CN111915893B

CN111915893B - Road bottleneck point identification method and device, electronic equipment and storage medium

Info

Publication number: CN111915893B
Application number: CN202010768191.1A
Authority: CN
Inventors: 吴学新; 孙伟力; 刘磊; 梁源
Original assignee: Beijing Didi Infinity Technology and Development Co Ltd
Current assignee: Beijing Didi Infinity Technology and Development Co Ltd
Priority date: 2019-04-15
Filing date: 2019-04-15
Publication date: 2021-05-11
Anticipated expiration: 2039-04-15
Also published as: CN111915893A; CN110782656B; CN110782656A

Abstract

The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for identifying a road bottleneck point, an electronic device, and a storage medium, where the method includes: acquiring running track information of each vehicle passing through a target road and position information of each road node in the target road; and identifying a node which becomes a road traffic bottleneck of the target road from the road nodes of the target road based on the acquired running track information and the position information of each road node in the target road. According to the method and the device, the node of the road traffic bottleneck can be automatically identified from the road node of the target road by utilizing the driving track information of the vehicle, and the problems that the labor cost is high and the efficiency is low when the bottleneck point identification is carried out by the existing manual observation are solved.

Description

Road bottleneck point identification method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for identifying a road bottleneck point, an electronic device, and a storage medium.

Background

The present application is a divisional application of the patent application with application number 201910300811.6.

With the continuous and rapid increase of urban vehicles, the urban traffic jam situation is continuously aggravated, and the traffic jam becomes a focus of common attention of all countries in the world and a problem to be solved urgently. Due to the occurrence of events such as hardware facility damage, severe weather, traffic accidents and the like, certain roads are blocked, and further, due to the mutual association between the roads and intersections, other roads or intersections are failed to form a linkage effect, and finally, the traffic network is locally or completely crashed, which may cause the roads and intersections which are crowded and spread or spread, and are collectively called traffic bottlenecks. Therefore, the identification of the traffic bottleneck point is of great significance for improving the road network design and adopting a reasonable traffic control method to relieve traffic jam.

The method can determine the traffic bottleneck points according to the related distribution condition of the congested roads in the jurisdiction range of the traffic police, which is known by the traffic police. However, manual observation requires a large amount of labor cost, and the recognition efficiency is low.

Disclosure of Invention

In view of this, an object of the embodiments of the present application is to provide a method and an apparatus for identifying a road bottleneck point, an electronic device, and a storage medium, which can automatically identify a road bottleneck point, save time and labor, and have high accuracy of identification.

Mainly comprises the following aspects:

in a first aspect, an embodiment of the present application provides a method for identifying a road bottleneck point, where the method includes:

acquiring running track information of each vehicle passing through a target road and position information of each road node in the target road;

and identifying a node which becomes a road traffic bottleneck of the target road from the road nodes of the target road based on the acquired running track information and the position information of each road node in the target road.

In one embodiment, the travel track information includes position information of travel track points; the identifying, based on the acquired travel track information and the position information of each road node in the target road, a node that becomes a road traffic bottleneck of the target road from among the road nodes of the target road includes:

for any road node, determining the number of target vehicles with start-stop running track points matched with the road node according to the position information of the road node and the position information of each running track point; the start-stop running track points comprise running track points for starting the vehicle and/or running track points for stopping the vehicle;

and if the number of the target vehicles is larger than a preset number threshold, determining that any road node is a node of a road traffic bottleneck of the target road.

In another embodiment, before the identifying a node which becomes a bottleneck of road traffic of the target road from the road nodes of the target road, the method further includes:

dividing the target road according to the preset length to obtain a plurality of sub-target roads;

determining the average speed and the number of low-speed track points of the vehicle corresponding to each sub-target road; the number of the low-speed track points is used for representing the number of the running track points with speed values smaller than a first speed threshold value;

determining speed change information on the target road and determining first position range information corresponding to the speed change information based on the average speed of the vehicle corresponding to each sub-target road and the position range information corresponding to each sub-target road; determining track point density peak value information on the target road and determining second position range information corresponding to the track point density peak value information based on the number of low-speed track points corresponding to each sub-target road and the position range information corresponding to each sub-target road;

the identifying, from the road nodes of the target road, a node which becomes a road traffic bottleneck of the target road includes:

and for any road node, if the position information of the road node is matched with the determined first position range information and the determined second position range information, determining that the road node is a node of the road traffic bottleneck of the target road.

In some embodiments, the determining the average speed of the vehicle corresponding to each sub-target road includes:

for each sub-target road, determining the vehicle running speed of any vehicle corresponding to the sub-target road based on the starting position information and the ending position information of the sub-target road and the time length information occupied by any vehicle from the starting position information to the ending position information;

and determining the average speed of the vehicles corresponding to the sub-target roads based on the driving speeds of all the vehicles corresponding to the sub-target roads.

