CN110688775A - Traffic bottleneck identification method in urban traffic network - Google Patents
Traffic bottleneck identification method in urban traffic network Download PDFInfo
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Abstract
The invention discloses a traffic bottleneck identification method in an urban traffic network, which relates to the technical field of traffic information control and comprises the following steps: step one, constructing virtual road network information; step two, collecting traffic flow data in the virtual road network; step three, fusing the acquired data; step four, generating a virtual road network PA back-stepping model by using a PA back-stepping technology; step five, establishing a virtual road network database based on the original road network database; step six, calculating data in the virtual road network database; calculating and mining to obtain bottleneck points according to the data, and diagnosing real bottleneck points; and step eight, outputting the reason of the bottleneck point, monitoring the road traffic flow information through the architecture virtual node and the virtual road section, generating a real-time model through a PA (power amplifier) reverse-pushing technology, simulating the road traffic flow information through the model, accurately deducing the bottleneck point, and providing more accurate and scientific suggestions for traffic jam treatment and optimization.
Description
Technical Field
The invention relates to the technical field of traffic information control, in particular to a traffic bottleneck identification method in an urban traffic network.
Background
Due to the complex nodes and roads in the topology of the urban traffic network, the complex interaction between the interior of the nodes and the nodes, the functional relationship between people, vehicles, roads and environment involved in network operation, policy management, laws and regulations and the like, the urban traffic network has high complexity, so that the urban traffic problem needs to be fundamentally solved, the urban traffic problem needs to be fundamentally explained to realize the sustainable development of the city, and the evolution law of the complex network, namely the urban traffic network, needs to be deeply researched.
With the increasing of the urban traffic jam problem, the traffic flow operation of a plurality of nodes is often in an oversaturated state, even the queuing length of part of road sections is close to or equal to the length of the road sections, the phenomenon of backtracking occurs in queuing, a road section bottleneck is formed, and the operation efficiency of the traffic flow of the urban road network is seriously influenced. Generally, when the queuing length of a road segment is close to the length of the road segment, the traffic state of the road segment can be regarded as a bottleneck state, and the road segment can be called as a bottleneck road segment. The bottleneck state belongs to a special oversaturation state, is the performance of extremely worsening of the traffic state of a road section, can acquire a large amount of real-time traffic data along with the popularization of vehicle navigation and mobile phone terminal navigation and the perfect arrangement of detectors on the road, can reflect the traffic condition of the road, cannot timely feed back road traffic flow information, and is not easy to monitor the traffic flow of the road in real time.
Disclosure of Invention
The invention aims to solve the defects that vehicle-mounted navigation and mobile phone terminal navigation in the prior art are popularized, detectors on a road are arranged perfectly, a large amount of real-time traffic data can be obtained, the traffic condition of the road can be reflected, road traffic flow information cannot be fed back in time, and real-time monitoring of road traffic flow is difficult to carry out, and provides a traffic bottleneck identification method in an urban traffic network.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for identifying traffic bottlenecks in an urban traffic network comprises the following steps:
step one, constructing virtual road network information;
step two, collecting traffic flow data in the virtual road network;
step three, fusing the acquired data;
step four, generating a virtual road network PA back-stepping model by using a PA back-stepping technology;
step five, establishing a virtual road network database based on the original road network database;
step six, calculating data in the virtual road network database;
calculating and mining to obtain bottleneck points according to the data, and diagnosing real bottleneck points;
and step eight, outputting the reason of the bottleneck point.
Preferably, the constructing of the virtual road network information includes constructing virtual nodes and constructing virtual road segments.
Preferably, the virtual node is constructed by abstracting a basic group of cities or a road network of some areas, representing a road network of a larger intersection and a road network of a neighborhood, and the number of the virtual nodes is determined according to actual conditions of different cities, so that not only the original road network can be simplified, but also the actual condition of the original road network can be better reflected, the integrated flow, the integrated capacity and the integrated impedance value of the virtual node are the results of the integrated analysis and calculation of the flow, the capacity and the impedance value of the original road network in the group, during the macro-level analysis, the virtual node is only considered as a point with the flow, the capacity and the impedance value, during the micro-level analysis, the virtual node is considered as a sub-road network, and the road sections connected with the virtual node are considered as nodes.
Preferably, the virtual road section is constructed by integrating one or more road sections in the current trunk road network, and performing superposition calculation on the integrated traffic volume, the integrated traffic capacity and the integrated impedance value of the virtual road section.
Preferably, the specific process of the second step is to determine traffic flow data for predicting road traffic conditions, and collect mobile navigation data and road fixed detector data of road vehicles.
Preferably, the specific process of the third step is to preprocess the acquired data, match the data from different sources, and determine the flow-velocity distribution of each detector cross section.
