CN105913658B - A kind of method that traffic flow speculates OD positions and OD matrixes - Google Patents
A kind of method that traffic flow speculates OD positions and OD matrixes Download PDFInfo
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Abstract
The present invention proposes a kind of method that traffic flow speculates OD positions and OD matrixes, comprises the following steps：Obtain historical traffic flows：One group of network of highways being made up of N number of crossroad is selected, obtains the historical traffic flows data S of the network of highways in 24 hours；Cluster analysis：Based on the historical traffic flows data S of the network of highways in 24 hours, the traffic flow data in each section forms a characteristic group, and cluster analysis is carried out to characteristic group；OD points are set；Estimate OD matrixes.Present invention, avoiding cumbersome in workflow, there is convenience, flexibility and accuracy, the road conditions under real-time Simulation different periods, available for systems such as traffic simulation, trip analyses, the problems such as effectively avoiding traffic congestion.
Description
Technical Field
The invention relates to a traffic management technology, in particular to a method for speculating OD positions and an OD matrix by traffic flow.
Background
Many existing traffic OD estimation schemes are not flexible, and the reason is that in the early stage, a large number of traffic questionnaires are needed to be investigated or traffic road conditions are observed for a long time to obtain the OD estimation schemes, so that the working process is complicated, workers can work repeatedly, and the workers have more subjective judgments, and the accuracy of results is influenced.
Disclosure of Invention
In order to solve the problems that the existing traffic flow OD position estimation is greatly influenced by subjectivity and has low accuracy, the invention provides a method for estimating the OD position and the OD matrix by the traffic flow.
The technical scheme adopted by the invention is as follows: a method for speculating OD positions and an OD matrix by traffic flow comprises the following steps:
s1, acquiring historical traffic flow: selecting a group of road network consisting of N crossroads, wherein each crossroad is connected with N road sections, N and N are natural numbers, and acquiring historical traffic flow data S of the road network within 24 hours;
s2, clustering analysis: based on the historical traffic flow data S of the road network within 24 hours, the traffic flow data of each road section forms aCharacteristic data group, clustering the characteristic data group to obtain k classes and k traffic flows in different time periods, and recording the k classes and the k traffic flows as S in sequence 1 To S k K is a natural number and is more than or equal to 1 and less than or equal to 24;
s3, setting an OD point: setting departure points O and departure points D according to the k classes and k different time periods of traffic flow obtained in the step S2 within a set time t to obtain a departure point O set and a departure point D set of the selected road network in each class;
s4, estimating an OD matrix: and estimating an OD matrix according to the positions of the departure point O and the receiving point D obtained in the step S3.
According to the invention, the position of the OD point and the number of the receiving and dispatching vehicles can be estimated through historical flow data; under the condition of unknown OD departure distribution, an OD matrix is calculated.
Preferably, the method for performing cluster analysis on the feature data set in step S2 includes the following steps:
a. set S for acquiring a group of N historical traffic flow data of road network n ＝{S 1 ,S 2 ,…,S n Waiting for clustering;
b. setting k value in 24-hour time period, randomly selecting k classes C, each class having a clustering center, and dividing the k classes into C k ＝{C 1 ,C 2 ,…,C k K is a natural number and is more than or equal to 1 and less than or equal to 24;
c. in a 24-hour time period, different time periods have different traffic flow data, a point Si in S is used as a k-th clustering center, an objective function f (k) is taken to enable the difference value of the traffic flows in the two time periods to be a distance l from the clustering center, and if the distance l approaches zero, the nearest clustering center is located;
d. when all historical traffic flow data S are divided into corresponding classes, entering next iteration, recalculating the distance l for clustering adjustment, and if the distance between the clustering centers of two adjacent times is not changed, indicating that the clustering function of the adjustment has converged to a fixed value, thereby obtaining k-class division C k ＝{C 1 ,C 2 ,…,C k }。
Preferably, theThe method for setting the position of the OD point in the step S3 comprises the following steps: four road sections n of a crossing 1 、n 2 、n 3 And n 4 Setting a section n within a set time t 1 The number of departure is Sn 1 Section n of road 2 The traffic flow is Sn 2 Section n of road 3 The traffic flow is Sn 3 Section n of road 4 Traffic flow is Sn 4 If, ifThen the road section n 1 Set as departure point O; if it isThen the road section n 1 Set as a pick-up point D; if it isThen the road section n 1 No OD spot was set.
Preferably, the OD matrix estimating method includes: road network at C 1 O Point set under class { O 1 ,O 2 ,…,O i And D point set { D 1 ,D 2 ,…,D j }，O i To O j The probability of passing a certain road segment n is f (d), and an OD matrix is set
The constraint condition isAnd
calculating to obtain each O from the constraint condition i D j A value of (d);
by the formula(For the t-th iteration) to obtain O i To O j Traffic flow S through a certain section n n The traffic flow of the road section is obtained by historical traffic data flow statistics
Order to(m is the flow difference, t is the number of iterations),
by the formula
To obtainA value of (d);
if it is(θ m Is a threshold value), the calculated O is i D j The estimated OD matrix;
if it isThen carry over into the iterative formula(t is the number of iterations and α is the agreed optimization coefficient), i.e.
