CN111275975B - Method and device for acquiring intersection turning flow data and storage medium - Google Patents

Method and device for acquiring intersection turning flow data and storage medium Download PDF

Info

Publication number
CN111275975B
CN111275975B CN202010131101.8A CN202010131101A CN111275975B CN 111275975 B CN111275975 B CN 111275975B CN 202010131101 A CN202010131101 A CN 202010131101A CN 111275975 B CN111275975 B CN 111275975B
Authority
CN
China
Prior art keywords
intersection
bayonet
vehicle
steering
reachable
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010131101.8A
Other languages
Chinese (zh)
Other versions
CN111275975A (en
Inventor
杨子
裴欣
李兴山
张四维
张毅
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tsinghua University
Original Assignee
Tsinghua University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tsinghua University filed Critical Tsinghua University
Priority to CN202010131101.8A priority Critical patent/CN111275975B/en
Publication of CN111275975A publication Critical patent/CN111275975A/en
Application granted granted Critical
Publication of CN111275975B publication Critical patent/CN111275975B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • 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

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)

Abstract

The embodiment of the application provides a method, a device and a storage medium for acquiring intersection turning flow data, wherein the method comprises the following steps: acquiring bayonet vehicle passing data and road network data; establishing a bayonet steering reachable matrix according to the road network data, wherein the bayonet steering reachable matrix is used for representing the accessibility between the bayonets in each intersection in the road network; and calculating intersection turning flow data corresponding to each intersection in the road network based on the intersection passing data and the intersection turning reachable matrix. Therefore, accurate intersection turning traffic flow data with large scale, fine time granularity, low cost and high accuracy can be obtained.

