CN116168539A - Prediction method and prediction device for highway traffic capacity parameters - Google Patents
Prediction method and prediction device for highway traffic capacity parameters Download PDFInfo
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Abstract
The application provides a prediction method and a prediction device for highway traffic capacity parameters, comprising the following steps: acquiring an actual traffic capacity influence parameter set of each type of road section on a target road, and inputting a road traffic capacity parameter prediction model of each type of road section to obtain predicted road section traffic capacity parameters so as to determine full-line predicted traffic capacity parameters of the target road; and carrying out cluster analysis on the historical traffic capacity parameter set of each type of road section to obtain the state historical traffic capacity parameter of each traffic flow state, taking the historical traffic capacity influence parameter set of the type of road section as input, taking the state historical traffic capacity parameter of each traffic flow state as output, and constructing a highway traffic capacity parameter prediction model. Therefore, the three-phase traffic flow state theory and the unsupervised data prediction model can be fused, and the influence of various factors on the traffic capacity of different road sections is fully considered, so that the road traffic capacity parameters can be predicted more accurately.
Description
Technical Field
The present disclosure relates to the field of traffic technologies, and in particular, to a method and an apparatus for predicting a highway traffic capacity parameter.
Background
The highway traffic capacity is taken as the basic basis of highway planning, design and management, and penetrates through each stage of highway engineering construction in China. The main roles of traffic capacity analysis can be summarized as: the method is used for road planning and design, traffic operation analysis, service level analysis and traffic volume prediction of the existing or potential bottleneck road sections, highway engineering and traffic management measures for improving traffic operation quality and traffic management, and traffic management measures of each stage are planned according to predicted traffic volume increase conditions and analysis of operation quality change conditions.
At present, in the prior art, an empirical value given in relevant road standard regulation is often directly determined as a traffic capacity parameter of a road, and the manner cannot reflect the actual running characteristic of road traffic, so that the determined traffic capacity is inaccurate.
Disclosure of Invention
In view of the above, an object of the present application is to provide a method and a device for predicting a road traffic capacity parameter, which predict and obtain a full-line predicted traffic capacity parameter of a target road through multiple traffic capacity influence parameters of each type of road section and a road traffic capacity parameter prediction model; therefore, the three-phase traffic flow state theory and the unsupervised data prediction model can be fused, and the influence of various factors on the traffic capacity of different road sections is fully considered, so that the road traffic capacity parameters can be predicted more accurately.
The embodiment of the application provides a prediction method of highway traffic capacity parameters, which comprises the following steps:
acquiring an actual traffic capacity influence parameter set of each type of road section on a target road;
inputting the actual traffic capacity influence parameter set of each type of road section into a road traffic capacity parameter prediction model of each type of road section to obtain predicted road section traffic capacity parameters of each type of road section, and determining the full-line predicted traffic capacity parameters of the target road according to the predicted road section traffic capacity parameters of each type of road section;
the road traffic capacity parameter prediction model of each type of road section is constructed by the following steps:
acquiring a historical traffic capacity parameter set and a historical traffic capacity influence parameter set of each type of road section in the roads with different historical time nodes;
performing cluster analysis on the historical traffic capacity parameter set of each type of road section to obtain the state historical traffic capacity parameter of the type of road section when the type of road section is in each traffic flow state; wherein the traffic flow state includes free flow, synchronous-choked flow, and choked;
and taking the historical traffic capacity influence parameter set of the type of road section as input, and taking the state historical traffic capacity parameter of the type of road section in each traffic flow state as output to construct a road traffic capacity parameter prediction model of the type of road section.
Further, each type of road segment is divided into at least one sub-road segment, and each sub-road segment comprises an observation point; the actual traffic capacity influence parameter set of each type of road section comprises an actual traffic capacity influence parameter subset corresponding to each observation point position in the road section; the predicted road traffic capacity parameter of each type of road section comprises a predicted point traffic capacity parameter corresponding to each observation point in the road section; after inputting the actual traffic capacity influence parameter set of each type of road section into the road traffic capacity parameter prediction model of the type of road section to obtain the predicted road section traffic capacity parameter of each type of road section, and determining the full-line predicted traffic capacity parameter of the target road according to the predicted road section traffic capacity parameter of each type of road section, the prediction method further comprises:
determining an occupied area occupied by a construction road on the target road;
for each observation point located in the occupied zone, correcting the predicted road section traffic capacity parameter of the observation point by the following formula to obtain the corrected predicted road section traffic capacity parameter of the observation point:
N k ＝ max γ 1 γ 2 γ 3
wherein N is k Representing the predicted road section traffic capacity parameter after the observation point position is corrected; n (N) max The maximum single-lane road traffic capacity parameter representing the observation point is determined by dividing the full line prediction traffic capacity parameter corresponding to the observation point by the number of lanes; gamma ray 1 The number of open lanes representing the observation point; gamma ray 2 An open lane position correction coefficient indicating the observation point; gamma ray 3 An open lane width correction coefficient indicating the observation point; wherein, gamma 2 And gamma 3 And calculating by adopting a particle swarm optimization algorithm from the historical traffic capacity parameter set.
Further, after inputting the actual traffic capacity influence parameter set of each type of road section into the road traffic capacity parameter prediction model of the type of road section to obtain the predicted road section traffic capacity parameter of each type of road section, and determining the full-line predicted traffic capacity parameter of the target road according to the predicted road section traffic capacity parameter of each type of road section, the prediction method further comprises:
determining connection areas among different types of road sections on the target road; the road sections comprise basic road sections, ramp road sections and interweaving area road sections; the range of different types of road sections on the target road is determined by experience parameters;
for each connection area, determining the ratio of the predicted point traffic capacity parameter of each observation point included in the connection area to the road section length of the sub road section where the observation point is located;
Performing differential operation on the ratio corresponding to each two adjacent observation points in the connection area to obtain differential values corresponding to each two adjacent observation points;
determining the influence ranges of ramps in different types of road sections and the influence ranges of the interleaving areas according to the observation points with the difference values smaller than a preset threshold value;
and re-dividing the road types of the target road according to the influence range of the ramp road section and the influence range of the interleaving area road section, and correspondingly correcting the basic road sections positioned in the influence range of the ramp road section or the influence range of the interleaving area road section into ramp road sections or interleaving area road sections.
