CN115063975A - Short-time traffic flow data prediction method, system, computer device and storage medium - Google Patents
Short-time traffic flow data prediction method, system, computer device and storage medium Download PDFInfo
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The invention belongs to the technical field of traffic control, and discloses a short-time traffic flow data prediction method, a system, computer equipment and a storage medium, wherein the short-time traffic flow data prediction method comprises the following steps: acquiring historical traffic flow data of an area to be predicted and relevant areas of the area to be predicted; obtaining periodic traffic flow data of the current time period of the area to be predicted according to historical traffic flow data of the area to be predicted, and obtaining optimal relevant historical traffic flow data of the area to be predicted by combining historical traffic flow data of relevant areas of the area to be predicted through a preset space-time characteristic selection method; the method comprises the steps of obtaining external characteristic influence factor data of a current time period of an area to be predicted, inputting the external characteristic influence factor data of the current time period of the area to be predicted, periodic traffic flow data of the current time period and optimal related historical traffic flow data into a preset traffic flow prediction model, obtaining a short-time predicted value of traffic flow data of the area to be predicted, and effectively improving accuracy of short-time traffic flow data prediction.
Description
Technical Field
The invention belongs to the technical field of traffic control, and relates to a short-time traffic flow data prediction method, a short-time traffic flow data prediction system, computer equipment and a storage medium.
Background
Along with the rapid development of economy and the increasing improvement of living standard of people, the scales of various vehicles, especially private cars are continuously enlarged, thereby bringing various traffic problems and frequent occurrence of traffic jam. How to avoid serious traffic accidents and how to reduce the probability of traffic jam becomes important content of traffic flow research. In recent years, with the development of intelligent traffic systems, the construction of ITS is considered to be an effective method for reducing traffic jam and relieving urban traffic pressure at present, and it not only can provide real-time road condition information for trip personnel, and the trip personnel can select a proper route to effectively relieve the traffic jam and improve the service level of traffic facilities, but also can greatly improve the service level of traffic management while reducing the cost investment of traffic management departments, and plays a non-trivial role in reducing environmental pollution and ensuring the traffic safety of roads. The traffic flow prediction is used as an important branch of ITS, can provide decision basis for traffic participants, and has great application prospect in road network planning, congestion alleviation, traffic control and other aspects.
Since Stephanedes first considered the use of HA models to solve a series of problems in urban intelligent traffic control systems in 1981, research on traffic flow prediction direction HAs been over forty years old, and short-term traffic flow data prediction methods are becoming mature and saturated. Existing traffic flow prediction methods can be roughly classified into four categories: statistical methods models based on mathematical logic, traditional machine learning prediction models, neural network based prediction models, and combinatorial models. The statistical method model based on mathematical logic is earlier used for traffic flow prediction, and the HA model belongs to one of the models, and in addition, the HA model also comprises time series models such as ARIMA, VAR and the like. Traditional machine learning prediction models including KNN, SVM and the like start from training samples, balance between model interpretability and result effectiveness is achieved, and multi-dimensional sequence data and large-scale training samples cannot be well processed. Deep learning networks can use more data to achieve higher performance than traditional machine learning algorithms. In the short-time traffic flow data prediction model based on the neural network, the convolutional neural network and the cyclic neural network both occupy a place. However, with the continuous and deep development of related research, the defects of a single traffic flow prediction model are exposed, and some prediction models only consider the spatial correlation among short-time traffic flow data nodes and ignore the time dependence; and some consider only the time correlation among the sequence data, neglect the space dependence among the regional nodes.
In order to capture features in both the time dimension and the space dimension of traffic flow sequence data, researchers began to shift points of interest to combinatorial models and use them for short-term traffic flow data prediction. Although the existing research basically considers the time correlation and the spatial correlation of the traffic flow sequence data at the same time and tries to introduce external characteristic influence factors and attention mechanisms, the same data set can generate different prediction results and prediction accuracy due to different combination models, and the prediction results are closely related to the training situation of the models. Therefore, the short-time traffic flow data prediction model needs to be further improved in prediction accuracy and practical application.