Get O of t iterations i D j Of t iterations of O i D j Is substituted into equation (1) to obtain t iterationsIs judged to be satisfied withIf the matrix is not consistent, the iteration is continued.
The invention has the beneficial effects that: the method avoids the problems of complexity in the working process, convenience, flexibility and accuracy, simulates road conditions at different time intervals in real time, can be used for systems such as traffic simulation and trip analysis, and can effectively avoid traffic jam and the like.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 shows example N 1 Intersection traffic flow data distribution map.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
As shown in fig. 1, a method for estimating an OD position and an OD matrix from a traffic flow specifically includes the following steps:
s1, acquiring historical traffic flow: selecting a group of road network consisting of N crossroads, wherein each crossroad is provided with N road sections which are connected, N and N are natural numbers, and acquiring historical traffic flow data S of the road network within 24 hours.
S2, clustering analysis: based on historical traffic flow data S of the road network within 24 hours, the traffic flow data of each road section form a characteristic data group, the characteristic data group is subjected to cluster analysis to obtain k classes of traffic flows and k traffic flows in different time periods, and the k classes of traffic flows and the k traffic flows are sequentially marked as S 1 To S k K is a natural number and is more than or equal to 1 and less than or equal to 24. A K-means clustering algorithm can be adopted, and the following method can be specifically adopted:
a. obtaining a set of N historical intersections of a road networkSet of traffic data S n ＝{S 1 ,S 2 ,…,S n } to be clustered;
b. setting k value in 24 hr day, randomly selecting k classes C with each class having a clustering center, and dividing the classes into groups of k k ＝{C 1 ,C 2 ,…,C k K is a natural number, and k is more than or equal to 1 and less than or equal to 24;
c. in a 24-hour time period, different time periods have different traffic flow data, a point Si in S is used as a k-th clustering center, an objective function f (k) is taken to enable the difference value of the traffic flows in the two time periods to be a distance l from the clustering center, and if the distance l approaches zero, the nearest clustering center is located;
d. when all historical traffic flow data S are divided into corresponding classes, entering next iteration, recalculating the distance l for clustering adjustment, and if the distance between the clustering centers of two adjacent times is not changed, indicating that the clustering function of the adjustment has converged to a fixed value, thereby obtaining k-class division C k ＝{C 1 ,C 2 ,…,C k }。
Assuming that the k classes obtained by clustering are respectively C 1 ：0-6h,C 2 ：7-12h,C 3 ：13-18h,C 4 :19-24h, the corresponding traffic flow is S 1 、S 2 、S 3 And S 4 Resulting in a set of peak flows H = { H } for different time periods 1 ，H 2 ，H 3 ，H 4 Set of peace peak flows L = { L = } = { L 1 ，L 2 ，L 3 ，L 4 }。
S3, setting an OD point: and setting departure points O and receiving points D according to the k classes and k different time periods of traffic flow obtained in the step S2 within a set time t (t <5 minutes) to obtain a departure point O set and a receiving point D set of the selected road network in each class.
In the road network N 1 Four road sections n of intersection 1 、n 2 、n 3 And n 4 For example, as shown in FIG. 2. Within a short time t (t <5 min), k classes C, i.e. C, are obtained from the clustering k ＝{C 1 ,C 2 ,…,C k And traffic flow S at different time intervals 1 、S 2 、S 3 And S 4 Setting a road section n 1 The number of departure is Sn 1 Section n of road 2 The traffic flow is Sn 2 Section n of road 3 The traffic flow is Sn 3 Section n of road 4 The traffic flow is Sn 4 。
Suppose in C 1 In the category, the OD points are judged and set according to the traffic flow as follows:
a. if Sn 1 ＜Sn 2 +Sn 3 +Sn 4 Then road section n 1 It may be set as departure point O;
b. if Sn 1 ＞Sn 2 +Sn 3 +Sn 4 Then road section n 1 Then it can be set as the departure point D;
c. if Sn 1 ＝Sn 2 +Sn 3 +Sn 4 Then road section n 1 The flow of the transmitted and received vehicles is unchanged, and an OD point is not set.
Therefore, under ideal conditions, the road network A can be presumed to be at C 1 O Point set under class { O 1 ,O 2 ,…,O i And D point set { D 1 ,D 2 ,…,D j }，O i To O j The probability of passing a certain link n is f (d).
S4, estimating an OD matrix: and estimating an OD matrix according to the positions of the departure point O and the receiving point D obtained in the step S3.
