CN114202917A - Construction area traffic control and induction method based on dynamic traffic flow short-time prediction - Google Patents
Construction area traffic control and induction method based on dynamic traffic flow short-time prediction Download PDFInfo
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Abstract
The invention relates to traffic control, in particular to a construction area traffic control and induction method based on dynamic traffic flow short-time prediction, wherein a road network topological relation around a construction area is constructed, a construction traffic influence area range is scientifically defined based on analysis of the road network topological relation, traffic flow data of the construction traffic influence area is collected and subjected to multi-source data fusion analysis, traffic flow characteristics of the construction traffic influence area are analyzed, the traffic flow data and the road network topological relation are comprehensively analyzed, the flow space-time convergence key point distribution of the construction traffic influence area is obtained, a traffic flow short-time prediction model of the flow space-time convergence key point is constructed according to a traffic flow characteristic analysis result, and the traffic flow short-time prediction model is subjected to online training and iterative optimization; the technical scheme provided by the invention can overcome the defects that the timing optimization of traffic signal lamps of intersections around the construction area, which are easy to cause congestion, cannot be carried out and the effective guidance of drivers cannot be carried out.
Description
Technical Field
The invention relates to traffic control, in particular to a construction area traffic control and induction method based on dynamic traffic flow short-time prediction.
Background
With the economic development and the acceleration of the urbanization process of China, the owned quantity and the traffic flow of private cars of China are increased rapidly. With the continuous and rapid increase of the quantity of motor vehicles, the traffic jam condition is intensified and even traffic paralysis occurs at the peak time of commuting, which also puts higher requirements on urban traffic management and greatly improves the traffic management efficiency. In order to reduce the occurrence of traffic accidents, control traffic illegal behaviors, avoid traffic congestion and enable traffic infrastructure to exert the maximum efficiency, a plurality of cities start the construction project of the urban traffic comprehensive management and control system.
The urban traffic jam condition is aggravated, besides a plurality of vehicles, the urban traffic jam condition is also greatly related to urban construction, a plurality of cities with development potential are subjected to urban construction in urban areas, in addition, traffic signal lamps of intersections with jam are easy to occur around construction areas, the timing scheme is basically fixed and unchanged, and traffic polices, police assistants and traffic managers are required to perform manual intervention. In addition, drivers cannot know the congestion condition of surrounding intersections, and can only obtain traffic information through channels with large information delay, such as broadcasting stations, and the like, so that the limitation is large.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects in the prior art, the invention provides a construction area traffic control and induction method based on dynamic traffic flow short-time prediction, which can effectively overcome the defects that the prior art can not carry out timing optimization on traffic lights of intersections around a construction area, which are easy to be jammed, and can not effectively induce drivers.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme:
a construction area traffic control and induction method based on dynamic traffic flow short-time prediction comprises the following steps:
s1, constructing a topological relation of road networks around the construction area, and scientifically delimiting the construction traffic influence area range based on the analysis of the topological relation of the road networks;
s2, collecting traffic flow data of the construction traffic influence area, performing multi-source data fusion analysis, and analyzing traffic flow characteristics of the construction traffic influence area;
s3, comprehensively analyzing the topological relation between the traffic flow data and the road network to obtain the flow space-time convergence key point distribution of the construction traffic affected area;
s4, constructing a traffic flow short-term prediction model of a traffic flow space-time convergence key point by using a neural network model according to a traffic flow characteristic analysis result, and performing online training and iterative optimization on the traffic flow short-term prediction model;
and S5, taking the flow space-time convergence key point as a regional control key intersection, traversing and searching a key path through an algorithm, performing linear coordination control on the key path, performing phase difference optimization on a non-key path intersection, finally realizing regional signal intelligent control, synchronously making a dynamic induction scheme according to a signal intelligent control scheme, and performing intelligent induction on traffic.
Preferably, the online training and iterative optimization of the traffic flow short-time prediction model in S4 includes:
and performing traffic flow short-term prediction including traffic flow direction, average speed and queuing length on the traffic flow space-time convergence key point by using a long and short memory model (LSTM), simulating a short-term prediction result of the traffic flow short-term prediction model, performing comparative analysis on the short-term prediction result and actually-measured traffic flow data, and feeding back the traffic flow short-term prediction model to realize online training and iterative optimization of the traffic flow short-term prediction model.
