CN117116062A - Expressway traffic prediction method and system under construction occupation condition - Google Patents
Expressway traffic prediction method and system under construction occupation condition Download PDFInfo
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Abstract
The embodiment of the specification discloses a method and a system for predicting traffic volume of a highway under construction occupation conditions, wherein the method comprises the following steps: obtaining highway network structure data in a preset area to construct a highway directed graph, and extracting an OD matrix of the preset prediction area; according to the expressway directed graph and the OD matrix, the historical traffic capacity of each road section in a preset area is counted; determining construction occupation grades of all road sections based on construction occupation conditions in a preset area, and representing the construction occupation grades and key factors of road section traffic based on reduction coefficients to obtain actual traffic capacity of all road sections in the preset area; determining road sections to be split in a preset area based on the actual traffic capacity and the historical traffic capacity, and determining the traffic probability of each road section to be split and the replacement traffic road section of each road section to be split according to a preset gravitational field model; and predicting the actual traffic volume of the expressway network in a preset area based on the traffic probability.
Description
Technical Field
The specification relates to the field of expressway traffic prediction analysis, in particular to a method and a system for predicting expressway traffic under construction occupation conditions.
Background
Road traffic is rapidly upgraded along with the continuous development of urbanization, and the construction of expressway networks is also considered as an important means for improving traffic running efficiency, improving resident traveling conditions and coping with traffic demands of high-speed growing places. In order to fully exert the advantages of the expressway network, the accurate traffic prediction or traffic demand capacity analysis is paid attention to, and the expressway network has positive effects on the normal operation of the expressway, traffic management and experience of travelers, improves traffic efficiency, reduces traffic jam, enhances traffic safety, and provides reliable data support for traffic planning, decision making and other aspects.
In addition, the traditional traffic volume prediction is mainly based on historical traffic data and developed by combining a statistical model, and in addition, the machine learning-based method can be used for carrying out better traffic volume prediction by combining the time-space characteristics of traffic volume fully mined by traffic volume big data. However, in the construction, maintenance and transformation process of the expressway, due to factors such as construction occupation, road closure or traffic limitation, unstable and abnormal traffic flow can be caused, temporary traffic jam, path change and travel behavior change can be caused, and therefore prediction accuracy of a conventional prediction model is limited.
Disclosure of Invention
In order to solve the above technical problems, one or more embodiments of the present disclosure provide a method and a system for predicting traffic volume of a highway under a construction occupation situation.
One or more embodiments of the present disclosure adopt the following technical solutions:
one or more embodiments of the present disclosure provide a method for predicting traffic volume of a highway under construction occupancy, the method comprising:
acquiring highway network structure data in a preset area, constructing a highway directed graph based on the highway network structure data, and extracting an OD matrix of the preset area based on related node data of the highway directed graph;
according to the expressway directed graph and the OD matrix in the preset area, the historical traffic capacity of each road section in the preset area is counted;
determining the construction occupation grade of each road section based on the grade classification of the construction occupation condition and the preset construction occupation condition in a preset area, and representing the construction occupation grade and key factors of road section traffic based on a reduction coefficient to obtain the actual traffic capacity of each road section in the preset area;
determining road sections to be split in the preset area based on the actual traffic capacity and the historical traffic capacity, and determining the traffic probability of each road section to be split and the replacement traffic road section of each road section to be split according to a preset gravitational field model;
and predicting the actual traffic volume of the expressway network in the preset prediction area based on the traffic probability.
Optionally, in one or more embodiments of the present disclosure, the obtaining highway network structure data in the preset area to construct a highway directed graph based on the highway network structure data specifically includes:
dividing the expressway network by taking the administrative region as a boundary to obtain expressway network structure data in each preset region; wherein, highway network structure data includes: boundary portal frame data, charging station data in a prediction area, interchange data and highway section data;
classifying the expressway network structure data to obtain a node set and an edge set of an expressway directed graph; wherein the set of nodes comprises: presetting all boundary portal data in a preset area, charging station data in the preset area and intercommunication interchange data; the edge set includes: presetting data of each highway section in a preset area;
and establishing the expressway directed graph in the preset area based on the node set and the edge set of the expressway directed graph.
Optionally, in one or more embodiments of the present disclosure, the extracting the OD matrix of the preset prediction area based on the relevant node data of the expressway directional map specifically includes:
determining toll station nodes and boundary portal frames in the expressway directed graph to respectively acquire inbound vehicle ID information and outbound vehicle ID information of the toll stations and the boundary portal frames in the prediction area;
respectively comparing the inbound vehicle ID information of the toll station in the prediction area with the outbound vehicle ID information, and the boundary portal and the inbound vehicle ID information of the toll station in the prediction area with the outbound vehicle ID information so as to determine the starting point and the ending point of an initial OD matrix;
and determining the area range of the initial OD matrix based on the starting point and the end point of the initial OD matrix, and taking traffic in the area range as matrix elements of the initial OD matrix to obtain the OD matrix of a preset prediction area.
