CN112863176B - Traffic jam tracing method and device, electronic equipment and storage medium - Google Patents
Traffic jam tracing method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a traffic jam tracing method, a traffic jam tracing device, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring road network data of a target road network and GPS data of all vehicles in a preset time period on the target road network; determining a congestion path in a target road network according to the road network data and the GPS data of all vehicles; determining key nodes in the congestion path and node information of the key nodes; obtaining the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key nodes; and determining all congestion sources in the congestion path according to the contribution degree of each key node to congestion. Compared with the prior art, the method and the device have the advantages that the contribution degree of each key node in the congestion path to congestion is analyzed to find out all congestion sources in the congestion path, so that the congestion cause can be accurately and rapidly located.
Description
Technical Field
The present application relates to the field of traffic information processing technologies, and in particular, to a traffic congestion tracing method and apparatus, an electronic device, and a computer-readable storage medium.
Background
With the improvement of the living standard of people, the vehicle retention rate is greatly increased, and traffic jam becomes a very concerned problem for people. And the congestion source is mastered in time, and the method is very important for treating traffic congestion.
The congestion source can provide key information for congestion cause analysis, and plays an important role in monitoring and predicting congestion. The congestion tracing is quick and accurate, so that traffic participants can timely grasp accurate congestion information and provide key reference information for treating traffic congestion; otherwise, a large loss may be caused to the user.
At present, congestion tracing methods include: presetting monitoring nodes in a road network, and identifying the monitoring nodes with changed road conditions as congestion sources based on road condition cloud data and the monitoring nodes; the congestion tracing method further comprises the following steps: and constructing a speed space-time diagram of congestion, positioning a congestion source by methods such as image morphology and the like, and identifying congestion reasons by combining morphological characteristics of a speed gray level image with other data.
In an actual traffic scene, traffic jam is often complex, and a multi-jam source coupling phenomenon is easy to occur. The existing methods take the whole congestion as an analysis object and are not suitable for multi-congestion source coupling tracing.
Disclosure of Invention
The application aims to provide a traffic jam tracing method and device, an electronic device and a computer readable storage medium.
The first aspect of the present application provides a traffic congestion tracing method, including:
acquiring road network data of a target road network and GPS data of all vehicles in a preset time period on the target road network;
determining a congestion path in a target road network according to the road network data and the GPS data of all vehicles;
determining key nodes in the congestion path and node information of the key nodes;
obtaining the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key nodes;
and determining all congestion sources in the congestion path according to the contribution degree of each key node to congestion.
The second aspect of the present application provides a traffic jam tracing apparatus, including:
the acquisition module is used for acquiring road network data of a target road network and GPS data of all vehicles in a preset time period on the target road network;
the route determining module is used for determining a congestion route in a target road network according to the road network data and the GPS data of all vehicles;
the node determining module is used for determining key nodes in the congestion path and node information of the key nodes;
the contribution degree determining module is used for obtaining the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key nodes;
and the congestion source determining module is used for determining all congestion sources in the congestion path according to the contribution degree of each key node to congestion.
A third aspect of the present application provides an electronic device comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the computer program when executing the computer program to implement the method of the first aspect of the present application.
A fourth aspect of the present application provides a computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of the first aspect of the present application.
The traffic jam tracing method, the device, the electronic equipment and the storage medium provided by the application acquire road network data of a target road network and GPS data of all vehicles in a preset time period on the target road network; determining a congestion path in a target road network according to the road network data and the GPS data of all vehicles; determining key nodes in the congestion path and node information of the key nodes; obtaining the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key nodes; and determining all congestion sources in the congestion path according to the contribution degree of each key node to congestion. Compared with the prior art, the method and the device have the advantages that the contribution degree of each key node in the congestion path to congestion is analyzed to find out all congestion sources in the congestion path, so that the congestion cause can be accurately and rapidly located.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a traffic congestion traceability method provided by the present application;
FIG. 2 shows a flowchart of step S102 provided herein;
FIG. 3 shows one of the schematic diagrams of GPS data provided herein;
FIG. 4 shows a second schematic diagram of GPS data provided herein;
FIG. 5 shows a third schematic diagram of GPS data provided herein;
FIG. 6 is a schematic diagram illustrating velocity space-time curves provided herein;
fig. 7 is a schematic diagram illustrating a traffic congestion traceability device provided in some embodiments of the present application;
FIG. 8 illustrates a schematic diagram of an electronic device provided by some embodiments of the present application;
FIG. 9 illustrates a schematic diagram of a computer-readable storage medium provided by some embodiments of the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which this application belongs.