Preferably, in S5, the flow space-time convergence key point is used as a regional control key intersection, a key path is searched through algorithm traversal, linear coordination control is performed on the key path, phase difference optimization is performed on a non-key path intersection, and finally regional signal intelligent control is achieved, including:
the method comprises the steps of taking a flow space-time convergence key point as a regional control key intersection, traversing and searching a key path through an algorithm, calculating the cycle time of the key intersection according to a short-time prediction result of the flow space-time convergence key point, taking the cycle time as the cycle time of all intersections of the key path, carrying out linear coordination control on the key path under the target condition of setting the saturation, the average speed and the vehicle delay of the flow space-time convergence key point, carrying out phase difference optimization on intersections of the non-key path, finally realizing regional signal intelligent control, guiding traffic flow to be distributed in each road section and intersection in a construction traffic influence region in a balanced manner, and dynamically controlling the saturation of the flow space-time convergence key point.
Preferably, the step S5 of synchronously making a dynamic guidance scheme according to the signal intelligent control scheme to intelligently guide traffic includes:
and under the constraint conditions that the vehicle travel time is shortest and the construction influence vehicle delay is minimum, carrying out real-time optimization of path selection on the traffic flow passing through the construction traffic influence area, and dynamically generating a traffic guidance scheme of the key point position of flow space-time convergence.
Preferably, the traffic flow data comprises license plate numbers, traffic flow direction, vehicle speed, headway, occupancy, queuing length and vehicle type proportion;
the traffic flow data are collected through a bayonet type electric police, a video flow detector and a microwave flow detector which are arranged on the boundary of a construction traffic affected area and at each main road section and intersection inside the traffic flow data.
Preferably, the traffic flow characteristics include traffic flow direction, traffic flow OD, travel time characteristics and route selection characteristics of each road section and intersection inside the construction traffic influence area.
Preferably, the constructing of the topological relation of the road network around the construction area in S1 includes:
abstracting a road network around a construction area into a graph structure G (N, M) with N nodes and M edges based on graph theory knowledge;
the nodes represent intersections, edges are connecting road sections between the two nodes, an adjacent matrix between the nodes represents accessibility of the two nodes, 0 represents unreachability between the two nodes, and 1 represents accessibility between the two nodes.
Preferably, based on the analysis of the topological relation of the road network in S1, the scientific definition of the construction traffic influence area range includes:
according to the position of the construction area, the lane occupation condition, the road attribute and the traffic operation characteristic, the influence mechanism of the construction area on the traffic flow is analyzed by using the complex network propagation dynamics knowledge, so that the construction traffic influence area range is scientifically defined.
Preferably, S6, the intelligent control scheme is issued through the signal control and optimization platform to realize intelligent control of the traffic space-time convergence key point, and the intelligent guidance of traffic is realized through the information distribution platform and the prompting device installed at the traffic space-time convergence key point.
Preferably, the intelligent control scheme for issuing the signal through the signal control and optimization platform to realize the intelligent control of the traffic space-time convergence key point comprises:
the signal timing scheme after intelligent optimization is issued to a front-end networking signal machine through a signal control and optimization platform, and dynamic optimization adjustment of signal timing of flow space-time convergence key point locations is realized;
through information issuing platform and install the suggestion device at flow space-time key point location that converges, realize the intelligence induction to the traffic, include:
and arranging an LED traffic guidance screen at the key point position of flow space-time convergence, issuing dynamic road conditions and traffic guidance schemes of a road network of the construction traffic affected area in real time, guiding the traffic flow to run according to an optimal path, carrying out real-time information interaction on the road condition information and traffic guidance schemes of the construction traffic affected area and an internet map, and carrying out real-time optimization and adjustment on the navigation scheme of the internet map.