Optionally, in one or more embodiments of the present disclosure, comparing the inbound vehicle ID information of the toll station in the prediction area with the outbound vehicle ID information, and comparing the inbound vehicle ID information of the toll station in the boundary portal with the outbound vehicle ID information of the toll station in the prediction area, respectively, so as to determine a start point and an end point of an initial OD matrix, which specifically includes:
comparing the inbound vehicle ID information of the charging station in the prediction area with the outbound vehicle ID information, and if the inbound vehicle ID information of the charging station in the prediction area is determined to be consistent with the outbound vehicle ID information, determining the starting point and the ending point of the initial OD matrix as the charging station in the prediction area;
if the inbound vehicle ID information of the charging station in the prediction area is inconsistent with the outbound vehicle ID information, determining a corresponding boundary portal frame as a starting point based on the outbound vehicle ID information of each boundary portal frame in the node set, and determining the charging station in the prediction area as an end point;
if the outbound vehicle ID information is determined to be included in the inbound vehicle ID information and the outbound vehicle ID information is not included in the outbound vehicle ID information, determining a starting point of an initial OD matrix as a charging station in the prediction area, and determining a corresponding boundary portal as an ending point of the initial OD matrix based on the outbound vehicle ID information of each boundary portal in the node set;
and comparing the inbound vehicle ID information and the outbound vehicle ID information of the charging station in the boundary portal and the prediction area, and if the inbound vehicle ID information and the outbound vehicle ID information with the boundary portal are determined, and the charging station in the prediction area does not have the inbound vehicle ID information and the outbound vehicle ID information, determining the starting point boundary portal and the ending point boundary portal of the initial OD matrix based on the time sequence of the boundary portal.
Optionally, in one or more embodiments of the present disclosure, according to the expressway directed graph and the OD matrix of the preset prediction area, the statistics of the historical traffic capacity of each road section in the preset prediction area specifically includes:
determining a plurality of road sections in the preset prediction area based on a starting point and an ending point corresponding to an OD matrix of the preset prediction area;
and determining corresponding edges of each road section on the expressway directed graph, and acquiring road section traffic volume corresponding to the corresponding edges so as to determine the traffic capacity of the road section based on the road section traffic volume.
Optionally, in one or more embodiments of the present disclosure, the expressing the key factors of the construction road occupation level and road section traffic based on the reduction coefficient, to obtain the actual traffic capacity of each road section in the preset area specifically includes:
obtaining a reduction coefficient of key factors of the construction road occupation level and road section traffic;
weighting the actual traffic volume of the road sections among the nodes based on the reduction coefficient to obtain the actual traffic capacity of each road section in a preset area; wherein, the actual traffic capacity is:
，/>，/>，/>，/>the reduction coefficient of key factors related to the road occupation length, the proportion and the speed limit of the heavy vehicle, and +.>Is the actual traffic volume of the inter-node road segments.
Optionally, in one or more embodiments of the present disclosure, determining, based on the actual traffic capacity and the historical traffic capacity, a road segment to be split in the preset area, and determining, according to a preset gravitational field model, a traffic probability of each road segment to be split and an alternative traffic road segment of each road segment to be split, including:
comparing the actual traffic capacity with the historical traffic capacity to obtain a road section with changed traffic capacity as a road section to be split in the preset area, so as to determine a replacement traffic section of the road section to be split based on the expressway network directed graph;
acquiring the gravitational field parameters of the road section to be shunted and the alternative traffic road section to replace the gravitational field parametersEntering the preset gravitational field model to obtain the traffic probability of each road section to be split and the replacement traffic road section of each road section to be split; the preset gravitational field model is as follows:，/>the traffic probability of the road section; the gravitational field parameters include: minimum traffic capacity of road section->Actual traffic volume of road segment->Traffic cost of road section->Adjustable parameter of gravitational field model +.>。
Optionally, in one or more embodiments of the present disclosure, predicting, based on the traffic probability, an actual traffic volume of the highway network in the preset prediction area specifically includes:
the method comprises the steps of obtaining the passing probability of each road section, and carrying out standardized processing on each passing probability to obtain standardized passing probability;
and updating and distributing the traffic volume of each road section in the preset prediction area according to the standardized traffic probability to obtain the predicted traffic volume of each road section in the prediction area.
