CN112785735B - Expressway road condition monitoring method and device based on charging data - Google Patents
Expressway road condition monitoring method and device based on charging data Download PDFInfo
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Abstract
The invention discloses a highway road condition monitoring method and device based on charging data, computer equipment and a storage medium, wherein the method comprises the following steps: and judging whether the current running vehicle runs to the target point within the estimated arrival time point, if so, monitoring that the current running vehicle is not jammed on the target running path, otherwise, monitoring that the current running vehicle is jammed on a road section which is in the target running path and is associated with at least one target point. According to the embodiment of the application, the constructed road network topological graph for highway toll collection is introduced, so that whether congestion occurs on the target driving path or not can be accurately detected, and the fact that the road section which is specifically in the target driving path and is associated with a target point is congested can be monitored.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a highway road condition monitoring method and device based on charging data.
Background
At present, the dynamic traffic information service is mainly based on mobile position data and a floating car technology, calculates and publishes average traffic speed information of roads by collecting and processing data such as positions and running speeds of sampled vehicles on high-speed roads, and has the advantages of high information precision, fine granularity, high refreshing frequency and the like, so that the dynamic traffic information service is widely used. It should be noted that the mobile location data is limited by the essential characteristics of its sampled data, and is extremely unbalanced in spatial-temporal distribution. Generally, the sampling rate of the mobile position data on the expressway is lower than 10% on average, so that a lot of roads are not sampled and show no data although vehicles pass by, and the phenomenon is particularly obvious in off-peak hours such as night hours. The problems can be remedied by methods such as historical data mining and the like in a common scene, but if an extreme scene such as an accident and a disaster occurs, a road is blocked and is not monitored in time, serious consequences can be caused. Therefore, dynamic traffic information service providers, highway management departments and the like are actively exploring methods for solving the problem of low information coverage rate of the highway by introducing other data sources and multi-source data fusion.
With the popularization of charging networking and sensing equipment such as ETC charging and video detection, a road network operation monitoring scene with nearly full flow is provided on the expressway. However, the deployed portal point location distribution of the ETC charging and video detection equipment is limited, for example, two adjacent portals are generally spaced at ten kilometers, tens of roads and even farther, which means that the frequency and timeliness of data acquisition, granularity of information expression and the like are greatly challenged.
The existing monitoring method applied to highway road conditions usually adopts a GPS technology, but the GPS technology randomly samples and monitors the running conditions of vehicles running on a highway, so that each running vehicle provided with vehicle-mounted unit equipment cannot be monitored, and the road condition congestion phenomenon which may occur to each running vehicle cannot be accurately predicted.
Disclosure of Invention
The embodiment of the application provides a method and a device for monitoring highway road conditions based on charging data, computer equipment and a storage medium. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a method for monitoring highway conditions based on charging data, where the method includes:
constructing a road network topological graph for highway toll collection, wherein the road network topological graph comprises a macroscopic topological graph used for identifying the upstream and downstream relation among all gantries on all paths and an mesoscopic topological graph used for identifying the traveling path segments of all traveling vehicles, and the mesoscopic topological graph is provided with the identified intersection points among all the path segments;
estimating a preset distance between the running speed of the current running vehicle and a target point on a target running path, wherein the target point comprises a target downstream portal point marked on the macroscopic topological graph and a target intersection point marked on the mesoscopic topological graph;
predicting the time length required by the current running vehicle to run the preset distance at the running speed according to the predicted running speed of the current running vehicle and the preset distance; predicting an estimated arrival time point of the current running vehicle to the target point based on the initial time point and the time length determined by the charging data of the current running vehicle;
and judging whether the current running vehicle runs to the target point within the estimated arrival time point, if so, monitoring that the current running vehicle is not jammed on the target running path, otherwise, monitoring that the current running vehicle is jammed on a road section which is within the target running path and is associated with the at least one target point.
In one embodiment, the monitoring that the currently traveling vehicle is within the target traveling path and the road segment associated with the at least one target point is congested includes:
and if the target point is the target downstream portal point, monitoring that the current running vehicle is in the target running path and the road section associated with at least one portal point downstream of the target is congested.
In one embodiment, the monitoring that the currently traveling vehicle is within the target traveling path and the road segment associated with the at least one target point is congested includes:
and if the target point is the target intersection, monitoring that the current running vehicle is in the target running path and the road section associated with the at least one target intersection is congested.
In one embodiment, the road network topology map comprises a macro topology map for characterizing upstream and downstream relationships between respective gantries on respective paths and a mesoscopic topology map for characterizing travel path segments of respective traveling vehicles, the mesoscopic topology map having identified intersections between respective paths; the constructing of the road network topology graph for high-speed charging according to the path information comprises the following steps:
and constructing the macro topological graph for highway toll according to the path information, and constructing the mesoscopic topological graph for highway toll according to the path information.
In one embodiment, said constructing said macro topology map for highway tolling from said path information comprises:
acquiring each path information corresponding to each running vehicle, wherein each path information comprises portal information arranged on each path and upstream and downstream incidence relation information between portals arranged on each path;
and constructing the macro topological graph for highway toll collection according to the portal information arranged on each path and the upstream and downstream incidence relation information between the portals arranged on each path.
In one embodiment, the constructing the mesoscopic topology map for highway tolling according to the path information comprises:
acquiring each piece of route information corresponding to each running vehicle, wherein each piece of route information further comprises an intersection point between each route;
and constructing the mesoscopic topological graph for highway toll collection according to the macroscopic topological graph and the intersection points between the paths.
In one embodiment, the method further comprises:
and under the condition of giving a macroscopic link effectiveness judgment result, establishing an inference relation between the effectiveness of the path segments in the mesoscopic topological graph and the effectiveness of the macroscopic links in the macroscopic topological graph based on the mesoscopic link monitoring model, and reversely deducing the priority order of the failure of each path segment in the mesoscopic topological graph based on the inference relation so as to carry out investigation and treatment.