In addition, the terms "first" and "second", etc. are used to distinguish different objects, rather than to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The embodiment of the application provides a traffic jam tracing method and device, an electronic device and a computer readable storage medium, which are described below with reference to the accompanying drawings.
Referring to fig. 1, which shows a flowchart of a traffic congestion traceability method according to some embodiments of the present application, as shown in fig. 1, the traffic congestion traceability method may include the following steps S101 to S105:
step S101: acquiring road network data of a target road network and GPS data of all vehicles in a preset time period on the target road network; GPS (Global Positioning System).
In step S101, the GPS data of the vehicle includes data of the vehicle' S real-time position, speed, and the like.
The road network data comprises road network structure data, shunting points, converging points, signal lamps, toll stations, road narrow mouths in the road network, POI points such as schools in hospitals and the like. POI is an abbreviation for "Point of Interest" that can be translated into "Point of Interest". In the geographic information, one POI may be a house, a shop, a mailbox, a bus station, etc.
Step S102: determining a congestion path in a target road network according to the road network data and the GPS data of all vehicles;
according to some embodiments of the present application, as shown in fig. 2, step S102 may be implemented as:
step S201: determining the driving track of the vehicle according to the GPS data and the road network data of the vehicle, and dividing the driving track of the vehicle into a low-speed segment and a high-speed segment;
a driving track: the series of position points recorded by the vehicle GPS track recorder are the GPS track (i.e., the driving track) of the vehicle, which can be represented by an ordered set of points.
Track segment: and (4) taking continuous subsets phi 'and phi' in the GPS track point set to form a track segment. The travel distance of the track segment is the average speed of the track segment compared with the travel time.
Low speed (high speed)) Fragment (b): congestion speed threshold v of road section where track segment is locatedblockThe average velocity of the trajectory segment is v. If v is<vblockIf the current track segment is a low-speed segment; otherwise, the high-speed segment is obtained. As in FIG. 3, track segment φ 'of vehicle 1'ABCIs a low-speed fragment, phi'CGIs a high-speed segment.
Specifically, step S201 can be implemented as follows:
determining a running track of the vehicle according to the GPS data of the vehicle;
dividing the driving track of the vehicle into a plurality of track segments according to the road network data;
calculating track segment speed according to GPS data in each track segment, wherein the track segment speed refers to the average speed of the vehicle in the track segment;
if the track segment speed is smaller than the congestion speed threshold, determining the corresponding track segment as a low-speed segment; otherwise, the high-speed segment is determined.
Step S202: selecting a road section covered by a low-speed segment from a target road network, searching adjacent road sections upwards and downwards by taking the road section as a starting point, and bringing the adjacent road sections meeting a first preset condition into a congestion path;
the first preset condition is as follows: dunblock<D, wherein DunblockThe length of the adjacent road section without low-speed track coverage is shown, and D is a path disconnection threshold value;
step S203: the method comprises the steps that the vehicle can be disconnected when a first preset condition is not met through searching, and a congestion path r is obtained;
as shown in the figure 3, the traffic flow direction is from A to B, the speed of the same vehicle track point in the figure can be reflected by the size of the track point, the speed is slow when the track point is small, and the speed is fast when the track point is large. For example, the track of the vehicle 1 has low speed of the ABC segment and high speed of the CG segment. And calculating the speeds of all vehicle running tracks, and segmenting into a low-speed segment and a high-speed segment. Searching along a certain direction (A to B direction), and connecting the road sections containing the low-speed segments to obtain the congestion path. The congested path in fig. 3 is ABCDM.