Disclosure of Invention
The invention aims to overcome the defect of low accuracy of short-time traffic flow data prediction in the prior art, and provides a short-time traffic flow data prediction method, a short-time traffic flow data prediction system, a computer device and a storage medium.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
in a first aspect of the present invention, a short-term traffic flow data prediction method includes:
acquiring historical traffic flow data of an area to be predicted and relevant areas of the area to be predicted;
obtaining periodic traffic flow data of the current time period of the area to be predicted according to historical traffic flow data of the area to be predicted, and obtaining optimal relevant historical traffic flow data of the area to be predicted by combining historical traffic flow data of relevant areas of the area to be predicted through a preset space-time characteristic selection method;
acquiring external characteristic influence factor data of the current time period of the area to be predicted, and inputting the external characteristic influence factor data of the current time period of the area to be predicted, the periodic traffic flow data of the current time period and the optimal related historical traffic flow data into a preset traffic flow prediction model to obtain a short-time predicted value of the traffic flow data of the area to be predicted.
Optionally, the periodic traffic data includes daily periodic traffic data, weekly periodic traffic data, and monthly periodic traffic data.
Optionally, the obtaining of the optimal relevant historical traffic flow data of the area to be predicted by the preset time-space feature selection method includes: calculating Pearson correlation coefficients between historical traffic flow data of the area to be predicted and each relevant area of the area to be predicted, and selecting relevant areas with large first preset number before the Pearson correlation coefficients as target relevant areas of the area to be predicted; and selecting traffic flow data of historical moments with large second preset quantity before the correlation with the traffic flow data of the current time period by adopting a simulated annealing algorithm based on the target related region of the region to be predicted to obtain the optimal related historical traffic flow data of the region to be predicted.
Optionally, the preset traffic flow prediction model includes a first deep neural network layer, a second deep neural network layer, a first time attention mechanism network layer, a second time attention mechanism network layer, a fusion layer, a first fully-connected neural network layer, and a second fully-connected neural network layer; the first deep neural network layer is sequentially connected with the first time attention mechanism network layer and the fusion layer, the second deep neural network layer is sequentially connected with the second time attention mechanism network layer and the fusion layer, and the fusion layer is sequentially connected with the first fully-connected neural network layer and the second fully-connected neural network layer; the input of the first deep neural network layer is optimal related historical traffic flow data; the input of the second deep neural network layer is periodic traffic data of the current time period of the area to be predicted; combining the external characteristic influence factor data of the current time period of the region to be predicted with the output of the fusion layer to serve as the input of the first fully-connected neural network layer; and the output of the second fully-connected neural network layer is a short-time predicted value of the traffic flow data of the area to be predicted.
Optionally, the first deep neural network layer includes a feature extraction unit, the second deep neural network layer includes a plurality of feature extraction units, and the feature extraction unit includes a time sequence convolution neural network and a gate control cycle unit network that are connected in sequence.
Optionally, the traffic flow data is traffic flow data or traffic speed data.
Optionally, when the traffic flow data is traffic flow data, the external characteristic influence factor data includes date type data, weather data, and traffic speed data of the current time period; when the traffic flow data is traffic speed data, the external characteristic influence factor data includes date type data, weather data, and traffic flow data of the current time period.
In a second aspect of the present invention, a short-term traffic flow data prediction system includes:
the data acquisition module is used for acquiring the area to be predicted and historical traffic flow data of each relevant area of the area to be predicted;
the characteristic extraction module is used for obtaining periodic traffic flow data of the current time period of the area to be predicted according to historical traffic flow data of the area to be predicted and obtaining optimal relevant historical traffic flow data of the area to be predicted by combining historical traffic flow data of relevant areas of the area to be predicted through a preset space-time characteristic selection method;
and the prediction module is used for acquiring external characteristic influence factor data of the current time period of the area to be predicted, inputting the external characteristic influence factor data of the current time period of the area to be predicted, the periodic traffic flow data of the current time period and the optimal related historical traffic flow data into a preset traffic flow prediction model, and obtaining the short-time predicted value of the traffic flow data of the area to be predicted.
In a third aspect of the present invention, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the short-time traffic flow data prediction method when executing the computer program.
In a fourth aspect of the present invention, a computer-readable storage medium stores a computer program, which when executed by a processor implements the steps of the short-time traffic flow data prediction method described above.