Further, for each type of road segment, performing cluster analysis on the historical traffic capacity parameter set of the type of road segment to obtain a state historical traffic capacity parameter when the type of road segment is in each traffic flow state, including:
clustering historical traffic capacity parameter sets of each type of road segments to obtain a free flow traffic capacity parameter subset, a blocking traffic capacity parameter subset and a synchronous flow traffic capacity parameter subset;
performing linear fitting on the blocking traffic capacity parameter subset to obtain a first fitting linear equation;
Dividing the synchronous flow capacity parameter subset into a synchronous-free flow capacity parameter subset and a synchronous-blocking flow capacity parameter subset according to the first fitting linear equation; the synchronous-free flow capacity parameter subset belongs to a synchronous flow state, but the blockage gradually dissipates along with the time and finally is converted into a free flow state; the synchronous-choked flow capacity parameter subset belongs to a synchronous flow state, but the choking is gradually increased along with the time, and finally the choking state is converted;
and respectively determining state history traffic capacity parameters of the road sections in a free flow state, a blocking state, a synchronous-free flow state and a synchronous-blocking flow state according to the free flow traffic capacity parameter subset, the blocking traffic capacity parameter subset, the synchronous-free flow traffic capacity parameter subset and the synchronous-blocking flow traffic capacity parameter subset.
Further, the determining, according to the free-flow traffic capacity parameter subset, the blocking traffic capacity parameter subset, the synchronous-free-flow traffic capacity parameter subset, and the synchronous-blocking traffic capacity parameter subset, the state history traffic capacity parameters when the type of road segments are in the free-flow state, the blocking state, the synchronous-free-flow state, and the synchronous-blocking flow state, respectively, includes:
Performing linear fitting on the free flow capacity parameter subset to obtain a second fitting linear equation;
determining the maximum flow value in the free flow capacity parameter subset as a state history capacity parameter when the type of road section is in a free flow state;
determining a flow value corresponding to the intersection point of the first fitting linear equation and the second fitting linear equation as a state history traffic capacity parameter when the type of road section is in a synchronous-free flow state;
and respectively determining the flow values in the synchronous-choking flow capacity parameter subset and the choking flow capacity parameter subset as a state history capacity parameter when the type of road section is in a synchronous-choking flow state and a state history capacity parameter when the type of road section is in a choking state.
Further, taking the historical traffic capacity influence parameter set of the type of road section as input, taking the state historical traffic capacity parameter of the type of road section in each traffic flow state as output, and constructing a road traffic capacity parameter prediction model of the type of road section, wherein the road traffic capacity parameter prediction model comprises the following steps:
taking a historical traffic capacity influence parameter set of the type of road section as input, taking a state historical traffic capacity parameter of the type of road section in each traffic flow state as output, and constructing a regression model by using a stepwise regression mode to serve as a road traffic capacity parameter prediction model of the type of road section; the road traffic capacity parameter prediction model of the road section of the type determines effective influence parameters and ineffective influence parameters in the historical traffic capacity influence parameter set of the road section of the type.
Further, the actual traffic capacity influencing parameter set comprises at least one of: road characteristic parameters, weather characteristic parameters, traffic composition characteristic parameters and traffic event characteristic parameters; the road characteristic parameters comprise the number of lanes, the width of lanes, the length of road sections, the design speed, the parameters of horizontal and vertical lines, the road surface condition, the interweaving configuration of the road sections of the interweaving area and the interweaving flow ratio; the weather characteristic parameters comprise weather types, corresponding weather levels, visibility derived from weather influences, road friction and vehicle stability; the traffic composition characteristics include the duty ratio of various types of vehicles in the vehicles traveling on the target highway and the familiarity of the driver with the target highway; the traffic event characteristic parameters include event location characteristics, event time characteristics, open lane characteristics, closed lane characteristics, transition zone length, isolation facility type, traffic control measures, median strip width when passing by opposite lanes, median strip opening length, and road arch slope.
The embodiment of the application also provides a prediction device for the road traffic capacity parameter, which comprises:
The acquisition module is used for acquiring an actual traffic capacity influence parameter set of each type of road section on the target road;
the prediction module is used for inputting the actual traffic capacity influence parameter set of each type of road section into the road traffic capacity parameter prediction model of each type of road section to obtain the predicted road section traffic capacity parameter of each type of road section, and determining the full-line predicted traffic capacity parameter of the target road according to the predicted road section traffic capacity parameter of each type of road section;
the construction module is used for constructing a highway traffic capacity parameter prediction model of each type of road section by the following modes:
acquiring a historical traffic capacity parameter set and a historical traffic capacity influence parameter set of each type of road section in the roads with different historical time nodes;
performing cluster analysis on the historical traffic capacity parameter set of each type of road section to obtain the state historical traffic capacity parameter of the type of road section when the type of road section is in each traffic flow state; wherein the traffic flow state includes free flow, synchronous-choked flow, and choked;
and taking the historical traffic capacity influence parameter set of the type of road section as input, and taking the state historical traffic capacity parameter of the type of road section in each traffic flow state as output to construct a road traffic capacity parameter prediction model of the type of road section.
The embodiment of the application also provides electronic equipment, which comprises: the system comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory are communicated through the bus when the electronic device is running, and the machine-readable instructions are executed by the processor to perform the steps of the method for predicting the highway traffic capacity parameter.
Embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method for predicting a road traffic capacity parameter as described above.