Description

Method and device for acquiring intersection turning flow data and storage medium
Technical Field
The application relates to the technical field of intelligent traffic, in particular to a method and a device for acquiring intersection turning flow data and a storage medium.
Background
The intersection is used as an important node of an urban road network, is a main intersection of vehicles, and has the characteristic of large traffic flow. At the intersection, due to the fact that traffic flow steering can cause traffic flow running states such as conflict, intersection and shunting among traffic flows, traffic conditions at the intersection are particularly complex, the traffic conditions are multiple points of traffic accidents, and urban traffic jam problems are often highlighted at the intersection.
The intersection turning flow rate (which may be referred to as an intersection turning traffic flow or an intersection turning traffic volume) is the number of vehicles that turn in each direction at an intersection in a unit time. The intersection turning flow can provide important data basis for analyzing the traffic capacity of the intersection, optimizing the signal timing of the intersection, analyzing the urban traffic condition, implementing traffic management measures and planning roads.
Currently, the main way to obtain the intersection turning flow is to send a meter to the intersection where the intersection turning flow needs to be counted, and observe the number of vehicles respectively turning in each direction of the intersection in unit time in the field. However, because a metering staff must be sent to the field for counting, the intersection turning flow data obtained by a manual counting mode has the problems of low accuracy, high acquisition cost, small coverage range and coarse time granularity.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and an apparatus for acquiring intersection turning traffic flow data, and a storage medium, which are capable of acquiring large-scale, fine-time-granularity, low-cost, and accurate intersection turning traffic flow data.
The embodiment of the application mainly provides the following technical scheme:
in a first aspect, an embodiment of the present application provides a method for acquiring intersection turning flow data, where the method includes: acquiring bayonet vehicle passing data and road network data; establishing a bayonet steering reachable matrix according to the road network data, wherein the bayonet steering reachable matrix is used for representing the accessibility between the bayonets in each intersection in the road network; and calculating intersection turning flow data corresponding to each intersection in the road network based on the intersection passing data and the intersection turning reachable matrix.
In a second aspect, an embodiment of the present application provides an apparatus for acquiring intersection turning flow data, where the apparatus includes: the first acquisition unit is used for acquiring bayonet vehicle passing data and road network data; the system comprises an establishing unit, a data processing unit and a data processing unit, wherein the establishing unit is used for establishing a bayonet steering reachable matrix according to the road network data, and the bayonet steering reachable matrix is used for representing the reachability between bayonets in each intersection in the road network; and the first calculating unit is used for calculating intersection turning flow data corresponding to each intersection in the road network based on the intersection passing data and the intersection turning reachable matrix.
In a third aspect, the present application provides a computer-readable storage medium, where the storage medium includes a stored program, where when the program runs, the computer device where the storage medium is located is controlled to execute the steps of the above-mentioned intersection turning flow data obtaining method.
In a fourth aspect, an embodiment of the present application provides a computer device, where the computer device includes: at least one processor; and at least one memory, bus connected with the processor; the processor and the memory complete mutual communication through the bus; the processor is used for calling the program instructions in the memory to execute the steps of the intersection turning flow data acquisition method.
According to the method, the device and the storage medium for acquiring intersection turning flow data, after the intersection passing data and the road network data are acquired, a intersection turning reachable matrix can be established according to the road network data, wherein the intersection turning reachable matrix is used for representing the reachability between intersections in the road network; and then, calculating intersection turning flow data corresponding to each intersection in the road network based on the intersection passing data and the intersection turning reachable matrix. Therefore, the intersection turning flow data is calculated through the intersection vehicle passing data, a metering person does not need to be dispatched to observe on site, and the large-scale, long-time, fine-time-granularity, low-cost and accurate intersection turning flow data can be obtained.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. Other advantages of the present application may be realized and attained by the instrumentalities and combinations particularly pointed out in the specification and the drawings.
Drawings
The accompanying drawings are included to provide an understanding of the present disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the examples serve to explain the principles of the disclosure and not to limit the disclosure.
Fig. 1 is a schematic flow chart of a method for acquiring intersection turning flow data in an embodiment of the present application;
fig. 2A is a schematic view of an intersection and a bayonet provided at the intersection in an embodiment of the present application;
FIG. 2B is a schematic view of a road network according to an embodiment of the present application;
FIG. 2C is a schematic diagram of a directed graph in an embodiment of the present application;
fig. 3 is a schematic structural diagram of an intersection turning flow data acquisition device in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
The embodiment of the application provides a method for acquiring intersection turning flow data. In practical application, the method for acquiring the turning flow data of the intersection can be applied to various occasions needing the traffic data of the intersection, such as analysis of the traffic capacity of the intersection, optimization of signal timing of the intersection, analysis of urban traffic conditions, implementation of traffic management measures, road planning, traffic safety evaluation, accident risk prediction and the like.
In the application embodiment, the type of the intersection can be a T-shaped intersection, a Y-shaped intersection, a cross-shaped intersection, an X-shaped intersection, a circular intersection and the like. Here, the embodiment of the present application is not particularly limited.
Fig. 1 is a schematic flow chart of a method for acquiring intersection turning flow data in an embodiment of the present application, and referring to fig. 1, the method for acquiring intersection turning flow data may include:
step 101: and acquiring the bayonet vehicle passing data and the road network data.
Here, the gate, which may also be referred to as a gate device, may refer to a traffic networking device that mainly uses a photoelectric technology, an image processing technology, and a pattern recognition technology to photograph, recognize, and store related data of each passing vehicle on a road, which are provided at each intersection in the field of intelligent traffic.
Further, the gate passing data refers to related data collected by the gate when the vehicle passes through the gate. As an example, the gate passing data may include information such as a vehicle identification (e.g., a license plate number), a camera orientation, a shooting time, a driving direction, a gate identification, a gate position, a lane identification, a vehicle type, and the like.
In particular, the road network data, which may also be referred to as road network data, may include urban road distribution data, traffic networking device distribution data, such as distribution data of gates, and the like.
In practical applications, the smart gate system may be formed by all gate devices provided in the target road network. According to the direction of the intersection, 1 to 4 bayonets can be arranged at each intersection in the road network. For example, referring to fig. 2A, taking an intersection in a road network of a certain urban area as an example of a cross intersection, an entry intersection of the cross intersection includes: the cross-shaped intersection is provided with 4 directions of a south-north direction, an east-west direction, a north-south direction and a west-east direction, and then a bayonet 201, a bayonet 202, a bayonet 203 and a bayonet 204 are respectively arranged in the 4 directions of the cross-shaped intersection. In this way, each direction of the intersection may correspond to each bayonet within the intersection.
Then, when the card port vehicle passing data needs to be acquired, the required card port vehicle passing data can be derived from the intelligent card port system.
Further, in order to improve the accuracy of the intersection turning flow data, the step of acquiring the intersection passing data may include: deriving original bayonet passing data from the intelligent bayonet system; carrying out data cleaning on the original bayonet vehicle passing data to obtain cleaned bayonet vehicle passing data; and taking the cleaned gate vehicle passing data as the gate vehicle passing data required to be acquired in the step 101. And then, intersection turning flow data corresponding to each intersection in the road network can be accurately calculated according to the cleaned intersection passing data and the intersection turning reachable matrix.
As an example, the data cleansing of the raw bayonet pass-by data may include: deleting one or more of abnormal data (such as null value recorded due to no bayonet or partial bayonet fault, flow rate recorded due to partial bayonet fault and obviously smaller than normal value, and the like), repairing dislocation information, standardizing data format, and the like.
For example, assuming that 207 bayonets are provided in all intersections in a road network in a certain urban area, 1406324 pieces of hourly flow data are actually counted, wherein 1371636 pieces of hourly flow data are obtained for data with hourly flow greater than 100 between 8 and 22 points, and the cleaned bayonet traffic data may include 1371636 pieces of hourly flow data.
Step 102: and establishing a gate steering reachable matrix according to the road network data.
Wherein the gate turn reachability matrix is used to characterize reachability between gates within each intersection in the road network.
In an exemplary embodiment, in a bayonet turn reachable matrix, accessibility may be indicated between bayonets in two adjacently connected crossbars, and inaccessibility may be indicated between bayonets in two crossbars that are in communication but not directly connected.
In one exemplary embodiment, in the bayonet turn reachable matrix, there may be included an origin intersection, an origin bayonet, a turning action, an arrival intersection, an arrival bayonet, and accessibility. In practical application, the directions of the intersection and the intersection are in one-to-one correspondence, that is, the intersection turning reachable matrix comprises a starting intersection, a starting direction, a turning action, a reaching intersection, a reaching direction and accessibility.
For example, as shown in fig. 2B, for example, 4 intersections in a partial road network are provided, and each intersection is provided with one bayonet in each direction, where the intersection 21 includes 4 bayonets (i.e., 4 directions), which are respectively denoted as a bayonet 211, a bayonet 212, a bayonet 213, and a bayonet 214; intersection 22 includes 4 bayonets (i.e. 4 directions), which are respectively marked as bayonet 221, bayonet 222, bayonet 223, and bayonet 224; intersection 23 includes 4 bayonets (i.e., 4 directions), which are respectively marked as bayonet 231, bayonet 232, bayonet 233, and bayonet 234; junction 24 includes 4 bayonets (i.e., 4 directions), which are respectively identified as bayonet 241, bayonet 242, bayonet 243, and bayonet 244. Intersection 21 communicates with but is not directly connected to intersection 24, and intersection 22 communicates with but is not directly connected to intersection 23; intersection 21 is directly connected with intersection 22, intersection 21 is directly connected with intersection 23, intersection 22 is directly connected with intersection 24, and intersection 23 is directly connected with intersection 24.