In a second aspect, an embodiment of the present application provides a device for monitoring highway conditions based on charging data, the device includes:
the system comprises a construction module, a road network topological graph and a road toll collection module, wherein the road network topological graph comprises a macroscopic topological graph used for identifying the upstream and downstream relations among all gantries on all paths and an mesoscopic topological graph used for identifying the traveling path segments of all traveling vehicles, and the mesoscopic topological graph is provided with identified intersection points among all path segments;
the estimation module is used for estimating the running speed of the current running vehicle and the preset distance between target points on a target running path, wherein the target points comprise target downstream portal points marked on the macro topological graph constructed by the construction module and target cross points marked on the mesoscopic topological graph constructed by the construction module;
the prediction module is used for predicting the time length required by the current running vehicle to run the preset distance at the running speed according to the running speed of the current running vehicle predicted by the prediction module and the preset distance; predicting an estimated arrival time point of the current running vehicle to the target point based on the initial time point and the time length determined by the charging data of the current running vehicle;
and the processing module is used for judging whether the current running vehicle runs to the target point within the estimated arrival time point predicted by the prediction module, monitoring that the current running vehicle is not jammed on the target running path if the current running vehicle is judged to run to the target point within the estimated arrival time point, and monitoring that the current running vehicle is jammed on a road section which is within the target running path and is associated with the at least one target point if the current running vehicle is judged to run to the target point within the estimated arrival time point.
In a third aspect, embodiments of the present application provide a computer device, including a memory and a processor, where the memory stores computer-readable instructions, and the computer-readable instructions, when executed by the processor, cause the processor to perform the above-mentioned method steps.
In a fourth aspect, embodiments of the present application provide a storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in the embodiment of the application, the time length required by the current running vehicle to run the preset distance at the running speed is predicted according to the predicted running speed and the preset distance of the current running vehicle; predicting an estimated arrival time point of the current running vehicle to the target point based on the initial time point and the duration determined by the charging data of the current running vehicle; and judging whether the current running vehicle runs to the target point within the estimated arrival time point, if so, monitoring that the current running vehicle is not jammed on the target running path, otherwise, monitoring that the current running vehicle is jammed on a road section which is in the target running path and is associated with at least one target point. According to the embodiment of the application, the constructed road network topological graph for highway toll collection is introduced, so that whether congestion occurs on the target driving path or not can be accurately detected, and the fact that the road section which is specifically in the target driving path and is associated with a target point is congested can be monitored.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic flow chart of a method for monitoring highway conditions based on charging data according to an embodiment of the present disclosure;
fig. 2 is a schematic view illustrating scene difference analysis of charging data and mobile location data in a specific application scene according to an embodiment of the present application;
fig. 3 is a schematic diagram of a macro topological graph and a meso topological graph in a road network topological graph for highway toll collection, which is constructed in a specific application scenario according to an embodiment of the present application;
fig. 4 is a schematic diagram of a flow coupling analysis unit in a specific application scenario provided in the embodiment of the present application;
fig. 5 is a schematic diagram of a mesoscopic link monitoring module in a specific application scenario provided in the embodiment of the present application;
fig. 6 is a schematic diagram illustrating an inference relationship between a path segmentation validity and a macro link validity in a specific application scenario according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a device for monitoring highway conditions based on charging data according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method and the device for monitoring the highway road condition based on the charging data are provided, so that the problems that each running vehicle provided with the vehicle-mounted unit equipment cannot be monitored, and the road condition congestion phenomenon possibly occurring in each running vehicle cannot be predicted accurately are solved. According to the technical scheme, the method comprises the steps that according to the estimated running speed and the estimated preset distance of the current running vehicle, the time length required by the current running vehicle to run the preset distance at the running speed is predicted; predicting an estimated arrival time point of the current running vehicle to the target point based on the initial time point and the duration determined by the charging data of the current running vehicle; and judging whether the current running vehicle runs to the target point within the estimated arrival time point, if so, monitoring that the current running vehicle is not jammed on the target running path, otherwise, monitoring that the current running vehicle is jammed on a road section which is in the target running path and is associated with at least one target point. In the embodiment of the application, due to the introduction of the constructed road network topological graph for highway toll collection, not only can whether congestion occurs on the target driving route be accurately detected, but also a road section which is specifically in the target driving route and is associated with which target point can be monitored to be congested, and the following detailed description is made by adopting an exemplary embodiment.
The method for monitoring highway conditions based on charging data according to the embodiment of the present application will be described in detail with reference to fig. 1 to 6. The highway condition monitoring method based on the charging data can be realized by depending on a computer program and can be operated on a highway condition monitoring device based on the charging data.
As shown in fig. 1, the method for monitoring highway traffic based on charging data according to the embodiment of the present application is a schematic flow chart, and the method for monitoring highway traffic based on charging data is applied to a server, especially a cloud; as shown in fig. 1, the method for monitoring highway conditions based on charging data according to the embodiment of the present application may include the following steps:
s101, constructing a road network topological graph for highway toll collection, wherein the road network topological graph comprises a macroscopic topological graph used for identifying the upstream and downstream relations among all gantries on all paths and an mesoscopic topological graph used for identifying the traveling path segments of all traveling vehicles, and the mesoscopic topological graph has intersection points among all the identified path segments.
Fig. 2 is a schematic view illustrating scene difference analysis of charging data and mobile location data in a specific application scene according to an embodiment of the present application.
