CN115620524B - Traffic jam prediction method, system, equipment and storage medium - Google Patents
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Abstract
The invention discloses a traffic jam prediction method, a system, equipment and a storage medium, wherein the method acquires traffic event data, jam event data and station flow data by acquiring a multi-source heterogeneous data set; extracting the space correlation characteristics among the congestion event data, the traffic event data and the traffic congestion to be predicted by adopting a convolutional neural network; extracting time dependency relationship characteristics of the station traffic processing data and the congestion through a characteristic extraction network; acquiring various factor data except traffic event data, congestion event data and station flow data, and performing multi-classification processing and single-hot coding processing on the various factor data to obtain various factor characteristics; and splicing the spatial correlation characteristics, the time dependency relationship characteristics and the multiple factor characteristics to obtain space-time joint characteristics, and inputting the space-time joint characteristics into a multilayer perceptron model to obtain a prediction result of the traffic jam to be predicted. The invention can improve the accuracy of traffic jam prediction.
Description
Technical Field
The present invention relates to the field of traffic congestion prediction technologies, and in particular, to a traffic congestion prediction method, system, device, and storage medium.
Background
The problem of traffic jam is always a very concern for citizens going out, and traffic jam prediction is also an important research field of an intelligent traffic system. By combining big data, the traffic jam prediction model can effectively predict future traffic conditions according to road conditions, station traffic and historical jam data, so that citizens can be guided to go out, detour and peak shifting. The existing research methods mainly comprise a statistics-based method, a traditional machine learning method and a deep learning model-based method, wherein the statistics-based method is mainly designed for small data sets, is not suitable for processing complex and dynamic data and cannot capture the relationship among features; the traditional machine learning method needs manual feature extraction and cannot extract complex space-time features.
In China, a high-speed road section with the average traffic speed lower than 30km/h is regarded as a congestion road section, a data source provided by an APP indicates that congestion data can be uploaded to the cloud when the driving speed is lower than 30km/h, and no relevant congestion data is recorded when the driving speed is higher than 30 km/h. Due to the characteristics of data discontinuity, difficulty in calculating congestion lengths, difficulty in distinguishing congestion events and the like, congestion prediction is more difficult than prediction of other traffic flows (such as high-speed station traffic, high-speed road section traveling speed and the like).
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, the invention provides a traffic jam prediction method, a system, equipment and a storage medium, which can improve the accuracy of traffic jam prediction.
In a first aspect, an embodiment of the present invention provides a traffic congestion prediction method, where the traffic congestion prediction method includes:
acquiring a multi-source heterogeneous data set, and acquiring traffic event data, congestion event data and station flow data from the multi-source heterogeneous data set;
extracting the congestion event data, the spatial correlation characteristics between the traffic event data and the traffic congestion to be predicted by adopting a convolutional neural network;
extracting time dependency relationship characteristics of the station traffic data and congestion through a characteristic extraction network; the feature extraction network is made by fusing a gated neural unit and an attention mechanism;
acquiring various factor data except traffic event data, congestion event data and station flow data from the multi-source heterogeneous data set, and performing multi-classification processing and single-hot coding processing on the various factor data to obtain various factor characteristics;
and splicing the spatial correlation characteristic, the time dependency relationship characteristic and the multiple factor characteristics to obtain a space-time combined characteristic, and inputting the space-time combined characteristic into a multilayer perceptron model to obtain a prediction result of the traffic jam to be predicted.
Compared with the prior art, the first aspect of the invention has the following beneficial effects:
the method comprises the steps of obtaining a multi-source heterogeneous data set, obtaining traffic event data, congestion event data and station flow data from the multi-source heterogeneous data set, obtaining various data sets, predicting traffic congestion in multiple aspects, and comprehensively predicting the traffic congestion condition, so that the accuracy of traffic congestion prediction is improved; extracting congestion event data, space correlation characteristics between the traffic event data and traffic congestion to be predicted by adopting a convolutional neural network, and extracting time dependence relation characteristics of station traffic data and the congestion through a characteristic extraction network; the characteristic extraction network is formed by fusing a gate control neural unit and an attention mechanism, obtains multiple factor data except traffic event data, congestion event data and station traffic data from a multi-source heterogeneous data set, performs binary processing and single-hot-code processing on the multiple factor data to obtain multiple factor characteristics, performs characteristic extraction on different data sets in different modes, can capture the relation among the characteristics, and improves the effectiveness of the characteristic extraction; the space correlation characteristics, the time dependency relationship characteristics and the multiple factor characteristics are spliced to obtain space-time combined characteristics, the space-time combined characteristics are input into the multilayer perceptron model to obtain a prediction result of the traffic jam to be predicted, the traffic jam condition is comprehensively predicted through the multiple types of characteristics, and the accuracy of traffic jam prediction can be improved.
According to some embodiments of the invention, the obtaining traffic event data, congestion event data and station traffic data from the multi-source heterogeneous data set comprises:
and carrying out unique thermal coding processing on the traffic events and congestion events in the multi-source heterogeneous data set to obtain the traffic event data and the congestion event data, and carrying out normalization processing on the station traffic in the multi-source heterogeneous data set to obtain the station traffic data.
According to some embodiments of the invention, the extracting the spatial correlation characteristics between the congestion event data, the traffic event data and the traffic congestion to be predicted by using a convolutional neural network comprises:
presetting a first historical time step, and acquiring historical data of a first quantity of congestion event data and historical data of traffic event data which are adjacent to geographical positions in the preset first historical time step;
splicing the historical data of the traffic event data and the historical data of the congestion event data to obtain a spliced data sequence;
and inputting the spliced data sequence into the convolutional neural network to obtain the congestion event data, the traffic event data and the spatial correlation characteristics between the traffic congestion to be predicted.