Preferably, the specific process of the fourth step is that the initial PA matrix is corrected for a limited number of times to achieve infinite approach of theoretically distributing traffic volume and actually measured traffic volume. The impedance of the circuit section is constant in the whole process; the flow rate of any PA point on each section is kept constant; the distribution process of any PA point to the distribution quantity is independent and does not influence each other, and finally a virtual road network PA back-stepping model is generated.
Preferably, the concrete process of the fifth step is to establish a road network database according to the actual road network condition and the current traffic situation, wherein the road network database mainly comprises: the method comprises the following steps of establishing a virtual road network database by combining a virtual road network PA back-pushing technology based on a primary road network database, utilizing a virtual road section and a virtual node related parameter calculation method and the virtual road network PA back-pushing technology, wherein the primary road network node coordinate and adjacent directory database, the primary road network section geometric element database and the primary road network section traffic capacity and traffic volume database mainly comprise the following steps: the system comprises a virtual node coordinate adjacency directory database, a virtual road network comprehensive traffic capacity database, a virtual road network comprehensive traffic volume database, a virtual road network OD distribution database and a virtual road network traffic impedance distribution database.
Preferably, the specific process of the seventh step is to utilize a virtual road network traffic balanced distribution model based on a virtual road network database to perform balanced flow distribution on the virtual road network, seek a flow distribution rule of the virtual road network under balanced conditions, analyze supply and demand 'balanced matching parameters' of the current virtual nodes and the virtual road sections by combining traffic capacity parameters of the virtual nodes and the virtual road sections, evaluate a macro matching level of the current road network by analyzing the set of parameters, and identify a 'real bottleneck point' of the road network, thereby discovering a macro-micro systematic defect of the road network.
The invention has the beneficial effects that:
the method monitors the road traffic flow information by constructing the virtual nodes and the virtual road sections, generates a real-time model by a PA (power amplifier) reverse-thrust technology, simulates the road traffic flow information by the model, accurately deduces the bottleneck point by comparing the road traffic flow information with the original road traffic flow information, and provides more accurate and scientific suggestions for traffic jam treatment and optimization.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The embodiment provides a traffic bottleneck identification method in an urban traffic network, which comprises the following steps:
step one, constructing virtual road network information comprises constructing virtual nodes and virtual road sections, abstracting a city basic group or a road network in some areas, representing a road network at a larger intersection and in the adjacent areas and a road network in a piece of area, wherein the number of the virtual nodes is determined according to actual conditions of different cities, not only can the original road network be simplified, but also the actual condition of the original road network can be well reflected, the comprehensive flow, the comprehensive capacity and the comprehensive impedance value of the virtual nodes are the results of comprehensive analysis and calculation of the flow, the capacity and the impedance value of the original road network in the group, during macro-level analysis, the virtual nodes are only regarded as points with flow, capacity and impedance values, during micro-level analysis, the virtual nodes are regarded as sub-road networks, and the road sections connected with the virtual nodes are regarded as nodes, and the virtual nodes are constructed; constructing and completing a virtual road section by integrating one or more road sections in the current trunk road network and performing superposition calculation on the integrated traffic volume, the integrated traffic capacity and the integrated impedance value of the virtual road section;
collecting traffic flow data in the virtual road network, determining traffic flow data for predicting road traffic conditions, and collecting mobile navigation data and road fixed detector data of road vehicles;
step three, performing fusion processing on the acquired data, preprocessing the acquired data, matching the data from different sources, and determining the flow-velocity distribution of each detector section;
step four, generating a virtual road network PA back-pushing model by utilizing a PA back-pushing technology, and achieving infinite approach of theoretically distributing traffic volume and actually measured traffic by correcting an initial PA matrix for a limited number of times. The impedance of the circuit section is constant in the whole process; the flow rate of any PA point on each section is kept constant; the distribution process of any PA point to the distribution quantity is independent and does not influence each other, and a virtual road network PA back-stepping model is finally generated;
step five, establishing a virtual road network database based on the road network database, and establishing a road network database according to the actual road network condition and the current traffic situation, wherein the road network database mainly comprises: establishing a virtual road network database by combining a virtual road network PA back-pushing technology and a virtual road network coordinate and adjacent directory database, a geometric element database of a road network section, and a traffic capacity and traffic volume database of the road network section based on the road network database by using a virtual road section and virtual node related parameter calculation method;
step six, calculating data in the virtual road network database;
step seven, calculating and mining bottleneck points according to data, diagnosing real bottleneck points, carrying out balanced flow distribution on a virtual road network by utilizing a virtual road network traffic balanced distribution model based on a virtual road network database, seeking a flow distribution rule of the virtual road network under balanced conditions, analyzing supply and demand 'balanced matching parameters' of the current virtual nodes and the virtual road sections by combining traffic capacity parameters of the virtual nodes and the virtual road sections, evaluating the macro matching level of the current road network by analyzing the parameters, and identifying the 'real bottleneck points' of the road network, thereby discovering the macro-micro systematic defects of the road network;
and step eight, outputting the reason of the bottleneck point.