As shown in fig. 2, A, B, C is three toll portals, portal B and portal C downstream of portal a. The vehicles Car1 and Car2 travel from gantry a to gantry B and gantry C, respectively, and the points in fig. 2 can be regarded as their moving position point sequences. No matter based on ETC charging or video identification, the mechanism is that the vehicle information is collected by the portal and uploaded to the background and recorded when the vehicle passes through the portal. Only when a vehicle passes either portal B or C will portals B and C know that a vehicle is being sent from portal A and arrives at both portals B and C. Therefore, all the details of travel between a → B and a → C can be expressed with respect to the movement position data, for example, a block occurs at f between a → C, the movement position point of Car2 can be expressed clearly, and the charge data can be learned only when Car2 reaches the portal C through the block point. The above example illustrates well the latency disadvantage of fixed point perception data compared to moving data. For example, Car2 breaks through f and is observed when arriving at portal C, and there is already a large delay in the detection of blocking at f, and in the extreme case, if Car2 cannot break through f all the time, the C portal can never know the presence of Car2, and cannot sense the blocking of the link between a → C.
To address the above, the conventional approach is to monitor the flow change at portal C, which will drop significantly if there is a problem with the link between a → C. The premise of the method is that the flow is stable, and the flow coupling relation between the door frames in the running process of the vehicle is ignored. For example, if all vehicles from a drive through B at a time, the flow rate reduction of the gantry C is normal; the problem is further compounded if there is a flow entry upstream of C for other legs (e.g., portal D). In fact, the toll road network is a network topology, and the coupling between the upstream and downstream gate frames makes the monitoring method based on the traffic change very challenging, but also provides a certain convenience for the location link problem.
According to the highway road condition monitoring method based on the charging data, based on highway charging data, the problems of the links between the upstream portal frame and the downstream portal frame are found by utilizing the flow coupling among different portal frames, the space of the link problems is further positioned in the mesoscopic scale, and data support is provided for highway network operation monitoring.
In the embodiment of the application, the charging data is introduced, and the charging data of the running vehicle can be directly known as follows: whether the traveling vehicle smoothly passes through each downstream mast (e.g., downstream mast B, or downstream mast C, as shown in fig. 2), or smoothly passes through an intersection between the respective routes (e.g., intersection g, as shown in fig. 2, between route DC and route AC). The charging data is different from the mobile position data, and basically, the position data is mobile data, and the charging data is fixed point data.
Fig. 3 is a schematic diagram of a macro topological graph and a meso topological graph in a road network topological graph for highway toll collection, which is constructed in a specific application scenario according to an embodiment of the present application.
In order to facilitate description of the flow coupling relationship between different portals, in the method for monitoring highway road conditions based on charging data according to the embodiment of the present application, a macroscopic and intermediate two-layer topological relationship of a highway network is abstracted, as shown in fig. 3. The macroscopic topological relation refers to the upstream and downstream topological relation among the portal frames, and the mesoscopic topological relation inserts an important branch and confluence point on the basis of the macroscopic topological relation so as to express the influence of the branch and confluence on flow coupling. According to the monitoring method provided by the embodiment of the application, the possible problems on the mesoscopic topological link are predicted by defining the flow coupling relation between the macroscopic topological layer gantries. In the example shown in fig. 2, the monitoring method provided in the embodiment of the present application can detect that there is a problem in the link between the mesoscale e → g based on the charging data, and although the problem point f cannot be located at the microscopic scale due to the limitation of the macroscopic data such as the charging data, it can at least accurately determine that the link between e → g shown in fig. 2 and 3 is congested, and the monitored traveling vehicle cannot smoothly reach the point g shown in fig. 2 and 3.
In the embodiment of the present application, the charging data is recorded in the following manner, which is specifically described as follows:
the toll data record is a data record associated with the gate frame of a vehicle generated by ETC equipment or video recognition when the vehicle passes through the high-speed toll gate frame and is expressed by the following quintuple:
the method comprises the following steps that CR is < Pid, Cid, Ctype and t >, wherein CR is a charging record, Pid is the unique identification of a portal frame, Cid is the unique identification of a vehicle, Ctype records the vehicle type of the vehicle, and the CR is obtained by calling registration information of the vehicle by a background or based on image identification; and t is the time when the vehicle passes through the door frame.
It should be noted that the monitoring method provided in the embodiment of the present application is also applicable to other application scenarios for performing road network operation monitoring based on fixed point data, and is not described herein again.
In an embodiment of the application, the road network topological graph comprises a macroscopic topological graph used for representing the upstream and downstream relation between the gantries on each path and a mesoscopic topological graph used for representing the driving path segments of each driving vehicle, wherein the mesoscopic topological graph is provided with identified intersection points between the paths; the method for constructing the road network topological graph for high-speed charging according to the path information comprises the following steps:
and constructing a macroscopic topological graph for highway charging according to the path information, and constructing a mesoscopic topological graph for highway charging according to the path information.
Specifically, the step of constructing the macro topological graph for highway toll collection according to the path information comprises the following steps:
acquiring each path information corresponding to each running vehicle, wherein each path information comprises portal information arranged on each path and upstream and downstream incidence relation information between portals arranged on each path;
and constructing a macro topological graph for highway toll collection according to the portal information arranged on each path and the upstream and downstream incidence relation information between the portals arranged on each path.
Specifically, the construction of the mesoscopic topological graph for highway toll collection according to the path information comprises the following steps:
acquiring each piece of route information corresponding to each running vehicle, wherein each piece of route information further comprises an intersection point between each route;
and constructing a mesoscopic topological graph for highway toll according to the macroscopic topological graph and the intersection points between the paths.
In a specific application scenario, the method for constructing the road network topological graph for highway toll collection specifically comprises the following steps:
step a 1: the portal frames are marked on an electronic map G of the expressway, and a two-tuple pl ═ Pid, linkid > formed by each portal frame and the link where the portal frame is located is obtained, and the set of pl is recorded as omega.