Compared with the prior art, the invention has the following beneficial effects:
the invention relates to a short-time traffic flow data prediction method, which obtains optimal relevant historical traffic flow data of a region to be predicted by combining historical traffic flow data of relevant regions of the region to be predicted according to the historical traffic flow data of the region to be predicted and a preset space-time characteristic selection method, then combines periodic traffic flow data of the current time period of the region to be predicted and external characteristic influence factor data of the current time period of the region to be predicted to be used as input data of a traffic flow prediction model together to perform short-time traffic flow data prediction, strengthens characteristic expression of key input data while reducing model training cost and improving model training efficiency, obviously improves the accuracy of short-time traffic flow data prediction based on a multi-characteristic fusion mode, can effectively improve road operation efficiency based on a prediction result, and provides guarantee for road traffic safety, the system and the method have the advantages that managers can take timely induction control measures to reduce or avoid road traffic accidents as much as possible, and the system and the method have important significance and research value for relieving and controlling traffic jam conditions and improving travel efficiency.
Drawings
FIG. 1 is a flow chart of a short-term traffic flow data prediction method according to an embodiment of the present invention;
FIG. 2 is a diagram of an example of an input time series segment of periodic traffic data according to an embodiment of the present invention;
FIG. 3 is a detailed flowchart of a short-term traffic flow data prediction method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a time attention mechanism network architecture according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, in an embodiment of the present invention, a short-term traffic flow data prediction method is provided, which can construct a suitable deep network prediction model for short-term traffic flow prediction based on multi-feature fusion, and the achievement of the method improves road operation efficiency, provides guarantee for road traffic safety, and enables a management department to take timely guidance control measures to reduce or avoid road traffic accidents as much as possible. The method has important significance and research value for relieving and controlling traffic jam conditions and improving travel efficiency, and is a problem which is very urgently needed to be solved in traffic flow research. Specifically, the short-time traffic flow data prediction method comprises the following steps:
acquiring historical traffic flow data of an area to be predicted and relevant areas of the area to be predicted; obtaining periodic traffic flow data of the current time period of the area to be predicted according to historical traffic flow data of the area to be predicted, and obtaining optimal relevant historical traffic flow data of the area to be predicted by combining historical traffic flow data of relevant areas of the area to be predicted through a preset space-time characteristic selection method; acquiring external characteristic influence factor data of the current time period of the area to be predicted, and inputting the external characteristic influence factor data of the current time period of the area to be predicted, the periodic traffic flow data of the current time period and the optimal related historical traffic flow data into a preset traffic flow prediction model to obtain a short-time predicted value of the traffic flow data of the area to be predicted.
In one possible embodiment, the periodic traffic data includes daily periodic traffic data, weekly periodic traffic data, and monthly periodic traffic data.
Specifically, referring to fig. 2, the period is divided into entry points, and the short-term prediction accuracy of the traffic flow data is improved by analyzing the dependency relationship among the daily periodicity, the weekly periodicity, and the monthly periodicity of the traffic flow data.
In a possible implementation manner, the obtaining of the optimal relevant historical traffic flow data of the area to be predicted by the preset spatio-temporal feature selection method includes: calculating Pearson correlation coefficients between historical traffic flow data of the area to be predicted and each relevant area of the area to be predicted, and selecting relevant areas with large first preset number before the Pearson correlation coefficients as target relevant areas of the area to be predicted; and selecting traffic flow data of historical moments with large second preset quantity before the correlation with the traffic flow data of the current time period by adopting a simulated annealing algorithm based on the target related region of the region to be predicted to obtain the optimal related historical traffic flow data of the region to be predicted.
Specifically, a Filter-based spatio-temporal feature selection method is adopted, correlation among features is calculated, corresponding scores are given, features with low correlation are removed according to the scores, and the selected optimal spatio-temporal sequence features are input into a prediction model and serve as one of input data of a traffic flow prediction model.
The core objective of the spatial feature selection is to find a relevant region with strong correlation with a region to be predicted, so that a data sequence of the region to be predicted in any time period has strong correlation with a data sequence of the relevant region in the same time period. Because the short-time traffic flow data has a complex nonlinear relation, the correlation strength among all relevant regions is measured by utilizing Pearson correlation coefficients, a similarity matrix is formed by ranking according to the correlation degree from high to low, the relevant regions with high similarity to the regions to be predicted are screened out on the basis, and the relevant regions with low significance to the regions to be predicted are ignored. In order to avoid the problem that the similarity between related areas is different due to different time periods of the input sequence, the traffic flow data of all the moments of each related area in the data set are regarded as the characteristics of the related area to depict the related area, and the time window is long so as to find the related area which is really similar to the area to be predicted.