The method and the device for predicting the road traffic capacity parameter provided by the embodiment of the application comprise the following steps: acquiring an actual traffic capacity influence parameter set of each type of road section on a target road; inputting the actual traffic capacity influence parameter set of each type of road section into a road traffic capacity parameter prediction model of each type of road section to obtain predicted road section traffic capacity parameters of each type of road section, and determining the full-line predicted traffic capacity parameters of the target road according to the predicted road section traffic capacity parameters of each type of road section; the road traffic capacity parameter prediction model of each type of road section is constructed by the following steps: acquiring a historical traffic capacity parameter set and a historical traffic capacity influence parameter set of each type of road section in the roads with different historical time nodes; performing cluster analysis on the historical traffic capacity parameter set of each type of road section to obtain the state historical traffic capacity parameter of the type of road section when the type of road section is in each traffic flow state; wherein the traffic flow state includes free flow, synchronous-choked flow, and choked; and taking the historical traffic capacity influence parameter set of the type of road section as input, and taking the state historical traffic capacity parameter of the type of road section in each traffic flow state as output to construct a road traffic capacity parameter prediction model of the type of road section.
Predicting and obtaining the full-line predicted traffic capacity parameters of the target road through a plurality of traffic capacity influence parameters of each type of road section and a road traffic capacity parameter prediction model; therefore, the three-phase traffic flow state theory and the unsupervised data prediction model can be fused, and the influence of various factors on the traffic capacity of different road sections is fully considered, so that the road traffic capacity parameters can be predicted more accurately.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a method for predicting a highway traffic capacity parameter according to an embodiment of the present disclosure;
FIG. 2 illustrates a three-phase traffic flow base graph provided by an embodiment of the present application;
Fig. 3 is a schematic structural diagram of a prediction apparatus for highway traffic capacity parameters according to an embodiment of the present application;
fig. 4 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. Based on the embodiments of the present application, every other embodiment that a person skilled in the art would obtain without making any inventive effort is within the scope of protection of the present application.
The research shows that the highway traffic capacity is used as the basic basis for highway planning, design and management, and penetrates through each stage of highway engineering construction in China. The main roles of traffic capacity analysis can be summarized as: the method is used for road planning and design, traffic operation analysis, service level analysis and traffic volume prediction of the existing or potential bottleneck road sections, highway engineering and traffic management measures for improving traffic operation quality and traffic management, and traffic management measures of each stage are planned according to predicted traffic volume increase conditions and analysis of operation quality change conditions.
At present, in the prior art, an empirical value given in relevant road standard regulation is often directly determined as a traffic capacity parameter of a road, and the manner cannot reflect the actual running characteristic of road traffic, so that the determined traffic capacity is inaccurate.
Based on the above, the embodiment of the application provides a prediction method and a prediction device for road traffic capacity parameters, which can fuse a three-phase traffic flow state theory and an unsupervised data prediction model, fully consider the influence of various factors on the traffic capacity of different road sections, and further accurately predict the road traffic capacity parameters.
Referring to fig. 1, fig. 1 is a flowchart of a method for predicting a highway traffic capacity parameter according to an embodiment of the present disclosure. As shown in fig. 1, the prediction method provided in the embodiment of the present application includes:
s101, acquiring an actual traffic capacity influence parameter set of each type of road section on a target road.
The type of the road section on the target road comprises: basic road segments, interleaved region road segments, and ramp road segments. Here, taking the expressway as an example, the basic road section refers to an expressway road section which is not affected by additional confluence, diversion and interweaving flows of the ramp; an intersection refers to a highway section along which two or more traffic streams travel through each other's course along a length of highway. The interweaved road section is generally composed of a converging region and a diverging region which are relatively close to each other; the ramp can be divided into an entrance ramp and an exit ramp, and is a road on the right side of the exit or entrance of the expressway.
Here, the ideal road traffic capacity refers to a road traffic capacity under good road conditions, no traffic accident, and good weather conditions. Traffic conditions in reality are often not ideal conditions, and road traffic capacity is affected by various factors. The road traffic capacity influencing factors mainly comprise: road characteristics (lane width and lateral clearance, number of lanes, design speed), traffic composition, driver overall characteristics, weather characteristics, traffic events (divided into planned events, such as road construction, and emergencies, such as accidents).
Thus, the actual traffic capacity influencing parameter set comprises at least one of the following: road characteristic parameters, weather characteristic parameters, traffic composition characteristic parameters and traffic event characteristic parameters. The road characteristic parameters comprise the number of lanes, the width of lanes, the length of road sections, the design speed, the parameters of the horizontal and vertical lines, the road surface condition, the interweaving configuration of the interweaving area road sections and the interweaving flow ratio. The weather characteristic parameters comprise weather types, corresponding weather levels, visibility derived from weather influences, road friction and vehicle stability. The traffic composition characteristics include the duty ratio of various types of vehicles in the vehicles traveling on the target highway and the familiarity of the driver with the target highway; for example, the vehicle types include trucks (type one, type two, type three, type four, type five), buses (type one, type two, type three, type four, type five), special working vehicles (type one, type two, type three, type four, type five, type six), and ordinary vehicles; the familiarity of the driver with the target highway can be characterized by the driving frequency of the driver on the present route. The traffic event characteristic parameters include event location characteristics, event time characteristics, open lane characteristics, closed lane characteristics, transition zone length, isolation facility type, traffic control measures, median strip width when passing by opposite lanes, median strip opening length, and road arch slope.
Specifically, traffic events include planned events and sudden events. The planned event includes road maintenance construction, road construction, and the sudden event includes an accident. For planned events, the traffic event characteristic parameters may specifically be a construction location, a construction time, a number of open lanes, a number of closed lanes, a closed lane position (left side, right side), a lane width, a transition zone length, an isolation facility type, and the like. When the traffic of the opposite lane is borrowed, the width of the middle belt, the opening length of the middle belt, the road arch gradient and the like can be also included. When the construction area has control measures, the method also comprises speed limiting and other construction area control measures. For sudden event, the traffic event characteristic parameters can be event type, event position, event occurrence time and event duration; other information is the same as the planned event.
It should be noted that, for the numerical value type feature, the numerical value can be directly used as the actual traffic capacity influence parameter in the actual traffic capacity influence parameter set; for non-numeric features, such as weather types, different non-numeric features can be converted into numeric features in a coded form by binary coding or the like, and then the numeric features are used as actual traffic capacity influence parameters in an actual traffic capacity influence parameter set.
S102, inputting an actual traffic capacity influence parameter set of each type of road section into a road traffic capacity parameter prediction model of each type of road section to obtain predicted road section traffic capacity parameters of each type of road section, and determining the full-line predicted traffic capacity parameters of the target road according to the predicted road section traffic capacity parameters of each type of road section.
