CN111383452A - Method for estimating and predicting short-term traffic running state of urban road network - Google Patents

Method for estimating and predicting short-term traffic running state of urban road network Download PDF

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CN111383452A
CN111383452A CN201911219004.8A CN201911219004A CN111383452A CN 111383452 A CN111383452 A CN 111383452A CN 201911219004 A CN201911219004 A CN 201911219004A CN 111383452 A CN111383452 A CN 111383452A
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任刚
宋建华
曹奇
李豪杰
李大韦
张洁斐
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Southeast University
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Abstract

The invention discloses a method for estimating and predicting short-term traffic running states of an urban road network, which comprises the following steps: (1) acquiring heterogeneous data, preprocessing the data, and reconstructing a speed field of a research unit by using a GASM algorithm by taking a road section between two signalized intersections in a city as the research unit; (2) constructing a spatial weight matrix of an urban road network, calculating the time-space correlation among all road sections, and identifying and quantifying the fragile road sections by adopting TOPSIS (technique for order preference by similarity to similarity); (3) taking the average value of the speeds according to the reconstructed speed field of the research unit and selecting a reasonable and fragile road section to construct a space-time characteristic matrix of the urban road network; (4) and estimating and predicting the traffic state of the whole road network according to the Bi-ConvLSTM. The method has the advantages that the speed field of the research unit is reconstructed by fusing heterogeneous data, the prediction limitation caused by a single data source is solved, meanwhile, Bi-ConvLSTM is adopted to consider the influence of the traffic speed of the upstream and downstream of the research unit, the space-time characteristic of the traffic flow is fully excavated, the prediction accuracy is further improved, and the like.

Description

Method for estimating and predicting short-term traffic running state of urban road network
Technical Field
The invention belongs to the technical field of intelligent traffic, and particularly relates to a method for estimating and predicting short-term traffic running states of an urban road network.
Background
With the rapid development of social economy and the arrival of the 5G information technology revolution, the life of people becomes more convenient and fast, and meanwhile, a new opportunity is brought to the traffic industry. Particularly, the rapid development of the intelligent traffic field is expected to solve the traffic problems such as traffic jam, traffic environment and the like.
Real-time monitoring of urban road traffic states and accurate traffic state information release are important foundations for guaranteeing traffic safety and operation efficiency. The reasonable and scientific management and control of traffic can be realized according to the real-time traffic state information of the road, the occurrence of congestion is reduced, road network resources are fully utilized, and the method has important practical significance in the aspects of shortening travel time and the like for road users. Therefore, real-time and accurate traffic state information estimation and prediction become a crucial link. However, the current research does not fully consider the effect of heterogeneous data on the estimation of the traffic state of the whole road network and the mutual influence of the traffic flow on the upstream and downstream, so that the prediction accuracy of the road network layer cannot meet the requirement.
When the traffic state of the whole road network is predicted, the upstream and downstream traffic states among the road sections in the road network cannot be ignored, but the general deep learning method is used for predicting in a one-way mode, for example, a Conv-LSTM model, some researches do not well dig the traffic space characteristics although Bidirectional traffic state prediction is carried out, for example, a Bidirectional LSTM model, some key information may be filtered by the model during prediction, and the final prediction result has certain deviation.