Optionally, in one or more embodiments of the present disclosure, updating and distributing the traffic volume of each road segment in the preset area according to the normalized traffic probability to obtain the predicted traffic volume of each road segment in the preset area, which specifically includes:
determining the road condition type of the road section which is the same as the standardized traffic probability; wherein, the road condition type includes: the road conditions of construction occupation are available and the road conditions of construction occupation are not available;
substituting the standardized traffic probability into a corresponding preset traffic distribution formula based on the road condition type, and updating and distributing traffic of each road section to obtain predicted traffic of each road section in the prediction area; wherein, the preset traffic distribution formula comprises:
the preset traffic distribution formula under the condition of no construction occupying the road:，，/>for normalizing the probability of passage->For minimum probability of passage->The maximum probability of pass is set;
the method comprises the following steps of:。
one or more embodiments of the present specification provide a system for highway traffic prediction under construction occupancy, the system comprising:
the construction unit is used for acquiring highway network structure data in a preset prediction area, constructing a highway directed graph based on the highway network structure data, and extracting an OD matrix of the preset prediction area based on relevant node data of the highway directed graph;
the historical traffic capacity calculation unit is used for counting the historical traffic capacity of each road section in the preset area according to the expressway directed graph and the OD matrix in the preset area;
the actual traffic capacity calculation unit is used for determining the construction occupation grade of each road section based on the construction occupation condition in the preset area and the grade classification of the preset construction occupation condition, and representing the construction occupation grade and key factors of road section traffic based on the reduction coefficient to obtain the actual traffic capacity of each road section in the preset area;
the traffic probability determining unit is used for determining the road sections to be split in the preset area based on the actual traffic capacity and the historical traffic capacity, and determining the traffic probability of each road section to be split and the replacement traffic road section of each road section to be split according to a preset gravitational field model;
and the traffic quantity prediction unit is used for predicting the actual traffic quantity of the expressway network in the preset prediction area based on the traffic probability.
The above-mentioned at least one technical scheme that this description embodiment adopted can reach following beneficial effect:
the road network structure of the expressway is abstracted into a directed graph with a node-side set, so that an OD matrix capable of expressing traffic capacity of each road section in the expressway is constructed, and the effect of providing accurate data support for traffic volume of the expressway under the condition of predicting construction occupation is achieved. The influence of different construction occupation conditions on the road section traffic capacity is fully considered after the actual traffic capacity is judged by obtaining the reduction coefficient under the different construction occupation conditions, the accuracy of real-time monitoring of the actual traffic capacity is improved, the traffic probability of each road section to be split and the replacement traffic road section of each road section to be split is determined based on the preset gravitational field model, the subjective selection of drivers on different road sections is simulated by the preset gravitational field model, the traffic probability of each road section is further determined, and the traffic quantity is conveniently redistributed from the angle of the drivers to obtain the predicted traffic quantity after the redistribution of each road section.
Drawings
In order to more clearly illustrate the embodiments of the present description or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some of the embodiments described in the present description, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
fig. 1 is a schematic flow chart of a method for predicting traffic volume of a highway under a construction occupation situation according to an embodiment of the present disclosure;
fig. 2 is a schematic diagram of constructing a directional diagram of an expressway according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a table format of information for acquiring an OD matrix according to an embodiment of the present disclosure;
fig. 4 is a schematic diagram of an internal structure of a system for predicting traffic volume of a highway under a construction occupation situation according to an embodiment of the present disclosure.
Detailed Description
The embodiment of the specification provides a method and a system for predicting traffic volume of a highway under construction occupation conditions.
In order to make the technical solutions in the present specification better understood by those skilled in the art, the technical solutions in the embodiments of the present specification will be clearly and completely described below with reference to the drawings in the embodiments of the present specification, and it is obvious that the described embodiments are only some embodiments of the present specification, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present disclosure.
As shown in fig. 1, a flow chart of a method for predicting traffic volume of a highway under a construction scenario is provided in an embodiment of the present disclosure. As can be seen from fig. 1, in one or more embodiments of the present disclosure, a method for predicting traffic volume of a highway under a construction occupation situation specifically includes the following steps:
s101: and acquiring expressway network structure data in a preset prediction area, constructing an expressway directed graph based on the expressway network structure data, and extracting an OD matrix of the preset prediction area based on related node data of the expressway directed graph.