Step a 2: for any one pl E omega, with the link where the portal pl is located as a starting point, depth-first traversal is executed on the electronic map G to obtain all downstream portal sets phi of the portal plpl(is provided with
). One downstream portal pl' epsilon is not setplThen their macroscopic topological relationship is noted as
topomacro(pl，pl′)＝<rela(pl，pl′)，path(pl，pl′)，dis(pl，pl′)，t(pl，pl′)>。
Wherein, the rela (pl, pl ') uniquely identifies that the upstream and downstream relationship exists between the gantries pl.pid and pl'. Pid;
path (pl, pl ') pl.linkid → … → pl '. linkid gives the path between the gantries pl.pid and pl '. Pid;
dis (pl, pl ') gives the length of the path (pl, pl');
t (pl, pl ') gives the elapsed time required for the path (pl, pl') to travel at the estimated normal vehicle speed (e.g., 80 km/h).
Through the operation, the macroscopic topological relation topo taking the gantries pl and pl' as nodes is constructedmacro(pl, pl') sided macroscopic map Gmacro。
Step a 3: marking the upstream and downstream relationship of the portal to the sections of the electronic map G of all the paths (pl, pl'), taking fig. 2 as an example, all the links on the A → B path are marked with the rela (pl)A，plB) All links on the A → C path are labeled rela (pl)A，plC) All links on the D → C path are labeled rela (pl)D，plC)；
Step a 4: and performing topological clustering on links marked with the same rela (,) marks, and taking the clustered path segments as elements of the viewing topology in the toll road network. Still taking FIG. 2 as an example, the label of all links on the path segment A → e path is rela (pl)A，plB) And rela (pl)A，plC) (ii) a The label of all links on the e → B path is rela (pl)A，plB) (ii) a e → the label of all links on the g path is rela (pl)A，plC) (ii) a Marker of all links on the g → C path is rela (pl)A，plC) And rela (pl)D，plC) (ii) a The label of all links on the D → g path is rela (pl)D，plC). Through the clustering operation, some important branch and merge points (e and G in fig. 2) are screened out to form a mesoscopic map G with the portal frame and the branch and merge points as nodes and the path segments as edgesmeco. At GmecoIn (1), we use topomeso(pl, pl ') records the set of path segments between portal portals pl and pl'. For example, topomeso(plA，plC) As shown in fig. 3, { a → e, e → g, g → C }.
In a possible implementation manner, the monitoring method provided in the embodiment of the present application further includes the following steps:
based on macroscopic map GmacroAnd constructing a flow coupling analysis unit.
Fig. 4 is a schematic diagram of a flow coupling analysis unit in a specific application scenario provided in the embodiment of the present application.
In order to capture the flow coupling relationship between the upstream and downstream gantries and between different downstream gantries of the same upstream gantry, the monitoring method provided in the embodiment of the present application defines a flow coupling analysis unit using the upstream gantry as a computation core, as shown in fig. 4.
On a macroscopic map GmacroIn the method, for any portal pl epsilon omega, a downstream portal set phi of the portal pl epsilon omega is extractedplAnd constructing a flow coupling analysis unit. As shown in fig. 4, the upstream portal pl is composed of a master communication module and a link monitoring module, and each downstream portal pl' is composed of a slave communication module and a timing module. As can be seen from fig. 4, the flow coupling analysis units are formed around the upstream portal, and therefore, one flow coupling analysis unit can be uniquely identified by the upstream portal pl.
The operation mechanism of the flow coupling analysis unit in fig. 4 is described as follows:
1) for upstream portal pl, if the charging data is recorded<pl.Pid，c1，Ctype，t0>Shows a vehicle c1 passing through portal pl at time t0, the master communication module forwards each downstream portal pl' e ΦplSending messages from the communication module<c1，t0+t(pl，pl′)+Δt>That is, the downstream portal pl 'is notified, and it is expected that the vehicle c1 passes through the portal pl' before the time t0+ t (pl, pl ') + Δ t, please monitor pl'; wherein t (pl, pl ') is the normal elapsed time for the vehicle to pass through the gantries pl and pl', and Δ t is a tolerance of the elapsed time;
2) link monitoring Module of upstream Portal pl, Add c1 to Topo for each downstream Portal Linkmacro(pl, pl') a set of monitoring tasks;
3) for any downstream portal pl' ∈ ΦplWhen is coming into contact withIt receives the information sent by the main communication module from the communication module<c1，t0+t(pl，pl′)+Δt>Meanwhile, a tracking task for the vehicle c1 is established, if the vehicle c1 passes through the portal pl 'is monitored before the time t0+ t (pl, pl') + Δ t, the main communication module is replied to inform the link monitoring module of the pl, and the vehicle c1 is monitored normally; otherwise, return to undetected vehicle c 1;
4) if the link monitoring module of pl receives a successful monitoring message to vehicle c1 in reply to portal pl', topo will be sentmacro(pl, pl') the link is active; and the vehicle c1 is taken from the other downstream portal pl "(pl ∈ Φ)plAnd pl ≠ pl') is removed from the set of monitoring tasks; if a message of failed monitoring of vehicle c1 is received in reply to portal pl', topo will be sentmacro(pl, pl') monitoring the task placement of vehicle c1 in the task set as failed;
5) the link monitoring module of the pl periodically counts each downstream portal link topomacro(pl, pl ') monitoring the number n (pl, pl') of failed tasks in the task set to topomacro(pl, pl') number of failed vehicle monitoring tasks, proportion
If n (pl, pl ') or δ (pl, pl') exceeds a certain threshold, the output link topomacro(pl, pl ') fails, and the probability of failure is δ (pl, pl').
By constructing the flow coupling unit, particularly a link monitoring module built around an upstream portal, data exchange and flow coupling analysis between the upstream portal and the downstream portal and between different downstream portals of the same upstream portal can be well realized, the theoretical time delay of link detection is t (pl, pl') + delta t, and the problems that the detection time delay is large or even the detection cannot be effectively carried out when a link fails in the conventional scheme and the detection accuracy based on the flow are effectively solved.
Further, in the case of link blocking, if the downstream portal pl' eventually detects the vehicle c1 at time t2, t2-t0 is link topomacroThe time consumption of (pl, pl') can be analyzed as the travel cost of the link, which is not described herein.
Fig. 5 is a schematic view of an mesoscopic link monitoring module in a specific application scenario provided in the embodiment of the present application.
Based on the macroscopic map GmacroEach macro link topo can be obtained by the constructed flow coupling analysis unitmacro(pl, pl') validity. GmacroMedium macro link availability and GmecoThe qualitative relation of the effectiveness of the middle path segments is that when the macro link is effective, all corresponding path segments can be deduced to be effective, and if the macro link is ineffective, each path segment is possibly ineffective; the quantitative relation is that when a certain macroscopic link effective condition judgment is satisfied, the conditional probability of the path segment failure is calculated, specifically, the probability of the failure path segment is estimated, and is defined as p (path segment failure | { topo)macro(pl, pl') validity determination }).
In a possible implementation manner, the monitoring method provided in the embodiment of the present application further includes the following steps:
based on mesoscopic map GmecoAnd constructing a mesoscopic link monitoring module.
Under a specific application scene, based on the mesoscopic map GmecoThe method for constructing the mesoscopic link monitoring module specifically comprises the following steps:
step b 1: establishment of GmecoMiddle path segmentation significance and GmacroInference relationship of medium macro link validity.
Fig. 6 is a schematic diagram illustrating an inference relationship between a path segment validity and a macro link validity in a specific application scenario provided by the embodiment of the present application, where the relationship between the macro link validity is inferred when a path segment validity assumption is given. As shown in FIG. 3, a total of 5 path segments are defined, A → e, e → B, e → g, g → C and D → g, respectively; and 3 macro links are defined, a → B, A → C and D → C, respectively.
According to the setting of the validity condition of the path segment, the validity conclusion of the corresponding macro link can be deduced. Suppose there are 5 path segments, each path segment having a valid sumTwo cases of invalidation, then 2 can be defined5Setting validity condition, corresponding to result space of macro link validity having 23And (4) respectively. However, on the premise that the effectiveness of some macro links is known, the space set by the condition and the number of the corresponding result spaces of the effectiveness of the macro links can be compressed. For example, knowing that the macro link A → B is valid, the validity of the path segments A → e and e → B can be determined, as shown in FIG. 6, to compress the validity condition space of the path segments to 23Respectively using mu1，μ2…μ8Identification, corresponding macroscopic link validity result space compression to 22And (4) respectively.
Step b 2: the probability of failure of each path segment is inferred given the macroscopic link validity decision.
In this embodiment of the present application, in order to facilitate the examination and the treatment, the monitoring method provided in this embodiment of the present application further includes the following steps:
and under the condition of giving a macroscopic link effectiveness judgment result, establishing an inference relation between the effectiveness of the path segments in the mesoscopic topological graph and the effectiveness of the macroscopic links in the macroscopic topological graph based on the mesoscopic link monitoring model, and reversely deducing the priority order of the failure of each path segment in the mesoscopic topological graph based on the inference relation so as to perform investigation and treatment.
Specifically, under the condition of a given macroscopic link effectiveness judgment result, G is established based on a mesoscopic link monitoring algorithmmecoMiddle topology path segmentation validation and GmacroDeducing relationship of medium and macro link effectiveness, and reversely deducing a medium topology graph G based on the deduced relationshipmecoThe failure priority order of each topological path segment is convenient for troubleshooting and disposal.
It should be noted that the mesoscopic link monitoring model is a model established based on a mesoscopic link monitoring algorithm, and the adopted method for establishing the model is a conventional method, which is not described herein again. If a given path segment tps and a macro link validity determination condition is Ψ, then the conditional probability of tps failure is defined as:
wherein k is the space capacity of the path segmentation validity condition; mu.skUniquely identifying a path segment validity condition; p (mu)k) Segmenting the significance condition mu for a pathkThe probability of occurrence is set as tau (tau < 0.5) and p (mu)k)＝τa×(1-τ)bWherein a is mukWherein tps is 0, and b is μkIn the present invention, the number of tps is 1, and τ is 0.2.
f1(tps，μk) Giving path segment tps a path segment validity condition mukThe determination of whether to fail is defined as follows:
σkis the path segment validity condition mukCorresponding macro link validity indication, f2(σkΨ) gives σkThe determination as to whether or not the macro link validity determination condition Ψ coincides is defined as follows:
based on the above formula, when topo is shown in FIG. 2macro(plA，plB) Effective topomacro(plD，plC) Effective and topomacro(plA，plC) When the device is out of service,
comprises the following steps: p (e → g ═ 0| (a → C ═ 0, D → C ═ 1)) ═ p (e → g ═ 0| μ | (g ═ 0 |)4)＝p(μ4)＝0.8×0.2×0.2＝0.032；
The same principle is as follows:
p(g→C＝0|(A→C＝0，D→C＝1))＝0，p(D→g＝0|(A→C＝0，D→C＝1))＝0。
normalized, p (e → g | (a → C ═ 0, D → C ═ 1)) is 1, i.e., the problem occurs on the path segment e → g with probability 1.
As another example, the first and second light-emitting diodes can be used,
when topomacro(plA，plB) Effective topomacro(plD，plC) And topomacro(plA，plC) When all fail, have:
p(e→g＝0|(A→C＝0，D→C＝0))＝p(μ1)+p(μ2)+p(μ3)＝0.072；p(g→C＝0|(A→C＝0，D→C＝0))＝p(μ1)+p(μ2)+p(μ5)+p(μ6)＝0.2；p(D→g＝0|(A→C＝0，D→C＝0))＝p(μ1)+p(μ2)+p(μ5)+p(μ6)＝0.072。
after the normalization is carried out, the result is obtained,
p(g→C＝0|(A→C＝0，D→C＝0))＝0.582，
p(e→g＝0|(A→C＝0，D→C＝0))＝0.209；
p(D→g＝0|(A→C＝0，D→C＝0))＝0.209。
the explanation problem occurs with a probability of 0.582 in the path segment g → C, but the probability of failure of D → g and e → g cannot be excluded, and is only low.
Based on the calculation of the observing link monitoring module, the failure probability of each path segment can be given, so that management departments can be helped to perform investigation and treatment according to the failure probability, and the efficiency of problem positioning and treatment is improved.
In an embodiment of the present application, the method for monitoring highway conditions based on charging data further includes the following steps:
responding to a viewing request of a specified user for viewing the road network topological graph, and pushing the road network topological graph displayed in the graphic form to terminal equipment of the specified user; in this way, the designated user can intuitively see the road network topology map as shown in fig. 2 and 3, so as to select a travel path in advance.
In a possible implementation manner, the monitoring method provided in the embodiment of the present application further includes the following steps:
and acquiring the estimated driving speed of the current driving vehicle and a preset distance between target points on a target driving path, wherein the target points comprise target downstream portal points marked on a macroscopic topological graph and target cross points marked on a mesoscopic topological graph.
In the embodiment of the present application, the target point may be a downstream gantry point B or a downstream gantry point C as shown in fig. 2 and 3, or the target point may also be an intersection e or an intersection g as shown in fig. 2 and 3.
S102, estimating a preset distance between the current running vehicle running speed and a target point on a target running path, wherein the target point comprises a target downstream portal point marked on a macroscopic topological graph and a target intersection point marked on a mesoscopic topological graph.
In this step, for the detailed description of the macro topological graph and the meso topological graph, refer to the description of the same or similar parts, which is not repeated herein.
S103, predicting the time length required by the current running vehicle to run for the preset distance at the running speed according to the estimated running speed and the preset distance of the current running vehicle; and predicting the estimated arrival time point of the current running vehicle to the target point based on the initial time point and the duration determined by the charging data of the current running vehicle.
For the description of the charging data, refer to the description of the same or similar parts in the foregoing S101, and are not described in detail here.
And S104, judging whether the current running vehicle runs to a target point within the estimated arrival time point, if so, monitoring that the current running vehicle is not jammed on the target running path, otherwise, monitoring that the current running vehicle is jammed on a road section which is within the target running path and is associated with at least one target point.
In one possible implementation, the step of monitoring that the currently traveling vehicle is within the target traveling path and the road segment associated with the at least one target point is congested comprises the following steps:
and if the target point is a target downstream portal point, monitoring that the current running vehicle is in the target running path and the road section associated with at least one portal point downstream of the target is congested.
In one possible implementation, the step of monitoring that the currently traveling vehicle is within the target traveling path and the road segment associated with the at least one target point is congested comprises the following steps:
and if the target point is the target intersection, monitoring that the current running vehicle is in the target running path and the road section associated with at least one target intersection is jammed.
In the embodiment of the application, the time length required by the current running vehicle to run the preset distance at the running speed is predicted according to the predicted running speed and the preset distance of the current running vehicle; predicting an estimated arrival time point of the current running vehicle to the target point based on the initial time point and the duration determined by the charging data of the current running vehicle; and judging whether the current running vehicle runs to the target point within the estimated arrival time point, if so, monitoring that the current running vehicle is not jammed on the target running path, otherwise, monitoring that the current running vehicle is jammed on a road section which is in the target running path and is associated with at least one target point. According to the embodiment of the application, the constructed road network topological graph for highway toll collection is introduced, so that whether congestion occurs on the target driving path or not can be accurately detected, and the fact that the road section which is specifically in the target driving path and is associated with a target point is congested can be monitored.
The following is an embodiment of the highway condition monitoring device based on the charging data, which can be used for implementing the highway condition monitoring method based on the charging data. For details not disclosed in the embodiment of the highway condition monitoring device based on the charging data, please refer to the embodiment of the highway condition monitoring method based on the charging data.
Fig. 7 is a schematic structural diagram of a monitoring device for highway traffic based on charging data according to an exemplary embodiment of the present invention. The highway road condition monitoring device based on the charging data is applied to a server, particularly a cloud. The highway condition monitoring device based on the charging data comprises a construction module 701, an estimation module 702, a prediction module 703 and a processing module 704.
Specifically, the construction module 701 is used for constructing a road network topological graph for highway toll collection, wherein the road network topological graph comprises a macroscopic topological graph used for identifying the upstream-downstream relation between all gantries on all paths and an intermediate topological graph used for identifying the driving path segments of all driving vehicles, and the intermediate topological graph is provided with identified intersection points among all the path segments;
the estimation module 702 is configured to estimate a preset distance between a current driving speed of a vehicle and a target point on a target driving path, where the target point includes a target downstream portal point identified on the macro topological graph constructed by the construction module 701 and a target intersection point identified on the meso topological graph constructed by the construction module 701;
the prediction module 703 is configured to predict, according to the driving speed and the preset distance of the current driving vehicle predicted by the prediction module 702, a time length required for the current driving vehicle to drive the preset distance at the driving speed; predicting an estimated arrival time point of the current running vehicle to the target point based on the initial time point and the duration determined by the charging data of the current running vehicle;
the processing module 704 is configured to determine whether the currently-traveling vehicle travels to the target point within the estimated arrival time point predicted by the prediction module 703, monitor that the currently-traveling vehicle is not congested on the target travel path if it is determined that the currently-traveling vehicle travels to the target point within the estimated arrival time point, and monitor that the currently-traveling vehicle is congested on a road segment that is within the target travel path and is associated with at least one target point if it is determined that the currently-traveling vehicle travels to the target point within the estimated arrival time point.
Optionally, the processing module 704 is configured to:
and if the target point is a target downstream portal point, monitoring that the current running vehicle is in the target running path and the road section associated with at least one portal point downstream of the target is congested.
Optionally, the processing module 704 is configured to:
and if the target point is the target intersection, monitoring that the current running vehicle is in the target running path and the road section associated with at least one target intersection is jammed.