In some embodiments, the determining speed variation information on the target road based on the average speed of the vehicle corresponding to each sub-target road and the position range information corresponding to each sub-target road includes:

fitting a speed change determination function based on the vehicle average speed corresponding to each sub-target road and the position range information corresponding to each sub-target road;

and carrying out derivation operation on the speed change determining function to obtain speed change information on the target road.

In yet another embodiment, the fitting the speed change determination function based on the average speed of the vehicle corresponding to each sub-target road and the position range information corresponding to each sub-target road includes:

determining independent variables and dependent variables of a speed change determination function based on the position range information corresponding to each sub-target road and the average speed of the vehicle;

fitting the speed variation determination function based on the determined respective variables and the respective dependent variables.

In a further embodiment, the travel track information includes speed information of travel track points; the determining the number of the low-speed track points corresponding to each sub-target road comprises the following steps:

for each sub-target road, determining the speed information of the running track points matched with the sub-target road according to the position information of each running track point and the position range information of the sub-target road;

determining the number of the travel track points with the speed value smaller than a first speed threshold value on each sub-target road based on the determined speed information of the travel track points matched with the sub-target road;

and taking the determined number of the running track points as the number of the low-speed track points corresponding to each sub-target road.

In another embodiment, the determining, based on the number of low-speed track points corresponding to each sub-target road and the position range information corresponding to each sub-target road, track point density peak information on the target road includes:

constructing a kernel density analysis function based on a preset kernel function, the number of low-speed track points corresponding to each sub-target road and the position range information corresponding to each sub-target road;

determining a maximum value of the kernel density analysis function;

and taking the determined maximum value of the kernel density analysis function as the track point density peak value information on the target road.

In another embodiment, the trace point density peak information is multiple; after determining the trace point density peak information on the target road and before determining the second position range information corresponding to the trace point density peak information, the method further includes:

determining the position range information of the sub-target road corresponding to the track point density peak value information aiming at each track point density peak value information; determining whether other trace point density peak value information larger than the trace point density peak value information exists or not according to the determined position range information and a preset distance range threshold value;

if the target track point density peak value does not exist, the track point density peak value information is used as target track point density peak value information;

the determining of the second position range information corresponding to the trace point density peak information includes:

and determining second position range information corresponding to the target track point density peak value information.

In a further embodiment, the travel track information includes speed information of travel track points; after the acquiring of the traveling track information of each vehicle traveling on the target road, before the identifying of the node which becomes the road traffic bottleneck of the target road from the road nodes of the target road, the method further includes:

determining whether the speed information of any driving track point of any vehicle is smaller than a second speed threshold value or not, and whether the speed information of a preset number of driving track points before the driving track point is larger than the second speed threshold value or not;

if the speed information of the travel track point is smaller than a second speed threshold value and the speed information of a preset number of travel track points before the travel track point is larger than the second speed threshold value, deleting other travel track points behind the travel track point from all the travel track points;

and taking the travel track information corresponding to the deleted travel track point as the updated travel track information of any vehicle.

In yet another embodiment, the method further comprises:

determining the influence degree of the node of the road traffic bottleneck of the target road on the traffic condition based on at least one of the following influence factors:

speed drop information, low-speed vehicle occupancy, and vehicle speed passing the node.

In some embodiments, determining the degree of influence of the node of the road traffic bottleneck of the target road on the traffic condition based on at least one influence factor comprises:

and determining the influence degree of the node of the road traffic bottleneck of the target road on the traffic condition based on at least one influence factor and the weight coefficient of each influence factor.

In a second aspect, an embodiment of the present application further provides a device for identifying a road bottleneck point, where the device includes:

the information acquisition module is used for acquiring the running track information of each vehicle passing through a target road and the position information of each road node in the target road;

and the bottleneck point identification module is used for identifying a node which becomes a road traffic bottleneck of the target road from the road nodes of the target road based on the acquired running track information and the position information of each road node in the target road.

In some embodiments, the travel track information includes position information of travel track points; the bottleneck point identification module is specifically configured to:

In some embodiments, the apparatus further comprises:

the position range determining module is used for dividing the target road according to the preset length to obtain a plurality of sub-target roads;

the bottleneck point identification module is specifically configured to:

In some embodiments, the position range determination module is specifically configured to:

In an embodiment, the position range determining module is specifically configured to:

In another embodiment, the travel track information includes position information and speed information of travel track points; the position range determination module is specifically configured to:

In another embodiment, the position range determining module is specifically configured to:

determining a maximum value of the kernel density analysis function;

In another embodiment, the trace point density peak information is multiple; the position range determination module is specifically configured to:

after determining the track point density peak value information on the target road and before determining the second position range information corresponding to the track point density peak value information, determining the position range information of the sub-target road corresponding to the track point density peak value information aiming at each track point density peak value information; determining whether other trace point density peak value information larger than the trace point density peak value information exists or not according to the determined position range information and a preset distance range threshold value;

In a further embodiment, the travel track information includes speed information of travel track points; the device further comprises:

the track updating module is used for determining whether the speed information of any driving track point of any vehicle is smaller than a second speed threshold value or not and whether the speed information of a preset number of driving track points before the driving track point is larger than the second speed threshold value or not;

In yet another embodiment, the apparatus further comprises:

the influence determination module is used for determining the influence degree of the node of the road traffic bottleneck of the target road on the traffic condition based on at least one of the following influence factors:

In some embodiments, the influence determining module is specifically configured to:

In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, when an electronic device runs, the processor and the storage medium communicate through the bus, and the processor executes the machine-readable instructions to execute the steps of the method for identifying a bottleneck point of a road according to the first aspect.