Because of the current single charging data in administrative area units, it is difficult to describe all high-speed traffic conditions in administrative area units, and the real-time performance is poor. Therefore, in order to obtain traffic flow conditions of the expressway more intuitively and completely, obtain more accurate traffic distribution information, and provide more real data support for traffic flow distribution of a construction road section. The OD matrix is a matrix in which all traffic areas are ordered by rows (starting area) and columns (destination area) and the traveling quantity (OD quantity) of residents or vehicles between any two areas is taken as an element. The two sections are divided into rectangular matrixes and triangular matrixes according to the form, the rectangular matrixes can distinguish the travel amounts in different directions in the two sections, the latter represents only the sum of the travel amounts in both directions in both sections. There are various matrices of the split-out row purpose, the split-out row way, or the no split-out purpose ("full purpose"), and the no split-out way ("full way") by content. The OD matrix is an english abbreviation of source-destination matrix, the so-called point is actually a traffic divided area, and the data in the matrix is the traffic flow from area a to area B, which is actually the congestion level on the path from one place to another, and is generally used in the traffic field.
Specifically, in one or more embodiments of the present disclosure, obtaining highway network structure data in a preset area to construct a highway directed graph based on the highway network structure data specifically includes:
firstly, dividing an expressway network with an administrative region as a boundary to obtain preset prediction regions, for example, an external dotted outline shown in fig. 2 is a boundary line of the administrative region, and obtaining expressway network structure data in each preset prediction region after dividing to obtain the preset prediction regions. It should be noted that the highway network structure data includes: boundary portal data, charging station data in a prediction area, interchange data and highway section data. The highway network structure data is then classified,thus, a node set and an edge set of the expressway directed graph are obtained, and it can be understood that the node set includes: the boundary portal data within the preset zone is as in FIG. 2、/>、/>、/>Nodes, forecast in-area charging station data as +.2>、/>Node, interchange data as +.>、/>、/>Nodes, and the edge set includes: and presetting data of each highway section in a preset area. And then establishing the expressway directed graph in a preset area according to the node set and the edge set of the expressway directed graph. In other words, in an embodiment of the present disclosure, firstly, the structures are classified, boundary portal frames, toll stations in a preset area, interchange are taken as nodes, expressway sections among the nodes are taken as edges, and a expressway network structure directed graph set in the preset area is established, as shown in the following formula:
(1)
(2)
(3)
(4)
wherein,is a set of nodes in the directed graph; />For the collection of edges in the directed graph, the element +.>Comprising the properties of two road sections, i.e +.>Representing road distance>Indicating the number of lanes of the road section, & lt & gt>Representing road segment traffic volume; />Is a adjacency matrix, in which the subscripts and sets of elements are +.>And the same is expressed as whether two nodes of the road section are communicated or not.
Further, in one or more embodiments of the present disclosure, the OD matrix of the preset prediction area is extracted based on the related node data of the expressway directed graph, which specifically includes the following steps:
first, toll station nodes and boundary portal frames in a highway directed graph are determined, so that inbound vehicle ID information and outbound vehicle ID information of toll stations and the boundary portal frames in a preset area are respectively acquired. And then, by respectively comparing the inbound vehicle ID information of the toll station in the predicted area with the outbound vehicle ID information, and comparing the boundary portal frame with the inbound vehicle ID information of the toll station in the predicted area with the outbound vehicle ID information, the starting point and the ending point of the initial OD matrix can be determined. Specifically, the method for determining the initial OD matrix includes the steps of comparing the inbound vehicle ID information and the outbound vehicle ID information of the toll station in the prediction area with the inbound vehicle ID information and the outbound vehicle ID information of the toll station in the boundary portal and the prediction area, respectively, to determine the starting point and the ending point of the initial OD matrix, specifically including:
first, comparing the inbound vehicle ID information of the toll station in the predicted area with the outbound vehicle ID information, and if the inbound vehicle ID information of the toll station in the predicted area is determined to be consistent with the outbound vehicle ID information, determining the starting point and the end point of the initial OD matrix as the toll station in the predicted area. If the inbound vehicle ID information of the toll station in the prediction area is inconsistent with the outbound vehicle ID information, searching is carried out according to the outbound vehicle ID information of each boundary portal in the node set, so that the corresponding boundary portal is determined as a starting point, and the toll station in the prediction area is determined as an end point.
If the inbound vehicle ID information of the toll station in the prediction area is compared with the outbound vehicle ID information, it is determined that the toll station in the prediction area has inbound vehicle ID information and does not have outbound vehicle ID information, then the start point of the initial OD matrix can be determined as the toll station in the prediction area, and the end point of the corresponding boundary portal frame is determined as the initial OD matrix based on the outbound vehicle ID information of each boundary portal frame in the node set.