In the specific implementation, each type of road section is divided into at least one sub-road section, and the dividing mode can be uniform division or nonuniform division; each sub-section comprises an observation point; the actual traffic capacity influence parameter set of each type of road section comprises an actual traffic capacity influence parameter subset corresponding to each observation point position in the road section; the predicted road traffic capacity parameter of each type of road segment comprises a predicted point traffic capacity parameter corresponding to each observation point in the road segment. In the embodiment of the application, the traffic capacity parameter comprises traffic flow; after S102, the prediction method further includes:
the first step: and determining an occupied area occupied by the construction road on the target road.
And a second step of: for each observation point located in the occupied zone, correcting the predicted road section traffic capacity parameter of the observation point by the following formula to obtain the corrected predicted road section traffic capacity parameter of the observation point:
N k ＝N max γ 1 γ 2 γ 3
Wherein N is k Representing the predicted road section traffic capacity parameter after the observation point position is corrected; n (N) max The maximum single-lane road traffic capacity parameter representing the observation point is determined by dividing the full line prediction traffic capacity parameter corresponding to the observation point by the number of lanes; gamma ray 1 The number of open lanes representing the observation point; gamma ray 2 An open lane position correction coefficient indicating the observation point; gamma ray 3 An open lane width correction coefficient indicating the observation point; wherein, gamma 2 And gamma 3 And calculating by adopting a particle swarm optimization algorithm from a historical traffic capacity parameter set when the construction road occupies.
In another possible implementation manner, after S102, the prediction method further includes: and determining the connection area between the road sections of different types on the target road. The road sections comprise basic road sections, ramp road sections and interweaving area road sections; the range of the different types of road segments on the target road is determined by empirical parameters. And determining the ratio of the predicted point traffic capacity parameter of each observation point included in each connection area to the road section length of the sub road section where the observation point is located according to each connection area. And carrying out differential operation on the ratio corresponding to each two adjacent observation points in the connection area to obtain differential values corresponding to each two adjacent observation points. And determining the influence ranges of the ramps in the road sections of different types and the influence ranges of the interleaving areas according to the observation points with the difference values smaller than the preset threshold. And re-dividing the road types of the target road according to the influence range of the ramp road section and the influence range of the interleaving area road section, and correspondingly correcting the basic road sections positioned in the influence range of the ramp road section or the influence range of the interleaving area road section into ramp road sections or interleaving area road sections.
Here, in an actual road, different types of road segments such as a basic road segment, an interleaved region road segment, and a ramp road segment are often mixed and alternately appeared, and the interleaved region and the ramp road segment may affect other adjacent road segments because of a complex traffic state. In the prior art, the range of influence of road segments of different types is usually given by empirical parameters in the relevant road standard specifications, for example, the relevant standard specifications: for an import ramp, from a ramp joint, the range of 150m upstream and 760m downstream is the influence range of the import ramp; for the exit ramp, from the ramp connection, the range of 760m upstream and 150m downstream is the influence range of the exit ramp; for the interleaving region, 150m at the upstream of the current combining point is the starting point of the interleaving region, 150m at the downstream of the current splitting point is the end point of the interleaving region, and the influence range of the interleaving region is arranged between the starting point and the end point. The road types of the target roads can be divided according to the experience parameters, and the range of the road sections of different types on the target roads can be determined.
However, in actual traffic, the influence range of the merging, splitting or interweaving area will change with traffic flow conditions, and especially in the case of traffic congestion and blockage, the merging, splitting or interweaving area may form a vehicle queuing phenomenon, and the queuing length may vary widely and may be as long as several kilometers. Therefore, the result of road segment division using the fixed influence range parameter specified by the relevant road standard cannot reflect the actual running characteristics of road traffic.
According to the method provided by the embodiment of the application, a certain range can be selected between different types of road sections on a target road as a connection area, for example, a certain number of sub-road sections are respectively selected at the junction of two types of road sections to be determined as a connection area; and then, determining observation points with difference values smaller than a preset threshold according to the predicted point traffic capacity parameters of each observation point included in the connection area, and determining the starting point and the end point of the influence range according to the observation points, so as to timely determine the influence ranges of different road sections under different traffic flow states. And then, according to the influence range of the ramp road section and the influence range of the road section of the interweaving area, correspondingly dividing the basic road section in the influence range into the ramp road or the interweaving area, namely, re-dividing the road section types, thereby accurately and timely reflecting the actual road section composition under different traffic conditions.
In one possible embodiment, the road traffic capacity parameter prediction model for each type of road segment is constructed by:
step 1, acquiring a historical traffic capacity parameter set and a historical traffic capacity influence parameter set of each type of road section in roads with different historical time nodes.
Here, the historical traffic ability influence parameter set may refer to the description of the actual traffic ability influence parameter set, which is not described herein.
And 2, performing cluster analysis on the historical traffic capacity parameter set of each type of road section to obtain the state historical traffic capacity parameter of the type of road section when the type of road section is in each traffic flow state. Wherein the traffic flow states include free flow, synchronous-choked flow, and choked flow.
The basic road section data set, the interweaving area data set and the ramp data set comprise flow, density and speed data; in the step, firstly, traffic flow data without traffic events and under no traffic control measures are respectively extracted from a basic road section data set, an interweaving area data set and a ramp data set, so that historical traffic capacity parameter sets of each type of road section are obtained. Carrying out state division on historical traffic capacity parameter sets of each type of road section based on a three-phase traffic flow theory to obtain traffic capacity parameter subsets in different traffic flow states; and finally, determining the state history traffic capacity parameters of the road sections of the type in each traffic flow state according to the traffic capacity parameter subset in each traffic flow state. Here, the traffic capacity parameter refers to the vehicle flow.