In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method for identifying a bottleneck point of a road according to the first aspect.

By adopting the scheme, the driving track information of each vehicle passing through the target road and the position information of each road node in the target road can be obtained firstly, and then the node which becomes the road traffic bottleneck of the target road is identified from the road nodes of the target road based on the obtained driving track information and the position information of each road node in the target road. That is, the embodiment of the application can automatically identify the nodes of the road traffic bottleneck from the road nodes of the target road by using the driving track information of the vehicle, does not need to consume the labor cost, and has higher identification efficiency.

In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a flowchart illustrating a method for identifying a road bottleneck point according to an embodiment of the present application;

fig. 2 shows a flowchart of a road bottleneck point identification method provided in the second embodiment of the present application;

fig. 3 shows a flowchart of a road bottleneck point identification method provided in the third embodiment of the present application;

fig. 4 shows a flowchart of a road bottleneck point identification method provided in the third embodiment of the present application;

fig. 5 is a diagram illustrating an application example of a road bottleneck point identification method provided in the third embodiment of the present application;

fig. 6 shows a flowchart of a road bottleneck point identification method provided in the fourth embodiment of the present application;

fig. 7 is a schematic structural diagram illustrating a road bottleneck point identification device provided in the fifth embodiment of the present application;

fig. 8 shows a schematic structural diagram of an electronic device according to a sixth embodiment of the present application.

Detailed Description

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, 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, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.

In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

In consideration of the fact that a large amount of labor cost is consumed in a method for determining a traffic bottleneck point based on a manual observation mode in the related art, and quantitative data cannot be given to the specific position of the bottleneck point. Based on this, an embodiment of the present application provides a method for identifying a road bottleneck point, so as to be able to automatically identify the road bottleneck point, save time and labor, and the accuracy of identification is higher. This is described in more detail below by way of several examples.

Example one

As shown in fig. 1, which is a flowchart of a method for identifying a road bottleneck point according to an embodiment of the present application, the method for identifying a road bottleneck point may be applied to an electronic device, and the method specifically includes the following steps:

s101, acquiring the running track information of each vehicle passing through a target road and the position information of each road node in the target road.

Here, the travel track information may be determined based on information recorded by a vehicle traveling apparatus (e.g., a drive recorder) of the vehicle, may be acquired from an existing network reservation service platform, or may be determined by another means capable of grasping the travel track of the vehicle traveling on the target road. In consideration of the wide application of the network car booking service platform, the running track information acquired by the network car booking service platform is richer and more comprehensive, and therefore, in the embodiment of the application, the running track information can be directly acquired from the network car booking service platform. In order to better clarify the information of the driving track, the working process of the network appointment service platform is briefly described below.

When a passenger needs to take a car, the corresponding car taking information (such as trip starting point information, trip end point information and the like) can be input at a passenger client, after the car taking information is determined, the server of the network car appointment service platform can generate a corresponding trip order according to the car taking information, the trip order can be distributed to a driver client corresponding to the driver, and the driver can carry out network car appointment service through the driver client. Here, the travel order corresponding to each vehicle can be clarified by the vehicle identification information determined in the travel order. The travel order can determine travel track information (such as travel starting point information and travel ending point information) of the starting track point and the ending track point, and can also record the travel track information of each travel track point in the travel process, such as time information, position information, speed information and the like of each track point of the travel path.

It should be noted that the position information in the travel track information may be determined by using a positioning technique. For the position information, various Positioning devices may be used to obtain the position information, and the Positioning technology used in the present application may be based on Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), COMPASS Navigation System (COMPASS), galileo Positioning System, Quasi-Zenith Satellite System (QZSS), Wireless Fidelity (WiFi), and the like, or any combination thereof. One or more of the above-described positioning systems may be used interchangeably in this application.

Considering that the travel track information of the start track point and the end track point mainly refers to a travel start point position corresponding to the start point and a travel end point position corresponding to the end point, the position information may be determined by the positioning technology. For example, the online car appointment service platform can automatically locate the current position of the user as a travel starting point position. Alternatively, the user may select a specific travel starting point position on the map, or manually input the travel starting point position, for example, manually input the travel starting point position of "capital airport", and the travel ending point position is mainly determined by using a mode selected or manually input by the user on the map, which is not described herein again.

In addition, the information on the speed in the travel track information may be determined by using a sensor technique. For the speed information, the embodiment of the present application may be determined by using a speed sensor disposed on the running vehicle or other sensors capable of measuring the speed of the running vehicle, and details thereof are not repeated here.