For example, assume that the straight movement in the turning motion of the gate is 0, the left turn is 1, the u-turn is 2, and the right turn is 3. Then, taking the intersection 21 as the main intersection, and taking the intersection turning reachable matrix including the start intersection, the start gate, the turning action, the arrival intersection, the arrival gate and the reachability as an example, the following table 1 may be established based on the road network data corresponding to the partial road network as shown in fig. 2B. Wherein, the gate 213 of the intersection 21 makes different turning motions to directly reach different gates of the intersection 22, and then is displayed as reachable in the gate turning reachable matrix in the following table 1; the gate 214 of the intersection 21 makes different turning motions to directly reach different gates of the intersection 23, and the gate turning reachable matrix shown in table 1 below is reachable; the gate 213 at intersection 21 communicates with intersection 24 through intersection 22 but is not directly connected to it, and is shown as unreachable in the gate turn reachable matrix in table 1 below; the bayonet 214 at intersection 21 communicates with intersection 24 through intersection 23 but is not directly connected to it, and is shown as unreachable in the bayonet turn reachable matrix in table 1 below.
Initial intersection Initial bayonet Steering action Reach the intersection Reach the bayonet Accessibility
Intersection
21 Bayonet 211 … … … …
Intersection 21 Bayonet 212 … … … …
Intersection 21 Bayonet213 0 Intersection 22 Bayonet 223 Can reach
Intersection 21 Bayonet 213 1 Intersection 22 Bayonet 221 Can reach
Intersection 21 Bayonet 213 2 Intersection 22 Bayonet 222 Can reach
Intersection 21 Bayonet 213 3 Intersection 22 Bayonet 224 Can reach
Intersection 21 Bayonet 213 3 Intersection 24 Bayonet 241 Is not reachable
Intersection
21 Bayonet 214 0 Intersection 23 Bayonet 234 Can reach
Intersection 21 Bayonet 214 1 Intersection 23 Bayonet 233 Can reach
Intersection 21 Bayonet 214 2 Intersection 23 Bayonet 231 Can reach
Intersection 21 Bayonet 214 2 Intersection 24 Bayonet 243 Is not reachable
Intersection
21 Bayonet 214 3 Intersection 23 Bayonet 232 Can reach
TABLE 1
In another embodiment of the present application, the step 102 may include the following steps 1021 to 1023:
step 1021: generating a directed graph based on the road network data;
wherein the directed graph includes: the intersection comprises a composite vertex set and an edge set, wherein one composite vertex represents an intersection, each composite vertex comprises at least one sub-vertex, each sub-vertex represents one direction of the intersection and a bayonet arranged in the direction, and each edge represents a path between every two intersections.
For example, a directed graph as shown in fig. 2C may be generated from road network data corresponding to a part of the road network as shown in fig. 2B. Referring to fig. 2B to 2C, a composite vertex a, a composite vertex B, a composite vertex C, and a composite vertex D respectively represent an intersection 21, an intersection 22, an intersection 23, and an intersection 24 in a partial road network as shown in fig. 2B. Sub vertices a1, a2, A3, and a4 included in the composite vertex a respectively indicate bayonets 211, 212, 213, and 214 provided in 4 directions of the intersection 21 and 4 directions of the intersection 21; the sub-vertices B1, B2, B3, and B4 included in the compound vertex B respectively indicate the bayonets 221, 222, 223, and 224 provided in the 4 directions of the intersection 22 and the 4 directions of the intersection 22; sub vertices C1, C2, C3, and C4 included in the composite vertex C respectively indicate bayonets 231, 232, 233, and 234 provided in 4 directions of the intersection 23 and 4 directions of the intersection 23; sub vertices D1, D2, D3, and D4 included in composite vertex D respectively indicate bayonets 241, 242, 243, and 244 provided in 4 directions of intersection 24 and 4 directions of intersection 24, respectively. The side L1 represents the path between the intersection 21 and the intersection 22; side L2 represents the path between intersection 21 and intersection 23; side L3 represents the path between intersection 23 and intersection 24; the sides L4 respectively represent the path between intersection 22 and intersection 24.
In an exemplary embodiment, the edge set includes: the directed links (also called edges) between the vertices. For example, referring to fig. 2B-2C, if the direction 3 (assumed as sub-vertex A3) at the intersection 21 leads to the direction 3 (assumed as sub-vertex B3) at the intersection 22, there is a connection line between the sub-vertex A3 at the composite vertex a and the sub-vertex B3 at the composite vertex B, which leads from the sub-vertex A3 to the sub-vertex B3 (straight at the intersection 22). For another example, if the direction 3 (assumed as sub-vertex A3) at the intersection 21 can turn to the direction 4 (assumed as sub-vertex B4) at the intersection 22, there is a connection line between the sub-vertex A3 at the composite vertex a and the sub-vertex B4 at the composite vertex B, which points from the sub-vertex A3 to the sub-vertex B4 (turn right at the intersection B).
In practical applications, the directed graph can be implemented by a weighted directed connected graph. As an example, the edges of the directed graph may represent routes and the weights may represent distances.
As an example, the obtained road network data may be abstracted into a weighted directed connectivity graph with composite vertices by using a weighted directed connectivity graph as a data structure, where each composite vertex may include 1 to 4 sub-vertices, each of which corresponds to a different direction of an intersection represented by the composite vertex, according to the number of directions included in each intersection, that is, one direction of each intersection corresponds to one sub-vertex.
In addition, the conventional bayonet steering judgment method does not involve a connecting line inside a composite vertex, and the bayonet only records the vehicle state once inside each intersection (composite vertex), so that the bayonet is judged from the connecting line of two sub-vertexes of different intersections (composite vertexes). The reason for distinguishing the sub-vertexes inside the composite vertex is that the shortest path between two intersections is more than one, and the specific path can be distinguished by distinguishing the sub-vertexes. For example, when intersection a is passed to intersection D, if intersection B, C is missing, intersection B is passed if recording gates A3 and Dx are passed, and intersection C is passed if recording gates a4 and Dx are passed.
Step 1022: respectively determining at least one corresponding reachable bayonet for each steering action of each bayonet in each intersection after going through the directed graph;
step 1023: and establishing a bayonet steering reachable matrix based on each bayonet in each intersection and at least one reachable bayonet corresponding to each steering action.
Here, the turning motion of the bayonet may include: a plurality of right turn, straight running, left turn, and turning around.
In the embodiment of the present application, in the directed graph, if there is a path that can reach the bayonet 2 in another intersection after proceeding from the bayonet 1 in a certain intersection to perform a certain turning motion, the bayonet 2 can be referred to as a reachable bayonet of the bayonet 1; if there is no gap between the bayonets 1 and 2 in other cross ports, the bayonets 2 can be called adjacent bayonets of the bayonets 1. If turning to bayonet 2 from bayonet 1 is possible, and there is no bayonet in another intersection between bayonet 1 and bayonet 2, bayonet 2 may be said to be an adjacent reachable bayonet of bayonet 1, and at this time, the search depth value between bayonet 2 and bayonet 1 is 1. If there is a path that can be diverted to the bayonet 2 after a certain turning motion is performed from the bayonet 1, and only one bayonet 3 in another intersection is spaced between the bayonet 1 and the bayonet 2, the bayonet 2 can be called a non-adjacent reachable bayonet of the bayonet 1, and at this time, the search depth value between the bayonet 2 and the bayonet 1 is 2.
For example, referring to FIGS. 2B-2C, the adjacent reachable bayonets corresponding to bayonets 213 within intersection 21 represented by sub-vertex A3 of compound vertex A may include: the compound vertex B includes a sub-vertex B1, a sub-vertex B2, a sub-vertex B3, and a sub-vertex B4, which respectively represent the bayonet 221, the bayonet 222, the bayonet 223, and the bayonet 224 at the intersection 22. The non-adjacent reachable bayonets corresponding to bayonets 213 within intersection 21 represented by sub-vertex a3 in compound vertex a may include: at intersection 24, of composite vertex D, sub-vertices D1, D2, D3, and D4 indicate bayonet 241, bayonet 242, bayonet 243, and bayonet 244, respectively.
In the embodiment of the present application, in order to determine the corresponding at least one reachable bayonet for each turning motion of each bayonet in each intersection, the step 1022 described above may exist but is not limited to include the following implementation manners.
The implementation mode is as follows: when the internal road network between the checkpoints is simple, in order to match as few reachable checkpoints (e.g. at least 1 checkpoint) as possible for a certain turning motion of each checkpoint according to reachability, a smaller first search depth value may be used for traversal, and then, in a specific implementation process, the step 1022 may include: and traversing the directed graph by adopting a preset first search depth value, and respectively matching at least one reachable bayonet for each steering action of each bayonet in each intersection.
The implementation mode two is as follows: when the road network inside the gates is complicated, if the branch road network is not considered, the turning actions of the gates in part of the intersections may not match the reachable gates, and then the branch road network needs to be considered properly in order to improve the recognition rate of the turning actions. At this point, a larger second search depth value may be employed for traversal. Thus, in a specific implementation process, the step 1022 may include: when the directed graph is traversed by adopting a preset first search depth value, and at least one corresponding reachable bayonet is not matched for at least one turning action of part of bayonets in each intersection, the directed graph is traversed again by adopting a preset second search depth value, and at least one corresponding reachable bayonet is matched for at least one turning action of the part of bayonets, wherein the preset second search depth value is larger than the preset first search depth value.
Here, at least one turning motion of a part of the bayonets in each intersection does not match up with a corresponding at least one reachable bayonet, which can be understood as follows: one or more bayonets may be present in each of the cross-ports, and one or more turning motions of each of the one or more bayonets may not match the accessible bayonets. For example, there is an intersection C among the respective intersections of the road network, the intersection C including: bayonet c1, bayonet c2, and bayonet c3, wherein bayonet c1 includes: however, the steering motion 1 of the bayonet c1 cannot match the accessible bayonet, although the steering motion 1 and the steering motion 2 are performed. For another example, an intersection D is present among the intersections of the road network, and the intersection D includes: bayonet d1 and bayonet d2, wherein bayonet d2 includes: however, the steering motion 1 and the steering motion 2 of the bayonet d2 cannot match the reachable bayonet in both the steering motion 1 and the steering motion 2.
In an exemplary embodiment, in order to increase the recognition speed of the steering action and ensure the recognition rate of the steering action, during the first pass, only the first search depth value may be used for searching, i.e., the steering action of each bayonet matches the directly reachable adjacent bayonet. And then, only when the first search depth value is used for matching part of turning motions of part of bayonets with adjacent reachable bayonets, the second search depth value is used for re-matching the turning motions of the part of bayonets which are searched by the first search depth value and are not matched with reachable bayonets.
In an exemplary embodiment, in order to avoid the complicated steering action, the difficulty in determining the real path, and the practical calculation performance, the first search depth value may be implemented by 1, and the second search depth value may be implemented by 2.