Referring now to fig. 2, fig. 2 is a basic diagram of a three-phase traffic flow according to an embodiment of the present application. As shown in fig. 2, curve F is a relationship curve between traffic flow and density in a free-flow state, a diagonal line region in a closed curve is a relationship between traffic flow and density in a synchronous flow state, and curve J is a relationship curve between traffic flow and density in a blocking state. The synchronous flow data is derived from external interference, including measurement errors, different density calculation methods, large vehicles, complex weather, road alignment and gradient, traffic accidents, vehicle lane changing behaviors and the like. Above the synchronous flow J line (region C) is the synchronous-free flow state where the blockage is continuously dissipated over time and eventually converted to a free flow state. The synchronous flow J line below (zone D) is the synchronous-choked flow state where the choking is widened over time and finally converted to a choked state. Based on the distribution characteristics of the traffic flow data of FIG. 2, the horizontal axis represents density and the vertical axis represents traffic flow, where a max Is the maximum traffic flow of free flow, is the maximum traffic flow collected in the actual running process, a out Is the maximum traffic flow, k i Is the vehicle density value at the time of traffic congestion.
In one possible embodiment, this step may comprise:
S201, clustering historical traffic capacity parameter sets of each type of road segments to obtain a free flow traffic capacity parameter subset, a blocking traffic capacity parameter subset and a synchronous flow traffic capacity parameter subset.
In specific implementation, the input data can be clustered by using a spectral clustering algorithm, and clustered class data is 3 classes, and the class data correspond to three states of free flow, synchronous flow and blocking respectively to obtain a free flow capacity parameter subset, a blocking capacity parameter subset and a synchronous flow capacity parameter subset.
S202, performing linear fitting on the blocking capacity parameter subset to obtain a first fitting linear equation.
In this step, a first fitted linear equation mk+- =0 is obtained, where a represents flow, k represents density, and m and n are linear parameters.
S203, dividing the synchronous flow capacity parameter subset into a synchronous-free flow capacity parameter subset and a synchronous-blocking flow capacity parameter subset according to the first fitting linear equation; the synchronous-free flow capacity parameter subset belongs to a synchronous flow state, but the blockage gradually dissipates along with the time and finally is converted into a free flow state; the synchronous-choked flow capacity parameter subset belongs to a synchronous flow state, but the choking is gradually increased with the passage of time, and finally the choking state is converted.
In the step, data in the synchronous flow capacity parameter subset can be respectively brought into a first fitting linear equation, and if the value is larger than 0, the data belongs to a C area, namely the synchronous-free flow capacity parameter subset; if the value is smaller than 0, the data belongs to the area D, namely a synchronous-choked flow capacity parameter subset; finally, the synchronous flow capacity parameter subset is divided into a synchronous-free flow capacity parameter subset and a synchronous-blocking flow capacity parameter subset.
S203, determining state history traffic capacity parameters of the road sections in a free flow state, a blocking state, a synchronous-free flow state and a synchronous-blocking flow state according to the free flow traffic capacity parameter subset, the blocking traffic capacity parameter subset, the synchronous-free flow traffic capacity parameter subset and the synchronous-blocking flow traffic capacity parameter subset.
In particular, step S203 may include: performing linear fitting on the free flow capacity parameter subset to obtain a second fitting linear equation; determining the maximum flow value in the free flow capacity parameter subset as a state history capacity parameter when the type of road section is in a free flow state; determining a flow value corresponding to the intersection point of the first fitting linear equation and the second fitting linear equation as a state history traffic capacity parameter when the type of road section is in a synchronous-free flow state; and respectively determining the flow values in the synchronous-choking flow capacity parameter subset and the choking flow capacity parameter subset as a state history capacity parameter when the type of road section is in a synchronous-choking flow state and a state history capacity parameter when the type of road section is in a choking state.
In this step, the maximum flow value in the subset of free-flow traffic capacity parameters may be extracted, and the maximum flow value may be determined as the maximum traffic capacity of the free flow, i.e. the state history traffic capacity parameter when the type of road segment is in the free-flow state. And (3) performing linear fitting on the free flow capacity parameter subset to obtain a second fitting linear equation pk+/- =0, wherein a represents flow, k represents density, and p and q are linear parameters. And calculating an intersection point of the first fitting linear equation and the second fitting linear equation, wherein the flow value at the intersection point is a C area flow value, namely a state history traffic capacity parameter when the type of road section is in a synchronous-free flow state.
And 3, taking the traffic capacity influence parameter set of the type of road section as input, and taking the state history traffic capacity parameter of the type of road section in each traffic flow state as output to construct a road traffic capacity parameter prediction model of the type of road section.
The highway traffic capacity parameter prediction model may be a machine learning model. In the specific implementation, the traffic capacity influence parameter set of the type of road section is taken as input, the state history traffic capacity parameter of the type of road section in each traffic flow state is taken as output, and a regression model is constructed by using a stepwise regression mode to be taken as a road traffic capacity parameter prediction model of the type of road section; the road traffic capacity parameter prediction model of the road section of the type determines effective influence parameters and ineffective influence parameters in traffic capacity influence parameter sets of the road section of the type, and further obtains an actual traffic capacity influence parameter set from the effective influence parameters to predict the predicted road section traffic capacity parameters of each road section of the type.
The method for predicting the road traffic capacity parameter provided by the embodiment of the application comprises the following steps: acquiring an actual traffic capacity influence parameter set of each type of road section on a target road; inputting the actual traffic capacity influence parameter set of each type of road section into a road traffic capacity parameter prediction model of each type of road section to obtain predicted road section traffic capacity parameters of each type of road section, and determining the full-line predicted traffic capacity parameters of the target road according to the predicted road section traffic capacity parameters of each type of road section; the road traffic capacity parameter prediction model of each type of road section is constructed by the following steps: acquiring a historical traffic capacity parameter set and a historical traffic capacity influence parameter set of each type of road section in the roads with different historical time nodes; performing cluster analysis on the historical traffic capacity parameter set of each type of road section to obtain the state historical traffic capacity parameter of the type of road section when the type of road section is in each traffic flow state; wherein the traffic flow state includes free flow, synchronous-choked flow, and choked; and taking the historical traffic capacity influence parameter set of the type of road section as input, and taking the state historical traffic capacity parameter of the type of road section in each traffic flow state as output to construct a road traffic capacity parameter prediction model of the type of road section.