Road network at C 1 O Point set under class { O 1 ,O 2 ,…,O i And D point set { D 1 ,D 2 ,…,D j }，O i To O j The probability of passing a certain road segment n is f (d), and an OD matrix is set
The constraint condition isAnd
calculating to obtain each O from the constraint condition i D j A value of (d);
by the formula(For the t-th iteration) to obtain O i To O j Traffic flow S through a certain section n n The traffic flow of the road section is obtained by historical traffic data flow statistics
Order to(m is the flow difference, t is the number of iterations),
by the formula
To obtainA value of (d);
if it is(θ m Is a threshold value), the calculated O is i D j The estimated OD matrix;
if it isThen carry over into the iterative formula(t is the number of iterations, α is the agreed optimization coefficient)I.e. by
Get O of t iterations i D j Of t iterations of O i D j Substituting the value of (A) into equation (1) to obtain t iterationsIs judged to be in accordance withIf the matrix is not consistent, the iteration is continued.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical solution according to the technical idea of the present invention fall within the protection scope of the present invention.
Claims (3)
1. A method for speculating OD position and OD matrix by traffic flow is characterized in that: the method comprises the following steps:
s1, acquiring historical traffic flow: selecting a group of road network consisting of N crossroads, wherein each crossroad is connected with N road sections, N and N are natural numbers, and acquiring historical traffic flow data S of the road network within 24 hours;
s2, clustering analysis: based on the historical traffic flow data S of the road network within 24 hours, the traffic flow data of each road section forms a characteristic data group, the characteristic data group is subjected to cluster analysis to obtain k classes and k traffic flows in different time periods, and the k classes and the k traffic flows are sequentially marked as S 1 To S k K is a natural number and is more than or equal to 1 and less than or equal to 24;
s3, setting an OD point: setting departure points O and receiving points D according to the k classes and k different time periods of traffic flow obtained in the step S2 within a set time t to obtain a set of departure points O and a set of receiving points D of the selected road network in each classA vehicle point D set; the method for setting the position of the OD point comprises the following steps: four road sections n of a crossing 1 、n 2 、n 3 And n 4 Setting a section n within a set time t 1 The number of departure is Sn 1 Section n of road 2 The traffic flow is Sn 2 Section n of road 3 The traffic flow is Sn 3 Section n of road 4 The traffic flow is Sn 4 ，
If it isThen the road section n 1 Set as departure point O;
if it isThen the road section n 1 Set as a pick-up point D;
if it isThen the road section n 1 No OD point is set;
s4, estimating an OD matrix: and D, estimating an OD matrix according to the positions of the departure point O and the receiving point D obtained in the step S3.
2. The method for estimating OD position and OD matrix according to traffic flow of claim 1, wherein: the method for performing cluster analysis on the feature data group in the step S2 includes the following steps:
a. set S for acquiring a group of N historical traffic flow data of road network n ＝{S 1 ,S 2 ,…,S n Waiting for clustering;
b. setting k value in 24 hr day, randomly selecting k classes C with each class having a clustering center, and dividing the classes into groups of k k ＝{C 1 ,C 2 ,…,C k K is a natural number and is more than or equal to 1 and less than or equal to 24;
c. in a 24-hour time period, traffic flow data in different time periods are different, a point Si in S is used as a k-th cluster center, an objective function f (k) is taken to enable the difference value of the traffic flows in the two time periods to be a distance l from the cluster center, and if the distance l approaches zero, the cluster center closest to the distance l is classified;
d. when all historical traffic flow data S are divided into corresponding classes, entering next iteration, recalculating the distance l for clustering adjustment, and if the distance between the clustering centers of two adjacent times is not changed, indicating that the clustering function of the adjustment has converged to a fixed value, thereby obtaining k-class division C k ＝{C 1 ,C 2 ,…,C k }。
3. The method for estimating OD position and OD matrix according to traffic flow of claim 1, wherein: the OD matrix estimation method comprises the following steps: road network at C 1 Set of O points under class { O 1 ,O 2 ,…,O i Set of points { D } and D 1 ,D 2 ,…,D j }，O i To O j The probability of passing a certain road segment n is f (d), and an OD matrix is set
The constraint condition isAnd
calculating to obtain each O from the constraint condition i D j A value of (d);
by the formula For the t-th iteration, O is calculated i To O j Traffic flow S through a certain section n n From a stream of historical traffic dataThe traffic flow of the road section is obtained by the quantity statistics
Order tom is the flow difference, t is the iteration number,
by the formula
To obtainA value of (d);
if it isθ m Is a threshold value, then calculated O i D j The estimated OD matrix;
if it isThen carry over into the iterative formulat is the number of iterations and α is the agreed optimization coefficient, i.e.
Get O of t iterations i D j Of t iterations of O i D j Substituting the value of (A) into equation (1) to obtain t iterationsIs judged to be satisfied withIf the matrix is not consistent, the iteration is continued.
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| CN108320504B (en) * | 2018-01-22 | 2020-06-16 | 北京工业大学 | Dynamic OD matrix estimation method based on monitoring data |
| CN110019633B (en) * | 2018-07-20 | 2021-08-10 | 深圳大学 | Line density statistical method and system based on ArcGIS secondary development |
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| 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 |
| CN113053110A (en) * | 2020-12-23 | 2021-06-29 | 沈阳世纪高通科技有限公司 | Method and device for calculating road flow based on 4g signaling data |
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