As an example, assuming that the first search depth value is implemented by 1 and the second search depth value is implemented by 2, first, in order to match at least 1 reachable card slot for each turning motion of each card slot in each intersection based on reachability based on the directed graph, when an internal road network between the card slots is simple, in a case where there is a branch or a microcapillary path between two adjacent card slots, as long as there is a reachable card slot, the influence of the middle capillary path on the direction determination is ignored according to the rule of closest distance. The influence of the interior road network on the direction judgment is limited, and the influence on the left direction and the right direction is basically the same, so that the influence on the intersection turning flow rate or the intersection turning proportion is not large. At this time, the search depth value may be set to 1, and only the gates in the intersections directly connected to the current intersection and not passing through other intersections are considered as reachable gates, that is, only the adjacent reachable gates are considered as reachable gates matched with the gates. Next, when the road network is complicated between the gates, if the branch is not taken into consideration, the turning operation of some gates may not be matched with the reachable gates, and the branch road network needs to be taken into consideration appropriately. In this case, the search depth value may be set to 2, and two previous and subsequent records may not belong to the gates in the adjacent intersections but may be considered to be accessible gates at other intersections, that is, non-adjacent accessible gates may be considered to be accessible gates matched with the gates. In practical application, the two-layer search can greatly improve the recognition rate of the steering action, because under the condition that the bayonets normally work, because each bayonet can only monitor 3 lanes at most, a vehicle has a certain missed detection probability R when passing through the bayonet, but the missed detection probability of 2 continuous bayonets is obviously smaller than R. In addition, for the situation that the vehicle cannot do some steering actions due to traffic control or road network structures (such as forbidding left turn, T-shaped intersections and the like), the subsequent bayonet of the corresponding steering action is marked as empty.
In the embodiment of the application, after the directed graph is traversed by adopting the preset search depth value and at least one reachable bayonet is matched for each turning action of each bayonet in each intersection, the matching results of the reachable bayonets can be integrated, and the reachable bayonets turning matrixes including all intersections and all bayonets in all intersections can be generated. As an example, in the bayonet turn reachable matrix, the straight line can be recorded as 0, the left turn as 1, the u-turn as 2, and the right turn as 3. When 1-layer search is carried out, the reachable bayonet sets of 4 types of steering actions of each bayonet (intersection-direction) are the same for 4 bayonets A-1, A-2, A-3 and A-4 (if existing) in the same intersection A.
Step 103: and calculating intersection turning flow data corresponding to each intersection in the road network based on the intersection passing data and the intersection turning reachable matrix.
Here, the intersection turning flow rate data may be referred to as an intersection turning traffic flow rate or an intersection turning traffic volume, and is the number of vehicles that turn in each direction at the intersection in a unit time. In practical applications, the steering action may include a plurality of right turn, straight run, left turn, and turning around. As an example, assuming that a certain intersection is a cross intersection, the intersection includes four directions of north-south direction, east-west direction, north-south direction, west-east direction, and the steering actions that can be performed in each direction of the intersection include: three types of right turn, straight running and left turn, then the intersection turning flow data corresponding to the intersection may include: and the right-turn flow data, the straight-going flow data and the left-turn flow data correspond to each direction of the intersection in unit time.
By way of example, the unit time may refer to every 30 minutes, every hour, every day, and the like. Here, the embodiment of the present application is not particularly limited.
In practical application, the intersection capable of acquiring the intersection turning flow data may be an intersection provided with a gate in a road network.
In another embodiment of the present application, the step 103 may include the following steps 1031 to 1033:
step 1031: generating vehicle track information of each vehicle within a preset time period based on the bayonet vehicle passing data;
it should be understood that each vehicle is referred to herein as each vehicle in which a valid record can be found in the bayonet pass data.
As an example, taking the preset time period as a day as an example, the vehicle trajectory information corresponding to all vehicles per day may be counted by using the vehicle identifier in the vehicle passing data at the gate, such as the license plate number, as a key word. Wherein each piece of track information may include: vehicle identification, bayonet position, shooting time, etc.
Step 1032: determining the steering action of each vehicle at a bayonet where each track point corresponding to each vehicle is based on a bayonet steering reachable matrix aiming at each track point corresponding to each vehicle in the vehicle track information of each vehicle;
in practical applications, for each vehicle, a certain track point where the vehicle is located at a certain time in the vehicle track information of the vehicle may refer to the position information of the vehicle recorded by a certain gate when the vehicle passes through the gate at the certain time. For example, a certain track point where the vehicle is located at a certain time can be represented by the position information of the gate through which the vehicle passes at the certain time.
Step 1033: and counting intersection steering flow data corresponding to each intersection in the road network based on the steering action of each vehicle at the gate where each corresponding track point is located.
As an example, for each trace point corresponding to each vehicle in the vehicle trace information corresponding to each vehicle, the steering action of each vehicle at the gate where each corresponding trace point is located may be determined according to the gate steering reachable matrix and the preset time threshold.
In this embodiment of the present application, the step 1032 may include: the following operations are performed for each vehicle, respectively: determining whether the bayonet where the (i + 1) th track point of the vehicle is located belongs to a steering reachable bayonet set corresponding to the bayonet where the ith track point of the vehicle is located on the basis of the bayonet steering reachable matrix, wherein i is a positive integer greater than 0; when the fact that the bayonet where the (i + 1) th track point corresponding to the vehicle is located belongs to the steering reachable bayonet set corresponding to the bayonet where the (i) th track point corresponding to the vehicle is located is determined, whether the time interval between the time t1 corresponding to the vehicle located at the (i + 1) th track point and the time t2 corresponding to the vehicle located at the i th track point is smaller than a preset time threshold value is determined; and when the time interval between the time t1 corresponding to the vehicle at the i +1 th track point and the time t2 corresponding to the vehicle at the i th track point is smaller than a preset time threshold value, determining the steering action of the vehicle at the bayonet where the corresponding i th track point is located based on the bayonet steering reachable matrix. .
In another embodiment of the present application, in order to improve the accuracy of the intersection turning flow data, a preset time threshold may be set as: one of a first time threshold and a second time threshold; and the preset search depth value may be set including: if one of the first search depth value corresponding to the first time threshold and the second search depth value corresponding to the second time threshold is determined, then, the step of determining whether the gate where the i +1 th track point corresponding to the vehicle is located belongs to the steering reachable gate set corresponding to the gate where the i th track point corresponding to the vehicle is located based on the gate steering reachable matrix may include: and determining whether the gate where the ith +1 track point of the vehicle is located belongs to a steering reachable gate set corresponding to the gate where the ith track point of the vehicle is located based on the gate steering reachable matrix and by adopting a breadth-first search algorithm according to the preset search depth value.
For example, assuming that the first search depth value is implemented by 1 and the second search depth value is implemented by 2, in order to determine the steering action of each vehicle at the gate where each track point corresponding to each vehicle is located according to the gate-based steering reachable matrix, the search depth may be limited to 1 layer or 2 layers by traversing the reachable gate with one of the first search depth value and the second search depth value based on the gate-based steering reachable matrix by using the breadth-first search algorithm. Specifically, when the search depth is 1 layer, if, based on the bayonet steering reachable matrix, it is determined that the distance between the vertex M1 of the bayonet at which the i +1 th track point corresponding to the vehicle k is located and the vertex N1 of the bayonet at which the i th track point corresponding to the vehicle k is located is 1, and it is determined that the time interval between the time T1 corresponding to the vehicle k when the vehicle k is located at the i +1 th track point and the time T2 corresponding to the vehicle k when the vehicle k is located at the i th track point is smaller than the preset time threshold T1, the steering action of the vehicle k at the bayonet at which the i th track point corresponding to the vehicle k is located can be determined based on the bayonet steering reachable matrix. Here, a distance between a vertex M1 used for characterizing that the vehicle k is located at a corresponding bayonet where the i +1 th track point is located and a vertex N1 used for characterizing that the vehicle k is located at a corresponding bayonet where the i th track point is located is 1, which may indicate that the bayonet where the vehicle k is located at the corresponding i +1 th track point belongs to a set of steering reachable bayonets corresponding to the vehicle k at the bayonet where the i th track point is located.
Similarly, when the search depth is 2 layers, if, based on the bayonet steering reachable matrix, it is determined that the distance between the vertex M1 of the bayonet where the i +1 th track point corresponding to the vehicle k is located and the vertex N1 of the bayonet where the i th track point corresponding to the vehicle k is located is 2, and it is determined that the time interval between the time T1 corresponding to the vehicle k when the vehicle k is located at the i +1 th track point and the time T2 corresponding to the vehicle k when the vehicle k is located at the i th track point is smaller than the preset time threshold T2, it is possible to determine the steering action of the vehicle k at the bayonet where the i th track point corresponding to the vehicle k is located based on the bayonet steering reachable matrix. Here, a distance between a vertex M1 used for representing that the vehicle k is located at the corresponding bayonet where the i +1 th track point is located and a vertex N1 used for representing that the vehicle k is located at the corresponding bayonet where the i th track point is located is 2, which may also indicate that the bayonet where the vehicle k is located at the corresponding i +1 th track point belongs to the set of steering reachable bayonets corresponding to the bayonet where the vehicle k is located at the corresponding ith track point.
In the embodiment of the present application, the preset time threshold value used in determining the steering action is different according to the difference of the search depth value used. In practical applications, the preset time threshold can be set by a person skilled in the art according to practical situations.