According to some embodiments of the invention, the inputting the concatenated data sequence into the convolutional neural network to obtain the congestion event data, the spatial correlation characteristics between the traffic event data and the traffic congestion to be predicted comprises:
inputting the spliced data sequence into the convolutional neural network, processing the spliced data sequence through a convolutional layer and a pooling layer:
wherein,
and
representing the output of the convolutional layer, E representing the concatenated data sequence,
and
a matrix of weights is represented by a matrix of weights,
、
、
and
representing a deviation matrix, reLU representing an activation function,
the value of the maximum function is represented,
and
the output of the pooling layer is represented as,
representing a convolution operation;
after the convolution layer and the pooling layer process the concatenated data sequence, the concatenated data sequence will be processed
Inputting the spatial correlation characteristics into a full connection layer, and obtaining the spatial correlation characteristics, wherein the spatial correlation characteristics are expressed as:
wherein,
representing a spatial correlation characteristic between the congestion event data at time t, the traffic event data and the traffic congestion to be predicted,
a matrix of weights is represented by a matrix of weights,
a deviation matrix is represented.
According to some embodiments of the invention, the extracting, by the feature extraction network, the time dependency feature of the station traffic data and the congestion includes:
presetting a second historical time step, and acquiring a second quantity of inbound site traffic data and outbound site traffic data with the top geographical position rank in the second historical time step;
splicing the inbound site traffic data and the outbound site traffic data to obtain spliced site traffic data;
inputting the flow data of the splicing site into the gated neural unit to obtain a first vector, and outputting the first vector in the t step of the gated neural unit
Expressed as:
wherein,
showing the spliced site traffic data of step t-1,
representing the flow data of the splicing site in the t step, wherein the GRU represents a gated neural unit;
inputting the first vector into the attention mechanism to obtain a second vector, wherein the attention mechanism is calculated by the formula:
wherein,
representing the vector output by the gated neural unit at time t
The value of the attention distribution of (1),
and
the weight coefficient is represented by a weight coefficient,
the coefficient of variation is represented by a coefficient of variation,
representing the vector output by the gated neural unit at time j
The value of the attention distribution of (1),
indicating the attention weight, i indicates the total time;
calculating the vector output by the attention mechanism through a full-connection layer to obtain the time dependency relationship characteristics, wherein the calculation formula of the full-connection layer is as follows:
wherein,
representing the time dependency characteristics at time t,
a matrix of weights is represented by a matrix of weights,
representing the deviation vector and ReLU the activation function.
According to some embodiments of the invention, the multi-classification processing and the one-hot coding processing are performed on the multi-factor data to obtain the multi-factor characteristics, including:
if the multi-factor data in the multi-source heterogeneous data set are classified variables, representing the multi-factor data as classified 0-1 variables through multi-classification processing to obtain two-classification factor data, and mapping the two-classification factor data into multiple factor characteristics through single-hot coding;
and if the multi-factor data in the multi-source heterogeneous data set are multi-classification variables, mapping the multi-factor data into multi-factor characteristics by adopting single-hot coding.
According to some embodiments of the present invention, the stitching the spatial correlation feature, the temporal dependency relationship feature, and the multi-factor feature to obtain a spatiotemporal union feature, and inputting the spatiotemporal union feature into a multi-layered sensor model to obtain the prediction result of the traffic congestion to be predicted, includes:
inputting the space-time joint features into a multilayer perceptron model, and calculating through a hidden layer and an output layer to obtain a traffic jam prediction result, wherein the calculation of the hidden layer comprises the following steps:
wherein,
representing the spatial correlation characteristic at time t,
representing the time dependency characteristics at time t,
representing the characteristics of the various factors at time t,
a function representing the function of splicing is shown,
representing the spatio-temporal union features,
a feature vector representing an output of the hidden layer,
a matrix of weights is represented by a matrix of weights,
representing a deviation matrix, reLU representing an activation function,
representing a convolution operation;
inputting the feature vectors output by the hidden layer to the output layer, the computing of the output layer comprising:
wherein,
a prediction result representing the traffic congestion to be predicted at time t +1,
a matrix of weights is represented by a matrix of weights,
a matrix of deviations is represented which is,
representing the activation function.
In a second aspect, an embodiment of the present invention further provides a traffic congestion prediction system, where the traffic congestion prediction system includes:
the data acquisition unit is used for acquiring a multi-source heterogeneous data set and acquiring traffic event data, congestion event data and station flow data from the multi-source heterogeneous data set;
the first feature extraction unit is used for extracting the spatial correlation features among the congestion event data, the traffic event data and the traffic congestion to be predicted by adopting a convolutional neural network;
the second feature extraction unit is used for extracting the time dependency relationship features of the site traffic data and the congestion through a feature extraction network; the feature extraction network is made by fusing a gated neural unit and an attention mechanism;
the third feature extraction unit is used for acquiring various factor data except traffic event data, congestion event data and station flow data from the multi-source heterogeneous data set, and performing multi-classification processing and single-hot coding processing on the various factor data to acquire various factor features;
and the prediction result acquisition unit is used for splicing the spatial correlation characteristic, the time dependency relationship characteristic and the multiple factor characteristics to obtain a space-time combined characteristic, and inputting the space-time combined characteristic into the multilayer perceptron model to obtain the prediction result of the traffic jam to be predicted.
In a third aspect, an embodiment of the present invention further provides a traffic congestion prediction apparatus, including at least one control processor and a memory, which is communicatively connected to the at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform a method of traffic congestion prediction as described above.
In a fourth aspect, the present invention also provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute a traffic congestion prediction method as described above.
It is to be understood that the advantageous effects of the second aspect to the fourth aspect compared to the related art are the same as the advantageous effects of the first aspect compared to the related art, and reference may be made to the related description of the first aspect, which is not repeated herein.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a traffic congestion prediction method according to an embodiment of the present invention;
fig. 2 is a block diagram of a traffic congestion prediction system according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, if there are first, second, etc. described, it is only for the purpose of distinguishing technical features, and it is not understood that relative importance is indicated or implied or that the number of indicated technical features is implicitly indicated or that the precedence of the indicated technical features is implicitly indicated.
In the description of the present invention, it should be understood that the orientation or positional relationship referred to, for example, the upper, lower, etc., is indicated based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, but does not indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention.
In the description of the present invention, it should be noted that unless otherwise explicitly defined, terms such as arrangement, installation, connection and the like should be broadly understood, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.
First, several terms referred to in the present application are resolved:
decision tree: the non-parameter supervised learning algorithm is a hierarchical tree structure and consists of root nodes, branches, internal nodes and leaf nodes.
Extreme gradient lifting tree: the method is an integrated learning algorithm based on the decision tree.
Random forest: is a classifier that contains multiple decision trees and whose output classes are dependent on the mode of the class output by the individual tree.
K-nearest neighbor algorithm: is a non-parametric statistical method for classification and regression.
Long and short term memory network: the method is a variant of a recurrent neural network and is widely applied to a deep learning model of time series prediction.
The problem of traffic jam is always a very concern for citizens going out, and traffic jam prediction is also an important research field of an intelligent traffic system. By combining big data, the traffic jam prediction model can effectively predict future traffic conditions according to road conditions, station traffic and historical jam data, so that citizens can be guided to go out, detour and peak shifting. The existing research methods mainly comprise a statistics-based method, a traditional machine learning method and a deep learning model-based method, wherein the statistics-based method is mainly designed for small data sets, is not suitable for processing complex and dynamic data and cannot capture the relationship among features; the traditional machine learning method needs manual feature extraction and cannot extract complex space-time features.
In China, a high-speed road section with the average traffic speed lower than 30km/h is regarded as a congestion road section, a data source provided by an APP indicates that the congestion data can be uploaded to a cloud end when the driving speed is lower than 30km/h, and no related congestion data is recorded when the driving speed is higher than 30 km/h. Due to the characteristics of discontinuous data, difficulty in calculating congestion lengths, difficulty in distinguishing congestion events and the like, congestion prediction is more difficult than prediction of other traffic flows (such as high-speed station flow, high-speed road section running speed and the like).
In order to solve the problems, the method and the device can comprehensively predict the traffic jam situation by acquiring the multi-source heterogeneous data set, acquiring traffic event data, jam event data and station flow data from the multi-source heterogeneous data set and predicting the traffic jam from multiple aspects by acquiring various data sets, thereby improving the accuracy of traffic jam prediction; extracting congestion event data, space correlation characteristics between the traffic event data and traffic congestion to be predicted by adopting a convolutional neural network, and extracting time dependence relation characteristics of station traffic data and the congestion through a characteristic extraction network; the characteristic extraction network is formed by fusing a gate control neural unit and an attention mechanism, obtains multiple factor data except traffic event data, congestion event data and station traffic data from a multi-source heterogeneous data set, performs multi-classification processing and single-hot-code processing on the multiple factor data to obtain multiple factor characteristics, performs characteristic extraction on different data sets in different modes, can capture the relation among the characteristics, and improves the effectiveness of the characteristic extraction; the space correlation characteristics, the time dependency relationship characteristics and the multiple factor characteristics are spliced to obtain space-time combined characteristics, the space-time combined characteristics are input into the multilayer perceptron model to obtain a prediction result of the traffic jam to be predicted, the traffic jam condition is comprehensively predicted through the multiple types of characteristics, and the accuracy of traffic jam prediction can be improved.
Referring to fig. 1, an embodiment of the present invention provides a traffic congestion prediction method, which includes, but is not limited to, steps S100 to S500:
s100, acquiring a multi-source heterogeneous data set, and acquiring traffic event data, congestion event data and station flow data from the multi-source heterogeneous data set;
s200, extracting congestion event data, traffic event data and spatial correlation characteristics between traffic congestion to be predicted by adopting a convolutional neural network;
step S300, extracting time dependency relationship characteristics of the site traffic data and the congestion through a characteristic extraction network; the feature extraction network is made by fusing a gated neural unit and an attention mechanism;
s400, acquiring various factor data except traffic event data, congestion event data and station traffic data from a multi-source heterogeneous data set, and performing multi-classification processing and one-hot coding processing on the various factor data to obtain various factor characteristics;
and S500, splicing the spatial correlation characteristics, the time dependency relationship characteristics and the multiple factor characteristics to obtain space-time combined characteristics, and inputting the space-time combined characteristics into the multilayer perceptron model to obtain a prediction result of the traffic jam to be predicted.
In steps S100 to S500 of some embodiments, in order to comprehensively predict a traffic jam condition in consideration of traffic jam from multiple aspects, thereby improving accuracy of traffic jam prediction, traffic event data, congestion event data, and station traffic data are acquired from a multi-source heterogeneous data set by acquiring the multi-source heterogeneous data set; in order to capture the relationship among the characteristics and improve the effectiveness of characteristic extraction, the spatial correlation characteristics among congestion event data, traffic event data and traffic congestion to be predicted are extracted by adopting a convolutional neural network, and the time dependency relationship characteristics between station flow data and congestion are extracted by a characteristic extraction network; the characteristic extraction network is formed by fusing a gate control neural unit and an attention mechanism, acquires various factor data except traffic event data, congestion event data and station traffic data from a multi-source heterogeneous data set, and performs multi-classification processing and single-hot-code processing on the various factor data to obtain various factor characteristics; in order to improve the accuracy of traffic jam prediction, space-time joint characteristics are obtained by splicing the space correlation characteristics, the time dependency relationship characteristics and the multiple factor characteristics, and the space-time joint characteristics are input into the multilayer perceptron model to obtain a prediction result of traffic jam to be predicted.
In some embodiments, obtaining traffic event data, congestion event data, and site traffic data from a multi-source heterogeneous dataset comprises:
the traffic event and the congestion event in the multi-source heterogeneous data set are subjected to one-hot coding processing to obtain traffic event data and congestion event data, and the station flow in the multi-source heterogeneous data set is subjected to normalization processing to obtain station flow data.
In the embodiment, different methods are adopted to extract multiple types of data from the multi-source heterogeneous data set, and different extraction methods are adopted for different types of data, so that the data can be effectively extracted.
In some embodiments, extracting spatial correlation features between congestion event data, traffic event data, and traffic congestion to be predicted using a convolutional neural network comprises:
presetting a first historical time step, and acquiring historical data of a first quantity of congestion event data and historical data of traffic event data which are adjacent to geographical positions in the preset first historical time step;
splicing the historical data of the traffic event data and the historical data of the congestion event data to obtain a spliced data sequence;
and inputting the spliced data sequence into a convolutional neural network to obtain the spatial correlation characteristics among the congestion event data, the traffic event data and the traffic congestion to be predicted.
It should be noted that the first historical time step and the first quantity of this embodiment may be changed according to an actual situation, and this embodiment is not limited specifically.
In the embodiment, the convolutional neural network is adopted, so that the spatial correlation characteristics between the congestion event data and the traffic event data can be effectively extracted, the relation between the characteristics can be captured, and the feature extraction effectiveness is improved.
In some embodiments, inputting the stitched data sequence into a convolutional neural network to obtain spatial correlation characteristics between congestion event data, traffic event data, and traffic congestion to be predicted, includes:
inputting the spliced data sequence into a convolutional neural network, processing the spliced data sequence through a convolutional layer and a pooling layer:
wherein,
and
representing the output of the convolutional layer, E the concatenated data sequence,
and
a matrix of weights is represented by a matrix of weights,
、
、
and
representing a deviation matrix, reLU representing an activation function,
the value of the maximum function is represented,
and
the output of the pooling layer is represented as,
representing a convolution operation;
after the concatenated data sequences are processed at the convolutional and pooling layers, the data sequence will be
Inputting to a full connection layer, and obtaining a spatial correlation characteristic, wherein the spatial correlation characteristic is expressed as:
wherein,
representing the spatial correlation characteristics between the congestion event data at time t, the traffic event data and the traffic congestion to be predicted,
a matrix of weights is represented by a matrix of weights,
a deviation matrix is represented.
In some embodiments, extracting the time dependency characteristics of the station traffic data and the congestion through the characteristic extraction network comprises:
presetting a second historical time step, and acquiring a second quantity of inbound site traffic data and outbound site traffic data with the top geographical position rank in the second historical time step;
splicing the inbound site traffic data and the outbound site traffic data to obtain spliced site traffic data;
inputting the flow data of the splicing site into a gated neural unit to obtain a first vector, and outputting the first vector in the t step of the gated neural unit
Expressed as:
wherein,
showing the flow data of the splicing station in the t-1 step,
representing the flow data of the splicing site in the t step, wherein GRU represents a gating neural unit;
inputting the first vector into an attention mechanism to obtain a second vector, wherein the attention mechanism is calculated by the formula:
wherein,
representing the vector output by the gated neural unit at time t
The value of the attention distribution of (2),
and
the weight coefficient is represented by a weight coefficient,
the coefficient of variation is represented by a coefficient of variation,
representing the vector output by the gated neural unit at time j
The value of the attention distribution of (2),
representing the attention weight, i represents the total time;
calculating a vector output by the attention mechanism through a full-connection layer to obtain a time dependency relationship characteristic, wherein a calculation formula of the full-connection layer is as follows:
wherein,
representing the time dependency characteristics at time t,
a matrix of weights is represented by a matrix of weights,
representing the deviation vector and ReLU the activation function.
It should be noted that the second historical time step and the second quantity in this embodiment may be changed according to actual situations, and this embodiment is not particularly limited.
In the embodiment, the time dependency relationship characteristics of the site flow data and the congestion can be effectively extracted by adopting the gate control neural unit and the attention mechanism, and the relationship between the characteristics can be captured, so that the effectiveness of characteristic extraction is improved.
In some embodiments, the multi-classification process and the one-hot encoding process are performed on the multi-factor data to obtain the multi-factor characteristics, including:
if the multi-factor data in the multi-source heterogeneous data set are classified variables, the multi-factor data are subjected to multi-classification processing and are expressed as classified 0-1 variables, two-classification factor data are obtained, and the two-classification factor data are mapped into multi-factor characteristics through single-hot coding;
and if the multi-factor data in the multi-source heterogeneous data set are multi-classification variables, mapping the multi-factor data into multi-factor characteristics by adopting single-hot coding.
In the embodiment, the two-classification processing and the one-hot coding processing can effectively extract multiple factor features and capture the relationship between the features, thereby improving the effectiveness of feature extraction.
In some embodiments, the obtaining a space-time combined feature by splicing the spatial correlation feature, the temporal dependency relationship feature and the multiple factor features, and inputting the space-time combined feature into the multilayer perceptron model to obtain a prediction result of traffic congestion to be predicted includes:
inputting the space-time joint characteristics into a multi-layer perceptron model, and calculating through a hidden layer and an output layer to obtain a traffic jam prediction result, wherein the calculation of the hidden layer comprises the following steps:
wherein,
representing the spatial correlation characteristic at time t,
representing the time dependency characteristics at time t,
indicating the characteristics of a number of factors at time t,
the representation of the splicing function is shown,
the characteristics of the spatio-temporal union are represented,
a feature vector representing the output of the hidden layer,
a matrix of weights is represented by a matrix of weights,
representing a deviation matrix, reLU representing an activation function,
representing a convolution operation;
inputting the feature vector output by the hidden layer into an output layer, wherein the calculation of the output layer comprises the following steps:
wherein,
a prediction result representing the traffic congestion to be predicted at time t +1,
a matrix of weights is represented by a matrix of weights,
a matrix of deviations is represented which is,
representing an activation function.
In the embodiment, after the spatial correlation characteristic, the time dependency relationship characteristic and the multiple factor characteristics are spliced, the traffic jam condition is comprehensively predicted through the multiple types of characteristics, and the accuracy of traffic jam prediction can be improved.
To facilitate understanding by those skilled in the art, the following sets of preferred embodiments are provided:
1. and processing the multi-source heterogeneous data set.
According to the characteristics of multi-source heterogeneous data, the data are divided into two types of data: the category data and the continuous data adopt different processing methods for different categories of data in the multi-source heterogeneous data set, such as:
in the embodiment, congestion event data (the vehicle running speed is lower than 30 km/h) returned by a high-grade map is combined, the congestion event data is combined with a stake number, so that a congestion event is accurately positioned on an expressway, the congestion event data is recorded as 1 when a road section n is congested in a time period [ t, t + t1] (t 1 takes 0.5h, namely 30 min), and the congestion event data is recorded as 0 if the road section n does not have any congestion event information in the time period [ t, t + t1] (t 1 takes 0.5h, namely 30 min). After being processed, the congestion event is processed into congestion event data with category variables of 0-1.
The data set of the embodiment comprises the records of the toll stations entering and leaving the toll stations of a certain provincial highway network system, and the data set is cleaned and preprocessed to count and obtain the traffic of the toll stations entering and leaving the toll stations at all times. In the embodiment, the traffic of the station m at the time t is represented as
Where M represents the total number of sites, resulting in site traffic data for ingress and egress sites for 268 billed sites. The statistical interval of the station traffic is 0.5 hour, each station traffic data comprises station traffic data of the toll station entering and leaving station from 1/2019 to 31/2019/12/31/year, and the traffic unit is per hour (namely, vehicle/hour). NeedleFor the station traffic data, the embodiment uses the normalization layer to process the station traffic data, and when the purpose of the normalization layer is to solve the optimization problem by using a gradient descent method, the solution speed of the gradient descent can be increased after normalization, that is, the convergence speed of the model is increased. After the station traffic is normalized, each sample set contains 17520 pieces of data, and is divided into a training set, a verification set and a test set of the model according to the following ratio of 6. The flow data is a continuous variable, namely how many vehicles exist in a certain time step, and becomes a floating point number from 0 to 1 after normalization processing.
2. And a spatial correlation feature extraction module.
Because the influence factors of the congestion of the highway network are complex, the congestion events of the highway network are not only influenced by the upstream road sections of the highway network, but also influenced by the downstream road sections of the highway network. The structure of the highway network is complicated and complicated, and the spatial influence is difficult to extract. Therefore, the embodiment captures the spatial correlation characteristics of the highway network congestion through the convolutional neural network. The method comprises the following specific steps:
the first historical time step adopted by the embodiment is T, and the historical data of the traffic event data of the adjacent N road sections with the nearest geographic positions is constructed as
Wherein
representing the condition of the traffic event of the jth historical time step of the adjacent i-number road section; the historical data of the congestion event data of the adjacent N road sections with the nearest geographic positions is constructed as
Wherein, in the process,
and the congestion condition of the jth historical time step of the adjacent i-number road section is shown. Splicing the historical data C of the traffic event data and the historical data D of the congestion event data to obtain the highwaySequence of traffic events and congestion events for adjacent sections of the network
And E is input into the convolutional neural network. The traffic incident data comprises the total traffic incident information of each urban area and each expressway in the whole province in 2019, and comprises incident types (such as large traffic flow, traffic control, sudden traffic accidents, road construction and the like), duration, starting-point expressway stake numbers, ending-point expressway stake numbers and occurrence time data. In this embodiment, the first historical time step T is 6, and the number N of links with the closest geographical position is 8.
In this embodiment, a convolutional neural network framework composed of 2 convolutional layers and 2 pooling layers is constructed, according to the characteristics of a highway network, the 2 convolutional layers are all designed as two-dimensional convolutions, the pooling mode is selected as maximum pooling, and the ReLU activation function is selected as the activation function of the convolutional layers. The processing of the convolutional and pooling layers can be expressed as follows:
after being processed by the convolutional layer and the pooling layer, historical traffic events and congestion events are mapped into the hidden layer feature space and then are processed by the convolutional layer and the pooling layer
Input to the fully-connected layer to obtain spatial correlation features, the fully-connected layer employs the activation function ReLU. The spatial correlation characteristic output by the convolutional neural network at the time t can be expressed as:
the method comprises the steps that the spatial correlation between the congestion events of the adjacent road sections and the traffic jam to be predicted can be captured through the convolutional neural network, the spatial correlation between the traffic events of the adjacent road sections and the traffic jam to be predicted can be captured through the convolutional neural network, and therefore the spatial correlation characteristics between the congestion event data and the traffic jam to be predicted can be obtained, and the spatial correlation characteristics between the traffic event data and the traffic jam to be predicted can be obtained.
3. And a time dependency relationship feature extraction module.
Since the high-speed station traffic has strong periodicity and time dependency, and the neighboring high-speed station traffic has strong nonlinear time correlation with the congestion of the high-speed road section, the embodiment captures the time periodicity of the station traffic data and captures the nonlinear time correlation of the congestion and the station traffic data through the gate control neural unit and the attention mechanism. The method specifically comprises the following steps:
in this embodiment, the second historical time step is T, and inbound site traffic data of M top-ranked high-speed sites with the closest geographic location is constructed as
Wherein
for historical data of inbound site traffic data with the length of site M being T, outbound site traffic data of M high-speed sites with the nearest geographic positions and the top rank are constructed as
Wherein
the historical data of outbound station traffic data with the length T of the station m. Historical data of inbound site traffic data
And historical data of outbound site traffic data
After the splicing operation is carried out, the flow data of the splicing station is obtained as
And inputting the flow data of the splicing site into a gating neural unit, and fully learning the features to be extracted so as to capture the time dependence relationship. In this embodiment, the second historical time step T is 6, and the number M of high-speed stations with the closest geographical position is 10. The output of the gated neural unit is a first vector, and the output of the gated neural unit is the first vector output in the t step
Can be expressed as:
then, inputting the first vector after the gated neural unit activation processing into an attention mechanism for summarizing, calculating weights corresponding to different feature vectors through weight distribution, and continuously calculating a parameter matrix with more optimal iteration, wherein the calculation mode of the attention mechanism can be represented as follows:
calculating the output of the attention mechanism through a full-connection layer to obtain a time dependency relationship characteristic, wherein the activation function of the full-connection layer is ReLU, and the time dependency relationship characteristic output at the time t is obtained as follows:
4. and a multi-factor feature extraction module.
The embodiment designs an embedded layer and a full connection layer to extract various factor data which affect the congestion of the highway network, such as time, holidays, weather and the like. For example:
for the traffic of each time of the toll station, category features with time characteristics, such as the hour in one day, the day in one week, whether the day is weekend, whether the day is holiday, whether the day before the holiday is holiday, whether the day after the holiday is holiday, and the like, are extracted, and the time features of the corresponding time are converted into an embedded vector Other by adopting a one-hot coding mechanism, wherein the embedded vector Other comprises the hour in one day (24 features), the day in one week (7 features), whether the day is holiday (2 features), whether the day before the holiday (2 features), whether the day after the holiday (2 features), current road section historical traffic event data (2 features), and the like. In this embodiment, for data of two classification variables (if the data is a holiday, the data is represented as a two-classification 0-1 variable through multi-classification processing, two classification factor data are obtained, and the two classification factor data are mapped into multiple factor features through one-hot coding; for the data of the multi-classified category variables, the embodiment adopts a one-hot coding method to map the data into a plurality of 0-1 binary features (i.e., multiple factor features) so as to ensure that the distances between different categories are the same, thereby facilitating better extraction of the relationship between the features. The present embodiment sets the historical time step to 6 (3 hours in the past), that is, predicts the road congestion condition of a single time step in the future by historical 6 time steps (3 hours).
The processing for the weather factors is: firstly, carrying out one-hot coding processing, then inputting the one-hot coding and other time factors into the embedding layer for embedding operation, and then inputting the output of the embedding layer into the full-connection layer to obtain the characteristics of multiple factors. The weather data in this embodiment includes weather data of 2019, which is refined to each urban area and each half hour, and includes data of weather conditions (such as cloudy, sunny, cloudy, rainy, and snowy), temperature, wind power, wind direction, and the like.
5. And predicting the condition of traffic jam.
Inputting the space-time joint characteristics into a multi-layer sensor model, and calculating through a hidden layer and an output layer to obtain a prediction result of traffic jam to be predicted, wherein the calculation of the hidden layer comprises the following steps:
wherein,
representing the characteristics of various factors at time t.
Inputting the feature vector output by the hidden layer into an output layer, wherein the calculation of the output layer comprises the following steps:
for better illustration, the following experiments were performed in this example:
1. performance index
The present embodiment uses the F1 Score (F1 Score), the positive sample classification accuracy (P), and the positive sample classification recall (R) as the evaluation indexes of the prediction model performance, and these three indexes are widely applied to the classification situation where the sample distribution is extremely uneven. Wherein TP (True Positive), FP (False Positive) and FN (False Negative) indicate True Positive and False Negative, respectively. The performance index is calculated according to the following formula:
2. and (4) carrying out comparative experiments.
In order to evaluate the performance of the model, the processed multi-source heterogeneous data set is divided into a training set, a verification set and a test set according to a certain proportion, the training set is used for model training, the verification set and the test set are used for model evaluation, and the effectiveness of the model is evaluated by comparing with other baseline models. In the embodiment, the overall performance is compared and evaluated by using a decision tree, an extreme gradient lifting tree, a random forest, a K-nearest neighbor algorithm, a long-short term memory network and other baseline prediction methods and the technical scheme of the embodiment.
Through comparative evaluation, the technical scheme of the embodiment has the best prediction effect, and the result is shown in table 1, and 3 conclusions can be obtained:
(1) The technical scheme of the embodiment is obviously superior to other methods in all indexes, especially in indexes of F1 score, accuracy rate and recall rate, and has very important guiding effect on the travel of citizens;
(2) Because the deep learning network can fully learn the nonlinear relation among the characteristics, the model has greater advantages when modeling the relevant data of the highway network;
(3) Since the baseline model cannot learn the spatial correlation of the highway network, the technical scheme of the embodiment can learn the spatial correlation through the convolutional neural network, and therefore the prediction performance is better.
TABLE 1
3. Ablation experiment
To evaluate the effectiveness of the various components in the model, the present embodiment is validated by ablation experiments, i.e., the performance of the model is evaluated by reducing certain design components in the model, which can reflect the effectiveness of various components in the model.
The effectiveness of different components in the technical solution of the present embodiment is evaluated through ablation experiments, and the results are shown in table 2, which shows three indexes, namely F1 score, accuracy rate and recall rate. The result shows that deleting any component will affect the performance of the solution of the present embodiment. When the spatial correlation characteristic extraction module is deleted, the F1 score, the accuracy rate and the recall rate are respectively reduced from 0.683, 0.685 and 0.683 to 0.488, 0.502 and 0.475; when the time dependency relationship characteristic extraction module is deleted, the F1 score, the precision rate and the recall rate are respectively reduced from 0.683, 0.685 and 0.683 to 0.605, 0.623 and 0.589; when the multi-factor feature extraction module is deleted, the F1 score, the precision rate and the recall rate are respectively reduced from 0.683, 0.685 and 0.683 to 0.637, 0.633 and 0.642.
This illustrates: 1) The congestion events occurring on the highway network are obviously influenced by the road network structure and the traffic conditions of adjacent road sections, so that the congestion events occurring on the highway network are emphasized and are processed in time so as to avoid causing congestion of the adjacent road sections; 2) The influence of the spatial correlation of the high-speed road network congestion is greater than the influence of the traffic station flow; 3) Since the spatial correlation of the highway network and the influence of the highway site traffic is more direct than the influence of time factors (such as day of the week, whether it is a holiday today), the prediction accuracy and recall rate drop off more when the first two components are ablated.
TABLE 2
Referring to fig. 2, an embodiment of the present invention further provides a traffic congestion prediction system, which includes a data obtaining unit 100, a first feature extracting unit 200, a second feature extracting unit 300, a third feature extracting unit 400, and a prediction result obtaining unit 500, where:
the data acquisition unit 100 is configured to acquire a multi-source heterogeneous data set, and acquire traffic event data, congestion event data, and station traffic data from the multi-source heterogeneous data set;
a first feature extraction unit 200, configured to extract spatial correlation features between congestion event data, traffic event data, and traffic congestion to be predicted by using a convolutional neural network;
a second feature extraction unit 300, configured to extract, through a feature extraction network, a time dependency relationship feature between site traffic data and congestion; the feature extraction network is made by fusing a gated neural unit and an attention mechanism;
a third feature extraction unit 400, configured to obtain multiple factor data, excluding traffic event data, congestion event data, and station traffic data, from the multi-source heterogeneous data set, and perform multi-classification processing and single-hot coding processing on the multiple factor data to obtain multiple factor features;
the prediction result obtaining unit 500 is configured to splice the spatial correlation feature, the temporal dependency relationship feature, and the multiple factor features to obtain a spatiotemporal union feature, and input the spatiotemporal union feature into the multilayer perceptron model to obtain a prediction result of the traffic congestion to be predicted.
It should be noted that, since a traffic congestion prediction system in the present embodiment and a traffic congestion prediction method described above are based on the same inventive concept, the corresponding contents in the method embodiments are also applicable to the present system embodiment, and are not described in detail herein.
An embodiment of the present invention further provides a traffic congestion prediction apparatus, including: at least one control processor and a memory for communicative connection with the at least one control processor.
The memory, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. Further, the memory may include high speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory located remotely from the processor, and these remote memories may be connected to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Non-transitory software programs and instructions required to implement a traffic congestion prediction method of the above-described embodiments are stored in a memory, and when executed by a processor, perform a traffic congestion prediction method of the above-described embodiments, for example, performing the method steps S100 to S500 in fig. 1 described above.
The above described system embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
Embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which are executed by one or more control processors, and may cause the one or more control processors to execute a traffic congestion prediction method in the above method embodiments, for example, to execute the functions of the above method steps S100 to S500 in fig. 1.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as is well known to those skilled in the art.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present invention.
Claims (10)
1. A traffic congestion prediction method, characterized by comprising:
acquiring a multi-source heterogeneous data set, and acquiring traffic event data, congestion event data and station flow data from the multi-source heterogeneous data set;
extracting the congestion event data, the traffic event data and the spatial correlation characteristics between the traffic congestion to be predicted by adopting a convolutional neural network;
extracting the time dependency relationship characteristics of the station traffic data and the congestion through a characteristic extraction network; the feature extraction network is made by fusing a gated neural unit and an attention mechanism;
acquiring multiple factor data except traffic event data, congestion event data and station flow data from the multi-source heterogeneous data set, and performing multi-classification processing and single-hot coding processing on the multiple factor data to obtain multiple factor characteristics; wherein the multi-factor data includes time, holidays, and weather;
and splicing the spatial correlation characteristic, the time dependency relationship characteristic and the multiple factor characteristics to obtain a space-time combined characteristic, and inputting the space-time combined characteristic into a multilayer perceptron model to obtain a prediction result of the traffic jam to be predicted.
2. The method of claim 1, wherein the obtaining traffic event data, congestion event data, and station traffic data from the multi-source heterogeneous data set comprises:
and carrying out one-hot coding processing on the traffic events and the congestion events in the multi-source heterogeneous data set to obtain the traffic event data and the congestion event data, and carrying out normalization processing on the station flow in the multi-source heterogeneous data set to obtain the station flow data.
3. The method of claim 1, wherein said extracting spatial correlation characteristics between the congestion event data, the traffic event data and the traffic congestion to be predicted using a convolutional neural network comprises:
presetting a first historical time step, and acquiring historical data of a first quantity of congestion event data and historical data of traffic event data which are adjacent to geographical positions in the preset first historical time step;
splicing the historical data of the traffic event data and the historical data of the congestion event data to obtain a spliced data sequence;
and inputting the spliced data sequence into the convolutional neural network to obtain the congestion event data, the traffic event data and the spatial correlation characteristics between the traffic congestion to be predicted.