In the embodiment of the application, the travel track information may be historical travel track information or real-time travel track information so as to meet the requirements of different application scenarios. In this way, when it is necessary to identify a node of a road traffic bottleneck of the target road (i.e., a road bottleneck point), the travel track information of the vehicle traveling on the target road may be identified from the travel track information of each vehicle, and the travel track information of the vehicle may be all travel track information related to the travel order, part of the travel track information corresponding to the target road extracted from all the travel track information, or the travel track information corresponding to the target road identified based on the time information, such as the travel track information of the vehicle traveling on the target road during the peak time period.

In the embodiment of the present application, the actual geographic position of the target road may be known, such as determining the actual geographic position information of the target road from the map data. In addition, a plurality of road nodes may be provided on the target road, and each road node may have unique node identification information (node id) corresponding thereto in the map data, so that the position information of each road node may also be determined based on the map data.

According to the embodiment of the application, when the target road is selected, the target road can be directly selected according to the requirements of users, and the target road can also be automatically selected. For automatic selection, it is mainly considered that the more complex the traffic condition corresponding to one road is, the higher the influence on road traffic is likely to be, so that the embodiment of the application can automatically select the target road by comprehensively considering the information of road complexity, historical congestion condition and the like.

S102, identifying a node which becomes a road traffic bottleneck of the target road from the road nodes of the target road based on the acquired running track information and the position information of each road node in the target road.

Here, in order to identify a node that becomes a bottleneck in road passage of the target road from among the road nodes of the target road, it is necessary to rely on the acquired travel track information in addition to the position information of each road node in the target road. The travel track information may include only the position information of the travel track point, and may include both the position information of the travel track point and the speed information of the travel track point. The two methods for determining the bottleneck point of the road are specifically described in the following embodiments two and three, respectively.

Example two

As shown in fig. 2, a flowchart of a method for determining a bottleneck point of a road based on position information of a driving track point provided in an embodiment of the present application is specifically provided, where the method specifically includes the following steps:

s201, aiming at any road node, determining the number of target vehicles with start-stop running track points matched with the road node according to the position information of the road node and the position information of each running track point;

s202, if the number of the target vehicles is larger than a preset number threshold, determining that any road node is a node of a road traffic bottleneck of the target road.

Here, the determination of the road bottleneck point mainly depends on the matching result of the start-stop travel track point and the road node of each vehicle, that is, in the embodiment of the present application, for any road node, when it is determined that the number of target vehicles having the start-stop travel track point matched with the road node is greater than a preset number threshold (for example, 1000 vehicles), the road node may be basically determined as the node of the road bottleneck of the target road.

The starting and stopping travel track points comprise travel track points of vehicle starting and/or travel track points of vehicle stopping, and in specific application, the travel track points of vehicle starting can be trip starting points, and the travel track points of vehicle stopping can be trip end points.

It should be noted that, when determining the number of target vehicles based on the position information of the driving track points, the driving track points of the vehicles may be first screened to determine the real start-stop driving track points. For example, for a plurality of sequentially arranged travel track points related to the vehicle, the embodiment of the present application may delete, based on the position information of each travel track point, the relevant travel track point within a certain distance (e.g., 45 meters) from the starting point of the target road, and determine the start-stop travel track point from the deleted travel track points, which mainly considers that in practical applications, the vehicle may have a buffer area during traveling, so as to avoid an influence of the factor on the determination of the bottleneck point of the road.

EXAMPLE III

As shown in fig. 3, a flowchart of a method for determining a bottleneck point of a road provided in the embodiment of the present application is provided, where the method specifically includes the following steps:

s301, dividing the target road according to the preset length to obtain a plurality of sub-target roads;

s302, aiming at each sub-target road, determining the average speed of the vehicle and the number of low-speed track points corresponding to the sub-target road;

s303, determining speed change information on the target road and determining first position range information corresponding to the speed change information based on the average speed of the vehicle corresponding to each sub-target road and the position range information corresponding to each sub-target road; determining track point density peak value information on the target road and determining second position range information corresponding to the track point density peak value information based on the number of low-speed track points corresponding to each sub-target road and the position range information corresponding to each sub-target road;

and S304, aiming at any road node, if the position information of the road node is matched with the determined first position range information and the determined second position range information, determining that the road node is a node of the road traffic bottleneck of the target road.

Here, in the embodiment of the present application, for any road node, on one hand, it may be determined whether the road node is a node of a bottleneck of road traffic of the target road by using a first matching result between the position information of the road node and the first position range information corresponding to the speed change information, and on the other hand, it may also be determined whether the road node is a node of a bottleneck of road traffic of the target road by using a second matching result between the position information of the road node and the second position range information corresponding to the track point density peak information. It is worth noting that in the embodiment of the application, whether a road node is a road bottleneck point or not can be determined by directly adopting the first matching result, whether the road node is the road bottleneck point or not can be determined by directly adopting the second matching result, and whether the road node is the road bottleneck point or not can be determined by combining the first matching result and the second matching result. In order to further improve the accuracy of identifying the bottleneck point of the road, the embodiment of the application can be determined by selecting a combination mode. Next, the determination of whether the road node is a bottleneck point will be described in detail.