And comparing the boundary portal with the inbound vehicle ID information and the outbound vehicle ID information of the charging station in the prediction area, if the inbound vehicle ID information and the outbound vehicle ID information of the boundary portal are determined, and the charging station in the prediction area does not have the inbound vehicle ID information and the outbound vehicle ID information, determining that the charging station is an OD matrix of the boundary portal-the boundary portal, and determining that the specific starting point and the terminal are the starting point boundary portal of the initial OD matrix and the terminal boundary portal with the early time based on the time sequence of the boundary portal, namely by extracting the time of the two boundary portals which are passed by the vehicle.
Filling the starting point and the end point of the initial OD matrix determined in the process according to the information table format of the acquired OD matrix shown in fig. 3 to determine the area range of the initial OD matrix, and taking the traffic in the area range as matrix elements of the initial OD matrix to obtain the OD matrix of the preset prediction area.
S102: and counting the historical traffic capacity of each road section in the preset area according to the expressway directed graph and the OD matrix in the preset area.
In order to obtain the change situation of the road section communication capability, so that the original traffic flow of the fault road section and the like are distributed to the fault road section and the tie road section in proportion in time, in the embodiment of the present disclosure, the historical traffic capability of each road section in the preset area needs to be counted according to the expressway directional diagram obtained in the step S101 and the OD matrix in the preset area. Specifically, in one or more embodiments of the present disclosure, according to the expressway directed graph and the OD matrix of the preset prediction area, the historical traffic capacity of each road section in the preset prediction area is counted, which specifically includes the following steps: firstly, determining a plurality of road sections in a preset prediction area according to a starting point and a terminal point corresponding to an OD matrix of the preset prediction area, thereby determining edges corresponding to each road section on a highway directed graph, further obtaining road section traffic volume corresponding to the corresponding edges, and determining traffic capacity of the road section based on the road section traffic volume.
S103: and determining the construction occupation grade of each road section based on the grade classification of the construction occupation condition in the preset area and the preset construction occupation condition, and representing the construction occupation grade and key factors of road section traffic based on the reduction coefficient to obtain the actual traffic capacity of each road section in the preset area.
The construction occupation condition of the expressway maintenance operation can influence normal traffic flow operation, and the main reason is that the number of lanes of the original road section is reduced, so that the road traffic capacity is reduced. In addition, factors such as the construction track occupation length, the proportion of heavy vehicles, the speed limit and the like are also influence factors closely related to the traffic capacity. Therefore, in order to improve the prediction accuracy, the problem that the actual traffic capacity judgment and the actual situation fit degree are low due to the fact that the related shadow key factors of the traffic capacity are not considered in the prior art is solved. In the embodiment of the specification, the construction occupation level of each road section is determined according to the classification of the construction occupation condition in the preset area and the preset construction occupation condition shown in the following table 1, so that the construction occupation level and the key factors of road section traffic are represented according to the reduction coefficient, and the actual traffic capacity of each road section in the preset area is obtained.
TABLE 1 class Classification Table for different construction conditions
Specifically, in one or more embodiments of the present disclosure, key factors of construction road occupation level and road section traffic are represented based on reduction coefficients, and actual traffic capacity of each road section in a preset area is obtained, which specifically includes the following steps: firstly, obtaining a reduction coefficient of key factors of construction road occupation level and road section traffic, and weighting the actual traffic of road sections among nodes based on the reduction coefficient to obtain the actual traffic capacity of each road section in a preset area; the actual traffic capacity is as follows:
；/>，/>，/>，/>the reduction coefficient of key factors related to the road occupation length, the proportion and the speed limit of the heavy vehicle, and +.>Is the actual traffic volume of the inter-node road segments.
S104: and determining the road sections to be split in the preset area based on the actual traffic capacity and the historical traffic capacity, and determining the traffic probability of each road section to be split and the replacement traffic road section of each road section to be split according to a preset gravitational field model.
When the expressway has a construction occupation situation, the situation that the traffic capacity of the road section is reduced is usually accompanied, so that the road section with changed traffic capacity, that is, the road section affected by the construction occupation situation, can be determined after the actual traffic capacity and the historical traffic capacity of each road section are obtained based on the steps S102 and S103. At the moment, the application relies on historical OD data, considers the selection influence of construction road sections and functional tie road sections on vehicles in the construction road occupation situation, and adds an attraction model to describe subjective selection behaviors of people so as to obtain the passing probability of each road section, so that traffic can be redistributed from the angle of drivers in the follow-up process.