(III) advantageous effects
Compared with the prior art, the construction area traffic control and induction method based on dynamic traffic flow short-time prediction has the following beneficial effects:
1) collecting traffic flow data of a construction traffic influence area, carrying out multi-source data fusion analysis, analyzing traffic flow characteristics of the construction traffic influence area, constructing a traffic flow short-time prediction model of a flow space-time convergence key point position according to a traffic flow characteristic analysis result, intelligently optimizing a signal timing scheme of the flow space-time convergence key point position based on the short-time prediction result of the traffic flow short-time prediction model, guiding the traffic flow to be distributed in each road section and intersection in the construction traffic influence area in a balanced manner through signal control, and dynamically controlling the saturation of the flow space-time convergence key point position;
2) and synchronously establishing a dynamic induction scheme according to the signal timing scheme, performing real-time optimization on path selection of traffic flow passing through the construction traffic influence area, dynamically generating a traffic induction scheme of a flow space-time convergence key point position, issuing the dynamic road condition and traffic induction scheme of the road network of the construction traffic influence area in real time, performing real-time information interaction on the road condition information and traffic induction scheme of the construction traffic influence area and an internet map, performing real-time optimization and adjustment on the navigation scheme of the internet map, and realizing effective induction on drivers.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A construction area traffic control and induction method based on dynamic traffic flow short-time prediction is disclosed, as shown in FIG. 1, S1, a road network topological relation around a construction area is constructed, and a construction traffic influence area range is scientifically defined based on analysis of the road network topological relation.
The method for constructing the network topological relation of the roads around the construction area comprises the following steps:
based on the graph theory knowledge, the road network around the construction area is abstracted into a graph structure G (N, M) with N nodes and M edges.
The nodes represent intersections, edges are connecting road sections between the two nodes, the adjacent matrix between the nodes represents the accessibility of the two nodes, 0 represents the unreachability between the two nodes, and 1 represents the accessibility between the two nodes.
Wherein, based on the analysis to road network topological relation, the scientific construction traffic influence regional scope of ruling includes:
according to the position of the construction area, the lane occupation condition (the number of occupied lanes and the length of the occupied lanes), the road attribute and the traffic operation characteristic, the influence mechanism of the construction area on the traffic flow is analyzed by using the complex network propagation dynamics knowledge, so that the construction traffic influence area range is scientifically defined.
And S2, collecting traffic flow data of the construction traffic influence area, performing multi-source data fusion analysis with an internet map and the like, and analyzing traffic flow characteristics of the construction traffic influence area.
The traffic flow data comprises license plate numbers, traffic flow directions, vehicle speeds, vehicle headway, occupancy rates, queuing lengths and vehicle type proportions. The traffic flow data are collected through a bayonet type electric police, a video flow detector and a microwave flow detector which are arranged at the boundary of a construction traffic affected area and at each main road section and intersection inside the construction traffic affected area.
The traffic flow characteristics comprise traffic flow direction, traffic flow OD, travel time characteristics and path selection characteristics of all road sections and intersections in the construction traffic influence area.
In the technical scheme of the application, analyzing the traffic flow characteristics of the construction traffic affected zone comprises:
extracting all the passing data in a set time period according to the bayonet passing data, wherein the passing data comprise a vehicle number plate ID, a passing time PassTime and a passing point; the vehicle license plate ID obtains a passing date PassDay according to a passing time PassTime, and the vehicle track of the day d is processed into a sequence P _ d ═ P _ d1, P _ d2, … and P _ di according to a time ascending order, wherein i is the ith point passing by the day d;
associating and matching longitude and latitude information of the passing point position PassPoint with longitude and latitude information of road network sections and intersections, associating a sequence P _ d (P _ d1, P _ d2, … and P _ di) with a road network, and obtaining a vehicle running track of each vehicle on the road network;
and analyzing and obtaining traffic flow characteristics such as traffic flow direction, traffic flow OD, travel time characteristics, path selection characteristics and the like of each road section and each intersection in the construction traffic influence area based on all vehicle running tracks.
S3, comprehensively analyzing the topological relation between the traffic flow data and the road network to obtain the flow space-time convergence key point distribution of the construction traffic affected area.
In the technical scheme of the application, the traffic space-time convergence key point position of the traffic affected area can be analyzed based on P-gray weighted association, and the method comprises the following specific steps:
firstly, assuming that the road network has N nodes and m node evaluation indexes, the comparison number of the a (a is more than or equal to 1 and less than or equal to N) th node is listed as
ya(j)＝[ya(1),ya(2),ya(3),...,ya(m)]
Wherein, ya(j) Is the jth principal component of the a-th node;
② obtaining evaluation index reference sequences of the nodes after comprehensive comparison
Y＝[Y(1),Y(2),...,Y(m)]
Wherein Y (j) in the sequence is the optimal value of the jth principal component in all nodes;
quantity dimensionless processing, which can adopt SPSS to standardize data to obtain comparison data sequence
ya*(j)＝[ya*(1),ya*(2),ya*(3),...,ya*(m)]
Obtaining a reference data sequence after dimensionless
Y*＝[Y*(1),Y*(2),...,Y*(m)]
According to
Δa(j)＝|Y*(j)-ya*(j)|
Respectively finding the maximum value DeltamaxAnd a minimum value Δmin；
Determining the weight corresponding to each index, and determining the weight omega (j), j (1, 2.., m), corresponding to each index approximately according to the characteristic value in the principal component analysis;
calculating the index correlation coefficient
Wherein rho is a resolution coefficient, the value is between 0 and 1, and is usually 0.5;
sixthly, calculating gray weighted association degree to obtain the importance degree of the intersection
And seventhly, calculating the gray weighted association degree of each node, wherein the more important the node with the greater association degree is, and the node with the maximum association degree is the key node.