The core goal of the time characteristic selection is to search a time period with strong correlation with the prediction time period and to search critical data which really plays a role, so that the optimal time sequence data is not changed due to the addition of other spatial nodes in the prediction time period, and the prediction time period has higher similarity with the selected optimal time sequence data. And determining the optimal time sequence data on the basis of the found optimal Spatial features through a Time Feature Selection Algorithm (TFSABS) Based on a simulated annealing Algorithm, and finally showing higher prediction accuracy on the data set.
In one possible embodiment, referring to fig. 3, the preset traffic flow prediction model includes a first deep neural network layer, a second deep neural network layer, a first time attention mechanism network layer, a second time attention mechanism network layer, a fusion layer, a first fully-connected neural network layer, and a second fully-connected neural network layer; the first deep neural network layer is sequentially connected with the first time attention mechanism network layer and the fusion layer, the second deep neural network layer is sequentially connected with the second time attention mechanism network layer and the fusion layer, and the fusion layer is sequentially connected with the first fully-connected neural network layer and the second fully-connected neural network layer; the input of the first deep neural network layer is the optimal relevant historical traffic flow data, which is expressed as X t (ii) a The input of the second deep neural network layer is periodic traffic data of the current time interval of the area to be predicted, which are respectively expressed as X m 、X w And X d (ii) a Combining the external characteristic influence factor data of the current time period of the region to be predicted with the output of the fusion layer to serve as the input of the first fully-connected neural network layer; the output of the second full-connection neural network layer is a short-time predicted value of the traffic flow data of the area to be predicted and is expressed as
Comparing with actual traffic flow data Y, optimizing the whole model by minimizing loss functionAnd (4) transforming.
Optionally, the first deep neural network layer includes a feature extraction unit, the second deep neural network layer includes a plurality of feature extraction units, and the feature extraction unit includes a time sequence convolution neural network and a gate control cycle unit network that are connected in sequence.
Specifically, a time series Convolutional Network (TCN) is used to capture the spatial dependency relationship between the nodes, and the causal relationship in the spatial dimension of the traffic flow data is taken into account by adding a causal Convolutional neural Network. The output of the TCN network is used as the input of a Gated Current Unit (GRU), the GRU network is used for capturing the time dependence relationship among the areas, and the change trend of historical traffic flow data is still kept while the traffic flow data of the current time period is captured. Referring to fig. 4, the different effects of different historical time periods on the predicted time period are taken into account by weighting each historical time period by introducing a time attention mechanism network. Before the preset traffic flow prediction model is used, the preset traffic flow prediction model can be used only after training and testing are completed based on a historical data set.
In one possible embodiment, the traffic flow data is traffic flow data or traffic speed data. The short-time traffic flow data prediction method can realize short-time prediction of traffic flow data or traffic speed data, and can be used for predicting the traffic flow data at the next moment or at the following moments.
In one possible embodiment, when the traffic flow data is traffic flow data, the external characteristic influence factor data includes date type data, weather data, and traffic speed data of the current period; when the traffic flow data is traffic speed data, the external characteristic influence factor data includes date type data, weather data, and traffic flow data of the current time period.
Specifically, in addition to the common external characteristic influence factor data such as date type data (such as working days and holidays), weather data and the like, the traffic speed data or traffic flow data of the current time period is added, namely, the traffic speed data or the traffic flow data in the process of predictionWhen the traffic flow data is processed, the traffic speed data in the current time period is used as one of the external characteristic influence factors; in predicting the traffic speed data, the traffic flow data of the current time period is taken as one of the external characteristic influence factors. Representing some extrinsic feature factor of the current time period as a vector e ext ∈R l Where l represents the length of some extrinsic feature influencing factor data. The discrete data, such as date type data, weather data and other external characteristic influence factor data, are processed into binary vectors in a one-hot coding mode; and (3) carrying out normalization processing on continuous data such as speed, temperature, humidity, wind speed and the like through Min-Max operation, and finally realizing the reaction of external characteristic factor data to the area to be predicted based on two layers of fully-connected neural networks.