Specifically, in one or more embodiments of the present disclosure, based on an actual traffic capacity and a historical traffic capacity, a to-be-split road section in a preset area is determined, and a traffic probability of each to-be-split road section and a replacement traffic road section of each to-be-split road section is determined according to a preset gravitational field model, which specifically includes the following procedures:
firstly, comparing the actual traffic capacity with the historical traffic capacity, so as to obtain a road section with changed traffic capacity, namely a road section with reduced traffic capacity, as a road section to be split in a preset area, and determining a replacement traffic section corresponding to the road section to be split according to a highway network directed graph. It can be understood that the road section to be split determined at this time has a tendency to shift and split in the alternative traffic area. Therefore, in order to simulate subjective selection of different road sections by a driver through a preset gravitational field model and further determine the passing probability of each road section, in the embodiment of the specification, gravitational field parameters of the road sections to be split and the replacement passing road sections are obtained, so that the gravitational field parameters are substituted into the preset gravitational field model, and the passing probability of each road section to be split and the replacement passing road section of each road section to be split is obtained.
The subjective selection of the driver on the traffic road section is mainly influenced by the traffic capacity of the road section, the section length and the traffic time, so that the preset gravitational field model is determined by considering the gravitational field parameters, and the preset gravitational field model is as follows:，/>the traffic probability of the road section; the gravitational field parameters include: minimum traffic capacity of road section->Actual traffic volume of road segment->Traffic cost of road section->Adjustable parameter of gravitational field model +.>。
S105: and predicting the actual traffic volume of the expressway network in the preset prediction area based on the traffic probability.
After the traffic probability of each road section is obtained based on the steps, the original traffic capacity of the expressway network is redistributed according to the traffic probability, so that the prediction of the actual traffic volume of the expressway network in a preset area is realized. Here, the traffic volume that can be redistributed is a vehicle that can still reach the end point in the OD matrix after changing the driving node. Specifically, in one or more embodiments of the present disclosure, the prediction of the actual traffic volume of the highway network in the preset area based on the traffic probability specifically includes the following procedures:
firstly, acquiring the passing probability of each road section, and carrying out standardized processing on each passing probability to obtain standardized passing probability; the following description is needed: the normalized probability of passage is:，/>for normalizing the probability of passage->For minimum probability of passage->Is the maximum probability of passage. And then, updating and distributing the traffic volume of each road section in the preset area according to the standardized traffic probability obtained after the standardized processing to obtain the predicted traffic volume of each road section in the preset area. Specifically, updating and distributing the traffic volume of each road section in a preset area according to the standardized traffic probability to obtain the predicted traffic volume of each road section in the preset area, which specifically comprises the following steps:
firstly, determining and standardizing the road condition type of the road section with the probability of passing. The road condition types comprise: the road conditions of construction occupation are available and the road conditions of construction occupation are not available. Substituting the standardized traffic probability into a corresponding preset traffic distribution formula according to the road condition type so as to update and distribute traffic of each road section and obtain predicted traffic of each road section in the predicted area; wherein, the preset traffic distribution formula comprises: the preset traffic distribution formula under the condition of no construction occupying the road:and a preset traffic distribution formula under the condition that construction occupies the road:the method comprises the steps of carrying out a first treatment on the surface of the Upper partIn (1) the->Actual traffic between nodes under the condition that no construction occupies the road for the section; />The section has actual traffic volume between nodes under construction occupying road conditions.
As shown in fig. 4, in the embodiment of the present disclosure, a schematic diagram of an internal structure of a system for predicting traffic volume of a highway under a construction occupancy condition is provided. As can be seen from fig. 4, in one or more embodiments of the present disclosure, a system for predicting traffic volume of an expressway under a construction occupancy situation includes:
a construction unit 401, configured to obtain highway network structure data in a preset prediction area, construct a highway directed graph based on the highway network structure data, and extract an OD matrix of the preset prediction area based on relevant node data of the highway directed graph;
a historical traffic capacity calculation unit 402, configured to calculate, according to the expressway directed graph and an OD matrix in the preset area, historical traffic capacities of road sections in the preset area;
the actual traffic capacity calculation unit 403 is configured to determine a construction occupation level of each road section based on a construction occupation condition in a preset area and a level classification of the preset construction occupation condition, and represent the construction occupation level and key factors of road section traffic based on a reduction coefficient, so as to obtain an actual traffic capacity of each road section in the preset area;
the traffic probability determining unit 404 is configured to determine, based on the actual traffic capacity and the historical traffic capacity, road segments to be split in the preset area, and determine a traffic probability of each road segment to be split and a replacement traffic road segment of each road segment to be split according to a preset gravitational field model;
and the traffic prediction unit 405 is configured to predict an actual traffic volume of the highway network in the preset prediction area based on the traffic probability.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus, devices, non-volatile computer storage medium embodiments, the description is relatively simple, as it is substantially similar to method embodiments, with reference to the section of the method embodiments being relevant.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing is merely one or more embodiments of the present description and is not intended to limit the present description. Various modifications and alterations to one or more embodiments of this description will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, or the like, which is within the spirit and principles of one or more embodiments of the present description, is intended to be included within the scope of the claims of the present description.
