CN113538898A - Multisource data-based highway congestion management and control system - Google Patents
Multisource data-based highway congestion management and control system Download PDFInfo
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Abstract
The invention discloses a multisource data-based highway congestion control system, which comprises an intelligent perception subsystem, a data management subsystem, a congestion prediction early warning subsystem, an online simulation subsystem and a visual application subsystem, wherein the intelligent perception subsystem is interconnected with the data management subsystem, the data management subsystem is interconnected with the congestion prediction early warning subsystem, the congestion prediction early warning subsystem is interconnected with the online simulation subsystem and the visual application subsystem, the online simulation subsystem is interconnected with the visual application subsystem, the intelligent perception subsystem provides basic traffic data support, the data management subsystem processes and stores the basic traffic data, the congestion prediction early warning subsystem carries out traffic state evaluation, the online simulation subsystem carries out simulation test, and the online simulation subsystem carries out simulation test, And searching an optimal control scheme suitable for the current traffic flow state, wherein the visual application subsystem is used for effect presentation and information release.
Description
Technical Field
The invention relates to the field of traffic management, in particular to a multisource data-based highway congestion control system.
Technical Field
The highway congestion control system is considered as an effective means for promoting the running capacity of a highway network, however, the implementation efficiency of the management decision system under the congestion condition of the highway in China is not high, and the management decision system is mainly reflected in that: the traffic information resource integration optimization capability is low, and the traffic running condition cannot be fully acquired and comprehensively analyzed by utilizing multi-source traffic big data to realize information sharing; the advanced technology is relatively short of expansion, the existing highway congestion control technology is relatively backward, the traffic situation cannot be analyzed and predicted in a wider space-time range, and a refined and intelligent highway congestion management system is constructed; the congestion control implementation scheme has the advantages and disadvantages of single output result and low comprehensive integration level, and multiple congestion evacuation schemes cannot be generated and compared, so that the purpose of optimizing and implementing is achieved.
Aiming at the problems, the invention deeply excavates the multisource traffic big data by constructing a multisource data-based highway congestion control system and utilizing advanced technological means such as an intelligent traffic technology, a big data processing technology, a traffic simulation technology and the like, realizes all-dimensional road network traffic operation monitoring analysis and study and judgment and situation prediction, enhances the linkage among subsystems in a control platform, and provides and recommends an optimal plan in real time for congestion control decision-making.
Disclosure of Invention
In order to achieve the above purpose, the following technical units are adopted to achieve the following purpose:
a multi-source data-based highway congestion control system comprises an intelligent perception subsystem, a data management subsystem, a congestion prediction early warning subsystem, an online simulation subsystem and a visual application subsystem. The intelligent perception subsystem is interconnected with the data management subsystem, the data management subsystem is interconnected with the congestion prediction early warning subsystem, the congestion prediction early warning subsystem is interconnected with the online simulation subsystem and the visual application subsystem, and the online simulation subsystem is interconnected with the visual application subsystem. The intelligent perception subsystem is used for providing basic traffic data support for a multi-source data-based highway congestion control system, the data management subsystem is used for processing and storing basic traffic data, the congestion prediction early warning subsystem is used for carrying out traffic state evaluation on the basis of the processed basic traffic data, the online simulation subsystem is used for carrying out simulation test and searching for an optimal control scheme suitable for the current traffic flow state on the basis of the processed basic traffic data and the traffic state evaluation data, and the visual application subsystem is used for effect presentation and information release.
By the technical scheme, automatic prediction and congestion early warning of the traffic running state can be realized through construction of the highway congestion control system based on multi-source data, the optimal plan is selected by an assistant manager through simulation effect comparison, and decision making of the administrator is assisted through a visual and visual visualization system.
The multi-source data-based highway congestion control system is characterized in that the intelligent perception subsystem comprises a real-time data acquisition unit and a historical data acquisition unit, and the real-time data acquisition unit and the historical data acquisition unit are independent of each other.
Through the technical scheme, the intelligent sensing subsystem is arranged, so that real-time detection data and manually input historical data of traffic detectors and user mobile phones from different cross-dispatching stations and toll stations can be acquired and received, the source data can be accessed, and subsequent applications can be supported.
The intelligent sensing subsystem is characterized in that the real-time data acquisition unit acquires data of a traffic detector (such as a microwave radar, a high-definition camera, a meteorological monitor and the like) and mobile phone signaling data, specifically real-time traffic flow information (flow, average speed, vehicle type, locomotive spacing and time occupancy), real-time traffic event information (road traffic accidents, cargo scattering, traffic illegal behaviors and construction maintenance), real-time road meteorological information (rainfall, snowfall, dense fog and sand dust) and the like.
The intelligent perception subsystem is characterized in that the historical data acquisition unit acquires and inputs historical data including workday thematic data, holiday thematic data, large-scale activity thematic data and the like.
The multisource data-based highway congestion control system comprises a data processing unit, a data fusion unit and a data storage unit. The data processing unit is connected with the data fusion unit, and the data fusion unit is connected with the data storage unit.