In the embodiment of the present application, the inventors of the present application inventively propose that the preset time threshold can be determined by sensitivity analysis. As an example, the process of obtaining the preset time threshold T1 through the sensitivity analysis is as follows: when the distance between the vertexes is 1, that is, when the search depth is limited to 1 layer, for the data of the same license plate, 4 cases with time intervals of 10 minutes, 20 minutes, 30 minutes and no time limit are respectively taken, and the change of the turn recognition rate is respectively observed. The experimental results show that: the difference between the preset time threshold value which is limited to be less than 30 minutes and the no-time threshold value is not large, and when the preset time threshold value is less than 30 minutes, the steering identification rate is obviously reduced along with the reduction of the preset time threshold value. As such, the preset time threshold may be set to 30 minutes, i.e., the preset time threshold T1 may be set to 30 minutes. Similarly, it can be obtained in the same manner that when the inter-vertex distance is 2, that is, when the search depth is defined as 2 layers, the preset time threshold is defined as 60 minutes, that is, the preset time threshold T2 may be set as 60 minutes.
In other embodiments of the present application, in order to facilitate analyzing the intersection traffic capacity and optimizing the intersection signal matching, after step 103, the method may further include: step 104: and calculating the intersection turning proportion data corresponding to each intersection based on the intersection turning flow data corresponding to each intersection.
Here, the intersection turning ratio data may be a ratio between the number of vehicles performing each turning operation in each direction of the intersection and the total number of vehicles in each direction of the intersection. Wherein the total number of vehicles in each direction of the intersection is the sum of the number of vehicles performing each steering action in each direction of the intersection. As an example, it is assumed that the steering actions that can be performed in each direction at a certain intersection include: the intersection turning proportion data corresponding to the intersection can comprise a right-turning proportion, a straight-going proportion and a left-turning proportion which respectively correspond to each direction of the intersection in unit time.
In practical applications, after the step 104, the method may further include: determining whether the steering proportion of each intersection is zero or not in the corresponding intersection steering proportion data; when the situation that some steering proportion corresponding to the bayonets in the individual intersections is zero appears in the intersection steering proportion data corresponding to each intersection, the situation that steering cannot be performed in part of actual scenes is eliminated, and the steering flow data and the steering proportion of the current bayonets are approximately given according to the same type of bayonets.
In another embodiment of the present application, in order to improve the accuracy of the intersection turning flow data, after the step 103, the method may further include the following steps 105 to 106:
step 105: acquiring the number of lanes corresponding to each intersection from the road network data;
step 106: and correcting the intersection steering flow data corresponding to each intersection based on the number of lanes corresponding to each intersection.
In a specific implementation process, the step 106 may include: comparing the number of lanes corresponding to each intersection with the preset number of lanes respectively; according to the comparison result, the intersections with the number of the corresponding lanes larger than the preset number of the lanes are found out from all the intersections; and respectively carrying out the following correction operations on the intersection turning flow data corresponding to each searched intersection: determining the ratio of the number of lanes corresponding to the intersection to the preset number of lanes as a correction coefficient corresponding to the intersection; acquiring intersection turning flow data corresponding to each intersection from the intersection turning flow data corresponding to each intersection; and multiplying the intersection turning flow data corresponding to the intersection by the correction coefficient corresponding to the intersection to obtain the corrected intersection turning flow data corresponding to the intersection.
Here, the intersection found from each intersection according to the preset number of lanes means an intersection corresponding to the number of lanes greater than the preset number of lanes from among the number of lanes corresponding to each intersection.
In practical application, after the turning action of each vehicle at the gate where each track point corresponding to each vehicle is located is determined, the turning action numbers of the gates of all lanes can be counted for each intersection, and intersection turning flow data corresponding to the intersection is obtained, namely right turning flow data, straight-going flow data, left turning flow data and turning flow corresponding to each direction of the intersection. As an example, assume that the number of lanes entering the intersection in the intersection-direction where the gate a in a certain intersection is located is MAAnd the turning flow data corresponding to the intersection-direction where the bayonet A is positioned in the intersection is recorded as TVAnd the corrected steering flow rate is recorded as TVaAnd assuming that the number of the preset lanes is 3, if the number of the lanes M of the intersection-direction where the gate A is located is MAIf greater than 3, TVa＝TV*MA/3。
Therefore, the process of acquiring the intersection turning flow data based on the intersection vehicle passing data is completed.
As can be seen from the above, according to the method for acquiring intersection turning flow data provided in the embodiment of the present application, after the intersection passing data and the road network data are acquired, a intersection turning reachable matrix is established according to the road network data, where the intersection turning reachable matrix is used to represent reachability between intersections in the road network; and then, calculating intersection turning flow data corresponding to each intersection in the road network based on the intersection passing data and the intersection turning reachable matrix. Therefore, the intersection turning flow data is calculated through the intersection vehicle passing data, a metering person does not need to be dispatched to observe on site, and the large-scale, long-time, fine-time-granularity, low-cost and accurate intersection turning flow data can be obtained.
First, in the method for acquiring intersection turning flow data provided in the embodiment of the present application, the intersection turning flow data is acquired by using intersection passing data, and the acquired intersection passing data may be from all intersections where a gate is installed in an urban road, whereas a conventional manual counting method is difficult to cover numerous intersections within an urban area.
Secondly, in the method for acquiring intersection turning flow data provided by the embodiment of the application, the measurement of the acquired intersection turning flow data can be accurate to the minimum granularity of a system clock, and the number of each turning behavior in each hour is generally counted in consideration of statistical significance, but theoretically can be finer.
Thirdly, in the method for acquiring intersection turning flow data provided in the embodiment of the present application, the measurement of the intersection turning flow data may cover all the study time ranges. In theory, as long as the bayonet system operates normally, the data of passing vehicles by the bayonet can be recorded. And the traditional manual counting method can only carry out sampling investigation in a limited time period.
Fourth, compared with the conventional manual counting method, the intersection turning flow data acquisition method provided by the embodiment of the application is based on the intersection passing data acquired by the intersection systems installed and operated in many cities, does not need to additionally arrange observation equipment, does not additionally generate large-scale cost, and only needs to provide a little data processing overhead, so that the intersection turning flow data acquisition method has the advantage of low cost.
Fifth, in the method for acquiring intersection turning flow data provided in the embodiment of the present application, the intersection turning flow data is acquired through the card passing data, and is not dependent on other traffic data counted for a long time, so that accurate intersection turning flow data can be acquired.
In summary, the method for acquiring intersection turning traffic flow data provided by the embodiment of the application can acquire large-scale, long-time, fine-time-granularity, low-cost and accurate intersection turning traffic flow data.
It should be noted that the method for acquiring intersection turning flow data provided by the embodiment of the present application may be applied to a traffic network, and may also be used for other environment connectivity related analysis under a network structure, such as drainage pipelines, ventilation pipelines, and the like.
Based on the foregoing embodiments, the present application provides a method for acquiring intersection turning flow data, which may include the following steps:
step 1: importing original bayonet passing data exported by a bayonet system into computer equipment for data processing;
step 2: carrying out data cleaning on the original bayonet vehicle passing data through the computer equipment to obtain required cleaned bayonet vehicle passing data so as to calculate intersection turning flow data based on the bayonet vehicle passing data;
here, the data cleansing of the raw bayonet pass-by data may include: deleting abnormal records (null values, data with the record flow rate obviously smaller than a normal value and the like), repairing dislocation information and standardizing a data format. The reasons for missing records include that no bayonet is arranged in part of the directions of part of intersections, and that part of the bayonets have faults and do not record data.
And step 3: acquiring road network data, and abstracting the road network number into a weighted directed connected graph with composite vertexes (each vertex comprises 1-4 sub vertexes, and each intersection-direction is one sub vertex);
and 4, step 4: traversing the weighted directed connected graph, and matching each turning action of each gate in each intersection in the road network with at least one adjacent reachable gate;
during matching, the search depth is firstly set to be 1, namely, only the intersection internal bayonets which are directly connected with the current intersection and do not pass through other intersections are considered as reachable bayonets. For the condition that a branch or a micro capillary path is arranged between two adjacent bayonets, according to the principle of closest distance, as long as a bayonet capable of being reached is arranged, the influence of the middle capillary path on direction judgment is ignored. The influence of the interior road network on the direction determination is limited, and the influence on the left and right directions is almost the same, so that the influence on the steering ratio is not large.
And 5: when the turning actions of part of the bayonets cannot be matched with the reachable bayonets, considering a branch road network, and matching at least one non-adjacent reachable bayonets for each turning action of each bayonet in each intersection in the road network;
for the situation that the road network is complicated in the interior between the bayonets, if the branch is not considered, the turning action of part of the bayonets cannot be matched with the bayonets, the branch road network can be properly considered. In this case, the search depth may be set to 2, that is, the two front and rear records may not belong to the gates in the adjacent intersections but may be separated by the reachable gates of the other intersections. Two-layer searching can greatly improve the steering recognition rate.
Step 6: if the vehicle can not do some steering actions due to traffic control or road network structure (such as forbidding left turn and T-shaped intersection), marking the subsequent point of the corresponding steering action as empty;
and 7: and (4) integrating the results of the steps 4-6 to generate a gate turning reachable matrix comprising all intersections and all gates in all intersections. Wherein, the straight movement is marked as 0, the left rotation is marked as 1, the turning round is marked as 2, and the right rotation is marked as 3. When 1-layer search is carried out, 4 bayonets A-1, A-2, A-3 and A-4 (if existing) at the same intersection A are identical, and the reachable bayonets set for each bayonet (intersection-direction) to carry out 4 steering actions are identical and can be marked as SA。
And 8: and calculating track information of all vehicles every day by taking the license plate number in the passing vehicle data of the gate as a key word. And a license plate number, a bayonet position, time and the like are recorded in the vehicle passing data of each bayonet.