In a first aspect: for determining whether a road node is a road bottleneck point by using a first matching result, in the embodiment of the application, the target road may be divided according to a preset length, then speed change information on the target road is determined based on the vehicle average speed of each sub-target road obtained through division and the position range information corresponding to each sub-target road, first position range information corresponding to the speed change information is determined, and finally a matching result between the position information of the road node and the first position range information is determined as a first matching result.

Here, considering that traffic conditions near an intersection on a target road are relatively complex, a parking queuing phenomenon often occurs, and therefore the difficulty of judging a bottleneck point of a road in an intersection area is increased, that is, when the queue at a downstream intersection of the target road is too long, a large number of low-speed track points may be located at the tail of the target road, which may result in judging that the bottleneck point occurs at the tail of the road, and this is actually caused by the queue generated by the influence of an intersection signal lamp. In order to avoid the misjudgment caused by the above influences, the embodiment of the present application considers the influence of speed change information on the determination of the bottleneck point of the road, where the speed change information is related to the average speed of vehicles on each sub-target road and the corresponding position range information of each sub-target road.

In this embodiment, for each sub-target road, the vehicle running speed of the vehicle corresponding to the sub-target road may be determined based on the start position information and the end position information of the sub-target road and the time length information occupied by any vehicle running from the start position information to the end position information, and then the vehicle average speed corresponding to the sub-target road may be determined by obtaining the average value of the vehicle running speeds corresponding to the sub-target road based on the vehicle running speeds corresponding to all vehicles running on the sub-target road.

Here, the speed change determination function may be fitted based on the vehicle average speed of each sub-target road and the position range information corresponding to each sub-target road, and the minimum point of the speed change determination function, that is, the corresponding speed change information may be obtained by performing a derivation operation on the speed change determination function. In this way, based on the matching result between the first position range information corresponding to the obtained speed change information and the position information of any road node, it can be determined whether the any road node is a road bottleneck point of the target road. That is, if the location information of any road node falls into the first location range information corresponding to the speed change information, it may be determined that the road node is a road bottleneck point. The first position range information corresponding to the speed change information may be position range information of sub-target roads corresponding to each minimum point.

For the fitting of the speed change determination function, the embodiment of the present application may first determine independent variables and dependent variables of the speed change determination function based on the corresponding location range information of each sub-target road and the average speed of the vehicle, and then fit the speed change determination function based on the determined respective variables and dependent variables. In specific application, the embodiment of the present application may perform polynomial fitting by using a least square method to obtain a speed change determination function, and may also perform function fitting by using other methods, which is not described herein again.

It should be noted that the preset length can be determined according to different application requirements. For the same target road, the smaller the preset length is, the more the number of the obtained independent variable and dependent variable combinations is, and thus the better the fitting effect of the speed change determination function is, however, the smaller the preset length is, the larger the calculation amount is. In order to take the fitting effect and the calculated amount into consideration, the preset length in the embodiment of the present application may be selected to be 5 meters.

In a second aspect: for determining whether a road node is a road bottleneck point by using a second matching result, the embodiment of the application may determine track point density peak information on the target road based on the number of low-speed track points of each sub-target road obtained by dividing and the position range information corresponding to each sub-target road, determine second position range information corresponding to the track point density peak information, and finally determine the matching result between the position information of the road node and the second position range information as the second matching result.

The number of low-speed track points can be counted in each sub-target road, and the number of the low-speed track points can be the number of the driving track points with the speed value smaller than the first speed threshold value. As shown in fig. 4, an embodiment of the present application provides a method for determining the number of low-speed trace points, where the method specifically includes the following steps:

s401, aiming at each sub-target road, determining the speed information of the running track points matched with the sub-target road according to the position information of each running track point and the position range information of the sub-target road;

s402, determining the number of the travel track points with the speed value smaller than a first speed threshold value on each sub-target road based on the determined speed information of the travel track points matched with the sub-target road;

and S403, taking the determined number of the running track points as the number of the low-speed track points corresponding to each sub-target road.

Here, for each sub-landmark road, the travel track point matched with the sub-landmark road may refer to a travel track point whose position information falls within the position range information of the sub-landmark road. Based on the speed information of the matched running track points, the number of the low-speed track points on each sub-target road can be determined, that is, the number of the running track points (namely, the low-speed track points) with the speed values smaller than the first speed threshold value can be determined as the number of the low-speed track points.

In order to more accurately and completely grasp the influence of the low-speed track points on the determination of the bottleneck points of the road, the embodiment of the application can determine the distribution condition of the data set corresponding to the number of all the low-speed track points, namely the track point density peak value information, after determining the number of the low-speed estimation points of each sub-standard road, and can further determine which road nodes are the bottleneck points of the road by utilizing the position range information corresponding to the track point density peak value information.