The invention discloses a short-time traffic flow data prediction method, aiming at the problems that the input data of a current traffic flow prediction model is excessive but useless, and the optimal related historical traffic flow data of an area to be predicted is selected by adding a space-time characteristic selection algorithm. Based on the short-time traffic flow data set adopted by the existing traffic flow prediction model, the time span is short, at most, only consideration of the spatial dependence relationship between adjacent weeks can be considered, the interval is divided into entry points by the period, the monthly period is considered for the first time, and the periodic traffic flow data of the current time period of the area to be predicted is obtained; based on this, for four temporal characteristics of short-time traffic flow data: the method is characterized in that the optimal dependency relationship of the related historical traffic flow data, daily periodic traffic flow data, periodic traffic flow data and monthly periodic traffic flow data is modeled, and the feature expression of key input data is strengthened while the training cost of the traffic flow prediction model is reduced and the training efficiency of the traffic flow prediction model is improved. Aiming at the problem that the potential influence existing between the traffic flow and the traffic speed in the same time period is not considered by the conventional traffic flow prediction model, the influence of the traffic flow data or the traffic speed data in the current time period is considered while conventional external characteristic factors such as date type data, weather data and the like are considered, the connection between macroscopic parameters is added to the prediction problem, the applicability and the generalization of the traffic flow prediction model are improved, and the traffic flow prediction model has certain persuasiveness.
In still another embodiment of the invention, the data sets of two road types, namely, an expressway and an urban road, are taken as research objects to be predicted based on the short-term traffic flow data prediction method.
The highway data set adopts a public real-time vehicle flow data set collected by a PeMS system, comprises traffic flow data and traffic speed data, and is added with external characteristic information such as weather data and the like on the basis of the data set through a web crawler technology based on a Dark Sky platform; the urban road data set adopts video data disclosed by express way areas which are provided with intersections and have no signal lamp guidance in a certain two rings of a certain city; the data sets of the two road types are trained and tested independently, and through comparison experiments, the short-time traffic flow data prediction method has obvious advantages and performance in the aspects of single-step prediction and multi-step prediction of short-time traffic flow data.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details not disclosed in the device embodiments, reference is made to the method embodiments of the invention.
In another embodiment of the present invention, a short-term traffic flow data prediction system is provided, which can be used to implement the short-term traffic flow data prediction method described above, and specifically, the short-term traffic flow data prediction system includes a data obtaining module, a feature extracting module, and a prediction module.
The data acquisition module is used for acquiring the area to be predicted and historical traffic flow data of each relevant area of the area to be predicted; the characteristic extraction module is used for obtaining periodic traffic flow data of the current time period of the area to be predicted according to historical traffic flow data of the area to be predicted and obtaining optimal relevant historical traffic flow data of the area to be predicted by combining historical traffic flow data of relevant areas of the area to be predicted through a preset space-time characteristic selection method; the prediction module is used for acquiring external characteristic influence factor data of the current time interval of the area to be predicted, inputting the external characteristic influence factor data of the current time interval of the area to be predicted, the periodic traffic flow data of the current time interval and the optimal related historical traffic flow data into a preset traffic flow prediction model, and obtaining the short-time predicted value of the traffic flow data of the area to be predicted.
In one possible embodiment, the periodic traffic data includes daily periodic traffic data, weekly periodic traffic data, and monthly periodic traffic data.
In a possible implementation manner, the obtaining of the optimal relevant historical traffic flow data of the area to be predicted by the preset spatio-temporal feature selection method includes: calculating Pearson correlation coefficients between historical traffic flow data of the area to be predicted and each relevant area of the area to be predicted, and selecting relevant areas with large first preset number before the Pearson correlation coefficients as target relevant areas of the area to be predicted; and selecting traffic flow data of historical moments with large second preset quantity before the correlation with the traffic flow data of the current time period by adopting a simulated annealing algorithm based on the target related region of the region to be predicted to obtain the optimal related historical traffic flow data of the region to be predicted.
In one possible implementation, the preset traffic flow prediction model includes a first deep neural network layer, a second deep neural network layer, a first time attention mechanism network layer, a second time attention mechanism network layer, a fusion layer, a first fully-connected neural network layer and a second fully-connected neural network layer; the first deep neural network layer is sequentially connected with the first time attention mechanism network layer and the fusion layer, the second deep neural network layer is sequentially connected with the second time attention mechanism network layer and the fusion layer, and the fusion layer is sequentially connected with the first fully-connected neural network layer and the second fully-connected neural network layer; the input of the first deep neural network layer is optimal related historical traffic flow data; the input of the second deep neural network layer is periodic traffic data of the current time period of the area to be predicted; combining the external characteristic influence factor data of the current time period of the region to be predicted with the output of the fusion layer to serve as the input of the first fully-connected neural network layer; and the output of the second fully-connected neural network layer is a short-time predicted value of the traffic flow data of the area to be predicted.