And step 9: judging the steering action of each vehicle at the gate where each track point corresponding to the vehicle is located according to the gate steering reachable matrix and the time threshold;
based on the bayonet steering reachable matrix, the reachable bayonet is traversed by using a breadth-first search algorithm, and the search depth is limited to 1 layer or 2 layers. When the distance between the vertexes is 1, the bayonet where the next track of the vehicle is located belongs to the steering reachable bayonet set of the current bayonet, and the time interval between the current bayonet and the bayonet is smaller than a preset time threshold T1, so that the steering action of the vehicle at the current bayonet can be judged.
And after the judgment in the step 9 is finished, outputting the vehicle steering judgment results (license plate number, position, time and steering action) of all the gates in all the intersections every day.
Step 10: and (4) counting the number of the bayonet steering actions of all lanes at each intersection according to the vehicle steering judgment results of all the bayonets in all the intersections every day, which are obtained in the step 9, and obtaining a plurality of left-turn flow, right-turn flow, straight flow and U-turn flow of each bayonet in each intersection in each direction.
Step 11: and (4) correcting the intersection turning flow data obtained in the step (10) according to the number of the gates and the number of the lanes to obtain corrected intersection turning flow data.
Step 12: and for the situation that some turning proportions of individual bayonets are zero, except the situation that the turning can not be really performed in part of actual scenes, the turning flow and proportion of the current bayonets are approximately given according to the bayonets of the same type.
Based on the same invention concept, the embodiment of the application provides an intersection turning flow data acquisition device. Fig. 3 is a schematic structural diagram of an apparatus for acquiring intersection turning flow data according to an embodiment of the present application, and referring to fig. 3, the apparatus 30 may include:
a first obtaining unit 301, configured to obtain bayonet passing data and road network data;
an establishing unit 302, configured to establish a bayonet steering reachable matrix according to road network data, where the bayonet steering reachable matrix is used to characterize reachability between bayonets in each intersection in a road network;
the first calculating unit 303 is configured to calculate intersection turning flow data corresponding to each intersection in the road network based on the intersection passing data and the intersection turning reachable matrix.
In an embodiment of the present application, the establishing unit, configured to establish a bayonet steering reachable matrix according to road network data, may include: generating a directed graph based on the road network data, wherein the directed graph comprises: the method comprises the steps of combining a composite vertex set and an edge set, wherein one composite vertex is an intersection, each composite vertex comprises at least one sub-vertex, each sub-vertex is a direction of the intersection, and each edge is a path between every two intersections; respectively determining at least one corresponding reachable bayonet for each steering action of each bayonet in each intersection after going through the directed graph; and establishing a bayonet steering reachable matrix based on each bayonet in each intersection and at least one reachable bayonet corresponding to each steering action.
In an embodiment of the present application, the establishing unit, configured to traverse a directed graph, and determine, for each steering action of each bayonet in each intersection, corresponding at least one reachable bayonet, respectively, may include: and traversing the directed graph by adopting a preset first search depth value, and respectively matching at least one reachable bayonet for each steering action of each bayonet in each intersection.
In this embodiment of the application, the establishing unit is further configured to, when traversing the directed graph by using a preset first search depth value and at least one turning action of a part of the bayonets in each intersection is not matched with the corresponding at least one reachable bayonet, traverse the directed graph again by using a preset second search depth value and match the corresponding at least one reachable bayonet for the at least one turning action of the part of the bayonets, where the preset second search depth value is greater than the preset first search depth value.
In this embodiment of the application, the first calculating unit is configured to calculate intersection turning flow data corresponding to each intersection in a road network based on the intersection passing data and the intersection turning reachable matrix, and may include: generating vehicle track information of each vehicle within a preset time period based on the bayonet vehicle passing data; determining the steering action of each vehicle at a bayonet where each track point corresponding to each vehicle is based on a bayonet steering reachable matrix aiming at each track point corresponding to each vehicle in the vehicle track information of each vehicle; and counting intersection steering flow data corresponding to each intersection in the road network based on the steering action of each vehicle at the gate where each corresponding track point is located.
In this application embodiment, a first calculating unit, is used for turning to the reachable matrix based on the bayonet, determines the action that turns to at the bayonet of each track point place that it corresponds to every vehicle, can include: the following operations are performed for each vehicle, respectively: determining whether the bayonet where the (i + 1) th track point of the vehicle is located belongs to a steering reachable bayonet set corresponding to the bayonet where the ith track point of the vehicle is located on the basis of the bayonet steering reachable matrix, wherein i is a positive integer greater than 0; when the fact that the bayonet where the (i + 1) th track point corresponding to the vehicle is located belongs to the steering reachable bayonet set corresponding to the bayonet where the (i) th track point corresponding to the vehicle is located is determined, whether the time interval between the time t1 corresponding to the vehicle located at the (i + 1) th track point and the time t2 corresponding to the vehicle located at the i th track point is smaller than a preset time threshold value is determined; and when the time interval between the time t1 corresponding to the vehicle at the i +1 th track point and the time t2 corresponding to the vehicle at the i th track point is smaller than a preset time threshold value, determining the steering action of the vehicle at the bayonet where the corresponding i th track point is located based on the bayonet steering reachable matrix.
In the embodiment of the present application, the preset time threshold is: one of a first time threshold and a second time threshold; the first calculating unit is configured to determine, based on the gate steering reachable matrix, whether the gate where the i +1 th track point of the vehicle is located at the corresponding gate belongs to a steering reachable gate set corresponding to the gate where the i th track point of the vehicle is located, and may include: based on the accessible matrix of bayonet socket turning to preset search depth value adopts the preferential search algorithm of breadth, confirms whether this vehicle belongs to the accessible bayonet socket set of turning that this vehicle corresponds at the bayonet socket that the ith track point that its corresponds belongs to at its bayonet socket that corresponds, and wherein, preset search depth value is: one of a first search depth value corresponding to a first time threshold and a second search depth value corresponding to a second time threshold.
In other embodiments of the present application, the apparatus may further include: and the second calculating unit is used for calculating the intersection turning proportion data corresponding to each intersection based on the intersection turning flow data corresponding to each intersection.
In another embodiment of the present application, the apparatus may further include: the second acquisition unit is used for acquiring the number of the lanes corresponding to each intersection from the road network data; and the correction processing unit is used for correcting the intersection steering flow data corresponding to each intersection based on the number of lanes corresponding to each intersection.
In this embodiment of the application, the correction processing unit is configured to perform correction processing on the intersection turning flow data corresponding to each intersection based on the number of lanes corresponding to each intersection, and may include: comparing the number of lanes corresponding to each intersection with the preset number of lanes respectively; according to the comparison result, the intersections with the number of the corresponding lanes larger than the preset number of the lanes are found out from all the intersections; and respectively carrying out the following correction operations on the intersection turning flow data corresponding to each searched intersection: determining the ratio of the number of lanes corresponding to the intersection to the preset number of lanes as a correction coefficient corresponding to the intersection; acquiring intersection turning flow data corresponding to each intersection from the intersection turning flow data corresponding to each intersection; and multiplying the intersection turning flow data corresponding to the intersection by the correction coefficient corresponding to the intersection to obtain the corrected intersection turning flow data corresponding to the intersection.
Based on the same inventive concept, the embodiment of the application provides computer equipment. Fig. 4 is a schematic structural diagram of a computer device in an embodiment of the present application, and referring to fig. 4, the computer device 40 includes: at least one processor 401; and at least one memory 402, a bus 403 connected to the processor 401; the processor 401 and the memory 402 complete communication with each other through the bus 403; the processor 401 is configured to call program instructions in the memory 402 to perform the steps of the intersection turning flow data obtaining method in one or more embodiments described above.
The Processor may be implemented by a Central Processing Unit (CPU), a microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like. The Memory may include volatile Memory in a computer readable medium, Random Access Memory (RAM), and/or nonvolatile Memory such as Read Only Memory (ROM) or Flash Memory (Flash RAM), and the Memory includes at least one Memory chip.
It should be noted that, in the embodiments of the present application, if the method for acquiring intersection turning flow data in one or more of the above embodiments is implemented in the form of a software functional module, and is sold or used as a stand-alone product, it may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof that contribute to the prior art may be embodied in the form of a software product stored in a storage medium, and including several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present application.
Accordingly, based on the same inventive concept, an embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium includes a stored program, and when the program runs, the computer device in which the storage medium is located is controlled to execute the steps of the intersection turning flow data obtaining method in one or more embodiments described above.
Here, it should be noted that: the above description of the apparatus, computer device or computer-readable storage medium embodiments is similar to the description of the method embodiments above, with similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the apparatus, the computer device or the computer-readable storage medium of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
The present application describes embodiments, but the description is illustrative rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or instead of any other feature or element in any other embodiment, unless expressly limited otherwise.
The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive concept as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive aspects to form yet another unique inventive aspect, as defined by the claims. Thus, it should be understood that any of the features shown and/or discussed in this application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not limited except as by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.
Further, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other orders of steps are possible as will be understood by those of ordinary skill in the art. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Further, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.

