formula.
What wherein Fig. 4 meaned is hanging down under the coverage rate condition, the relative error of estimation OD and actual OD, and what Fig. 5 meaned is the schematic diagram to the improved effect of priori OD information.Even, when the AVI coverage rate is 60%, when priori OD precision is 40%, its relative error is only also 21.8%.
Claims (2)
1. the Dynamic OD Matrix Estimation method based on the AVI checkout equipment is characterized in that concrete steps are as follows:
(1) extraction of AVI information data and classification
The information that AVI is obtained is usingd the vehicle license data as main index data, and by vehicle license information, identical data are mated combination, and according to the quantity of information of arbitrary same vehicle licence plate, information of vehicles are divided into to following 3 large classes:
1., comprise the vehicle starting point, the data of all path nodes of terminal and process are called path omniscient type vehicle data;
2., the data of two path nodes that at least comprise the process of vehicle are called path and partly know the type vehicle data;
3., the data that only comprise a path node of vehicle process are called path and singly know the type vehicle data;
(2) the expansion sample of prior imformation
The vehicle number that the AVI of take catches is basis, and prior imformation is expanded to the sample processing according to the vehicle number of actual acquisition;
(3) filtration path omniscient type vehicle data
Convert path omniscient type vehicle data to the OD matrix information, and the prior imformation after this OD matrix information and expansion sample is subtracted each other, if OD corresponding in prior imformation is less than 0 to flow, gives a minimum flow 1, and then obtain the priori OD information after filtering;
(4) estimate the path journey time in the period
According to the laying information of different AVI, the journey time of the vehicle of two groups of AVI processes of arbitrary neighborhood is added up and obtained its average travel time; According to the different vehicle journey time, distribute, Based on Probability opinion model, set up AVI journey time probability distribution function, and by the possibility of all the other these two groups of AVI detecting devices of vehicles faileds process of this Functional Analysis; Then pass through highway section-path journey time relation function, the journey time of way to acquire, and this is judged to whether vehicle is estimating the criterion that enters road network in the period as one;
(5) path estimation of type vehicle is partly known in path
The part path of path partly being known to 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, by the Bayesian Estimation algorithm, the optional miss path of any vehicle is dwindled rapidly to hunting zone, and on the basis of filtering priori OD information, the selection probability of possible path is revised; Finally by the Monte Carlo random simulation, any vehicle is made to routing in its feasible path, and carry out the path reparation with this; The routing information of type vehicle is partly known in acquired any one group of path, and by the path journey time, the judgement vehicle enters the time of road network and leaves the time of road network;
(6) secondary filtration of priori OD information
Path after repairing is partly known to the type data-switching becomes the OD matrix information, and the prior imformation after this OD matrix information and expansion sample is subtracted each other, if OD corresponding in prior imformation is less than 0 to flow, give a minimum flow 1, simultaneously according to remaining, singly know that the type vehicle number carries out secondary and expands sample, finally obtain the OD information after secondary filtration;
(7) path estimation of type vehicle is singly known in path
Path singly knows that method that the type vehicle route estimates and path partly know that 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 obtains singly knowing 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 initial reparation OD matrix;
(9) initially repair the OD matrix information based on the flow adjustment
The link flow information of the OD matrix information of initially reparation and AVI detection is dynamically strolled to matching according to highway section-path flow funtcional relationship; When path flow and highway section measured discharge relative error are 5%, stop strolling matching, 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 step (5) partly knows that path in the path estimation of type vehicle and 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 while comprising next highway section detecting device, has any in following situation:
(1), vehicle passes through next highway section, but is not recorded owing to detecting 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,
, then according to following formula, analyze vehicle and can within this period, pass through next highway section, and dwindle the range of choice to residual paths with this;
Wherein
: mean the journey time between any two AVI;
: mean the distance between any two adjacent AVI detecting devices;
S: mean random chance numerical value;
N: mean time interval length, i.e. unit interval length;
For the residual paths that can select, according to the selection probability of following formula revised residual paths, obtain new routing probability set;
: the prior imformation that means n group miss path in the travel direction of downstream;
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| CN103903437B (en) * | 2014-02-27 | 2016-06-08 | 中国科学院自动化研究所 | The Vehicle emission OD matrix acquisition methods of data is detected based on video traffic |
| CN105631945B (en) * | 2014-10-30 | 2018-11-23 | 国际商业机器公司 | The method and apparatus of start point/end point pairing for identification |
| CN104915731B (en) * | 2015-06-11 | 2018-04-27 | 同济大学 | A kind of grand microcosmic integration method of vehicle driving reconstructing path based on automatic vehicle identification data |
| CN105679037B (en) * | 2016-03-29 | 2019-03-08 | 招商局重庆交通科研设计院有限公司 | A kind of dynamic path planning method based on user's trip habit |
| CN109035784B (en) * | 2018-09-17 | 2021-01-26 | 江苏智通交通科技有限公司 | Dynamic traffic flow OD estimation method based on multi-source heterogeneous data |
| CN109461307B (en) * | 2018-11-16 | 2021-09-24 | 中电科新型智慧城市研究院有限公司 | Method for estimating average vehicle flow and OD (origin-destination) requirements of road section |
| CN110245423B (en) * | 2019-06-14 | 2023-01-31 | 重庆大学 | Method for analyzing flow relation between highway toll stations |
| CN110491157B (en) * | 2019-07-23 | 2022-01-25 | 中山大学 | Vehicle association method based on parking lot data and checkpoint 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 |
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