Disclosure of Invention
In order to solve the problems, the invention discloses a method for estimating and predicting the short-term traffic running state of an urban road network, which solves the problem that the existing prediction method is not comprehensive enough in consideration of space-time dimension, further improves the prediction precision, provides accurate traffic information for urban traffic managers and users in the future, and has great significance for the construction of an intelligent traffic system.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a method for estimating and predicting short-term traffic running states of an urban road network comprises the following steps:
(1) acquiring urban taxi GPS data and urban road speed measuring card port data, and preprocessing heterogeneous data;
(2) the method comprises the steps that a road section between two intersections serves as a research unit, the GPS speed and the gate speed of a taxi are fused by utilizing a generalized adaptive smoothing algorithm (GASM), and the actual traffic state of the research unit (road section) is reconstructed;
(3) solving the average speed of the road section according to the fused traffic state;
(4) establishing a spatial weight matrix of an urban road network;
(5) calculating the space-time correlation between road sections;
(6) identifying and quantifying vulnerable road segments based on a near ideal point ranking method (TOPSIS);
(7) input data, i.e. a spatio-temporal matrix of the urban road network, a N x D characteristic matrix is generated, which describes the traffic speed over the road as a function of time. Wherein N is the number of vulnerable segments and D is the time interval;
(8) the method is based on the respective advantages of the Bi-LSTM and the CNN model and combines the advantages, namely, the Bi-ConvLSTM is used for extracting the space-time characteristics of the traffic state of the whole road network, and the estimation and the prediction values of the traffic state of the whole road network at the current moment and the next moment are obtained.
The invention has the beneficial effects that:
the method for estimating and predicting the short-term traffic running state of the urban road network comprises the steps of taking urban taxi GPS data and road network speed measuring gate data as basic data, fusing the taxi data and the gate data through a generalized adaptive smoothing algorithm (GASM), reconstructing an actual traffic flow, and truly reflecting the actual change of the traffic speed of the urban road section. The method effectively solves the problems of low traffic state estimation precision, large estimation error of a single data source and the like, effectively improves the traffic running state of a real road section, and lays a solid foundation for further estimating and predicting the traffic state of the whole road network. By defining the fragile road sections in the road network, the traffic state of the whole urban road network layer is accurately estimated and predicted in a short time by utilizing the bidirectional convolution long-short term memory neural network. By accurately reflecting the evolution law of the traffic state of the urban road network, optimal traffic control measures and travel plans are provided for road traffic managers and users.
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FIG. 1 is a flow chart of the method for estimating and predicting the short-term traffic operation state of the urban road network.
FIG. 2 is a schematic diagram of the structure of LSTM.
FIG. 3 is a schematic view of the BDC-LSTM structure.
Detailed Description
The present invention will be further illustrated with reference to the accompanying drawings and specific embodiments, which are to be understood as merely illustrative of the invention and not as limiting the scope of the invention.
Noun explanations of some abbreviations of the invention:
GASM (General adaptive smoothing method, generalized adaptive smoothing method),
TOPSIS (Technique for Order Preference by Similarity to Ideal solution, based on the approximation of the ideal point ordering method),
Bi-ConvLSTM (Bi-directional LSTM, bidirectional long-short time memory Network; CNN, convolutional neural Network, which is abbreviated as Bi-ConvLSTM after being fused, bidirectional convolutional long-short time memory neural Network).
Fig. 1 shows a flowchart of a method for estimating and predicting short-term traffic operation states of an urban road network, which is implemented by the invention, and the method specifically comprises the following steps:
001, acquiring urban taxi GPS data and urban road speed measuring card port data, and preprocessing heterogeneous data;
002, taking the road section between two intersections as a research unit, and reconstructing the actual traffic state of the research unit (road section) by fusing the taxi GPS speed and the gate speed by using a generalized adaptive smoothing algorithm (GASM);
003, calculating the average speed of the road section according to the fused traffic state;
004, establishing a city road network space weight matrix;
005, calculating the space-time correlation between the road sections;
006, identifying and quantifying vulnerable segments based on TOPSIS;
007 input data, i.e. a spatio-temporal matrix of the urban road network, a N x D feature matrix, which describes the traffic speed over the road as a function of time, is generated. Where N is the number of weak segments and D is the time interval.
008, the invention is based on the respective advantages of Bi-LSTM and CNN models and combines, namely Bi-ConvLSTM is used for extracting the space-time characteristics of the traffic state of the whole road network, and the estimation and prediction values of the traffic state of the whole road network at the current time and the next time are obtained.