Due to GPS sampling and the periodic movement of vehicles in front of traffic lights, intermittent coverage areas with no data or only high speed segments may occur even in a continuous congested route. By adopting steps S202 and S203, it is possible to avoid dividing the continuous congestion path into several independent paths due to no data or high-speed sections. For the situation that a plurality of independent congestion paths are combined into one congestion path by mistake, a subsequent key node analysis method can be adopted for disconnection, and the method is shown in the following text.
Step S204: the congestion path searching operation is carried out on all unprocessed road sections in the target road network to determine all congestion path sets phi in the target road networkr＝{r1,r2,...rn}。
It will be understood by those skilled in the art that the road segments processed by the upper numbers refer to the road segments in the target road network that have not been processed by S201-S203.
After all the congested paths in the target road network are determined, the process continues to step S103.
Step S103: determining key nodes in the congestion path and node information of the key nodes; the node information of the key node comprises node position information and node attribute information.
In step S103, determining a conventional congestion point, a POI point, a disturbance point, and a route end point of the congestion route in the congestion route as a key node of the congestion route;
and the key node is outside the congestion source key analysis node.
The conventional congestion point refers to nodes such as a diversion point, a confluence point, a signal lamp, a toll station, a road narrow opening and the like in a path, and the nodes have relatively definite attribute information (topological relation, traffic light duration, lane number and the like). The conventional congestion points and POI nodes are nodes which are easy to cause conventional congestion and are easy to become traffic congestion sources, so that the conventional congestion points and POI nodes are important nodes to be analyzed and can be obtained from road network data, and the nodes are marked as a set phinomal。
Besides, irregular disturbance (such as traffic accident) on the road and the like can cause irregular congestion, the nodes also need to be identified and analyzed, and the nodes are classifiedThe node is the disturbance point. The disturbance point is not frequently blocked or the position is not clear, the node is required to be searched first, and the search result is recorded as phiabnomal。
The disturbance point is determined in the following manner:
searching points on the congestion path along a fixed direction, finding out points meeting a second preset condition, and forming an alternative point set;
the second preset condition is as follows:
wherein, Npre、Nblock preThe total number of the upstream track segments and the number of the low-speed segments, Nnxt、Nblock nxtRespectively counting the total number of downstream track segments and the number of low-speed segments; rlAnd selecting a threshold value for the point, wherein the value is greater than 1. The selection of the upstream and downstream distances should meet the condition that the traffic scene and the traffic state are relatively consistent, for example, the upstream optional BC section of the point C in fig. 3 participates in the calculation, and the downstream optional CD section.
Through a second preset condition, an alternative point set phi can be selected from the congestion path preliminarilybSuch as point C and point M in fig. 3.
Matching the points in the alternative point set with the conventional congestion points and the POI points according to the distance, deleting the points of which the distance between the two points is less than a preset distance threshold value from the alternative point set, wherein the rest points in the alternative point set are disturbance points;
the specific matching method is as follows:
set phi of alternative pointsbThe points in (1) are matched with conventional congestion points and POI points. The matching method adopts a distance comparison method, takes out the point closest to the current point from the conventional congestion point and the POI point, calculates the distance d between the two points, and if the distance d is satisfied<dmax(dmaxIs a judgment threshold value), the distance between the two points is considered to be too small, and the matching can be regarded as the GPS positioning deviation or the calculation deviation, and the matching is successful. Will phibThe phi can be obtained by deleting the points successfully matched with the conventional key pointsabnomal。
Step S104: obtaining the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key nodes;
for the analysis and evaluation of the key nodes, more detailed information of congestion can be disclosed, and the effect of the nodes on the congestion can be quantitatively evaluated. As shown in fig. 4, the congestion route is ABC, and analysis and evaluation of the traffic flow near the diversion point B show that the congestion of the AB link does not affect the right-turn lane because of the congestion of the straight lane; in fig. 5, it is difficult for the road before the signal light of the CB segment to merge, and the vehicles need to queue for a certain distance before merging, which indicates that the CB segment may aggravate the congestion of the downstream road.