S4, according to the traffic flow feature analysis result, a neural network model is utilized to construct a traffic flow short-time prediction model of the flow space-time convergence key point, and the traffic flow short-time prediction model is subjected to online training and iterative optimization.
The method comprises the following steps of carrying out online training and iterative optimization on a traffic flow short-time prediction model, wherein the online training and iterative optimization comprise the following steps:
and performing traffic flow short-term prediction including traffic flow direction, average speed and queue length on the traffic flow space-time convergence key point by using a long and short memory model (LSTM), simulating a short-term prediction result of the traffic flow short-term prediction model by using traffic simulation software such as VISSIM (virtual visual subscriber identity module), performing comparative analysis on the short-term prediction result and actually-measured traffic flow data, feeding back the traffic flow short-term prediction model, and realizing online training and iterative optimization of the traffic flow short-term prediction model.
In the technical scheme of the application, a long and short memory model LSTM can be adopted to carry out short-term prediction of traffic flow on the key point position of flow space-time convergence, and the specific steps are as follows:
the traffic flow is counted according to a 5-minute interval, and can be represented as Q ═ Q1,q2,...,qiWherein q isiIndicating a traffic flow at the ith time;
secondly, training by using the long and short memory model LSTM, and calculating the long and short memory model LSTM model layer as follows:
an input node: g(t)＝σ(Wgx*x(t)+Wgh*h(t-1)+bg)；
An input gate: i.e. i(t)＝σ(Wix*x(t)+Wih*h(t-1)+bi)；
Forgetting to remember the door: f. of(t)＝σ(Wfx*x(t)+Wfh*h(t-1)+bf)；
An output gate: o(t)＝σ(Wox*x(t)+Woh*h(t-1)+bo)；
The relationship between them: s(t)＝g(t)*i(t)+s(t-1)*f(t)，h(t)＝s(t)*o(t)；
Wherein x is(t)Is the input of the loop layer, h(t)Is the output of the loop layer or layers,t is the value of the time step, σ is the Sigmoid function, Wgx、Wix、Wfx、WoxIs the relationship between input and output, Wgh、Wih、Wfh、WohIs the historical relevance of the output, bg、bi、bf、boIs an offset; the initial value of the parameter is a random value, h(t)Is zero.
Thirdly, using the trained long-short memory model LSTM to lead the traffic flow { q ] of 12 time intervals (2 hours) in historyi,qi-1,...,qi-11As input, predict traffic flow { q ] for 1-3 time intervals (5-15 minutes) in the futurei+1,qi+2,qi+3}。
And S5, taking the flow space-time convergence key point as a regional control key intersection, traversing and searching a key path through an algorithm, performing linear coordination control on the key path, performing phase difference optimization on a non-key path intersection, finally realizing regional signal intelligent control, synchronously making a dynamic induction scheme according to a signal intelligent control scheme, and performing intelligent induction on traffic.
The method comprises the following steps of taking flow space-time convergence key point positions as regional control key intersections, traversing and searching key paths through an algorithm, performing linear coordination control on the key paths, performing phase difference optimization on non-key path intersections, and finally realizing regional signal intelligent control, wherein the method comprises the following steps:
the method comprises the steps of taking a flow space-time convergence key point as a regional control key intersection, traversing and searching a key path through an algorithm, calculating the cycle time of the key intersection according to a short-time prediction result of the flow space-time convergence key point, taking the cycle time as the cycle time of all intersections of the key path, carrying out linear coordination control on the key path under the target condition of setting the saturation, the average speed and the vehicle delay of the flow space-time convergence key point, carrying out phase difference optimization on intersections of the non-key path, finally realizing regional signal intelligent control, guiding traffic flow to be distributed in each road section and intersection in a construction traffic influence region in a balanced manner, and dynamically controlling the saturation of the flow space-time convergence key point.