Claims (12)

1. A method for acquiring intersection turning flow data is characterized by comprising the following steps:
acquiring bayonet vehicle passing data and road network data;
establishing a bayonet steering reachable matrix according to the road network data, wherein the bayonet steering reachable matrix is used for representing the accessibility between the bayonets in each intersection in the road network;
calculating intersection turning flow data corresponding to each intersection in the road network based on the intersection passing data and the intersection turning reachable matrix, wherein the intersection turning flow data comprises the following steps:
generating vehicle track information of each vehicle within a preset time period based on the bayonet vehicle passing data;
determining the steering action of each vehicle at a bayonet where each track point corresponding to each vehicle is located based on the bayonet steering reachable matrix aiming at each track point corresponding to each vehicle in the vehicle track information of each vehicle;
and counting intersection steering flow data corresponding to each intersection in the road network based on the steering action of each vehicle at the gate where each corresponding track point is located.
2. The method according to claim 1, wherein said building a bayonet steering reachable matrix from said road network data comprises:
generating a directed graph based on the road network data, wherein the directed graph comprises: the method comprises the steps that a composite vertex set and an edge set are provided, wherein one composite vertex represents an intersection, each composite vertex comprises at least one sub-vertex, each sub-vertex represents one direction of the intersection and a bayonet arranged in the direction, and each edge represents a path between every two intersections;
traversing the directed graph, and respectively determining at least one corresponding reachable bayonet for each steering action of each bayonet in each intersection;
and establishing a bayonet steering reachable matrix based on each bayonet in each intersection and at least one reachable bayonet corresponding to each steering action.
3. The method of claim 2, wherein said traversing the directed graph to determine a corresponding at least one reachable bayonet for each steering action of each bayonet within each intersection, respectively, comprises:
and traversing the directed graph by adopting a preset first search depth value, and respectively matching at least one reachable bayonet for each steering action of each bayonet in each intersection.
4. The method of claim 3, wherein after traversing the directed graph with the preset first search depth value to match at least one reachable bayonet for each turning action of each bayonet within each intersection, respectively, the method further comprises:
when the preset first search depth value is adopted to traverse the directed graph, and at least one corresponding reachable bayonet is not matched for at least one turning action of part of bayonets in each intersection, the preset second search depth value is adopted to traverse the directed graph again, and at least one corresponding reachable bayonet is matched for at least one turning action of the part of bayonets, wherein the preset second search depth value is larger than the preset first search depth value.
5. The method according to claim 4, wherein the determining the turning action of each vehicle at the gate where each corresponding track point is located based on the gate turning reachable matrix comprises:
the following operations are performed for each vehicle, respectively:
determining whether the gate where the (i + 1) th track point of the vehicle is located belongs to a steering reachable gate set corresponding to the gate where the ith track point of the vehicle is located, based on the gate steering reachable matrix, wherein i is a positive integer greater than 0;
when the fact that the bayonet where the (i + 1) th track point corresponding to the vehicle is located belongs to the steering reachable bayonet set corresponding to the bayonet where the (i) th track point corresponding to the vehicle is located is determined, whether the time interval between the time t1 corresponding to the vehicle located at the (i + 1) th track point and the time t2 corresponding to the vehicle located at the (i) th track point is smaller than a preset time threshold value is determined;
and when the time interval between the time t1 corresponding to the vehicle when the vehicle is located at the (i + 1) th track point and the time t2 corresponding to the vehicle when the vehicle is located at the (i) th track point is determined to be smaller than the preset time threshold, the steering action of the vehicle at the gate where the (i) th track point corresponding to the vehicle is located is determined based on the gate steering reachable matrix.
6. The method according to claim 5, wherein the preset time threshold is: one of a first time threshold and a second time threshold;
based on the bayonet steering reachable matrix, determining whether the bayonet where the ith track point of the vehicle is located at the corresponding bayonet where the ith track point of the vehicle is located belongs to a steering reachable bayonet set corresponding to the bayonet where the ith track point of the vehicle is located, includes:
based on the bayonet steering reachable matrix, whether the bayonet where the ith track point corresponding to the vehicle is located at belongs to a steering reachable bayonet set corresponding to the bayonet where the ith track point corresponding to the vehicle is located is determined by adopting a breadth-first search algorithm according to a preset search depth value, wherein the preset search depth value is as follows: one of a first search depth value corresponding to the first time threshold and a second search depth value corresponding to the second time threshold.
7. The method of claim 1, wherein after said calculating intersection turn flow data for each intersection in the road network based on said bayonet transit data and said bayonet turn reachable matrix, the method further comprises:
and calculating the intersection turning proportion data corresponding to each intersection based on the intersection turning flow data corresponding to each intersection.
8. The method of claim 1, wherein after said calculating intersection turn flow data for each intersection in the road network based on said bayonet transit data and said bayonet turn reachable matrix, the method further comprises:
acquiring the number of lanes corresponding to each intersection from the road network data;
and correcting the intersection steering flow data corresponding to each intersection based on the number of lanes corresponding to each intersection.
9. The method according to claim 8, wherein the modifying the intersection turning flow data corresponding to each intersection based on the number of lanes corresponding to each intersection comprises:
comparing the number of lanes corresponding to each intersection with a preset number of lanes respectively;
according to the comparison result, the intersections with the number of the corresponding lanes larger than the preset number of the lanes are found out from all the intersections;
and respectively carrying out the following correction operations on the intersection turning flow data corresponding to each searched intersection:
determining the ratio of the number of lanes corresponding to the intersection to the preset number of lanes as a correction coefficient corresponding to the intersection;
acquiring intersection turning flow data corresponding to each intersection from the intersection turning flow data corresponding to each intersection;
and multiplying the intersection turning flow data corresponding to the intersection by the correction coefficient corresponding to the intersection to obtain the corrected intersection turning flow data corresponding to the intersection.
10. An intersection turning flow data acquisition device, characterized in that the device includes:
the first acquisition unit is used for acquiring bayonet vehicle passing data and road network data;
the system comprises an establishing unit, a data processing unit and a data processing unit, wherein the establishing unit is used for establishing a bayonet steering reachable matrix according to the road network data, and the bayonet steering reachable matrix is used for representing the reachability between bayonets in each intersection in the road network;
the first calculating unit is used for calculating intersection turning flow data corresponding to each intersection in a road network based on the intersection passing data and the intersection turning reachable matrix, and comprises the following steps:
generating vehicle track information of each vehicle within a preset time period based on the bayonet vehicle passing data;
determining the steering action of each vehicle at a bayonet where each track point corresponding to each vehicle is located based on the bayonet steering reachable matrix aiming at each track point corresponding to each vehicle in the vehicle track information of each vehicle;
and counting intersection steering flow data corresponding to each intersection in the road network based on the steering action of each vehicle at the gate where each corresponding track point is located.
11. A computer-readable storage medium characterized in that the storage medium includes a stored program, wherein the program when executed controls a computer device on which the storage medium is located to perform the steps of the intersection turning flow data acquisition method according to any one of claims 1 to 9.
12. A computer device, characterized in that the computer device comprises:
at least one processor;
and at least one memory, bus connected with the processor;
the processor and the memory complete mutual communication through the bus; the processor is adapted to invoke program instructions in the memory to perform the steps of the method of acquiring intersection turning flow data according to any one of claims 1 to 9.
CN202010131101.8A 2020-02-28 2020-02-28 Method and device for acquiring intersection turning flow data and storage medium Active CN111275975B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010131101.8A CN111275975B (en) 2020-02-28 2020-02-28 Method and device for acquiring intersection turning flow data and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010131101.8A CN111275975B (en) 2020-02-28 2020-02-28 Method and device for acquiring intersection turning flow data and storage medium