According to some embodiments of the present application, step S104 may be implemented as:
the method comprises the steps of unmixing GPS data of all vehicles on a congested path to obtain GPS data of all vehicles on different steering lanes, and distinguishing the GPS data of the vehicles which run normally from the GPS data of the vehicles which run abnormally;
specifically, the GPS data is mixed, the steering lanes are mixed, and the normal driving data and the abnormal driving data are mixed. Before analyzing the key nodes, the GPS hybrid data is unmixed to distinguish different steering data and normal driving data and abnormal driving data.
First, data samples of different steering are distinguished, as shown in fig. 4, in the section AB, the vehicles 1, 3, and 4 are straight samples, and the vehicle 2 is a right-turning sample. And for the samples with uncertain steering, calculating the speed space-time curve of the samples, and then performing similarity matching with the known steering samples to distinguish the samples.
Then, the turning sample is further screened for abnormal samples. And calculating the average speed of all track segments, clustering the track segments based on the speed, and distinguishing normal samples from abnormal samples according to the quantity of each classified sample. As shown in fig. 4, the AB road segment straight-ahead samples can be clustered into two classes, wherein the vehicles 1 and 3 are classified as normal samples, and the vehicle 4 is classified as abnormal sample. Moreover, in the scenario of fig. 4, the vehicle 4 is suspected to "queue" and travel in the right-turn lane, and can be used as a reference sample for analyzing the right-turn lane.
And analyzing the turning road condition, namely determining whether congestion exists in each turning in front of key nodes (shunting points, intersections, partial signal lamps and the like) related to the turning road condition or not based on the unmixed GPS data samples, so as to obtain a more accurate congestion path. As shown in fig. 4, the congestion path is the AB-lane-BC.
Aiming at each key node, according to GPS data of vehicles on different steering lanes and node position information and node attribute information of the key node, a speed space-time curve corresponding to the key node is constructed, and a congestion contribution degree R is calculated based on the speed space-time curveblock；
Specifically, a congestion contribution degree R is defined as a quantitative evaluation index of the influence of the key nodes on the upstream congestionblock. Calculating R based on unmixed GPS datablockThe method comprises the following steps:
the construction process of the speed space-time curve comprises the following steps: setting a road section L, and constructing a speed space-time curve by taking a distance d from a starting point of the road section L as an abscissa and taking a current average speed v of GPS data as an ordinate;
the average speed calculation method comprises the following steps: setting the distance from the target point p to the starting point L as d, and setting the set of vehicles passing through the point p in the current time period as { XiI | ═ 1,2, … n }, with a corresponding set of velocities { v | >, i | >, andi1,2, … n, the average speed at point p is
For the problems of sample loss and sample speed abnormality, methods such as curve interpolation, curve smoothing and the like can be adopted for processing, such as cubic spline interpolation, sliding window smoothing and the like, so that a continuous and complete speed space-time curve is obtained, as shown in fig. 6.
And according to interval screening conditions, respectively taking the nearest speed stable intervals upstream and downstream of the key node on a speed space-time curve, such as an ab interval and an ef interval in fig. 6.
The interval screening conditions are as follows:
wherein v ismaxAnd vminMaximum and minimum speeds within the interval, dmaxAnd dminRespectively an interval end point position and a starting point position, and epsilon is a screening threshold;
calculating the contribution degree of the key node to congestion according to a first formula;
the first formula is:
wherein R isblockRepresents the degree of contribution, vbeforeIs the average speed of the speed stable interval upstream of the key node, i.e. the stable speed before the traffic flow passes through the node, vafterAnd the average speed of the speed stable interval at the downstream of the key node is the stable speed of the traffic flow after passing through the node.
RblockThe ratio is a ratio, and the function of the key node in the congestion path can be quantitatively evaluated. Using 1 as watershed when R isblock<When 1, the upstream of the key node is smooth compared with the downstream, and the function of the key node is not obvious; when R isblock1 or RblockWhen the traffic congestion state is approximately equal to 1, the upstream and downstream congestion states are close, the downstream congestion is indicated to be diffused to the upstream of the key node, and the effect of the key node is not obvious at the moment; when R isblockWhen the traffic flow exceeds 1, the key node contributes a large amount of upstream congestion, and the traffic flow has a bottleneck at the key node.
When R isblockWhen the congestion route is larger than 1, the key node has a larger function and can be regarded as a sub-congestion source in the congestion route.
In addition, if two adjacent signal lamps exist in the congested path and no other node exists between the two signal lamps, the following calculation method can be adopted:
in the formula, tcur greenAnd tcur redIs the green time and red time of the current analysis signal lamp, tnxt greenAnd tnxt redWhen it is a green light of a downstream adjacent signal lightLong and red light duration.
Step S105: and determining all congestion sources in the congestion path according to the contribution degree of each key node to congestion.
According to some embodiments of the present application, step S105 may be implemented as: comparing the contribution degree of each key node to congestion with a preset threshold value; and taking all key nodes with the contribution degree to congestion larger than a preset threshold value as congestion sources.
Specifically, R in all analyzed nodes is extractedblockAnd if the number of the key nodes is more than 1, the nodes of the congestion-free path at the downstream are congestion sources, and the nodes of the congestion path at the downstream are congestion sources of the congestion path sub-congestion sources. All congestion paths and all congestion sources thereof can be obtained through the steps. And the tracing result is accurate, the efficiency is high, and the real-time calculation requirement is met.
In the traffic jam tracing method provided by the embodiment, road network data of a target road network and GPS data of all vehicles in a preset time period on the target road network are acquired; determining a congestion path in a target road network according to the road network data and the GPS data of all vehicles; determining key nodes in the congestion path and node information of the key nodes; obtaining the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key nodes; and determining all congestion sources in the congestion path according to the contribution degree of each key node to congestion. Compared with the prior art, the method and the device have the advantages that the contribution degree of each key node in the congestion path to congestion is analyzed, so that all congestion sources in the congestion path are found out, and the congestion cause can be accurately and rapidly located.
In the foregoing embodiment, a traffic congestion tracing method is provided, and correspondingly, a traffic congestion tracing apparatus is also provided in the present application. The traffic jam tracing device provided by the embodiment of the application can implement the traffic jam tracing method, and the traffic jam tracing device can be implemented through software, hardware or a software and hardware combined mode. For example, the traffic congestion traceability device may comprise integrated or separate functional modules or units to perform the corresponding steps of the methods described above. Referring to fig. 6, a schematic diagram of a traffic congestion tracing apparatus according to some embodiments of the present application is shown. Since the apparatus embodiments are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for relevant points. The device embodiments described below are merely illustrative.
As shown in fig. 7, the traffic congestion traceability device 10 may include:
the acquisition module 101 is configured to acquire road network data of a target road network and GPS data of all vehicles in a preset time period on the target road network;
a path determining module 102, configured to determine a congestion path in a target road network according to the road network data and GPS data of all vehicles;
the node determining module 103 is configured to determine a key node in a congestion path and node information of the key node;
the contribution degree determining module 104 is configured to obtain the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key node;
and the congestion source determining module 105 is configured to determine all congestion sources in the congestion path according to the contribution degree of each key node to congestion.
In some implementations of the embodiments of the present application, the path determining module 102 is specifically configured to:
determining the driving track of the vehicle according to the GPS data and the road network data of the vehicle, and dividing the driving track of the vehicle into a low-speed segment and a high-speed segment;
selecting a road section covered by a low-speed segment from a target road network, searching adjacent road sections upwards and downwards by taking the road section as a starting point, and bringing the adjacent road sections meeting a first preset condition into a congestion path; the first preset condition is that the length of an adjacent road section without low-speed segment coverage is less than a path disconnection threshold value;
the method comprises the steps that the vehicle can be disconnected when a first preset condition is not met, and a congestion path is obtained;
the above operation of searching for the congested paths is performed on all unprocessed road segments to determine all the congested paths in the target road network.
In some implementations of the embodiments of the present application, the path determining module 102 is specifically configured to:
determining a running track of the vehicle according to the GPS data of the vehicle;
dividing the driving track of the vehicle into a plurality of track segments according to the road network data;
calculating track segment speed according to GPS data in each track segment, wherein the track segment speed refers to the average speed of a vehicle in the track segment;
if the track segment speed is smaller than the congestion speed threshold, determining the corresponding track segment as a low-speed segment; otherwise, the high-speed segment is determined.
In some implementations of the embodiments of the present application, the node determining module 103 is specifically configured to:
determining a conventional congestion point, a POI point, a disturbance point and a route terminal point of a congestion route in the congestion route as a key node of the congestion route; the disturbance point is determined in the following manner:
searching points on the congestion path along a fixed direction, finding out points meeting a second preset condition, and forming an alternative point set;
matching the points in the alternative point set with the conventional congestion points and the POI points according to the distance, deleting the points of which the distance between the two points is less than a preset distance threshold value from the alternative point set, wherein the rest points in the alternative point set are disturbance points;
the second preset condition is as follows:
wherein N ispre、Nblock preThe total number of the upstream track segments and the number of the low-speed segments, Nnxt、Nblock nxtRespectively counting the total number of downstream track segments and the number of low-speed segments; rlAnd selecting a threshold value for the point, wherein the value is greater than 1.
In some implementations of the embodiments of the present application, the contribution determining module 104 is specifically configured to:
the GPS data of all vehicles on the congested route are unmixed to obtain the GPS data of each vehicle on different steering lanes, and the GPS data of the vehicles which run normally and run abnormally are distinguished;
aiming at each key node, constructing a speed space-time curve corresponding to the key node according to GPS data of a vehicle on different steering lanes and node position information and node attribute information of the key node;
respectively taking the nearest speed stable intervals of the upstream and the downstream of the key node on a speed space-time curve according to interval screening conditions;
calculating the contribution degree of the key node to congestion according to a first formula;
the construction process of the speed space-time curve comprises the following steps: setting a road section L, and constructing a speed space-time curve by taking a distance d from a starting point of the road section L as an abscissa and taking a current average speed v of GPS data as an ordinate;
the interval screening conditions are as follows:
wherein v ismaxAnd vminMaximum and minimum speeds within the interval, dmaxAnd dminRespectively an interval end point position and a starting point position, and epsilon is a screening threshold;
the first formula is:
wherein R isblockRepresents the degree of contribution, vbeforeIs the average velocity, v, of the velocity stability interval upstream of the key nodeafterIs the average speed of the speed stability interval downstream of the key node.
In some implementations of the embodiments of the present application, the congestion source determining module 105 is specifically configured to:
comparing the contribution degree of each key node to congestion with a preset threshold value;
and taking all key nodes with the contribution degree to congestion larger than a preset threshold value as congestion sources.
The traffic jam tracing device provided by the embodiment of the application obtains road network data of a target road network and GPS data of all vehicles in a preset time period on the target road network; determining a congestion path in a target road network according to the road network data and the GPS data of all vehicles; determining key nodes in the congestion path and node information of the key nodes; obtaining the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key nodes; and determining all congestion sources in the congestion path according to the contribution degree of each key node to congestion. Compared with the prior art, the method and the device have the advantages that the contribution degree of each key node in the congestion path to congestion is analyzed to find out all congestion sources in the congestion path, so that the congestion cause can be accurately and rapidly located.
The embodiment of the present application further provides an electronic device corresponding to the traffic congestion traceability method provided in the foregoing embodiment, where the electronic device may be a mobile phone, a notebook computer, a tablet computer, a desktop computer, or the like, so as to execute the traffic congestion traceability method.
Please refer to fig. 8, which illustrates a schematic diagram of an electronic device according to some embodiments of the present application. As shown in fig. 8, the electronic device 20 includes: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and the processor 200 executes the traffic congestion tracing method provided by any one of the foregoing embodiments when executing the computer program.
The Memory 201 may include a high-speed Random Access Memory (RAM) and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic device provided by the embodiment of the application and the traffic jam tracing method provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic device.
Referring to fig. 9, the computer readable storage medium is an optical disc 30, and a computer program (i.e., a program product) is stored thereon, and when being executed by a processor, the computer program may execute the traffic congestion traceability method according to any of the foregoing embodiments.
It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory, or other optical and magnetic storage media, which are not described in detail herein.
The computer-readable storage medium provided by the above-mentioned embodiment of the present application and the traffic congestion tracing method provided by the embodiment of the present application have the same beneficial effects as the method adopted, operated or implemented by the application program stored in the computer-readable storage medium.
It should be noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present disclosure, and the present disclosure should be construed as being covered by the claims and the specification.
Claims (8)
1. A traffic jam tracing method is characterized by comprising the following steps:
acquiring road network data of a target road network and GPS data of all vehicles in a preset time period on the target road network;
determining a congestion path in a target road network according to the road network data and the GPS data of all vehicles;
determining key nodes in the congestion path and node information of the key nodes; the node information of the key node comprises node position information and node attribute information;
obtaining the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key nodes;
determining all congestion sources in the congestion path according to the contribution degree of each key node to congestion;
the method for obtaining the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key nodes comprises the following steps:
the method comprises the steps of unmixing GPS data of all vehicles on a congested path to obtain GPS data of all vehicles on different steering lanes, and distinguishing the GPS data of the vehicles which run normally from the GPS data of the vehicles which run abnormally;
aiming at each key node, constructing a speed space-time curve corresponding to the key node according to GPS data of a vehicle on different steering lanes and node position information and node attribute information of the key node;
respectively taking the nearest speed stable intervals of the upstream and downstream of the key node on a speed space-time curve according to interval screening conditions;
calculating the contribution degree of the key node to congestion according to a first formula;
the construction process of the speed space-time curve comprises the following steps: setting a road section L, and constructing a speed space-time curve by taking a distance d from a starting point of the road section L as an abscissa and taking a current average speed v of GPS data as an ordinate;
the interval screening conditions are as follows:
wherein v ismaxAnd vminMaximum and minimum speeds within the interval, dmaxAnd dminRespectively an interval end point position and a starting point position, and epsilon is a screening threshold;
the first formula is:
wherein R isblockRepresents the degree of contribution, vbeforeIs the average velocity, v, of the velocity stability interval upstream of the key nodeafterIs the average speed of the speed stability interval downstream of the key node.
2. The method of claim 1, wherein said determining congestion paths in a target road network from said road network data and GPS data of all vehicles comprises:
determining the driving track of the vehicle according to the GPS data and the road network data of the vehicle, and dividing the driving track of the vehicle into a low-speed segment and a high-speed segment;
selecting a road section covered by a low-speed segment from a target road network, searching adjacent road sections upwards and downwards by taking the road section as a starting point, and bringing the adjacent road sections meeting a first preset condition into a congestion path; the first preset condition is that the length of an adjacent road section without low-speed segment coverage is less than a path disconnection threshold value;
the method comprises the steps that the vehicle can be disconnected when a first preset condition is not met, and a congestion path is obtained;
and performing the operation of searching the congestion path on all unprocessed road segments in the target road network to determine all congestion paths in the target road network.