In the above technical solution, the implementation method of step 002 includes:
since the collected traffic data is typically discrete and sparse, it is necessary to reconstruct the continuous velocity field using a generalized adaptive smoothing algorithm (GASM) to achieve accurate traffic conditions. The input data is a discrete set of data points { x }i,ti,v i1, the output is a continuous velocity field V (x, t), and the calculation formula is as follows:
Figure BDA0002300271460000031
wherein: x is a spatial coordinate; t time coordinate; v. ofiIs the velocity value of point i; smoothing kernel function phii(x, t) decreases as | x | and | t | increase.
The kernel function calculation formula is as follows:
Figure BDA0002300271460000032
wherein: point (x, t) is the evaluation point, (x)i,ti) For collected dataPoint, σ is half the distance between two adjacent detectors, τ is half the sampling time of the detectors; the normalization function is also defined as follows:
Figure BDA0002300271460000033
in order to truly reflect the propagation condition of the traffic flow, the GASM realizes the congestion traffic flow V by adjusting the kernel functioncong(x, t) and free stream VfreeThe velocity estimates of (x, t) are respectively as follows:
Figure BDA0002300271460000034
Figure BDA0002300271460000035
wherein: c. CfreeAnd ccongThe propagation velocities in case of congestion and free flow, respectively.
The continuous speed field of the traffic flow then consists of:
V(x,t)＝w(x,t)Vcong(x,t)+[1-w(x,t)]Vfree(x,t) (6)
where w (x, t) is a weighted function of congestion and free stream traffic conditions, which is represented by an S-shaped nonlinear function:
Figure BDA0002300271460000041
thus, the traffic state based on heterogeneous data is calculated by the following formula:
Figure BDA0002300271460000042
wherein: z (x, t) is the traffic state estimated based on the heterogeneous data;
Figure BDA0002300271460000043
is the kernel function corresponding to the data point i of the data source j (see formula (2)); α(j)(x, t) is a weighting factor α that measures the reliability dynamics of the data source j at point (x, t)(j)The calculation formula of (x, t) is as follows:
Figure BDA0002300271460000044
wherein:
Figure BDA0002300271460000045
the estimated average velocity of the data source j,
Figure BDA0002300271460000046
standard deviation of measurement error of data source j, index kjThe measurement error reflecting data source j varies with the change in average velocity.
In the above technical solution, the implementation method of step 004 is as follows:
the road network spatial adjacency matrix describes the adjacency relation between different spatial objects from the network topology perspective. A complex road network is generally abstracted into a directed graph G ═ N, L, which is composed of N nodes and L edges. The adjacency in the graph theory is expressed by the following formula:
Figure BDA0002300271460000047
Figure BDA0002300271460000048
is the weight of the adjacent edges i and j in the k-order adjacency matrix and forms an N × N space weight adjacency matrix EkThe comprehensive space adjacent matrix of the road network is
Figure BDA0002300271460000049
Converting the spatial adjacency matrix into a spatial weight matrix by means of row normalization, i.e.
Figure BDA00023002714600000410
Thus, a subject with N spatial elements has a spatial weight matrix expressed as follows:
Figure BDA00023002714600000411
in the above technical solution, the implementation method of step 005 is:
traffic states in a road network affect each other, and therefore it is necessary to quantify the spatio-temporal relationship between road segments. The correlation of two objects on the time series can be measured through the Pearson correlation function, meanwhile, the influence of the space relation is considered, and the space factor is introduced by utilizing the road section average speed obtained in the step 003
Figure BDA00023002714600000412
As a comprehensive speed for quantifying the adjacent links, the calculation formula is as follows:
Figure BDA00023002714600000413
wherein:representing the traffic speed, w, of the section i at time tijFor the weight adjacent to the road sections i and j, i ∈ [1, R]
R is the number of road segments, T ∈ [1, T ], T is the length of the statistical time period, T time interval.
The speed correlation of the road section i and its adjacent road sections is shown as follows:
Figure BDA0002300271460000051
wherein
Figure BDA0002300271460000052
Is the traffic speed of the road section i in the statistical time period T
Figure BDA0002300271460000053
Is determined by the average value of (a) of (b),
Figure BDA0002300271460000054
integrated speed
Figure BDA0002300271460000055
S represents a time delay.