In a possible implementation manner, the first deep neural network layer includes one feature extraction unit, the second deep neural network layer includes a plurality of feature extraction units, and the feature extraction unit includes a time-series convolutional neural network and a gated cyclic unit network which are connected in sequence.
In one possible embodiment, the traffic flow data is traffic flow data or traffic speed data.
In one possible embodiment, when the traffic flow data is traffic flow data, the external characteristic influence factor data includes date type data, weather data, and traffic speed data of the current period; when the traffic flow data is traffic speed data, the external characteristic influence factor data includes date type data, weather data, and traffic flow data of the current time period.
All relevant contents of each step related to the embodiment of the short-term traffic flow data prediction method can be cited to the functional description of the functional module corresponding to the short-term traffic flow data prediction system in the embodiment of the present invention, and are not described herein again.
The division of the modules in the embodiments of the present invention is schematic, and only one logical function division is provided, and in actual implementation, there may be another division manner, and in addition, each functional module in each embodiment of the present invention may be integrated in one processor, or may exist alone physically, or two or more modules are integrated in one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to load and execute one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor of the embodiment of the invention can be used for the operation of the short-time traffic flow data prediction method.
In yet another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer readable storage medium may be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in the computer readable storage medium may be loaded and executed by the processor to implement the corresponding steps of the short-time traffic flow data prediction method in the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A short-term traffic flow data prediction method is characterized by comprising the following steps:
acquiring historical traffic flow data of an area to be predicted and relevant areas of the area to be predicted;
obtaining periodic traffic flow data of the current time period of the area to be predicted according to historical traffic flow data of the area to be predicted, and obtaining optimal relevant historical traffic flow data of the area to be predicted by combining historical traffic flow data of relevant areas of the area to be predicted through a preset space-time characteristic selection method;
acquiring external characteristic influence factor data of the current time period of the area to be predicted, and inputting the external characteristic influence factor data of the current time period of the area to be predicted, the periodic traffic flow data of the current time period and the optimal related historical traffic flow data into a preset traffic flow prediction model to obtain a short-time predicted value of the traffic flow data of the area to be predicted.
2. The short-term traffic flow data prediction method according to claim 1, characterized in that the periodic traffic flow data includes daily periodic traffic flow data, weekly periodic traffic flow data, and monthly periodic traffic flow data.
3. The short-term traffic flow data prediction method according to claim 1, wherein the obtaining of the optimal relevant historical traffic flow data of the area to be predicted by a preset spatiotemporal feature selection method comprises:
calculating Pearson correlation coefficients between historical traffic flow data of the area to be predicted and each relevant area of the area to be predicted, and selecting relevant areas with large first preset number before the Pearson correlation coefficients as target relevant areas of the area to be predicted;
and selecting traffic flow data of historical moments with large second preset quantity before the correlation with the traffic flow data of the current time period by adopting a simulated annealing algorithm based on the target related region of the region to be predicted to obtain the optimal related historical traffic flow data of the region to be predicted.
4. The short-term traffic flow data prediction method according to claim 1, wherein the preset traffic flow prediction model comprises a first deep neural network layer, a second deep neural network layer, a first time attention mechanism network layer, a second time attention mechanism network layer, a fusion layer, a first fully-connected neural network layer and a second fully-connected neural network layer; the first deep neural network layer is sequentially connected with the first time attention mechanism network layer and the fusion layer, the second deep neural network layer is sequentially connected with the second time attention mechanism network layer and the fusion layer, and the fusion layer is sequentially connected with the first fully-connected neural network layer and the second fully-connected neural network layer;
the input of the first deep neural network layer is optimal related historical traffic flow data; the input of the second deep neural network layer is periodic traffic data of the current time period of the area to be predicted; combining the external characteristic influence factor data of the current time period of the region to be predicted with the output of the fusion layer to serve as the input of the first fully-connected neural network layer; and the output of the second fully-connected neural network layer is a short-time predicted value of the traffic flow data of the area to be predicted.
5. The short-term traffic flow data prediction method according to claim 4, wherein the first deep neural network layer comprises a feature extraction unit, the second deep neural network layer comprises a plurality of feature extraction units, and the feature extraction units comprise a time sequence convolution neural network and a gate control cycle unit network which are connected in sequence.
6. The short-term traffic flow data prediction method according to claim 1, characterized in that the traffic flow data is traffic flow data or traffic speed data.