The prediction method provided by the embodiment of the application combines the advantages of small data demand of the traffic theory model and the advantages of emphasized learning capacity of the machine learning model, reduces the data demand under the condition of ensuring the accuracy, and is suitable for the whole project construction period. Meanwhile, the prediction method provided by the embodiment of the application firstly generates the full road traffic capacity based on the historical data, then corrects the full road traffic capacity based on the traffic event information to obtain the perfect road traffic capacity, has good interpretability and good applicability under roads with different characteristics. The prediction method provided by the embodiment of the application extracts traffic capacity parameters based on the three-phase traffic flow theory and the non-supervision data learning method, fully considers the influence of each factor on the traffic capacity, establishes road traffic capacity models of different road segments under the influence of multiple factors, divides the expressway based on the change of traffic flow and road characteristics, and realizes the analysis of the expressway full-line traffic capacity, thereby predicting the road traffic capacity parameters more accurately.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a prediction apparatus for highway traffic capacity parameters according to an embodiment of the present application. As shown in fig. 3, the prediction apparatus 300 includes:
An obtaining module 310, configured to obtain an actual traffic capacity influence parameter set of each type of road section on the target highway;
the prediction module 320 is configured to input an actual traffic capacity influence parameter set of each type of road segment into a road traffic capacity parameter prediction model of each type of road segment, obtain a predicted road capacity parameter of each type of road segment, and determine a full-line predicted traffic capacity parameter of the target road from the predicted road capacity parameter of each type of road segment;
a construction module 330, configured to construct a highway traffic capacity parameter prediction model of each type of road segment by:
acquiring a historical traffic capacity parameter set and a historical traffic capacity influence parameter set of each type of road section in the roads with different historical time nodes;
performing cluster analysis on the historical traffic capacity parameter set of each type of road section to obtain the state historical traffic capacity parameter of the type of road section when the type of road section is in each traffic flow state; wherein the traffic flow state includes free flow, synchronous-choked flow, and choked;
and taking the historical traffic capacity influence parameter set of the type of road section as input, and taking the state historical traffic capacity parameter of the type of road section in each traffic flow state as output to construct a road traffic capacity parameter prediction model of the type of road section.
Further, each type of road segment is divided into at least one sub-road segment, and each sub-road segment comprises an observation point; the actual traffic capacity influence parameter set of each type of road section comprises an actual traffic capacity influence parameter subset corresponding to each observation point position in the road section; the predicted road traffic capacity parameter of each type of road section comprises a predicted point traffic capacity parameter corresponding to each observation point in the road section; the prediction device further comprises a correction module, wherein the correction module is used for:
determining an occupied area occupied by a construction road on the target road;
for each observation point located in the occupied zone, correcting the predicted road section traffic capacity parameter of the observation point by the following formula to obtain the corrected predicted road section traffic capacity parameter of the observation point:
N k ＝N max γ 1 γ 2 γ 3
wherein N is k Representing the predicted road section traffic capacity parameter after the observation point position is corrected; n (N) max The maximum single-lane road traffic capacity parameter representing the observation point is determined by dividing the full line prediction traffic capacity parameter corresponding to the observation point by the number of lanes; gamma ray 1 The number of open lanes representing the observation point; gamma ray 2 An open lane position correction coefficient indicating the observation point; gamma ray 3 An open lane width correction coefficient indicating the observation point; wherein, gamma 2 And gamma 3 And calculating by adopting a particle swarm optimization algorithm from the historical traffic capacity parameter set.
Further, the prediction device further comprises a determining module, wherein the determining module is used for:
determining connection areas among different types of road sections on the target road; the road sections comprise basic road sections, ramp road sections and interweaving area road sections; the range of different types of road sections on the target road is determined by experience parameters;
for each connection area, determining the ratio of the predicted point traffic capacity parameter of each observation point included in the connection area to the road section length of the sub road section where the observation point is located;
performing differential operation on the ratio corresponding to each two adjacent observation points in the connection area to obtain differential values corresponding to each two adjacent observation points;
determining the influence ranges of ramps in different types of road sections and the influence ranges of the interleaving areas according to the observation points with the difference values smaller than a preset threshold value;
and re-dividing the road types of the target road according to the influence range of the ramp road section and the influence range of the interleaving area road section, and correspondingly correcting the basic road sections positioned in the influence range of the ramp road section or the influence range of the interleaving area road section into ramp road sections or interleaving area road sections.
Further, when the construction module 330 is configured to perform cluster analysis on the historical traffic capacity parameter set of each type of road segment to obtain the state historical traffic capacity parameter of the type of road segment when the type of road segment is in each traffic flow state, the construction module 330 is configured to:
clustering historical traffic capacity parameter sets of each type of road segments to obtain a free flow traffic capacity parameter subset, a blocking traffic capacity parameter subset and a synchronous flow traffic capacity parameter subset;
performing linear fitting on the blocking traffic capacity parameter subset to obtain a first fitting linear equation;
dividing the synchronous flow capacity parameter subset into a synchronous-free flow capacity parameter subset and a synchronous-blocking flow capacity parameter subset according to the first fitting linear equation; the synchronous-free flow capacity parameter subset belongs to a synchronous flow state, but the blockage gradually dissipates along with the time and finally is converted into a free flow state; the synchronous-choked flow capacity parameter subset belongs to a synchronous flow state, but the choking is gradually increased along with the time, and finally the choking state is converted;
and respectively determining state history traffic capacity parameters of the road sections in a free flow state, a blocking state, a synchronous-free flow state and a synchronous-blocking flow state according to the free flow traffic capacity parameter subset, the blocking traffic capacity parameter subset, the synchronous-free flow traffic capacity parameter subset and the synchronous-blocking flow traffic capacity parameter subset.
Further, the building module 330 is configured to, when determining the state history traffic capability parameters of the type of road segments in the free-flow state, the blocking state, the synchronous-free-flow state and the synchronous-blocking state according to the free-flow traffic capability parameter subset, the blocking traffic capability parameter subset, the synchronous-free-flow traffic capability parameter subset and the synchronous-blocking traffic capability parameter subset, respectively, the building module 330 is configured to:
performing linear fitting on the free flow capacity parameter subset to obtain a second fitting linear equation;
determining the maximum flow value in the free flow capacity parameter subset as a state history capacity parameter when the type of road section is in a free flow state;
determining a flow value corresponding to the intersection point of the first fitting linear equation and the second fitting linear equation as a state history traffic capacity parameter when the type of road section is in a synchronous-free flow state;
and respectively determining the flow values in the synchronous-choking flow capacity parameter subset and the choking flow capacity parameter subset as a state history capacity parameter when the type of road section is in a synchronous-choking flow state and a state history capacity parameter when the type of road section is in a choking state.
Further, when the construction module 330 is configured to take the historical traffic capacity influence parameter set of the type of road segment as input and the state historical traffic capacity parameter of the type of road segment in each traffic flow state as output, the construction module 330 is configured to:
taking a historical traffic capacity influence parameter set of the type of road section as input, taking a state historical traffic capacity parameter of the type of road section in each traffic flow state as output, and constructing a regression model by using a stepwise regression mode to serve as a road traffic capacity parameter prediction model of the type of road section; the road traffic capacity parameter prediction model of the road section of the type determines effective influence parameters and ineffective influence parameters in the historical traffic capacity influence parameter set of the road section of the type.
Further, the actual traffic capacity influencing parameter set comprises at least one of: road characteristic parameters, weather characteristic parameters, traffic composition characteristic parameters and traffic event characteristic parameters; the road characteristic parameters comprise the number of lanes, the width of lanes, the length of road sections, the design speed, the parameters of horizontal and vertical lines, the road surface condition, the interweaving configuration of the road sections of the interweaving area and the interweaving flow ratio; the weather characteristic parameters comprise weather types, corresponding weather levels, visibility derived from weather influences, road friction and vehicle stability; the traffic composition characteristics include the duty ratio of various types of vehicles in the vehicles traveling on the target highway and the familiarity of the driver with the target highway; the traffic event characteristic parameters include event location characteristics, event time characteristics, open lane characteristics, closed lane characteristics, transition zone length, isolation facility type, traffic control measures, median strip width when passing by opposite lanes, median strip opening length, and road arch slope.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 4, the electronic device 400 includes a processor 410, a memory 420, and a bus 430.
The memory 420 stores machine-readable instructions executable by the processor 410, when the electronic device 400 is running, the processor 410 communicates with the memory 420 through the bus 430, and when the machine-readable instructions are executed by the processor 410, the steps of a method for predicting a highway traffic capacity parameter in the method embodiments shown in fig. 1 and fig. 2 may be executed, and detailed description thereof will be omitted.
The embodiment of the present application further provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps of a method for predicting a highway traffic capacity parameter in the method embodiments shown in fig. 1 and fig. 2 may be executed, and specific implementation manners may refer to the method embodiments and are not repeated herein.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present application, and are not intended to limit the scope of the present application, but the present application is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, the present application is not limited thereto. Any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the technical scope of the disclosure of the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for predicting a road traffic capacity parameter, the method comprising:
acquiring an actual traffic capacity influence parameter set of each type of road section on a target road;
inputting the actual traffic capacity influence parameter set of each type of road section into a road traffic capacity parameter prediction model of each type of road section to obtain predicted road section traffic capacity parameters of each type of road section, and determining the full-line predicted traffic capacity parameters of the target road according to the predicted road section traffic capacity parameters of each type of road section;
the road traffic capacity parameter prediction model of each type of road section is constructed by the following steps:
acquiring a historical traffic capacity parameter set and a historical traffic capacity influence parameter set of each type of road section in the roads with different historical time nodes;
performing cluster analysis on the historical traffic capacity parameter set of each type of road section to obtain the state historical traffic capacity parameter of the type of road section when the type of road section is in each traffic flow state; wherein the traffic flow state includes free flow, synchronous-choked flow, and choked;
and taking the historical traffic capacity influence parameter set of the type of road section as input, and taking the state historical traffic capacity parameter of the type of road section in each traffic flow state as output to construct a road traffic capacity parameter prediction model of the type of road section.
2. The prediction method according to claim 1, wherein each type of road segment is divided into at least one sub-road segment, each sub-road segment comprising an observation point; the actual traffic capacity influence parameter set of each type of road section comprises an actual traffic capacity influence parameter subset corresponding to each observation point position in the road section; the predicted road traffic capacity parameter of each type of road section comprises a predicted point traffic capacity parameter corresponding to each observation point in the road section; after inputting the actual traffic capacity influence parameter set of each type of road section into the road traffic capacity parameter prediction model of the type of road section to obtain the predicted road section traffic capacity parameter of each type of road section, and determining the full-line predicted traffic capacity parameter of the target road according to the predicted road section traffic capacity parameter of each type of road section, the prediction method further comprises:
determining an occupied area occupied by a construction road on the target road;
for each observation point located in the occupied zone, correcting the predicted road section traffic capacity parameter of the observation point by the following formula to obtain the corrected predicted road section traffic capacity parameter of the observation point:
N k ＝N max γ 1 γ 2 γ 3
Wherein N is k Representing the predicted road section traffic capacity parameter after the observation point position is corrected; n (N) max The maximum single-lane road traffic capacity parameter representing the observation point is determined by dividing the full line prediction traffic capacity parameter corresponding to the observation point by the number of lanes; gamma ray 1 The number of open lanes representing the observation point; gamma ray 2 Open lane position repair indicating the observation pointPositive coefficients; gamma ray 3 An open lane width correction coefficient indicating the observation point; wherein, gamma 2 And gamma 3 And calculating by adopting a particle swarm optimization algorithm from the historical traffic capacity parameter set.