In the above technical solution, the implementation method of step 006 is:
in order to avoid calculating the traffic state of the urban road network on a large scale, effective extraction of the fragile road sections is important for deducing the traffic state of the whole road network. The invention adopts TOPSIS method to realize the identification of the fragile road section, and the calculation steps are as follows:
① define Positive Ideal solution A+And negative ideal scheme A-
A+(s)＝{maxCori(s)|s∈(1,2,...,S),1≤i≤R} (14)
A-(s)＝{minCori(s)|s∈(1,2,...,S),1≤i≤R} (15)
Wherein A is+(s) and A-(s) are positive and negative ideal schemes, respectively; r is the number of road segments in the road network, and S is a set of values of time delay S.
② calculating the weight at each time delay
To account for the closer traffic conditions in the close time intervals, the weights at different time delays are calculated from the euclidean distances as follows:
Figure BDA0002300271460000056
since greater weight is assigned to more similar traffic states, it is translated as follows:
Figure BDA0002300271460000057
where max (ed) and min (ed) are the maximum and minimum values, respectively, in the Euclidean distance set.
③ calculating distance
And calculating the distance between the space-time correlation of each path segment and the positive and negative ideal schemes, and taking the Euclidean weighted distance to calculate the following formula:
Figure BDA0002300271460000058
Figure BDA0002300271460000059
wherein:
Figure BDA00023002714600000510
and
Figure BDA00023002714600000511
the weighted distances of the segment i from the positive and negative ideal respectively, and the other variables are defined as (14) - (15).
④ calculating similarity
And calculating the similarity of the road section i and the adjacent road sections thereof with the ideal scheme under the condition of all time delay so as to measure the influence degree between the traffic states of each road section and the adjacent road sections thereof. The calculation formula is as follows:
Figure BDA0002300271460000061
wherein C isiIs the similarity of the road section i to the ideal scheme, i.e. the degree of importance of the road section. C i1 means that the spatio-temporal correlation of the link i is the best case, which has the greatest influence, and vice versa. As defined for other variables and equations (18) - (19).
⑤ according to CiSequencing road section influence degrees and extracting fragile road sections
First according to C of each road sectioniThe values are sorted, then partial road sections are obtained based on the extraction proportion α, the partial road sections are regarded as weak road sections, namely the road sections which are most prone to congestion are regarded as weak road sections, and the traffic state of the whole road network is estimated and predicted by using the traffic characteristics of the weak road sections.
In the above technical solution, the implementation method of step 007 is as follows:
input data, i.e. a spatio-temporal feature matrix of the urban road network, an N x D feature matrix is generated, which describes the variation of the speed over time on the road. The time-space matrix is generally expressed as follows:
Figure BDA0002300271460000062
where N is the number of weak segments, D is the number of time delays, xitAnd (4) calculating the average traffic speed of the road section i at the time t, namely calculating the average speed of the road section i according to the fused traffic state.
In the above technical solution, the implementation method of step 008 is:
the invention is based on the respective advantages of the Bi-LSTM and the CNN models and combines the advantages, namely the Bi-ConvLSTM is utilized to realize the estimation and prediction of the traffic state of the whole road network. The model is used for extracting space related features and time related features from historical traffic speed data, and finally estimation and prediction are carried out by combining the features.
The spatial correlation characteristics are extracted from the traffic state of the current road section and the traffic state sequence of the adjacent road section of the current road section through a CNN model and are used for representing the correlation of the traffic states between the current road section and the adjacent road section; the time-related characteristics are extracted by adopting a Bi-LSTM model and considering that the traffic state at each moment is a time sequence and the current road section is influenced by upstream and downstream traffic flows, and the model finally obtains the forward and reverse traffic state information of the current road section, so that the actual traffic characteristics are better extracted, and the prediction error is further reduced.