Publications (2)

Publication Number Publication Date
CN111275975A CN111275975A (en) 2020-06-12
CN111275975B true CN111275975B (en) 2021-04-02

Family

ID=71004151

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010131101.8A Active CN111275975B (en) 2020-02-28 2020-02-28 Method and device for acquiring intersection turning flow data and storage medium

Country Status (1)

Country Link
CN (1) CN111275975B (en)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111932908B (en) * 2020-08-05 2021-07-23 浙江工业大学 Deep learning-based steering ratio and traffic flow statistical method
CN112634612B (en) * 2020-12-15 2022-09-27 北京百度网讯科技有限公司 Intersection flow analysis method and device, electronic equipment and storage medium
CN112700643A (en) * 2020-12-21 2021-04-23 北京百度网讯科技有限公司 Method and device for outputting vehicle flow direction, road side equipment and cloud control platform
CN114822049B (en) * 2022-03-23 2023-06-20 山东省交通规划设计院集团有限公司 Vehicle flow direction monitoring and analyzing method and system
CN117292547B (en) * 2023-10-27 2024-05-07 重庆交通大学 Method for evaluating connectivity of large-scale movable multistage influence area road network

Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100899789B1 (en) * 2008-09-29 2009-05-28 복 성 김 2-phase signalized grade separated intersection system
CN104715604A (en) * 2014-01-13 2015-06-17 杭州海康威视数字技术股份有限公司 Method and system for acquiring real-time traffic status information
CN105046956A (en) * 2015-06-24 2015-11-11 银江股份有限公司 Traffic flow simulating and predicting method based on turning probability
CN106683450A (en) * 2017-01-25 2017-05-17 东南大学 Recognition method for key paths of urban signal control intersection groups
CN107121146A (en) * 2017-06-02 2017-09-01 西安电子科技大学 Optimum path planning method based on road chain depth
CN108122069A (en) * 2017-12-08 2018-06-05 杭州电子科技大学 Based on huge traffic data resident trip starting and terminal point matrix extracting method
CN109979191A (en) * 2017-12-28 2019-07-05 杭州海康威视系统技术有限公司 Traffic signal control method, device, electronic equipment and computer readable storage medium
CN110319845A (en) * 2018-03-30 2019-10-11 日本电气株式会社 For determining the methods, devices and systems of the reachable path between two o'clock
CN110363984A (en) * 2019-06-25 2019-10-22 讯飞智元信息科技有限公司 Traffic flow forecasting method and equipment
CN110415523A (en) * 2019-08-13 2019-11-05 东南大学 A kind of signal control work zone division methods based on vehicle driving track data
CN110415514A (en) * 2019-06-10 2019-11-05 杨正 A kind of intersection lane prioritization scheme selection method, device and storage medium
CN110516846A (en) * 2019-07-26 2019-11-29 浙江工业大学 Intersection turning rate prediction technique based on LSTM neural network
CN110807931A (en) * 2019-11-08 2020-02-18 许凌 Traffic network directed graph path model construction and solving method based on steering relation

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000090389A (en) * 1998-09-16 2000-03-31 Mitsubishi Electric Corp Vehicle sensor
CN106991813A (en) * 2017-05-15 2017-07-28 东南大学 One class turn around vehicle and turn to limitation intersection flux and flow direction projectional technique
CN109816984B (en) * 2019-03-19 2021-08-31 太原理工大学 Traffic network region division and dynamic adjustment method

Patent Citations (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100899789B1 (en) * 2008-09-29 2009-05-28 복 성 김 2-phase signalized grade separated intersection system
CN104715604A (en) * 2014-01-13 2015-06-17 杭州海康威视数字技术股份有限公司 Method and system for acquiring real-time traffic status information
CN105046956A (en) * 2015-06-24 2015-11-11 银江股份有限公司 Traffic flow simulating and predicting method based on turning probability
CN106683450A (en) * 2017-01-25 2017-05-17 东南大学 Recognition method for key paths of urban signal control intersection groups
CN107121146A (en) * 2017-06-02 2017-09-01 西安电子科技大学 Optimum path planning method based on road chain depth
CN108122069A (en) * 2017-12-08 2018-06-05 杭州电子科技大学 Based on huge traffic data resident trip starting and terminal point matrix extracting method
CN109979191A (en) * 2017-12-28 2019-07-05 杭州海康威视系统技术有限公司 Traffic signal control method, device, electronic equipment and computer readable storage medium
CN110319845A (en) * 2018-03-30 2019-10-11 日本电气株式会社 For determining the methods, devices and systems of the reachable path between two o'clock
CN110415514A (en) * 2019-06-10 2019-11-05 杨正 A kind of intersection lane prioritization scheme selection method, device and storage medium
CN110363984A (en) * 2019-06-25 2019-10-22 讯飞智元信息科技有限公司 Traffic flow forecasting method and equipment
CN110516846A (en) * 2019-07-26 2019-11-29 浙江工业大学 Intersection turning rate prediction technique based on LSTM neural network
CN110415523A (en) * 2019-08-13 2019-11-05 东南大学 A kind of signal control work zone division methods based on vehicle driving track data
CN110807931A (en) * 2019-11-08 2020-02-18 许凌 Traffic network directed graph path model construction and solving method based on steering relation

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
The cardinal hypotheses of Holoecology: Facets for a general systems theory of the organism–environment relationship;Bernard C.Patten;《Ecological Modelling》;20160110;全文 *
基于RFID数据的城市路网交通运行态势识别技术及应用;郭鹏;《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑）》;20180315;论文第38、64-86页 *
基于监督式学习的城市路网动态OD估计方法研究;王寅朴;《中国优秀硕士学位论文全文数据库（工程科技Ⅱ辑）》;20200115;论文第14页 *
过车记录缺失下的瓶颈路段流量分析;刘畅;《武汉理工大学学报(交通科学与工程版)》;20191231;全文 *

Also Published As

Publication number Publication date
CN111275975A (en) 2020-06-12

Similar Documents

Publication Publication Date Title
CN111275975B (en) Method and device for acquiring intersection turning flow data and storage medium
CN104050817B (en) Speed limiting information base generation and speed limiting information detection method and system
CN109272756B (en) Method for estimating queuing length of signal control intersection
CN109961106B (en) Training method and device of trajectory classification model and electronic equipment
CN107885795B (en) Data verification method, system and device for card port data
CN110570660A (en) real-time online traffic simulation system and method
CN107195180B (en) Traffic travel track extraction method and device based on electric police data
CN112215427B (en) Vehicle driving track reconstruction method and system under condition of bayonet data loss
CN113570864B (en) Method and device for matching running path of electric bicycle and storage medium
CN110851490B (en) Vehicle travel common stay point mining method and device based on vehicle passing data
CN109243173B (en) Vehicle track analysis method and system based on road high-definition checkpoint data
CN110532250B (en) Method and device for processing traffic data
CN114239371A (en) Simulation-based parameter calibration method for vehicle delay model at entrance and exit of large parking lot
CN112686466A (en) Subway passenger path confirmation method and device
Cich et al. Threshold settings for TRIP/STOP detection in GPS traces
CN108646261A (en) A kind of data processing method and device of vehicle GPS data set
CN113850237B (en) Internet vehicle target detection and evaluation method and system based on video and track data
CN110675631A (en) Traffic flow traceability analysis method and system
CN109448379A (en) A kind of identification of sporadic traffic events of social media data and localization method
CN115631082B (en) Multi-scene path restoration method based on highway network topology structure
CN114758493B (en) Expressway traffic flow monitoring method and system based on data fusion
CN113538902B (en) Intersection vehicle track data restoration method based on traffic state
CN113470343B (en) Road blocking opening detection method, device, equipment and storage medium
CN114332174A (en) Track image alignment method and device, computer equipment and storage medium
CN112201041A (en) Trunk road path flow estimation method integrating electric alarm data and sampling trajectory data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant