CN115326180A - Large-scale vehicle dynamic positioning method based on communication technology - Google Patents
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Abstract
The invention provides a large-scale vehicle dynamic positioning method based on a communication technology, which comprises the following steps: collecting and storing structural object information in the area, planning a driving path between each node, and collecting vehicle passing data passing through each node; continuously and dynamically maintaining an online vehicle list, extracting the driving speed of adjacent nodes, analyzing the passing habits of the vehicles through historical passing data, analyzing the vehicle migration rules between the adjacent nodes through the historical data, estimating a destination node of the vehicle driving, estimating the distance traveled by the vehicle, and positioning the online vehicle. The large-scale vehicle dynamic positioning method based on the communication technology can fully master the distribution information of the vehicles and monitor the real-time positions of the important vehicles, so that the public trip is safer and smoother.
Description
Technical Field
The invention belongs to the technical field of vehicle positioning, and particularly relates to a large-scale vehicle dynamic positioning method based on a communication technology.
Background
ETC (Electronic Toll Collection), also known as an automatic road payment system and an Electronic Toll Collection system. The road toll collection method is a road toll collection method specially used for toll roads, and is usually found in expressways, bridges or tunnels for applying toll collection policies and city center part road sections to relieve the congestion condition of urban traffic. ETC can only carry out the location record to the vehicle when the vehicle is out of the way. Once the vehicle has traveled past, the ETC cannot record.
At present, a GNSS technology, a sensing ranging technology and a high-precision map are mainly adopted for vehicle positioning in an expressway environment, but the positioning methods need real-time positioning and depend too much on positioning signals. The positioning signals are very easy to interfere, and part of road sections even cannot receive the positioning signals, so that the positioning system has limitations.
Therefore, the invention provides a large-scale vehicle dynamic positioning method based on a communication technology.
Disclosure of Invention
In view of the above, the present invention provides a method for dynamically positioning a large-scale vehicle based on communication technology. The invention aims to provide a novel method for positioning a vehicle independent of a positioning signal.
In order to achieve the above object, the present invention provides a large-scale vehicle dynamic positioning method based on communication technology, which comprises the following steps;
s1, collecting structure information of a toll station, an ETC portal frame, a service area inlet and a service area outlet in the area, and writing the structure information into a cache container in a topological structure form;
s2, planning a driving path among all nodes: the toll station, the ETC portal frame, the service area inlet and the service area outlet are all regarded as nodes, and then the driving path of the adjacent node and the distance between the adjacent nodes are obtained from the GIS map according to the structure information of the nodes;
s3, collecting vehicle passing data passing through each node;
s4, continuously and dynamically maintaining an online vehicle list: when the vehicle generates data through the entrance of the toll station, the ETC portal frame and the service area, adding the vehicle into the network list; when the vehicle generates data through the exit of the toll station or the vehicle does not generate any data within a specified time range, removing the vehicle from the network list;
s5, extracting the driving speed of the adjacent node: the method comprises the steps of acquiring the driving speed of a road section between adjacent nodes from a GIS map at fixed frequency in a timing mode, and updating in real time;
s6, analyzing the passing habits of the vehicles through historical passing data: extracting the proportion of the total times of the vehicle passing through the node a to the next adjacent node b to the total times of the vehicle passing through the node a;
s7, analyzing a vehicle migration rule between adjacent nodes through historical data: extracting the proportion of the total number of the trains passing through the node a to the next adjacent node b in the history;
s8, presuming a destination node of the vehicle driving through the information obtained in the steps S6 and S7: taking a node in the latest passing information of the vehicle as a starting point o, and taking the next node with the largest ratio in the vehicle migration rule of the starting point o as a target node if the vehicle passes through the starting point o for the first time; if the vehicle does not pass through the starting point o for the first time, taking the next node with the highest probability in the passing habit of the vehicle at the starting point o as a target node;
s9, estimating the distance traveled by the vehicle according to the information obtained in the steps S5 and S8: obtaining a destination node of the vehicle according to the step S8, obtaining the speed of a road section between the starting point and the destination node according to the step S5, and calculating the distance traveled by the vehicle from the starting point by combining the time of passing the starting point;
s10, positioning the on-line vehicle in the S4 through the step S9.
Further, the structure information written in step S1 includes: the upstream and downstream relationship of the nodes, the longitude and latitude information of the nodes and the access identification information of the area boundary nodes.
Further, in step S2, the driving path is a curve formed by connecting the key points of the GIS map that need to deviate according to the driving direction in the trajectory.
Further, the road sections among the key points are equally divided by a fixed length, and the longitude and latitude of the dividing point and the distance from the starting point are calculated:
the calculation expression of the longitude of the kth division point among the pth keypoint and the (p + 1) th keypoint is as follows:
the calculation expression of the latitude of the kth segmentation point in the pth key point and the pth +1 key point is as follows:
the calculation expression of the distance from the kth segmentation point to the nth node in the pth keypoint and the (p + 1) th keypoint is as follows:
where Fa represents the distance between adjacent division points, d p Representing the distance of neighboring keypoints and x representing the xth keypoint.
Further, in step S5, the calculation expression of the traveling speed is as follows:
in the formula (I), the compound is shown in the specification,
for the distance between adjacent nodes in the GIS map interface,
and the time for passing through the adjacent node segments in the GIS map interface.
The invention has the beneficial effects that:
1. the invention provides a large-scale vehicle dynamic positioning method based on a communication technology, which can be used for positioning a vehicle on a large scale through an ETC (electronic toll collection), does not need other positioning devices and is not easy to be interfered by external information. In addition, the positioning method can fully master the vehicle distribution information around the event in the process of handling the high-speed event, is convenient for taking control measures in time and enables the public to travel more safely and smoothly. Meanwhile, the method can also monitor the real-time position of the key vehicle, thereby ensuring the safety of the expressway and reducing the occurrence probability of events.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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FIG. 1 is a flow chart of a large-scale vehicle dynamic positioning method based on communication technology of the present invention;
FIG. 2 is a distribution diagram of the structure in the middle and high speed road area according to one embodiment.
Detailed Description
In order to make the technical solutions, advantages and objects of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the present application.
The invention provides a large-scale vehicle dynamic positioning method based on a communication technology, which comprises the following steps of;
s1, collecting structure information of toll stations, ETC gantries, service area inlets, service area outlets and the like in the area, wherein the structure information can generate toll data, and writing the structure information into a cache container in a topological structure form in a Key-Value format, wherein the written data comprise: the method comprises the following steps of (1) the upstream and downstream relation of nodes in a topological structure (Key is an upstream node identifier, and Value is a downstream node identifier list), the longitude and latitude information of the nodes (Key is a node identifier, and Value is node longitude and latitude information), and the access identification information of regional boundary nodes (Key is a boundary node identifier, and Value is the access identification of the node).
S2, planning a driving path among all nodes: the toll station, the ETC portal frame, the service area inlet and the service area outlet are all regarded as nodes, then the driving path (the driving path is a curve formed by connecting key points of the GIS map which need to deviate according to the driving direction in the track) of the adjacent node and the distance between the adjacent nodes are obtained from the GIS map according to the structure information of the nodes, the road sections between the key points are equally divided at a fixed length, and the longitude and latitude of the dividing point and the distance between the dividing point and the starting point are calculated.
(1) The calculation expression of the distance from the nth node to the (n + 1) th node is as follows:
wherein m is the number of key points of the driving path in the GIS map, d x Distance to adjacent key points
(2) The calculation expression of the longitude of the kth division point among the pth keypoint and the (p + 1) th keypoint is as follows:
(3) The calculation expression of the latitude of the kth segmentation point in the pth key point and the pth +1 key point is as follows:
(4) The calculation expression of the distance from the kth segmentation point to the nth node in the pth keypoint and the (p + 1) th keypoint is as follows:
where Fa represents the distance between adjacent division points, d p Representing the distance of neighboring keypoints and x representing the xth keypoint.
And then writing the result data into a cache container in a Key-Value format, wherein the Key consists of an upstream node identifier and a downstream node identifier, and the Value is an ordered set.
And S3, collecting vehicle passing data passing through each node, including time of passing through an ETC portal frame, a service area entrance and exit and a toll station, storing the passing data in a Key-Value format, wherein Key is a unique identifier of vehicle passing information, the Value data format is a stack, and the passing data are sequentially stacked according to the sequence of passing time.
S4, continuously and dynamically maintaining an online vehicle list: the online vehicle list is the only identification of vehicle passing information, and when the vehicle generates data through a toll station entrance, an ETC portal frame and a service area, the vehicle is added into the online list; when the vehicle generates data through the exit of the toll station or the vehicle does not generate any data within a specified time range, the vehicle is removed from the net list.
S5, extracting the driving speed of the adjacent node: the driving speed of the road sections between adjacent nodes is obtained from the GIS map at fixed frequency and timing, and is updated in real time, and the calculation expression of the driving speed is as follows:
in the formula (I), the compound is shown in the specification,
for the distance between adjacent nodes in the GIS map interface,
the time for passing through the adjacent node segments in the GIS map interface.
S6, analyzing the passing habits of the vehicles through historical passing data: extracting the proportion of the total times of the vehicle history passing through the node a to the next adjacent node b to the total times of the vehicle passing through the node a, wherein the calculation expression of the probability is as follows:
in the formula, m a Is the sum of the number of pass nodes a in the history data of the vehicle A, m a→b The sum of the number of passes from node a to node b in the history data of vehicle a.
S7, analyzing the vehicle migration rule between adjacent nodes through historical data: and extracting the proportion of the total train number of the history passing through the node a to the next adjacent node b to the total train number passing through the node a. The calculation expression of the vehicle migration habit of the node a (the nodes downstream of the node a comprise the node b and the node c) is as follows:
in the formula, n a Is the sum of the number of passes, n, of the path nodes a in the historical data a→b The sum of the number of passes from node a to node b in the history data.
In the formula, n a Is the sum of the number of passes, n, of the path nodes a in the historical data a→c The sum of the number of cars passing from node a to node c in the history data.
S8, presuming a destination node of the vehicle driving through the information obtained in the steps S6 and S7: taking a node in the latest passing information of the vehicle as a starting point o, and taking the next node with the largest ratio in the vehicle migration rule of the starting point o as a target node if the vehicle passes through the starting point o for the first time; if the vehicle does not pass through the starting point o for the first time, taking the next node with the highest probability in the passing habit of the vehicle at the starting point o as a target node;
s9, estimating the distance traveled by the vehicle according to the information obtained in the steps S5 and S8: obtaining a destination node of the vehicle according to the step S8, obtaining the speed of a road section between the starting point and the destination node according to the step S5, and calculating the distance traveled by the vehicle from the starting point by combining the time of passing through the starting point;
s10, positioning the on-line vehicles in the step S4 through the step S9, and finding out a sequencing factor from the driving paths of the topological structure according to the distance traveled by the vehicles in the driving paths of the node sections and the division point with the shortest traveled distance as a position point for positioning the vehicles according to the latest traffic information of the vehicles and the destination nodes of the vehicles.
Example one
The present embodiment is based on a section of highway administered by highway operating company a, and the structure information in this section of highway is shown in table 1.
Watch 1
1. And processing the structure in the highway area, and writing the structure information into the cache container in a topological structure form.