Through the technical scheme, the data collected by the intelligent perception subsystem can be preprocessed through the data management subsystem, and the preprocessed data are fused and stored, so that the system performance requirement is met, and support is provided for the application of the next stage.
The data management subsystem is characterized in that the data processing unit can clean, filter and eliminate noise of incomplete, false, repeated and missing abnormal data, and finally completes reconstruction and extraction of traffic data.
And the data management subsystem is characterized in that the data fusion unit comprises a multi-source data fusion module and a data map matching module.
The data fusion unit is characterized in that the multi-source data fusion module firstly collects and combines data of traffic detectors (such as microwave radars, high-definition cameras and meteorological monitors) and mobile phone signaling data to obtain road fusion traffic data; then, on the basis of a neural network technology, carrying out consistency processing on the multi-source information from three dimensions of time, space and semantics; the data map matching module is used for matching the data at each space interval to the high-precision map so as to realize the fusion of the data information and the geographic information. And finally, sending the data fusion result to a fusion database for storage.
And the data storage unit of the data management subsystem stores massive data, processes the data and fuses the data, and stores the data into the data storage unit to support further application of the data.
The multi-source data-based highway congestion control system comprises a congestion prediction early warning subsystem, a traffic state estimation unit, a traffic state prediction unit and an early warning grade matching unit. The traffic state estimation unit is connected with the traffic state prediction unit, and the traffic state prediction unit is connected with the early warning grade matching unit.
According to the technical scheme, the congestion prediction and early warning subsystem can analyze, study and judge the traffic congestion of the road network and early warn in advance on the basis of data such as mobile phone signaling, inter-modulation and charging flow through multi-source traffic data fusion and road network traffic congestion early warning prediction.
The traffic state estimation unit firstly acquires data required by state estimation, processes the data based on a state estimation algorithm to generate uniformly expressed real-time road traffic operation condition data, and evaluates the real-time traffic operation condition data to realize the estimation of the traffic state of a real-time road.
The traffic state prediction unit realizes the prediction processing of the road condition information in a short term (15-30 minutes) in the future on the basis of processing and generating the current real-time road condition so as to meet the demand of traffic information release and forecast. Specifically, based on the current road traffic estimated state, the historical traffic state, meteorological information and other data, according to a specific state prediction algorithm, the short-term and medium-term traffic states of the expressway network are subjected to prediction processing, and short-term and medium-term traffic prediction state results are generated.
The congestion prediction early warning subsystem is characterized in that the early warning grade matching unit judges the congestion degree of the real-time traffic state and the future traffic state, completes large-flow congestion early warning under different grades, and matches corresponding early warning grades based on the congestion grades.
The multi-source data-based highway congestion control system comprises an online simulation subsystem, a traffic data filtering and extracting unit, a simulation plan storage unit, an intelligent plan testing unit and an optimal plan analyzing unit. The traffic data filtering and extracting unit can be associated with a simulation plan storage unit, the simulation plan storage unit can be associated with an intelligent plan testing unit, and the intelligent plan testing unit can be associated with an optimal plan analyzing unit.
Through the technical scheme, the simulation of the traffic situation evolution process under different events and scheduling control means can be realized through the setting of the online simulation subsystem, the optimal scheme is selected to manage and control the traffic field according to the simulation control effect of each plan and the comparison of the simulation control effect of each plan by an auxiliary manager, and a traveler can be helped to make better selection on the basis of accurately informing the road condition to be faced.
And the traffic data filtering and extracting unit is used for extracting historical data, real-time data and traffic jam prediction data required by simulation from the jam prediction and early warning subsystem, filtering the extracted data and reserving effective data.
The on-line simulation subsystem is characterized in that the simulation plan storage unit is used for designing typical traffic plan models of different types and is commonly stored in the unit plan library.
And the online simulation subsystem is characterized in that the intelligent plan test unit is used for calibrating actual road traffic flow characteristic parameters according to simulation data after filtering processing, and performing test comparison and selection on different measure plans to accurately simulate traffic running conditions.
And the online simulation subsystem is used for evaluating the comprehensive traffic implementation effect under different typical management and control measures and assisting a manager to select the optimal plan according to the optimal result of the simulation.
The multi-source data-based highway congestion control system comprises a visual application subsystem and an effect presentation unit. The effect presenting unit and the information publishing unit are mutually independent.
Through the technical scheme, the data results of traffic situation prediction early warning and online simulation can be visually displayed in the forms of electronic maps, various charts and 3D animations through the arrangement of the visual application subsystem, so that expressway managers can be assisted in comparing and selecting plans and making decisions, and the emergency command capability and the public trip service level of the road network traffic organization are further improved.
The visual application subsystem is characterized in that the effect presentation unit comprises a real-time traffic parameter display module, a short-time predicted traffic situation display module and a simulation result display module.
The effect presentation unit is used for displaying road traffic flow parameter data such as real-time flow, vehicle speed, traffic accidents and the like in a visualized manner by utilizing abundant and diverse chart combinations for a user to inquire and download; the short-time predicted traffic situation display module is used for determining a traffic jam grading threshold value according to a prediction algorithm model of the road section traffic flow and speed and by combining historical traffic conditions, so that traffic jam prediction and early warning service are realized; the simulation result display module is used for displaying different alternative plan traffic simulation 3D animation results and evaluation indexes in a typical control measure plan model library in a chart mode in a comparison mode, and visual and three-dimensional presentation of the scheme is achieved.