The Bi-ConvLSTM model is fully trained by the urban traffic speed data constructed through the steps. Similar to general LSTM, in the Bi-ConvLSTM model, as shown in FIG. 2, x is input1,...,xtCell output C1,...,CtHidden state h1,...,htAre all 3D tensors. Each cell comprises three parts, namely input gates itForgetting door ftOutput gate ot. The Bi-ConvLSTM model determines the future state of a cell by its local neighbors' inputs and past states. The ConvLSTM model is calculated as followsShown in the figure:
Figure BDA0002300271460000071
wherein it,ft,Ct,ot,htRespectively showing an input gate, a forgetting gate, a cell state updating gate, an output gate and a hidden state; sigma and tanh respectively represent activation functions of a sigmoid function and a hyperbolic tangent function; a and
Figure BDA0002300271460000072
respectively representing a convolution operator and a Hadamard product; w and b represent the corresponding weight matrix and bias.
In Bi-ConvLSTM, the result sequence is output in the forward direction as shown in FIG. 3
Figure BDA0002300271460000073
Is to use the forward input sequence to iterate calculation from time T-n to T-1 and output the result sequence reversely
Figure BDA0002300271460000074
Is iteratively computed from time T-n to T-1 using an inverse input sequence, eventually an output result vector in which the results for each element are fused by the following formula:
Figure BDA0002300271460000075
wherein sigmagA function is used to fuse the two result vectors output in the forward and reverse directions, which may be a summation function, an averaging function, etc.
When the model Bi-ConvLSTM is applied to an actual case, parameters such as the time sequence step length and the number of hidden layers of the involved Bi-ConvLSTM model, the number of network layers of a CNN model, the size and the step length of a convolution kernel, the number of neurons of all full connection layers and the like can be configured by those skilled in the art according to specific requirements, and are not described one by one here.
The method for estimating and predicting the short-term traffic running state of the urban road network provided by the embodiment of the invention is described in detail, the principle and the embodiment of the invention are explained in the text, and the description of the embodiment is only used for helping the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (7)

1. A method for estimating and predicting short-term traffic running states of an urban road network is characterized by comprising the following steps: the method comprises the following steps:
(1) acquiring urban taxi GPS data and urban road speed measuring card port data, and preprocessing heterogeneous data;
(2) the method comprises the steps that a road section between two intersections serves as a research unit, and the GPS speed and the gate speed of a taxi are fused by using a GASM (generalized adaptive smoothing algorithm), so that the actual traffic state of the research unit is reconstructed;
(3) solving the average speed of the road section according to the fused traffic state;
(4) establishing a spatial weight matrix of an urban road network;
(5) calculating the space-time correlation between road sections;
(6) identifying and quantifying vulnerable road sections based on a TOPSIS method, namely an approximate ideal point sorting method;
(7) input data, i.e. a spatio-temporal matrix of the urban road network, a N x D characteristic matrix is generated, which describes the traffic speed over the road as a function of time. Wherein N is the number of vulnerable segments and D is the time interval;
(8) based on the respective advantages of the Bi-LSTM and the CNN models, the Bi-ConvLSTM is used for extracting the space-time characteristics of the traffic state of the whole road network, and the estimation and prediction values of the traffic state of the whole road network at the current moment and the next moment are obtained.