Claims (10)
1. A method for predicting traffic volume of a highway under construction occupation condition, the method comprising:
acquiring highway network structure data in a preset area, constructing a highway directed graph based on the highway network structure data, and extracting an OD matrix of the preset area based on related node data of the highway directed graph;
according to the expressway directed graph and the OD matrix in the preset area, the historical traffic capacity of each road section in the preset area is counted;
determining the construction occupation grade of each road section based on the grade classification of the construction occupation condition and the preset construction occupation condition in a preset area, and representing the construction occupation grade and key factors of road section traffic based on a reduction coefficient to obtain the actual traffic capacity of each road section in the preset area;
determining road sections to be split in the preset area based on the actual traffic capacity and the historical traffic capacity, and determining the traffic probability of each road section to be split and the replacement traffic road section of each road section to be split according to a preset gravitational field model;
and predicting the actual traffic volume of the expressway network in the preset area based on the traffic probability.
2. The method for predicting traffic volume of a highway under construction occupation situation according to claim 1, wherein the obtaining highway network structure data in a preset area to construct a highway directed graph based on the highway network structure data specifically comprises:
dividing the expressway network by taking the administrative region as a boundary to obtain expressway network structure data in each preset region; wherein, highway network structure data includes: boundary portal frame data, charging station data in a prediction area, interchange data and highway section data;
classifying the expressway network structure data to obtain a node set and an edge set of an expressway directed graph; wherein the set of nodes comprises: presetting all boundary portal data in a preset area, charging station data in the preset area and intercommunication interchange data; the edge set includes: presetting data of each highway section in a preset area;
and establishing the expressway directed graph in the preset area based on the node set and the edge set of the expressway directed graph.
3. The method for predicting traffic volume of expressway under construction occupancy situation according to claim 2, wherein the extracting the OD matrix of the preset prediction area based on the related node data of the expressway directed graph specifically comprises:
determining toll station nodes and boundary portal frames in the expressway directed graph to respectively acquire inbound vehicle ID information and outbound vehicle ID information of the toll stations and the boundary portal frames in the prediction area;
respectively comparing the inbound vehicle ID information of the toll station in the prediction area with the outbound vehicle ID information, and the boundary portal and the inbound vehicle ID information of the toll station in the prediction area with the outbound vehicle ID information so as to determine the starting point and the ending point of an initial OD matrix;
and determining the area range of the initial OD matrix based on the starting point and the end point of the initial OD matrix, and taking traffic in the area range as matrix elements of the initial OD matrix to obtain the OD matrix of a preset prediction area.
4. A method for predicting traffic volume of a highway under construction occupancy, as set forth in claim 3, wherein comparing the inbound vehicle ID information of the toll station in the prediction area with the outbound vehicle ID information, and comparing the inbound vehicle ID information of the toll station in the border portal and the prediction area with the outbound vehicle ID information, respectively, to determine a start point and an end point of an initial OD matrix, comprises:
comparing the inbound vehicle ID information of the charging station in the prediction area with the outbound vehicle ID information, and if the inbound vehicle ID information of the charging station in the prediction area is determined to be consistent with the outbound vehicle ID information, determining the starting point and the ending point of the initial OD matrix as the charging station in the prediction area;
if the inbound vehicle ID information of the charging station in the prediction area is inconsistent with the outbound vehicle ID information, determining a corresponding boundary portal frame as a starting point based on the outbound vehicle ID information of each boundary portal frame in the node set, and determining the charging station in the prediction area as an end point;
if the outbound vehicle ID information is determined to be included in the inbound vehicle ID information and the outbound vehicle ID information is not included in the outbound vehicle ID information, determining a starting point of an initial OD matrix as a charging station in the prediction area, and determining a corresponding boundary portal as an ending point of the initial OD matrix based on the outbound vehicle ID information of each boundary portal in the node set;
and comparing the inbound vehicle ID information and the outbound vehicle ID information of the charging station in the boundary portal and the prediction area, and if the inbound vehicle ID information and the outbound vehicle ID information with the boundary portal are determined, and the charging station in the prediction area does not have the inbound vehicle ID information and the outbound vehicle ID information, determining the starting point boundary portal and the ending point boundary portal of the initial OD matrix based on the time sequence of the boundary portal.