3. The method of claim 2, wherein determining the driving trace of the vehicle according to the GPS data and the road network data of the vehicle and dividing the driving trace of the vehicle into a low-speed segment and a high-speed segment comprises:
determining a running track of the vehicle according to the GPS data of the vehicle;
dividing the driving track of the vehicle into a plurality of track segments according to the road network data;
calculating track segment speed according to GPS data in each track segment, wherein the track segment speed refers to the average speed of the vehicle in the track segment;
if the track segment speed is smaller than the congestion speed threshold, determining the corresponding track segment as a low-speed segment; otherwise, the high-speed segment is determined.
4. The method of claim 3, wherein determining the key node in the congested path comprises:
determining a conventional congestion point, a POI point, a disturbance point and a route terminal point of a congestion route in the congestion route as a key node of the congestion route; the disturbance point is determined in the following manner:
searching points on the congestion path along a fixed direction, finding out points meeting a second preset condition, and forming an alternative point set;
matching the points in the alternative point set with the conventional congestion points and the POI points according to the distance, deleting the points of which the distance between the two points is less than a preset distance threshold value from the alternative point set, wherein the rest points in the alternative point set are disturbance points;
the second preset condition is as follows:
wherein N ispre、Nblock preThe total number of the upstream track segments and the number of the low-speed segments, Nnxt、Nblock nxtRespectively counting the total number of downstream track segments and the number of low-speed segments; rlAnd selecting a threshold value for the point, wherein the value is greater than 1.
5. The method according to claim 1, wherein the determining all congestion sources in the congestion path according to the contribution degree of each key node to the congestion comprises:
comparing the contribution degree of each key node to congestion with a preset threshold value;
and taking all key nodes with the contribution degree to congestion larger than a preset threshold value as congestion sources.
6. A traffic jam tracing apparatus is characterized by comprising:
the acquisition module is used for acquiring road network data of a target road network and GPS data of all vehicles in a preset time period on the target road network;
the route determining module is used for determining a congestion route in a target road network according to the road network data and the GPS data of all vehicles;
the node determining module is used for determining key nodes in the congestion path and node information of the key nodes; the node information of the key node comprises node position information and node attribute information;
the contribution degree determining module is used for obtaining the contribution degree of each key node to congestion according to the GPS data of all vehicles on the congestion path and the node information of the key nodes;
the congestion source determining module is used for determining all congestion sources in the congestion path according to the contribution degree of each key node to congestion;
wherein, the contribution degree determining module is specifically configured to:
the method comprises the steps of unmixing GPS data of all vehicles on a congested path to obtain GPS data of all vehicles on different steering lanes, and distinguishing the GPS data of the vehicles which run normally from the GPS data of the vehicles which run abnormally;
aiming at each key node, constructing a speed space-time curve corresponding to the key node according to GPS data of a vehicle on different steering lanes and node position information and node attribute information of the key node;
respectively taking the nearest speed stable intervals of the upstream and downstream of the key node on a speed space-time curve according to interval screening conditions;
calculating the contribution degree of the key node to congestion according to a first formula;
the construction process of the speed space-time curve comprises the following steps: setting a road section L, and constructing a speed space-time curve by taking a distance d from a starting point of the road section L as an abscissa and taking a current average speed v of GPS data as an ordinate;
the interval screening conditions are as follows:
wherein v ismaxAnd vminMaximum and minimum speeds within the interval, dmaxAnd dminRespectively an interval end point position and a starting point position, and epsilon is a screening threshold;
the first formula is:
wherein R isblockRepresents the degree of contribution, vbeforeIs the average velocity, v, of the velocity stability interval upstream of the key nodeafterIs the average speed of the speed stability interval downstream of the key node.
7. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the method according to any of claims 1 to 5.
8. A computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to implement the method of any one of claims 1 to 5.
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