In the technical scheme, breadth-first search (BFS) is adopted for determining the key path, the congestion index, the speed and the like are used as key indexes to determine the key path of the region, and the key path is dynamically adjusted according to the traffic flow change condition.
Wherein, according to the synchronous dynamic induction scheme of making of signal intelligent control scheme, carry out intelligent induction to the traffic, include:
and under the constraint conditions that the vehicle travel time is shortest and the construction influence vehicle delay is minimum, carrying out real-time optimization of path selection on the traffic flow passing through the construction traffic influence area, and dynamically generating a traffic guidance scheme of the key point position of flow space-time convergence.
And S6, issuing a signal intelligent control scheme through the signal control and optimization platform to realize intelligent control on the traffic space-time convergence key point, and realizing intelligent induction on traffic through the information release platform and the prompting device installed at the traffic space-time convergence key point.
The intelligent control scheme is issued through the signal control and optimization platform, so that the intelligent control of the flow space-time convergence key point is realized, and the method comprises the following steps:
and the signal timing scheme after intelligent optimization is issued to a front-end networking signal machine through a signal control and optimization platform, so that dynamic optimization adjustment of signal timing of the traffic space-time convergence key point is realized.
Wherein, through information issuing platform and install the suggestion device at the key point position of flow space-time convergence, realize the intelligence induction to the traffic, include:
and arranging an LED traffic guidance screen at the key point position of flow space-time convergence, issuing dynamic road conditions and traffic guidance schemes of a road network of the construction traffic affected area in real time, guiding the traffic flow to run according to an optimal path, carrying out real-time information interaction on the road condition information and traffic guidance schemes of the construction traffic affected area and an internet map, and carrying out real-time optimization and adjustment on the navigation scheme of the internet map.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (10)
1. A construction area traffic control and induction method based on dynamic traffic flow short-time prediction is characterized by comprising the following steps: the method comprises the following steps:
s1, constructing a topological relation of road networks around the construction area, and scientifically delimiting the construction traffic influence area range based on the analysis of the topological relation of the road networks;
s2, collecting traffic flow data of the construction traffic influence area, performing multi-source data fusion analysis, and analyzing traffic flow characteristics of the construction traffic influence area;
s3, comprehensively analyzing the topological relation between the traffic flow data and the road network to obtain the flow space-time convergence key point distribution of the construction traffic affected area;
s4, constructing a traffic flow short-term prediction model of a traffic flow space-time convergence key point by using a neural network model according to a traffic flow characteristic analysis result, and performing online training and iterative optimization on the traffic flow short-term prediction model;
and S5, taking the flow space-time convergence key point as a regional control key intersection, traversing and searching a key path through an algorithm, performing linear coordination control on the key path, performing phase difference optimization on a non-key path intersection, finally realizing regional signal intelligent control, synchronously making a dynamic induction scheme according to a signal intelligent control scheme, and performing intelligent induction on traffic.
2. The construction area traffic control and induction method based on dynamic traffic flow short-time prediction according to claim 1, characterized in that: in S4, the traffic flow short-time prediction model is trained online and iteratively optimized, including:
and performing traffic flow short-term prediction including traffic flow direction, average speed and queuing length on the traffic flow space-time convergence key point by using a long and short memory model (LSTM), simulating a short-term prediction result of the traffic flow short-term prediction model, performing comparative analysis on the short-term prediction result and actually-measured traffic flow data, and feeding back the traffic flow short-term prediction model to realize online training and iterative optimization of the traffic flow short-term prediction model.
3. The construction area traffic control and induction method based on dynamic traffic flow short-time prediction according to claim 2, characterized in that: s5, taking the flow space-time convergence key point as a regional control key intersection, traversing and searching a key path through an algorithm, performing linear coordination control on the key path, and performing phase difference optimization on a non-key path intersection, and finally realizing regional signal intelligent control, wherein the method comprises the following steps:
the method comprises the steps of taking a flow space-time convergence key point as a regional control key intersection, traversing and searching a key path through an algorithm, calculating the cycle time of the key intersection according to a short-time prediction result of the flow space-time convergence key point, taking the cycle time as the cycle time of all intersections of the key path, carrying out linear coordination control on the key path under the target condition of setting the saturation, the average speed and the vehicle delay of the flow space-time convergence key point, carrying out phase difference optimization on intersections of the non-key path, finally realizing regional signal intelligent control, guiding traffic flow to be distributed in each road section and intersection in a construction traffic influence region in a balanced manner, and dynamically controlling the saturation of the flow space-time convergence key point.