5. The method for predicting traffic volume of expressway under construction occupancy situation according to claim 1, wherein statistics of historical traffic capacity of each road section in the preset area is performed according to the expressway directed graph and an OD matrix in the preset area, specifically comprising:
determining a plurality of road sections in the preset prediction area based on a starting point and an ending point corresponding to an OD matrix of the preset prediction area;
and determining corresponding edges of each road section on the expressway directed graph, and acquiring road section traffic volume corresponding to the corresponding edges so as to determine the traffic capacity of the road section based on the road section traffic volume.
6. The method for predicting traffic volume of expressway under construction occupation situation according to claim 1, wherein the key factors of construction occupation level and road section traffic are represented based on reduction coefficient, and actual traffic capacity of each road section in a preset prediction area is obtained, specifically comprising:
obtaining a reduction coefficient of key factors of the construction road occupation level and road section traffic;
weighting the actual traffic volume of the road sections among the nodes based on the reduction coefficient to obtain the actual traffic capacity of each road section in a preset area; wherein, the actual traffic capacity is:
，/>，/>，/>，/>the reduction coefficient of key factors related to the road occupation length, the proportion and the speed limit of the heavy vehicle, and +.>Is the actual traffic volume of the inter-node road segments.
7. The method for predicting traffic volume of expressway under construction occupancy of claim 1, wherein determining the road segments to be split in the preset area based on the actual traffic capacity and the historical traffic capacity, and determining the traffic probability of each road segment to be split and the replacement traffic road segment of each road segment to be split according to a preset gravitational field model, specifically comprises:
comparing the actual traffic capacity with the historical traffic capacity to obtain a road section with changed traffic capacity as a road section to be split in the preset area, so as to determine a replacement traffic section of the road section to be split based on the expressway network directed graph;
acquiring the gravitational field parameters of the road sections to be split and the replacement traffic road sections, so as to substitute the gravitational field parameters into the preset gravitational field model, and acquiring the traffic probability of the replacement traffic road sections of each road section to be split and each road section to be split; the preset primerThe force field model is as follows:，/>the traffic probability of the road section; the gravitational field parameters include: minimum traffic capacity of road section->Actual traffic volume of road segment->Traffic cost of road section->Adjustable parameter of gravitational field model +.>。
8. The method for predicting the traffic volume of the expressway under the construction occupation situation according to claim 1, wherein the predicting the actual traffic volume of the expressway network in the preset prediction area based on the traffic probability specifically comprises:
the method comprises the steps of obtaining the passing probability of each road section, and carrying out standardized processing on each passing probability to obtain standardized passing probability;
and updating and distributing the traffic volume of each road section in the preset prediction area according to the standardized traffic probability to obtain the predicted traffic volume of each road section in the prediction area.
9. The method for predicting traffic volume of highway under construction occupation situation according to claim 8, wherein the traffic volume of each road section in the preset prediction area is updated and distributed according to the standardized traffic probability, so as to obtain the predicted traffic volume of each road section in the prediction area, and the method specifically comprises the following steps:
determining the road condition type of the road section which is the same as the standardized traffic probability; wherein, the road condition type includes: the road conditions of construction occupation are available and the road conditions of construction occupation are not available;
substituting the standardized traffic probability into a corresponding preset traffic distribution formula based on the road condition type, and updating and distributing traffic of each road section to obtain predicted traffic of each road section in the prediction area; wherein, the preset traffic distribution formula comprises:
the preset traffic distribution formula under the condition of no construction occupying the road:，，/>for normalizing the probability of passage->For minimum probability of passage->The maximum probability of pass is set;
the method comprises the following steps of:。
10. a system for highway traffic prediction under construction occupancy, the system comprising:
the construction unit is used for acquiring highway network structure data in a preset prediction area, constructing a highway directed graph based on the highway network structure data, and extracting an OD matrix of the preset prediction area based on relevant node data of the highway directed graph;
the historical traffic capacity calculation unit is used for counting the historical traffic capacity of each road section in the preset area according to the expressway directed graph and the OD matrix in the preset area;
the actual traffic capacity calculation unit is used for determining the construction occupation grade of each road section based on the construction occupation condition in the preset area and the grade classification of the preset construction occupation condition, and representing the construction occupation grade and key factors of road section traffic based on the reduction coefficient to obtain the actual traffic capacity of each road section in the preset area;
the traffic probability determining unit is used for determining the road sections to be split in the preset area based on the actual traffic capacity and the historical traffic capacity, and determining the traffic probability of each road section to be split and the replacement traffic road section of each road section to be split according to a preset gravitational field model;
and the traffic quantity prediction unit is used for predicting the actual traffic quantity of the expressway network in the preset prediction area based on the traffic probability.
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