2. The method for estimating and predicting the short-term traffic operation state of the urban road network according to claim 1, wherein the method comprises the following steps: the implementation method of the step (2) comprises the following steps:
the input data is a discrete set of data points { x }i,ti,vi1, the output is a continuous velocity field V (x, t), which is calculated as follows:
Figure FDA0002300271450000011
wherein: x is the spatial coordinate, t time coordinate, viIs the velocity value of point i, smoothing kernel function phii(x, t) decreases as | x | and | t | increase;
the kernel function calculation formula is as follows:
Figure FDA0002300271450000012
wherein: point (x, t) is the evaluation point, (x)i,ti) For the collected data points, σ is half the distance between two adjacent detectors, and τ is half the sampling time of the detectors; the normalization function is also defined as follows:
Figure FDA0002300271450000013
GASM realizes congestion traffic flow V by adjusting kernel functioncong(x, t) and free stream VfreeThe velocity estimates of (x, t) are respectively as follows:
Figure FDA0002300271450000014
Figure FDA0002300271450000015
wherein: c. CfreeAnd ccongPropagation speeds under congested and free-flow conditions, respectively;
the continuous speed field of the traffic flow then consists of:
V(x,t)＝w(x,t)Vcong(x,t)+[1-w(x,t)]Vfree(x,t) (6)
where w (x, t) is a weighted function of congestion and free stream traffic conditions, which is represented by an S-shaped nonlinear function:
Figure FDA0002300271450000021
thus, the traffic state based on heterogeneous data is calculated by the following formula:
Figure FDA0002300271450000022
wherein: z (x, t) is the traffic state estimated based on the heterogeneous data;
Figure FDA0002300271450000023
is the corresponding kernel function of the data source j at the data point i α(j)(x, t) is a measure of the reliability dynamics of the data source j at point (x, t)
Weighting factor of index α(j)The calculation formula of (x, t) is as follows:
Figure FDA0002300271450000024
wherein:
Figure FDA0002300271450000025
the estimated average velocity of the data source j,
Figure FDA0002300271450000026
standard deviation of measurement error of data source j, index kjThe measurement error reflecting data source j varies with the change in average velocity.
3. The method for estimating and predicting the short-term traffic operation state of the urban road network according to claim 1, wherein the method comprises the following steps: the implementation method of the step (4) comprises the following steps:
the complex road network is abstracted into a directed graph G (N, L) to express the topological relation of the road network, a directed graph consisting of N nodes and L edges is established, and the adjacency relation in the graph theory is expressed by the following formula:
Figure FDA0002300271450000027
Figure FDA0002300271450000028
is the weight of the adjacent edges i and j in the k-order adjacency matrix and forms an N × N space weight adjacency matrix EkThe comprehensive space adjacent matrix of the road network is
Figure FDA0002300271450000029
Converting the spatial adjacency matrix into a spatial weight matrix by means of row normalization, i.e.
Figure FDA00023002714500000210
Thus, a subject with N spatial elements has a spatial weight matrix expressed as follows:
Figure FDA00023002714500000211
4. the method for estimating and predicting the short-term traffic operation state of the urban road network according to claim 1, wherein the method comprises the following steps: the implementation method of the step (5) comprises the following steps:
the correlation of two objects on a time sequence can be measured through a Pearson correlation function, meanwhile, the influence of a spatial relation is considered, and a spatial factor is introduced by utilizing the road section average speed obtained in the step (3)
Figure FDA0002300271450000031
As a comprehensive speed for quantifying the adjacent links, the calculation formula is as follows:
Figure FDA0002300271450000032
wherein:
Figure FDA0002300271450000033
representing the traffic speed, w, of the section i at time tijFor the weight adjacent to the road sections i and j, i ∈ [1, R]R is the number of road segments, T ∈ [1, T]T is the length of the statistical time period, T time interval;
the speed correlation of the road section i and its adjacent road sections is shown as follows:
Figure FDA0002300271450000034
wherein
Figure FDA0002300271450000035
Is the traffic speed of the road section i in the statistical time period T
Figure FDA0002300271450000036
Is determined by the average value of (a) of (b),
Figure FDA0002300271450000037
integrated speed
Figure FDA0002300271450000038
S represents a time delay.