4. The construction area traffic control and induction method based on dynamic traffic flow short-time prediction according to claim 3, characterized in that: and S5, synchronously making a dynamic induction scheme according to the signal intelligent control scheme, and intelligently inducing traffic, wherein the method comprises the following steps:
and under the constraint conditions that the vehicle travel time is shortest and the construction influence vehicle delay is minimum, carrying out real-time optimization of path selection on the traffic flow passing through the construction traffic influence area, and dynamically generating a traffic guidance scheme of the key point position of flow space-time convergence.
5. The construction area traffic control and induction method based on dynamic traffic flow short-time prediction according to any one of claims 1 to 4, characterized in that: the traffic flow data comprises license plate numbers, traffic flow direction, vehicle speed, vehicle headway, occupancy, queuing length and vehicle type proportion;
the traffic flow data are collected through a bayonet type electric police, a video flow detector and a microwave flow detector which are arranged on the boundary of a construction traffic affected area and at each main road section and intersection inside the traffic flow data.
6. The construction area traffic control and induction method based on dynamic traffic flow short-time prediction according to claim 5, characterized in that: the traffic flow characteristics comprise traffic flow direction, traffic flow OD, travel time characteristics and path selection characteristics of all road sections and intersections in the construction traffic influence area.
7. The construction area traffic control and induction method based on dynamic traffic flow short-time prediction according to claim 1, characterized in that: s1, constructing a topological relation of the road network around the construction area, including:
abstracting a road network around a construction area into a graph structure G (N, M) with N nodes and M edges based on graph theory knowledge;
the nodes represent intersections, edges are connecting road sections between the two nodes, an adjacent matrix between the nodes represents accessibility of the two nodes, 0 represents unreachability between the two nodes, and 1 represents accessibility between the two nodes.
8. The construction area traffic control and induction method based on dynamic traffic flow short-time prediction according to claim 7, characterized in that: in the step S1, based on the analysis of the road network topological relation, the construction traffic influence area range is scientifically defined, which includes:
according to the position of the construction area, the lane occupation condition, the road attribute and the traffic operation characteristic, the influence mechanism of the construction area on the traffic flow is analyzed by using the complex network propagation dynamics knowledge, so that the construction traffic influence area range is scientifically defined.
9. The construction area traffic control and induction method based on dynamic traffic flow short-time prediction according to claim 1, characterized in that: further comprising:
and S6, issuing a signal intelligent control scheme through the signal control and optimization platform to realize intelligent control on the traffic space-time convergence key point, and realizing intelligent induction on traffic through the information release platform and the prompting device installed at the traffic space-time convergence key point.
10. The construction area traffic control and induction method based on dynamic traffic flow short-time prediction according to claim 9, characterized in that: the intelligent control scheme for issuing the signal through the signal control and optimization platform to realize the intelligent control on the key point position of the flow space-time convergence comprises the following steps:
the signal timing scheme after intelligent optimization is issued to a front-end networking signal machine through a signal control and optimization platform, and dynamic optimization adjustment of signal timing of flow space-time convergence key point locations is realized;
through information issuing platform and install the suggestion device at flow space-time key point location that converges, realize the intelligence induction to the traffic, include:
and arranging an LED traffic guidance screen at the key point position of flow space-time convergence, issuing dynamic road conditions and traffic guidance schemes of a road network of the construction traffic affected area in real time, guiding the traffic flow to run according to an optimal path, carrying out real-time information interaction on the road condition information and traffic guidance schemes of the construction traffic affected area and an internet map, and carrying out real-time optimization and adjustment on the navigation scheme of the internet map.
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Denomination of invention: A Traffic Control and Guidance Method for Construction Areas Based on Dynamic Traffic Flow Short term Prediction Effective date of registration: 20231225 Granted publication date: 20221122 Pledgee: Huishang Bank Co.,Ltd. Hefei Dadongmen sub branch Pledgor: Anhui Lufeng Transportation Technology Co.,Ltd. Registration number: Y2023980073878 |