Preferably, the virtual network database mainly includes: the system comprises a virtual node coordinate adjacency directory database, a virtual road network comprehensive traffic capacity database, a virtual road network comprehensive traffic volume database, a virtual road network OD distribution database and a virtual road network traffic impedance distribution database.
The method monitors the road traffic flow information by constructing the virtual nodes and the virtual road sections, generates a real-time model by a PA (power amplifier) reverse-thrust technology, simulates the road traffic flow information by the model, accurately deduces the bottleneck point by comparing the road traffic flow information with the original road traffic flow information, and provides more accurate and scientific suggestions for traffic jam treatment and optimization.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (9)
1. A method for identifying traffic bottlenecks in an urban traffic network is characterized by comprising the following steps:
step one, constructing virtual road network information;
step two, collecting traffic flow data in the virtual road network;
step three, fusing the acquired data;
step four, generating a virtual road network PA back-stepping model by using a PA back-stepping technology;
step five, establishing a virtual road network database based on the original road network database;
step six, calculating data in the virtual road network database;
seventhly, calculating and mining to obtain bottleneck points according to the data, and diagnosing real bottleneck points
And step eight, outputting the reason of the bottleneck point.
2. The method according to claim 1, wherein the constructing of the virtual road network information comprises constructing virtual nodes and constructing virtual road segments.
3. The method according to claim 2, the method is characterized in that the virtual node is specifically constructed by abstracting the urban basic group or the road network of some areas, representing the road network of a larger intersection and the adjacent areas and the road network of a parcel, the number of the virtual nodes is determined according to the actual conditions of different cities, the original road network can be simplified, the actual condition of the original road network can be better reflected, the comprehensive flow, the comprehensive capacity and the comprehensive impedance value of the virtual node are the results of comprehensive analysis and calculation of the flow, the capacity and the impedance value of the original network in the group, in the macro layer analysis, the virtual node is only considered as a point with flow, capacity and impedance values, in the middle-micro level analysis, the virtual node is regarded as a sub-road network, and the road sections connected with the virtual node are regarded as nodes.
4. The method as claimed in claim 2, wherein the virtual road segment is a composite of one or more road segments in the current trunk road network, and the composite traffic volume, the composite traffic capacity and the composite impedance value of the virtual road segment are calculated by superposition.
5. The method according to claim 1, wherein the second step is performed by determining traffic flow data for predicting road traffic conditions, and collecting the mobile navigation data and the road fixed detector data of road vehicles.
6. The method according to claim 1, wherein the specific process of step three is to preprocess the collected data, match the data from different sources, and determine the flow-velocity distribution of each detector section.
7. The method according to claim 1, wherein the specific process of the fourth step is to achieve infinite approach between theoretically allocated traffic volume and actually measured traffic volume by finite correction of the initial PA matrix. The impedance of the circuit section is constant in the whole process; the flow rate of any PA point on each section is kept constant; the distribution process of any PA point to the distribution quantity is independent and does not influence each other, and finally a virtual road network PA back-stepping model is generated.
8. The method according to claim 1, wherein the concrete process of the fifth step is to establish a road network database according to the actual road network condition and the current traffic situation, and the road network database mainly comprises: the method comprises the following steps of establishing a virtual road network database by combining a virtual road network PA back-pushing technology based on a primary road network database, utilizing a virtual road section and a virtual node related parameter calculation method and the virtual road network PA back-pushing technology, wherein the primary road network node coordinate and adjacent directory database, the primary road network section geometric element database and the primary road network section traffic capacity and traffic volume database mainly comprise the following steps: the system comprises a virtual node coordinate adjacency directory database, a virtual road network comprehensive traffic capacity database, a virtual road network comprehensive traffic volume database, a virtual road network OD distribution database and a virtual road network traffic impedance distribution database.
9. The method according to claim 1, wherein the seventh step is implemented by performing balanced flow distribution on the virtual road network by using a virtual road network traffic balanced distribution model based on a virtual road network database, seeking a flow distribution rule of the virtual road network under balanced conditions, analyzing supply and demand 'balanced matching parameters' of the current virtual node and the virtual road section by combining traffic capacity parameters of the virtual node and the virtual road section, evaluating a macro matching level of the current road network by analyzing the parameters, and identifying a 'real bottleneck point' of the road network, thereby finding a macro-micro systematic defect of the road network.
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Cited By (2)
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