4. The method according to claim 3, wherein the inputting the concatenated data sequence into the convolutional neural network to obtain the spatial correlation characteristics between the congestion event data, the traffic event data and the traffic congestion to be predicted comprises:
inputting the spliced data sequence into the convolutional neural network, processing the spliced data sequence through a convolutional layer and a pooling layer:
wherein,
and &>
Represents the output of a convolutional layer, E represents the concatenated data sequence, R represents the concatenated data sequence>
And &>
Represents a weight matrix, based on the weight of the reference signal>
、/>
、/>
And &>
Represents a deviation matrix, reLU represents an activation function, ->
Represents the maximum function value, < >>
And &>
Represents the output of the pooling layer, < > or >>
Representing a convolution operation;
after the convolution layer and the pooling layer process the concatenated data sequence, the concatenated data sequence will be processed
Inputting the spatial correlation characteristics into a full connection layer, and obtaining the spatial correlation characteristics, wherein the spatial correlation characteristics are expressed as:
wherein,
a spatial correlation feature representing the congestion event data at time t, the traffic event data and the traffic congestion to be predicted, ->
Represents a weight matrix, based on the weight of the reference signal>
A deviation matrix is represented.
5. The method according to claim 1, wherein the extracting, through a feature extraction network, the time-dependent relationship feature of the station traffic data and the congestion includes:
presetting a second historical time step, and acquiring a second quantity of inbound site traffic data and outbound site traffic data with the top geographical position rank in the second historical time step;
splicing the inbound site traffic data and the outbound site traffic data to obtain spliced site traffic data;
inputting the flow data of the splicing site into the gated neural unit to obtain a first vector, and outputting the first vector in the t step of the gated neural unit
Expressed as:
wherein,
spliced station traffic data representing the t-1 th step, based on the data flow in the database, and based on the data flow in the database>
Representing the splice site traffic of the t-th stepData, GRU represents gated neural units;
inputting the first vector into the attention mechanism to obtain a second vector, wherein the attention mechanism is calculated by the formula:
wherein,
representing the output vector ≥from the gated neural unit at time t>
Is taken into consideration, based on the attention profile value of (4)>
And &>
Represents a weight coefficient, <' > based on>
Represents a deviation factor>
Represents the output vector @ by the gated neural unit at time j>
Is taken into consideration, based on the attention profile value of (4)>
Indicating the attention weight, i indicates the total time;
calculating the vector output by the attention mechanism through a full-connection layer to obtain the time dependency relationship characteristic, wherein the full-connection layer calculation formula is as follows:
wherein,
represents a time-dependency characteristic at time t>
Represents a weight matrix, based on the weight of the reference signal>
Representing the deviation vector and ReLU the activation function.
6. The method according to claim 1, wherein said performing multi-classification processing and one-hot coding processing on said multi-factor data to obtain multi-factor features comprises:
if the multi-factor data in the multi-source heterogeneous data set are classified variables, representing the multi-factor data as classified 0-1 variables through multi-classification processing to obtain two-classification factor data, and mapping the two-classification factor data into multiple factor characteristics through single-hot coding;
and if the multi-factor data in the multi-source heterogeneous data set are multi-classification variables, mapping the multi-factor data into multi-factor characteristics by adopting single-hot coding.
7. The traffic congestion prediction method according to claim 1, wherein the obtaining of the prediction result of the traffic congestion to be predicted by splicing the spatial correlation feature, the temporal dependency relationship feature and the multi-factor feature to obtain a spatiotemporal joint feature and inputting the spatiotemporal joint feature into a multi-layered perceptron model comprises:
inputting the space-time joint features into a multilayer perceptron model, and calculating through a hidden layer and an output layer to obtain a traffic jam prediction result, wherein the calculation of the hidden layer comprises the following steps:
wherein,
represents the spatial correlation characteristic at time t>
Representing the time dependency characteristics at time t,
characteristic of the various factors present at time t>
Represents a splicing function, <' > or>
Represents the spatiotemporal union feature>
A feature vector representing the output of the hidden layer, -a>
Represents a weight matrix, based on the weight of the reference signal>
Represents a deviation matrix, reLU represents an activation function, ->
Representing a convolution operation;
inputting the feature vectors output by the hidden layer to the output layer, the computing of the output layer comprising:
wherein,
represents the prediction result of the traffic jam to be predicted at the time t +1, is->
Represents a weight matrix, based on the weight of the reference signal>
Represents a deviation matrix, <' > or>
Representing an activation function.
8. A traffic congestion prediction system, characterized in that the traffic congestion prediction system comprises:
the data acquisition unit is used for acquiring a multi-source heterogeneous data set and acquiring traffic event data, congestion event data and station flow data from the multi-source heterogeneous data set;
the first feature extraction unit is used for extracting the spatial correlation features among the congestion event data, the traffic event data and the traffic congestion to be predicted by adopting a convolutional neural network;
the second feature extraction unit is used for extracting the time dependency relationship features of the site traffic data and the congestion through a feature extraction network; the feature extraction network is made by fusing a gated neural unit and an attention mechanism;
the third feature extraction unit is used for acquiring various factor data except traffic event data, congestion event data and station flow data from the multi-source heterogeneous data set, and performing multi-classification processing and single-hot coding processing on the various factor data to acquire various factor features; wherein the multiple factor data includes time, holidays, and weather;
and the prediction result acquisition unit is used for splicing the spatial correlation characteristic, the time dependency relationship characteristic and the multiple factor characteristics to obtain a space-time joint characteristic, and inputting the space-time joint characteristic into a multilayer sensor model to obtain a prediction result of the traffic jam to be predicted.
9. A traffic congestion prediction apparatus comprising at least one control processor and a memory for communicative connection with said at least one control processor; the memory stores instructions executable by the at least one control processor to enable the at least one control processor to perform a method of traffic congestion prediction according to any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform the method of traffic congestion prediction according to any one of claims 1 to 7.
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