3. The prediction method according to claim 2, wherein after inputting the actual traffic capacity influence parameter set of each type of road segment into the road traffic capacity parameter prediction model of that type of road segment to obtain the predicted road segment traffic capacity parameter of each type of road segment, and determining the all-line predicted traffic capacity parameter of the target road from the predicted road segment traffic capacity parameter of each type of road segment, the prediction method further comprises:
determining connection areas among different types of road sections on the target road; the road sections comprise basic road sections, ramp road sections and interweaving area road sections; the range of different types of road sections on the target road is determined by experience parameters;
For each connection area, determining the ratio of the predicted point traffic capacity parameter of each observation point included in the connection area to the road section length of the sub road section where the observation point is located;
performing differential operation on the ratio corresponding to each two adjacent observation points in the connection area to obtain differential values corresponding to each two adjacent observation points;
determining the influence ranges of ramps in different types of road sections and the influence ranges of the interleaving areas according to the observation points with the difference values smaller than a preset threshold value;
and re-dividing the road types of the target road according to the influence range of the ramp road section and the influence range of the interleaving area road section, and correspondingly correcting the basic road sections positioned in the influence range of the ramp road section or the influence range of the interleaving area road section into ramp road sections or interleaving area road sections.
4. The prediction method according to claim 1, wherein for each type of road segment, performing cluster analysis on a historical traffic capacity parameter set of the type of road segment to obtain a state historical traffic capacity parameter when the type of road segment is in each traffic flow state, includes:
clustering historical traffic capacity parameter sets of each type of road segments to obtain a free flow traffic capacity parameter subset, a blocking traffic capacity parameter subset and a synchronous flow traffic capacity parameter subset;
Performing linear fitting on the blocking traffic capacity parameter subset to obtain a first fitting linear equation;
dividing the synchronous flow capacity parameter subset into a synchronous-free flow capacity parameter subset and a synchronous-blocking flow capacity parameter subset according to the first fitting linear equation; the synchronous-free flow capacity parameter subset belongs to a synchronous flow state, but the blockage gradually dissipates along with the time and finally is converted into a free flow state; the synchronous-choked flow capacity parameter subset belongs to a synchronous flow state, but the choking is gradually increased along with the time, and finally the choking state is converted;
and respectively determining state history traffic capacity parameters of the road sections in a free flow state, a blocking state, a synchronous-free flow state and a synchronous-blocking flow state according to the free flow traffic capacity parameter subset, the blocking traffic capacity parameter subset, the synchronous-free flow traffic capacity parameter subset and the synchronous-blocking flow traffic capacity parameter subset.
5. The prediction method according to claim 4, wherein the determining the state history traffic capacity parameters when the type of road segment is in the free-flow state, the blocked state, the synchronous-free-flow state, and the synchronous-blocked-flow state according to the free-flow traffic capacity parameter subset, the blocked traffic capacity parameter subset, the synchronous-free-flow traffic capacity parameter subset, and the synchronous-blocked-flow traffic capacity parameter subset, respectively, includes:
Performing linear fitting on the free flow capacity parameter subset to obtain a second fitting linear equation;
determining the maximum flow value in the free flow capacity parameter subset as a state history capacity parameter when the type of road section is in a free flow state;
determining a flow value corresponding to the intersection point of the first fitting linear equation and the second fitting linear equation as a state history traffic capacity parameter when the type of road section is in a synchronous-free flow state;
and respectively determining the flow values in the synchronous-choking flow capacity parameter subset and the choking flow capacity parameter subset as a state history capacity parameter when the type of road section is in a synchronous-choking flow state and a state history capacity parameter when the type of road section is in a choking state.
6. The method according to claim 1, wherein constructing the road traffic capacity parameter prediction model for the type of road segment with the historical traffic capacity influence parameter set for the type of road segment as input and the state historical traffic capacity parameter for the type of road segment in each traffic flow state as output includes:
taking a historical traffic capacity influence parameter set of the type of road section as input, taking a state historical traffic capacity parameter of the type of road section in each traffic flow state as output, and constructing a regression model by using a stepwise regression mode to serve as a road traffic capacity parameter prediction model of the type of road section; the road traffic capacity parameter prediction model of the road section of the type determines effective influence parameters and ineffective influence parameters in the historical traffic capacity influence parameter set of the road section of the type.
7. The method of claim 1, wherein the actual traffic capacity influencing parameter set comprises at least one of: road characteristic parameters, weather characteristic parameters, traffic composition characteristic parameters and traffic event characteristic parameters; the road characteristic parameters comprise the number of lanes, the width of lanes, the length of road sections, the design speed, the parameters of horizontal and vertical lines, the road surface condition, the interweaving configuration of the road sections of the interweaving area and the interweaving flow ratio; the weather characteristic parameters comprise weather types, corresponding weather levels, visibility derived from weather influences, road friction and vehicle stability; the traffic composition characteristics include the duty ratio of various types of vehicles in the vehicles traveling on the target highway and the familiarity of the driver with the target highway; the traffic event characteristic parameters include event location characteristics, event time characteristics, open lane characteristics, closed lane characteristics, transition zone length, isolation facility type, traffic control measures, median strip width when passing by opposite lanes, median strip opening length, and road arch slope.
8. A prediction apparatus for a road traffic capacity parameter, the prediction apparatus comprising:
The acquisition module is used for acquiring an actual traffic capacity influence parameter set of each type of road section on the target road;
the prediction module is used for inputting the actual traffic capacity influence parameter set of each type of road section into the road traffic capacity parameter prediction model of each type of road section to obtain the predicted road section traffic capacity parameter of each type of road section, and determining the full-line predicted traffic capacity parameter of the target road according to the predicted road section traffic capacity parameter of each type of road section;
the construction module is used for constructing a highway traffic capacity parameter prediction model of each type of road section by the following modes:
acquiring a historical traffic capacity parameter set and a historical traffic capacity influence parameter set of each type of road section in the roads with different historical time nodes;
performing cluster analysis on the historical traffic capacity parameter set of each type of road section to obtain the state historical traffic capacity parameter of the type of road section when the type of road section is in each traffic flow state; wherein the traffic flow state includes free flow, synchronous-choked flow, and choked;
and taking the historical traffic capacity influence parameter set of the type of road section as input, and taking the state historical traffic capacity parameter of the type of road section in each traffic flow state as output to construct a road traffic capacity parameter prediction model of the type of road section.
9. An electronic device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating via said bus when the electronic device is running, said machine readable instructions when executed by said processor performing the steps of a method of predicting a road traffic capacity parameter according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of a method for predicting a road traffic capacity parameter according to any one of claims 1 to 7.
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