7. The short-term traffic flow data prediction method according to claim 6, characterized in that when the traffic flow data is traffic flow data, the external characteristic influence factor data includes date type data, weather data, and traffic speed data of the current period; when the traffic flow data is traffic speed data, the external characteristic influence factor data includes date type data, weather data, and traffic flow data of the current time period.
8. A short-term traffic flow data prediction system, comprising:
the data acquisition module is used for acquiring the area to be predicted and historical traffic flow data of each relevant area of the area to be predicted;
the characteristic extraction module is used for obtaining periodic traffic flow data of the current time period of the area to be predicted according to historical traffic flow data of the area to be predicted and obtaining optimal relevant historical traffic flow data of the area to be predicted by combining historical traffic flow data of relevant areas of the area to be predicted through a preset space-time characteristic selection method;
and the prediction module is used for acquiring external characteristic influence factor data of the current time period of the area to be predicted, inputting the external characteristic influence factor data of the current time period of the area to be predicted, the periodic traffic flow data of the current time period and the optimal related historical traffic flow data into a preset traffic flow prediction model, and obtaining the short-time predicted value of the traffic flow data of the area to be predicted.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the short-term traffic flow data prediction method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the short-term traffic flow data prediction method according to any one of claims 1 to 7.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006018435A (en) * | 2004-06-30 | 2006-01-19 | Matsushita Electric Ind Co Ltd | Traffic flow data prediction device and traffic flow data prediction method |
| US20100063715A1 (en) * | 2007-01-24 | 2010-03-11 | International Business Machines Corporation | Method and structure for vehicular traffic prediction with link interactions and missing real-time data |
| CN110223510A (en) * | 2019-04-24 | 2019-09-10 | 长安大学 | A kind of multifactor short-term vehicle flowrate prediction technique based on neural network LSTM |
| CN111524349A (en) * | 2020-04-14 | 2020-08-11 | 长安大学 | Context feature injected multi-scale traffic flow prediction model and method |
| US20200334979A1 (en) * | 2017-09-15 | 2020-10-22 | Velsis Sistemas E Tecnologia Viaria S/A | Predictive, integrated and intelligent system for control of times in traffic lights |
| CN111968375A (en) * | 2020-08-27 | 2020-11-20 | 北京嘀嘀无限科技发展有限公司 | Traffic flow prediction method and device, readable storage medium and electronic equipment |
| CN113192315A (en) * | 2020-01-14 | 2021-07-30 | 香港理工大学深圳研究院 | Traffic flow distribution prediction method, prediction device and terminal equipment |
| CN113674525A (en) * | 2021-07-30 | 2021-11-19 | 长安大学 | Signalized intersection vehicle queuing length prediction method based on sparse data |
-
2022
- 2022-06-09 CN CN202210648480.7A patent/CN115063975B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2006018435A (en) * | 2004-06-30 | 2006-01-19 | Matsushita Electric Ind Co Ltd | Traffic flow data prediction device and traffic flow data prediction method |
| US20100063715A1 (en) * | 2007-01-24 | 2010-03-11 | International Business Machines Corporation | Method and structure for vehicular traffic prediction with link interactions and missing real-time data |
| US20200334979A1 (en) * | 2017-09-15 | 2020-10-22 | Velsis Sistemas E Tecnologia Viaria S/A | Predictive, integrated and intelligent system for control of times in traffic lights |
| CN110223510A (en) * | 2019-04-24 | 2019-09-10 | 长安大学 | A kind of multifactor short-term vehicle flowrate prediction technique based on neural network LSTM |
| CN113192315A (en) * | 2020-01-14 | 2021-07-30 | 香港理工大学深圳研究院 | Traffic flow distribution prediction method, prediction device and terminal equipment |
| CN111524349A (en) * | 2020-04-14 | 2020-08-11 | 长安大学 | Context feature injected multi-scale traffic flow prediction model and method |
| CN111968375A (en) * | 2020-08-27 | 2020-11-20 | 北京嘀嘀无限科技发展有限公司 | Traffic flow prediction method and device, readable storage medium and electronic equipment |
| CN113674525A (en) * | 2021-07-30 | 2021-11-19 | 长安大学 | Signalized intersection vehicle queuing length prediction method based on sparse data |
Non-Patent Citations (1)
| Title |
|---|
| 程山英;: "基于模糊神经网络的短时交通流预测方法研究", 计算机测量与控制, no. 08 * |
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