The data set can be maximally approximated by using a kernel density estimation method, wherein the kernel density estimation can be performed by fitting observed data points (namely, the number of low-speed trace points) by using a smooth peak function, so as to simulate a real probability distribution curve. Based on this, in the embodiment of the application, a kernel density analysis function may be first constructed based on a preset kernel function, the number of low-speed track points corresponding to each sub-target road, and the position range information corresponding to each sub-target road, and then the maximum value of the kernel density analysis function is determined, and the determined maximum value of the kernel density analysis function may be used as the track point density peak information on the target road. The preset kernel function may be a gaussian kernel function, a triangular kernel function, or other kernel functions, and the gaussian kernel function may be selected in the embodiment of the present application in consideration of the usability of the gaussian kernel function in waveform synthesis calculation.

In addition, in order to improve the accuracy of determining the bottleneck point of the road by using the trace point density peak value information, the embodiment of the application can also screen the trace point density peak value information so as to avoid peak value interference caused by other trace point density peak value information. In the embodiment of the application, for each piece of track point density peak value information, the position range information of the sub-target road corresponding to the track point density peak value information is firstly determined, whether other track point density peak value information larger than the track point density peak value information exists is determined according to the determined position range information and a preset distance range threshold value, and when it is determined that other track point density peak value information does not exist, the track point density peak value information can be used as target track point density peak value information, and second position range information corresponding to the target track point density peak value information is determined. The second position range information corresponding to the target trace point density peak information may be position range information corresponding to one sub-waveform intercepted from the kernel density analysis function based on the target trace point density peak.

In order to further understand the influence of the speed variation information and the trace point density peak value information on the determination of the bottleneck point of the road, a specific example is described below.

As shown in fig. 5, the upper graph corresponds to the fitted speed change determination function, the horizontal axis corresponds to each position information of the target road, and the vertical axis corresponds to the vehicle average speed; the lower graph corresponds to a kernel density analysis curve drawn based on the statistical result of the number of low-speed trace points, the horizontal axis also corresponds to each position information of the target road, and the vertical axis corresponds to the kernel density value. In addition to this, the present invention is,

for representing the road nodes on the target road.

It can be seen from the upper graph of fig. 5 that A, B, C, D corresponds to four speed variation information, and from the lower graph of fig. 5, the regions [190m-210m ] and [614m-634m ] formed by two line frames correspond to the peak information of the density of the target track point, and from the upper graph and the lower graph, the road node corresponding to A, D can be determined as the node of the road traffic bottleneck of the target road.

In order to further avoid the road traffic bottleneck caused by queuing caused by the influence of intersection signal lamps, the method and the device can screen the running track information of each vehicle after acquiring the speed information of the running track points. This is illustrated in detail by the following example four.

Example four

As shown in fig. 6, a flowchart of a method for screening driving trace information according to a fourth embodiment of the present application is provided, where the method specifically includes the following steps:

s601, aiming at any driving track point of any vehicle, determining whether the speed information of the driving track point is smaller than a second speed threshold value or not, and whether the speed information of a preset number of driving track points before the driving track point is larger than the second speed threshold value or not;

s602, if it is determined that the speed information of the travel track point is smaller than a second speed threshold value and the speed information of a preset number of travel track points before the travel track point is larger than the second speed threshold value, deleting other travel track points behind the travel track point from all the travel track points;

and S603, taking the travel track information corresponding to the deleted travel track point as the updated travel track information of any vehicle.

Here, for any travel track point of any vehicle, it may be determined whether speed information of the travel track point is less than a second speed threshold (e.g., 5km/h) or not, and whether speed information of a preset number (e.g., 3) of travel track points before the travel track point is greater than the second speed threshold or not, if so, other travel track points after the travel track point are deleted from all the travel track points, that is, the travel track points that may be queued due to influence of an intersection signal lamp may be deleted in the embodiment of the present application, so as to reduce influence of the intersection signal lamp on determination of the road bottleneck point to the greatest extent, so that accuracy of the identified road bottleneck point is higher.

The method for identifying the road bottleneck point provided by the embodiment of the application can also determine the influence degree of the road bottleneck point of the target road on the traffic condition based on the following influence factors:

The speed drop information is used for representing the speed drop condition at the bottleneck point of the road, and can be determined by the following formula (1):

here, v2 is used to indicate speed information of N vehicles passing through a node of a road traffic bottleneck, v1 is used to indicate speed information of N vehicles passing through a previous road node corresponding to the node of the road traffic bottleneck, and v3 is used to indicate speed information of N vehicles passing through a next road node corresponding to the node of the road traffic bottleneck, where the speed information may be an average speed corresponding to the N vehicles.

In addition, the above-mentioned low-speed vehicle proportion is used to represent the proportion of low-speed vehicles passing through the bottleneck point of the road to all vehicles, and can be determined by the following formula (2):

here, nv is used to indicate the number of low-speed vehicles whose speed at the node passing the bottleneck of road passage is less than 10km/h, and N indicates the number of all vehicles passing the node of the bottleneck of road passage.