The upstream and downstream relations are as follows:
portal A1: [ gantry B1]
A gantry B1: [ toll station exit, service area entrance 1, gantry C1]
Service area entrance 1: [ service area Exit 1]
Service area exit 1: [ Portal frame C1]
The portal C2: [ service area entrance 2, toll booth exit, gantry B2]
Service area entrance 2: [ service area Exit 2]
Service area exit 2: [ exit of toll station, gantry B2]
A gantry B2: [ Portal A2]
An entrance of a toll station: [ service entrance 1, gantry C1, gantry B2]
And (3) node latitude and longitude:
portal A1: lng-a1, lat-a1
A gantry B1: lng-b1, lat-b1
Service area entrance 1: lng-si1, lat-si1
Service area exit 1: lng-se1, lat-se1
Portal C1: lng-c1, lat-c1
The portal C2: lng-c2, lat-c2
Service area entrance 2: lng-si2, lat-si2
Service area exit 2: lng-se2, lat-se2
A gantry B2: lng-b2, lat-b2
The portal A2: lng-a2, lat-a2
An entrance of a toll station: lng-ent1, lat-ent1
And (4) an exit of the toll station: lng-ext1, lat-ext1
List of area entries: ent: [ Portal A1, portal C2, entrance of toll station ]
Area export list: ext: [ Portal A2, portal C1, toll booth export ]
2. And planning a path of the road section of the adjacent nodes in the upstream and the downstream, and respectively calculating the position of the segmentation point and the distance from the starting point to obtain the following data:
taking the portal B1-portal C1 as an example:
portal B1-portal C1: [ { ling 1-lat1,100},
{lng2-lat2,200},
{lng3-lat3,300},
{lng4-lat4,400},
...,
{lng23-lat23,2300},
...,
{lngx-latx,n}]
3. the real-time consumption charging data and the accumulated consumption data are processed to obtain the following data:
vehicle a: [ { X toll station, 20220501083012369},
{ Y gantry, 20220501085209426},
{ Z gantry, 20220501091318334},
{ gantry a1,20220501092919311},
{ gantry B1,20220501095822345} ]
Vehicle B: [ { P gantry, 20220501085448267},
{ gantry B2,20220501092716319},
{ service area entrance 2,20220501095724444} ]
Vehicle C: [ { M gantry, 20220501084012369},
{ gantry B2,20220501092919311},
{ toll gate exit, 20220501095822345} ]
Vehicle D: [ { gantry B2,20220501052919311} ]
The vehicle E: [ { gantry B1,20220501092655311} ]
4. On the basis of the above 3, the following list of on-grid vehicles can be obtained.
The current time is: 2022-05-0110, the specified time of this example is 4 hours.
On-net vehicle list: [ vehicle A, vehicle B, vehicle E ].
Wherein the vehicle C exits the zone via the zone exit; the latest dynamic time for vehicle D is 2022-05-0105.
5. Through a GIS map path planning interface, the speed (unit meter/second) of each adjacent node segment is calculated, and the following speed data can be obtained:
portal A1-portal B1:23m/s;
portal B1-toll gate exit: 24m/s;
portal B1-service area entrance 1:24m/s;
portal B1-portal C1:24m/s;
service area egress 1-portal C1:24m/s;
portal C2-service area entrance 2:22m/s;
portal C2-toll gate exit: 22m/s;
portal C2-portal B2:22m/s;
service area exit 2-toll gate exit: 22m/s;
service area egress 2-gantry B2:22m/s;
portal B2-portal A2:20m/s;
entrance of toll station-entrance of service area 1:18m/s;
entrance of toll station-portal C1:18m/s;
entrance of toll station-portal B2:18m/s;
6. and analyzing the passing habits of the vehicles and the migration rules among the structures from the historical data.
Taking a vehicle A and a portal B1 as an example, the passing habit of the vehicle A is as follows: in the historical data, the number of times that the vehicle A passes through the portal B1 is 10, the number of times of going to the exit of the toll station is 0, the number of times of going to the entrance 1 of the service area is 3, and the number of times of going to the portal C1 is 7. The proportion of portal B1 to toll booth exit is 0, portal B1 to service entrance 1 is 30% and portal B1 to portal C1 is 70% for vehicle a.
Vehicle migration law of the portal B1: in the historical data, the total train number passing through the portal B1 is 100000, the total train number going to the exit of the toll station is 20000, the total train number going to the entrance 1 of the service area is 30000, and the total train number going to the portal C1 is 50000, so that for the portal B1, the vehicle migration rate of the portal B1-the exit of the toll station is 20%, the vehicle migration rate of the portal B1-the entrance 1 is 30%, and the vehicle migration rate of the portal B1-the portal C1 is 50%.