5. The method for estimating and predicting the short-term traffic operation state of the urban road network according to claim 1, wherein the method comprises the following steps: the implementation method of the step (6) comprises the following steps:
the TOPSIS method is adopted to realize the identification of the fragile road section, and the calculation steps are as follows:
① define Positive Ideal solution A+And negative ideal scheme A-
A+(s)＝{maxCori(s)|s∈(1,2,...,S),1≤i≤R} (14)
A-(s)＝{minCori(s)|s∈(1,2,...,S),1≤i≤R} (15)
Wherein A is+(s) and A-(s) are positive and negative ideal schemes, respectively; r is the number of road sections in the road network, and S is a value set of time delay S;
② calculating the weight at each time delay
Considering that traffic conditions are more similar in close time intervals, the weights at different time delays are calculated from the euclidean distance as follows:
Figure FDA0002300271450000039
since greater weight is assigned to more similar traffic states, it is translated as follows:
Figure FDA00023002714500000310
wherein max (ed) and min (ed) are the maximum and minimum values, respectively, in the Euclidean distance set;
③ calculating distance
And calculating the distance between the space-time correlation of each path segment and the positive and negative ideal schemes, and taking the Euclidean weighted distance to calculate the following formula:
Figure FDA0002300271450000041
Figure FDA0002300271450000042
wherein:
Figure FDA0002300271450000043
and
Figure FDA0002300271450000044
the weighted distances of the road section i and the positive and negative ideal schemes respectively, and other variables are defined as (14) - (15);
④ calculating similarity
Calculating the similarity between the road section i and the adjacent road sections thereof and the ideal scheme under the condition of all time delay so as to measure the influence degree between the traffic states of each road section and the adjacent road sections thereof; the calculation formula is as follows:
Figure FDA0002300271450000045
wherein C isiThe similarity of the road section i and the ideal scheme, namely the importance degree of the road section; ci1 represents that the space-time correlation of the road section i is the best case, the influence degree is the greatest, and vice versa; as defined for other variables and equations (18) - (19);
⑤ according to CiSequencing road section influence degrees and extracting fragile road sections
First according to C of each road sectioniThe values are sorted, partial road sections are obtained based on the extraction proportion α, the partial road sections are regarded as weak road sections, and the traffic state of the whole road network is estimated and predicted by using the traffic characteristics of the weak road sections.
6. The method for estimating and predicting the short-term traffic operation state of the urban road network according to claim 1, wherein the method comprises the following steps: the implementation method of the step (7) comprises the following steps:
generating input data, namely a space-time characteristic matrix of the urban road network, and an N-D characteristic matrix, wherein the N-D characteristic matrix describes the change of speed on a road along with time; the time-space matrix is generally expressed as follows:
Figure FDA0002300271450000046
where N is the number of weak segments, D is the number of time delays, xitThe average traffic speed of the road section i at the time t is the average speed of the road section i according to the fused traffic state in the step (3);
7. the method for estimating and predicting the short-term traffic operation state of the urban road network according to claim 1, wherein the method comprises the following steps: the implementation method of the step (8) comprises the following steps:
training a Bi-ConvLSTM model by using the urban traffic speed tensor data generated in the step (7); similar to general LSTM, in the Bi-ConvLSTM model, x is input1,...,xtCell output C1,...,CtHidden state h1,...,htAre all 3D tensors; each cell comprises three parts, namely input gates itForgetting door ftOutput gate ot(ii) a The Bi-ConvLSTM model determines the future state of a certain cell by the inputs of its local neighbors and the past state; the formula for the ConvLSTM model is as follows:
Figure FDA0002300271450000051
wherein it,ft,Ct,ot,htRespectively showing an input gate, a forgetting gate, a cell state updating gate, an output gate and a hidden state; sigma and tanh respectively represent activation functions of a sigmoid function and a hyperbolic tangent function; a and
Figure FDA0002300271450000052
respectively representing a convolution operator and a Hadamard product; w and b represent the respective weight matrix and bias;
in Bi-ConvLSTM, the result sequence is output in the forward direction
Figure FDA0002300271450000053
Is to use the forward input sequence to iterate calculation from time T-n to T-1 and output the result sequence reversely
Figure FDA0002300271450000054
Is iteratively computed from time T-n to T-1 using an inverse input sequence, eventually an output result vector in which the results for each element are fused by the following formula:
Figure FDA0002300271450000055
wherein sigmagThe function is used to fuse the two result vectors output in the forward and reverse directions.
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