Alternatively, the speed of the vehicle passing the node may be determined directly from v 2.

In this way, the influence degree of the node of the road bottleneck point of the target road on the traffic condition can be determined based on the at least one influence factor and the weighted sum of the weight coefficients corresponding to each influence factor, that is, the influence of different influence factors on different road bottleneck points is different, so that the evaluation requirements of various traffic conditions can be adapted.

Based on the above embodiments, the present application also provides a device for identifying a bottleneck point of a road, and the implementation of the following various devices can refer to the implementation of the method, and repeated details are not repeated.

EXAMPLE five

As shown in fig. 7, a road bottleneck point identification apparatus provided in the fifth embodiment of the present application includes:

the information acquisition module 701 is used for acquiring the running track information of each vehicle passing through a target road and the position information of each road node in the target road;

a bottleneck point identifying module 702, configured to identify, based on the obtained driving track information and the location information of each road node in the target road, a node that becomes a road traffic bottleneck of the target road from the road nodes of the target road.

In some embodiments, the travel track information includes position information of travel track points; the bottleneck point identifying module 702 is specifically configured to:

In some embodiments, the apparatus further comprises:

a position range determining module 703, configured to divide the target road according to a preset length to obtain multiple sub-target roads;

the bottleneck point identifying module 702 is specifically configured to:

In some embodiments, the position range determining module 703 is specifically configured to:

In an embodiment, the position range determining module 703 is specifically configured to:

In another embodiment, the travel track information includes position information and speed information of travel track points; the position range determining module 703 is specifically configured to:

In another embodiment, the position range determining module 703 is specifically configured to:

determining a maximum value of the kernel density analysis function;

In another embodiment, the trace point density peak information is multiple; the position range determining module 703 is specifically configured to:

the track updating module 704 is configured to determine, for any travel track point of any vehicle, whether speed information of the travel track point is smaller than a second speed threshold, and whether speed information of a preset number of travel track points before the travel track point is larger than the second speed threshold;

In yet another embodiment, the apparatus further comprises:

an influence determining module 705, configured to determine a degree of influence of a node of a road traffic bottleneck of the target road on the traffic condition based on at least one of the following influence factors:

In some embodiments, the influence determining module 705 is specifically configured to:

EXAMPLE six

As shown in fig. 8, a schematic structural diagram of an electronic device according to a sixth embodiment of the present application includes: a processor 801, a storage medium 802 and a bus 803, wherein the storage medium 802 stores machine-readable instructions executable by the processor 801, when the electronic device is operated, the processor 801 communicates with the storage medium 802 through the bus 803, and the processor 801 executes the machine-readable instructions to execute the following execution instructions stored in the storage medium 802:

In one embodiment, the travel track information includes position information of travel track points; the processor 801 executes a process of identifying a node that becomes a bottleneck of road passage of the target road from the road nodes of the target road based on the acquired travel track information and the position information of each road node in the target road, including:

In another embodiment, before the identifying a node which becomes a bottleneck of road traffic of the target road from the road nodes of the target road, the processing performed by the processor 801 further includes:

the processor 801 executes a process of identifying a node that becomes a bottleneck in road traffic of the target road from the road nodes of the target road, including:

In some embodiments, the processor 801 performs the process of determining the average speed of the vehicle corresponding to each sub-target road, which includes:

In some embodiments, in the processing performed by the processor 801, the determining speed variation information on the target road based on the average speed of the vehicle corresponding to each sub-target road and the position range information corresponding to each sub-target road includes:

In yet another embodiment, the above-mentioned processor 801 executes a process in which the fitting of the speed change determination function based on the average speed of the vehicle corresponding to each sub-target road and the position range information corresponding to each sub-target road includes:

In still another embodiment, in the processing executed by the processor 801, the travel track information includes speed information of travel track points; the determining the number of the low-speed track points corresponding to each sub-target road comprises the following steps:

In yet another embodiment, in the processing executed by the processor 801, the determining the track point density peak information on the target road based on the number of low-speed track points corresponding to each sub-target road and the position range information corresponding to each sub-target road includes:

determining a maximum value of the kernel density analysis function;

In another embodiment, the trace point density peak information is multiple; after determining the trace point density peak information on the target road and before determining the second position range information corresponding to the trace point density peak information, the processor 801 further performs the following processing:

in the processing performed by the processor 801, the determining the second position range information corresponding to the trace point density peak information includes:

In a further embodiment, the travel track information includes speed information of travel track points; after the acquiring of the driving trace information of each vehicle traveling on the target road, and before the identifying of the node that becomes the bottleneck of road traffic of the target road from the road nodes of the target road, the processing executed by the processor 801 further includes:

In yet another embodiment, the processing performed by the processor 801 further includes:

In some embodiments, the above processing performed by the processor 801, wherein determining the degree of influence of the node of the road traffic bottleneck of the target road on the traffic condition based on at least one influence factor, includes:

Example eight

An eighth embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for identifying a bottleneck point of a road corresponding to the foregoing embodiment are executed.

Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, and the like, and when a computer program on the storage medium is executed, the method for identifying the road bottleneck point can be executed, so that the problems that the labor cost is high and the identification efficiency is low in the existing manual observation method are solved, and the effects that the labor cost is not required to be consumed and the identification efficiency is high are achieved.

Based on the same technical concept, embodiments of the present application further provide a computer program product, which includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the steps of the method for identifying a bottleneck point of a road.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to corresponding processes in the method embodiments, and are not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for identifying a bottleneck point on a road, the method comprising:

identifying a node which becomes a road traffic bottleneck of the target road from the road nodes of the target road based on the acquired running track information and the position information of each road node in the target road;

before the identifying a node which becomes a road traffic bottleneck of the target road from the road nodes of the target road, the method further comprises:

determining the average speed of the vehicle corresponding to each sub-target road;

determining speed change information on the target road and determining first position range information corresponding to the speed change information based on the average speed of the vehicle corresponding to each sub-target road and the position range information corresponding to each sub-target road; the first position range information corresponding to the speed change information is position range information of sub-target roads corresponding to each minimum value point, each minimum value point is obtained by calculation according to a speed change function, and the speed change function is determined based on the vehicle average speed corresponding to each sub-target road and the position range information corresponding to each sub-target road;

and for any road node, if the position information of the road node is matched with the determined first position range information, determining that the road node is the node of the road traffic bottleneck of the target road.

2. The method according to claim 1, characterized in that the travel track information includes position information of travel track points; the identifying, based on the acquired travel track information and the position information of each road node in the target road, a node that becomes a road traffic bottleneck of the target road from among the road nodes of the target road includes:

3. The method of claim 1, wherein determining the average speed of the vehicle for each sub-target link comprises:

4. The method of claim 1, wherein the determining the speed variation information on the target road based on the average speed of the vehicle corresponding to each sub-target road and the position range information corresponding to each sub-target road comprises:

5. The method according to claim 1, wherein before the identifying a node that becomes a bottleneck of road traffic of the target road from the road nodes of the target road, further comprising:

aiming at each sub-target road, determining the number of low-speed track points corresponding to the sub-target road; the number of the low-speed track points is used for representing the number of the running track points with speed values smaller than a first speed threshold value;

determining track point density peak value information on the target road and determining second position range information corresponding to the track point density peak value information based on the number of low-speed track points corresponding to each sub-target road and the position range information corresponding to each sub-target road;

and for any road node, if the position information of the road node is matched with the determined second position range information, determining that the road node is the node of the road traffic bottleneck of the target road.

6. The method according to claim 5, wherein the travel track information includes position information and speed information of travel track points; the determining the number of the low-speed track points corresponding to each sub-target road comprises the following steps:

7. The method according to claim 5, wherein the determining of the track point density peak information on the target road based on the number of low-speed track points corresponding to each sub-target road and the position range information corresponding to each sub-target road comprises:

determining a maximum value of the kernel density analysis function;

8. The method according to claim 7, characterized in that the trace point density peak information is a plurality of; after determining the trace point density peak information on the target road and before determining the second position range information corresponding to the trace point density peak information, the method further includes:

9. The method according to claim 1, characterized in that the travel track information includes speed information of travel track points; after the acquiring of the traveling track information of each vehicle traveling on the target road, before the identifying of the node which becomes the road traffic bottleneck of the target road from the road nodes of the target road, the method further includes:

10. The method of claim 1, further comprising:

11. The method of claim 5, further comprising:

and for any road node, if the position information of the road node is matched with the third position range information, determining that the road node is the node of the road traffic bottleneck of the target road.

12. The method of claim 11, wherein the third location range information is location range information corresponding to an intersection between the first location range information and the second location range information.

13. A road bottleneck point identification device, the device comprising:

a bottleneck point identification module, configured to identify a node that becomes a road traffic bottleneck of the target road from the road nodes of the target road based on the acquired travel track information and the position information of each road node in the target road;

the device, still include:

the position range determining module is used for dividing the target road according to the preset length to obtain a plurality of sub-target roads; determining the average speed of the vehicle corresponding to each sub-target road; determining speed change information on the target road and determining first position range information corresponding to the speed change information based on the average speed of the vehicle corresponding to each sub-target road and the position range information corresponding to each sub-target road; the first position range information corresponding to the speed change information is position range information of sub-target roads corresponding to each minimum value point, each minimum value point is obtained by calculation according to a speed change function, and the speed change function is determined based on the vehicle average speed corresponding to each sub-target road and the position range information corresponding to each sub-target road;

a bottleneck point identification module comprising:

and the bottleneck point identification unit is used for determining any road node as the node of the road traffic bottleneck of the target road if the position information of the road node is matched with the determined first position range information.

14. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is running, the processor executing the machine-readable instructions to perform the steps of the method according to any one of claims 1 to 12.

15. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for identifying a bottleneck point according to any one of claims 1 to 12.