Optionally, the road network topological graph comprises a macro topological graph for representing the upstream and downstream relations between the gantries on the respective paths and a mesoscopic topological graph for representing the traveling path segments of the respective traveling vehicles, wherein the mesoscopic topological graph has the identified intersections between the respective paths; the building block 701 is configured to:
and constructing a macroscopic topological graph for highway charging according to the path information, and constructing a mesoscopic topological graph for highway charging according to the path information.
Optionally, the apparatus further comprises:
a first obtaining module (not shown in fig. 7) configured to obtain respective path information corresponding to respective traveling vehicles, where the respective path information includes portal information provided on the respective paths and upstream-downstream association relationship information between the respective portals provided on the respective paths;
the building block 701 is specifically configured to:
and constructing a macro topological graph for highway toll collection according to the portal information arranged on each path and the upstream and downstream incidence relation information between the portals arranged on each path, which are acquired by the first acquisition module.
Optionally, the apparatus further comprises:
a second acquisition module (not shown in fig. 7) configured to acquire respective pieces of route information corresponding to respective traveling vehicles, the respective pieces of route information further including intersections between the respective routes;
the building block 701 is specifically configured to:
and constructing a mesoscopic topological graph for highway toll collection according to the macroscopic topological graph and the intersection points between the paths acquired by the second acquisition module.
Optionally, the processing module 704 is further configured to:
and under the condition of giving a macroscopic link effectiveness judgment result, establishing an inference relation between the effectiveness of the path segments in the mesoscopic topological graph and the effectiveness of the macroscopic links in the macroscopic topological graph based on the mesoscopic link monitoring model, and reversely deducing the priority order of the failure of each path segment in the mesoscopic topological graph based on the inference relation so as to perform investigation and treatment.
It should be noted that, when the monitoring device for highway road condition based on charging data provided in the foregoing embodiment executes the monitoring method for highway road condition based on charging data, only the division of the functional modules is illustrated, and in practical application, the function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the functions described above. In addition, the monitoring device for highway traffic based on charging data and the monitoring method for highway traffic based on charging data provided by the above embodiments belong to the same concept, and the detailed implementation process is shown in the monitoring method for highway traffic based on charging data, and is not described herein again.
In the embodiment of the application, the estimation module is used for predicting the time length required by the current running vehicle to run the preset distance at the running speed according to the estimated running speed and the preset distance of the current running vehicle; predicting an estimated arrival time point of the current running vehicle to the target point based on the initial time point and the duration determined by the charging data of the current running vehicle; the processing module is used for judging whether the current running vehicle runs to the target point within the estimated arrival time point or not, if the current running vehicle is judged to run to the target point within the estimated arrival time point, the current running vehicle is monitored not to be jammed on the target running path, and otherwise, the current running vehicle is monitored to be jammed on a road section which is in the target running path and is associated with at least one target point. According to the embodiment of the application, the constructed road network topological graph for highway toll collection is introduced, so that whether congestion occurs on the target driving path or not can be accurately detected, and the fact that the road section which is specifically in the target driving path and is associated with a target point is congested can be monitored.
The invention also provides a computer readable medium, on which program instructions are stored, and the program instructions, when executed by a processor, implement the method for monitoring highway road conditions based on charging data provided by the above method embodiments.
The invention also provides a computer program product containing instructions, which when run on a computer causes the computer to execute the method for monitoring highway conditions based on toll data according to the above method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (9)
1. A method for monitoring highway road conditions based on charging data is characterized by comprising the following steps:
constructing a road network topological graph for highway toll collection, wherein the road network topological graph comprises a macroscopic topological graph used for identifying the upstream and downstream relation among all gantries on all paths and an mesoscopic topological graph used for identifying the traveling path segments of all traveling vehicles, and the mesoscopic topological graph is provided with the identified intersection points among all the path segments;
under the condition of giving a macroscopic link effectiveness judgment result, establishing an inference relation between the effectiveness of the path segments in the mesoscopic topological graph and the effectiveness of the macroscopic links in the macroscopic topological graph based on the mesoscopic link monitoring model, and reversely deducing the priority order of the failure of each path segment in the mesoscopic topological graph based on the inference relation so as to carry out investigation and disposal processing; under the condition of a given macroscopic link effectiveness judgment result, deducing the failure probability of each path segment; the mesoscopic link monitoring model is established based on a mesoscopic link monitoring algorithm; if a given path segment tps and a macro link validity determination condition is Ψ, then the conditional probability of tps failure is defined as:
wherein k is the space capacity of the path segmentation validity condition; mu.skUniquely identifying a path segment validity condition; p (mu)k) Segmenting the significance condition mu for a pathkThe probability of occurrence is set as tau (tau < 0.5) and p (mu)k)＝τa×(1-τ)bWherein a is mukWherein tps is 0, and b is μkWhere tps is 1, f1(tps，μk) Giving path segment tps a path segment validity condition mukDetermination of whether or not failure is in (a)kIs the path segment validity condition mukCorresponding macro link validity indication, f2(σkΨ) gives σkA determination as to whether or not the macro link validity determination condition Ψ coincides;
estimating a preset distance between the running speed of the current running vehicle and a target point on a target running path, wherein the target point comprises a target downstream portal point marked on the macroscopic topological graph and a target intersection point marked on the mesoscopic topological graph;
predicting the time length required by the current running vehicle to run the preset distance at the running speed according to the predicted running speed of the current running vehicle and the preset distance; predicting an estimated arrival time point of the current running vehicle to the target point based on the initial time point and the time length determined by the charging data of the current running vehicle;
and judging whether the current running vehicle runs to the target point within the estimated arrival time point, if so, monitoring that the current running vehicle is not jammed on the target running path, otherwise, monitoring that the current running vehicle is jammed on a road section which is within the target running path and is associated with at least one target point.
2. The method of claim 1, wherein the monitoring that the currently traveling vehicle is within the target travel path and that a segment associated with the at least one target point is congested comprises:
and if the target point is the target downstream portal point, monitoring that the current running vehicle is in the target running path and the road section associated with at least one portal point downstream of the target is congested.