7. For the vehicle a, according to the vehicle passing habit in step 6, it can be presumed that there is a high probability that the vehicle a will travel from the portal B1 to the portal C1, and in view of the fact that the time for the vehicle a to pass through the portal B1 is 2022-05-0109, the distance to the current time 2022-05-0110 is 98 seconds
8. From the distance traveled in step 7 being 2352m and the distribution of the division points in step 2, it is found that the vehicle a is in the vicinity of (ng 23, lat 23).
9. And (5) positioning the vehicles in the net vehicle list in the step (4) at a fixed frequency by adopting the method of the step (7) and the step (8).
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered in the protection scope of the present invention.
Claims (5)
1. A large-scale vehicle dynamic positioning method based on communication technology is characterized by comprising the following steps:
s1, collecting structure information of a toll station, an ETC portal frame, a service area inlet and a service area outlet in the area, and writing the structure information into a cache container in a topological structure form;
s2, planning a driving path among all nodes: the toll station, the ETC portal frame, the service area inlet and the service area outlet are all regarded as nodes, and then the driving path of the adjacent node and the distance between the adjacent nodes are obtained from the GIS map according to the structure information of the nodes;
s3, collecting vehicle passing data passing through each node;
s4, continuously and dynamically maintaining an online vehicle list: when the vehicle generates data through the entrance of the toll station, the ETC portal frame and the service area, adding the vehicle into the network list; when the vehicle generates data through an exit of a toll station or the vehicle does not generate any data within a specified time range, removing the vehicle from the network list;
s5, extracting the driving speed of the adjacent node: the driving speed of a road section between adjacent nodes is obtained from the GIS map at fixed frequency and timing, and is updated in real time;
s6, analyzing the passing habits of the vehicles through historical passing data: extracting the proportion of the total times of the vehicle passing through the node a to the next adjacent node b to the total times of the vehicle passing through the node a;
s7, analyzing the vehicle migration rule between adjacent nodes through historical data: extracting the proportion of the total number of the trains passing through the node a to the next adjacent node b in the history;
s8, presuming a destination node of the vehicle driving through the information obtained in the steps S6 and S7: taking the node in the latest traffic information of the vehicle as a starting point o, and if the vehicle passes through the starting point o for the first time, taking the next node with the largest proportion in the vehicle migration rule of the starting point o as a target node; if the vehicle does not pass through the starting point o for the first time, taking the next node with the highest probability in the passing habit of the vehicle at the starting point o as a target node;
s9, estimating the distance traveled by the vehicle according to the information obtained in the steps S5 and S8: obtaining a destination node of the vehicle according to the step S8, obtaining the speed of a road section between the starting point and the destination node according to the step S5, and calculating the distance traveled by the vehicle from the starting point by combining the time of passing the starting point;
s10, positioning the on-line vehicle in the S4 through the step S9.
2. The large-scale vehicle dynamic positioning method based on communication technology as claimed in claim 1, wherein the structure information written in step S1 includes: the upstream and downstream relationship of the nodes, the longitude and latitude information of the nodes and the access identification information of the area boundary nodes.
3. The large-scale vehicle dynamic positioning method based on communication technology as claimed in claim 1, wherein in step S2, the driving path is a curve formed by connecting GIS maps according to key points in the trajectory where the driving direction needs to deviate.
4. The large-scale dynamic vehicle positioning method based on communication technology as claimed in claim 3, wherein the road segments between the key points are equally divided in a fixed length, and the longitude and latitude of the division point and the distance from the starting point are calculated as follows:
the calculation expression of the longitude of the kth partition point among the pth keypoint and the (p + 1) th keypoint is as follows:
the calculation expression of the latitude of the kth segmentation point in the pth key point and the pth +1 key point is as follows:
the calculation expression of the distance from the kth segmentation point to the nth node in the pth keypoint and the (p + 1) th keypoint is as follows:
where Fa represents the distance between adjacent division points, d p Representing the distance of neighboring keypoints and x representing the xth keypoint.
5. The large-scale vehicle dynamic positioning method based on communication technology as claimed in claim 1, wherein in step S5, the formula for calculating the driving speed is as follows:
in the formula (I), the compound is shown in the specification,
for the distance between adjacent nodes in the GIS map interface,
and the time for passing through the adjacent node segments in the GIS map interface.
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