3. The method of claim 1, wherein the monitoring that the currently traveling vehicle is within the target travel path and that a segment associated with the at least one target point is congested comprises:
and if the target point is the target intersection, monitoring that the current running vehicle is in the target running path and the road section associated with the at least one target intersection is congested.
4. The method of claim 1, wherein the road network topology map comprises a macro topology map for characterizing upstream and downstream relationships between respective gantries on respective paths and a mesoscopic topology map for characterizing travel path segments for respective traveling vehicles, the mesoscopic topology map having identified intersections between respective paths; the constructing of the road network topology graph for high-speed charging according to the path information comprises the following steps:
constructing the macro topology map for highway tolling from the path information, an
And constructing the mesoscopic topological graph for highway toll collection according to the path information.
5. The method of claim 4, wherein the constructing the macro topology map for highway tolling from the path information comprises:
acquiring each path information corresponding to each running vehicle, wherein each path information comprises portal information arranged on each path and upstream and downstream incidence relation information between portals arranged on each path;
and constructing the macro topological graph for highway toll collection according to the portal information arranged on each path and the upstream and downstream incidence relation information between the portals arranged on each path.
6. The method of claim 4, wherein the constructing the mesoscopic topology map for highway tolling according to the path information comprises:
acquiring each piece of route information corresponding to each running vehicle, wherein each piece of route information further comprises an intersection point between each route;
and constructing the mesoscopic topological graph for highway toll collection according to the macroscopic topological graph and the intersection points between the paths.
7. A highway condition monitoring device based on charging data, the device comprising:
the system comprises a construction module, a road network topological graph and a road toll collection module, wherein the road network topological graph comprises a macroscopic topological graph used for identifying the upstream and downstream relations among all gantries on all paths and an mesoscopic topological graph used for identifying the traveling path segments of all traveling vehicles, and the mesoscopic topological graph is provided with identified intersection points among all path segments;
the inference relation establishing module is used for establishing an inference relation between the effectiveness of the path segments in the mesoscopic topological graph and the effectiveness of the macro links in the macro topological graph on the basis of the mesoscopic link monitoring model under the condition of giving the judgment result of the effectiveness of the macro links;
the inference module is used for inferring the failure probability of each path segment under the condition of a given macroscopic link effectiveness judgment result;
the backward-pushing module is used for reversely pushing the priority order of each path segment failure in the intermediate topological graph based on the inference relation obtained by the inference relation establishing module so as to perform investigation and disposal processing; the mesoscopic link monitoring model is established based on a mesoscopic link monitoring algorithm; if a given path segment tps and a macro link validity determination condition is Ψ, then the conditional probability of tps failure is defined as:
wherein k is the space capacity of the path segmentation validity condition; mu.skUniquely identifying a path segment validity condition; p (mu)k) Segmenting the significance condition mu for a pathkThe probability of occurrence is set as tau (tau < 0.5) and p (mu)k)＝τa×(1-τ)bWherein a is mukWherein tps is 0, and b is μkWhere tps is 1, f1(tps，μk) Giving path segment tps a path segment validity condition mukDetermination of whether or not failure is in (a)kIs the path segment validity condition mukCorresponding macro link validity indication, f2(σkΨ) gives σkA determination as to whether or not the macro link validity determination condition Ψ coincides;
the estimation module is used for estimating the running speed of the current running vehicle and the preset distance between target points on a target running path, wherein the target points comprise target downstream portal points marked on the macro topological graph constructed by the construction module and target cross points marked on the mesoscopic topological graph constructed by the construction module;
the prediction module is used for predicting the time length required by the current running vehicle to run the preset distance at the running speed according to the running speed of the current running vehicle predicted by the prediction module and the preset distance; predicting an estimated arrival time point of the current running vehicle to the target point based on the initial time point and the time length determined by the charging data of the current running vehicle;
and the processing module is used for judging whether the current running vehicle runs to the target point within the estimated arrival time point predicted by the prediction module, monitoring that the current running vehicle is not jammed on the target running path if the current running vehicle is judged to run to the target point within the estimated arrival time point, and monitoring that the current running vehicle is jammed on a road section which is within the target running path and is associated with at least one target point if the current running vehicle is judged to run to the target point within the estimated arrival time point.
8. A computer device comprising a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the monitoring method of any one of claims 1 to 6.
9. A storage medium having stored thereon computer-readable instructions which, when executed by one or more processors, cause the one or more processors to perform the steps of the monitoring method of any one of claims 1 to 6.
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| CN113724493B (en) * | 2021-07-29 | 2022-08-16 | 北京掌行通信息技术有限公司 | Method and device for analyzing flow channel, storage medium and terminal |
| CN114333305B (en) * | 2021-12-21 | 2023-05-02 | 北京掌行通信息技术有限公司 | Method and device for inducing vehicles to pass during highway congestion, storage medium and terminal |
| CN115223359A (en) * | 2022-06-08 | 2022-10-21 | 北京掌行通信息技术有限公司 | Method and device for early warning of toll station, electronic equipment and storage medium |
| CN115240414B (en) * | 2022-07-19 | 2024-04-02 | 陕西蓝德智慧交通科技有限公司 | Traffic condition investigation method based on ETC portal system |
| CN115527380B (en) * | 2022-09-15 | 2023-07-21 | 交通运输部科学研究院 | Vehicle running speed measuring and calculating method, device, computer equipment and storage medium |
| CN115240431B (en) * | 2022-09-20 | 2023-02-10 | 深圳市城市交通规划设计研究中心股份有限公司 | Real-time online simulation system and method for traffic flow of highway toll station |
| CN116611586B (en) * | 2023-07-19 | 2023-10-31 | 山东高速股份有限公司 | Newly built road network flow prediction method and system based on double-layer heterogeneous network |
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