The visual application subsystem is characterized in that the information issuing unit is used for combining the traffic control implementation plan finally selected by the user with the pushing and issuing by the aid of multimedia means such as road variable information boards, broadcasting and mobile phones, so that dynamic real-time traffic information service is provided for public trips, and emergency command capability of a road network traffic organization is improved.
Drawings
Fig. 1 is an overall architecture diagram of a multi-source data-based highway congestion control system according to the invention;
FIG. 2 is a diagram illustrating the overall architecture of the intelligent sensor subsystem of the present invention;
FIG. 3 is an overall architecture diagram of the data management subsystem of the present invention;
FIG. 4 is an overall architecture diagram of the data fusion unit of the present invention;
FIG. 5 is an overall architecture diagram of a congestion prediction early warning subsystem according to the present invention;
FIG. 6 is an overall architecture diagram of the on-line simulation subsystem of the present invention;
FIG. 7 is an overall architecture diagram of a visualization application subsystem of the present invention;
FIG. 8 is an overall architecture diagram of the effect presenting unit of the present invention;
FIG. 9 is a graph showing expected effects of real-time traffic parameters;
FIG. 10 is a diagram of a short-term predicted traffic situation showing expected effects;
fig. 11 is a diagram showing the interface effect of the simulation result.
Detailed Description
The invention will be described in detail with reference to the accompanying drawings and examples.
Fig. 1 is an overall block diagram of a multisource data-based highway congestion management and control system 1. As shown in the figure, the multi-source data-based highway congestion management and control system 1 includes five subsystems, which are respectively: the intelligent perception subsystem 11, the data management subsystem 12, the congestion prediction early warning subsystem 13, the online simulation subsystem 14 and the visualization application subsystem 15. The intelligent perception subsystem 11 can perform information interaction with the data management subsystem 12, the data management subsystem 12 can perform information interaction with the congestion prediction and early warning subsystem 13, the congestion prediction and early warning subsystem 13 can perform information interaction with the online simulation subsystem 14 and the visual application subsystem 15, and the online simulation subsystem 14 can perform information interaction with the visual application subsystem 15. The intelligent perception subsystem 11 is used for providing basic traffic data support for the multisource data-based highway congestion control system, the data management subsystem 12 is used for processing and storing the basic traffic data, the congestion prediction early warning subsystem 13 can perform traffic state assessment based on the processed basic traffic data, the online simulation subsystem 14 performs simulation test and searches for an optimal control scheme suitable for the current traffic flow state based on the processed basic traffic data and the traffic state assessment data, and the visual application subsystem 15 is mainly used for effect presentation and information release functions. The detailed description will be made next for each subsystem.
Fig. 2 is an overall architecture diagram of the smart sensor subsystem 11. As shown, the smart perception subsystem 11 includes a real-time data acquisition unit 111 and a historical data acquisition unit 112. The real-time data acquisition unit 111 and the historical data acquisition unit 112 are independent of each other.
Specifically, the smart sensor subsystem 11 can collect and receive real-time detection data from traffic detectors of different traffic stations and toll stations and mobile phones of users and manually input historical data based on the real-time data collection unit 111 and the historical data collection unit 112, complete access to source data, and support subsequent applications.
Specifically, the real-time data acquisition unit 111 is responsible for acquiring and inputting data obtained by real-time detection, including traffic detector data and user mobile phone signaling data. The traffic detector data is acquired in real time through traffic detectors such as microwave radars, high-definition cameras, weather monitors and the like, and specifically comprises real-time traffic flow (flow, average speed, vehicle type, locomotive spacing, time occupancy), real-time traffic events (road traffic accidents, cargo scattering, traffic violation behaviors, construction maintenance) and real-time road weather (rainfall, snowfall, dense fog, sand and dust) data. User mobile phone signaling data is obtained from operator communication system, when cell switching occurs, related data is automatically transmitted to BSC base station control system and reported to MSC mobile switching center. Analyzing a signaling No. 7 (SS7) in the communication system to obtain related data of the switching of the mobile phone; the mobile phone signaling data specifically includes fields such as an encrypted mobile phone identification number (MSID), a timestamp, a location area number, a cell number, an event type, a home location, and the like. The historical data acquisition unit 112 is responsible for acquiring and inputting historical data, including workday thematic data, holiday thematic data, and large-scale activity thematic data, wherein each thematic data specifically includes historical traffic flow data, historical traffic event data, and historical road meteorological data. The real-time traffic flow data and the historical traffic flow data are collectively referred to as traffic flow data, the real-time traffic event data and the historical traffic event data are collectively referred to as traffic event data, and the real-time road meteorological data and the historical road meteorological data are collectively referred to as road meteorological data.
Fig. 3 is an overall architecture diagram of the data management subsystem 12. As shown in fig. 3, the data management subsystem 12 includes a data processing unit 121, a data fusion unit 122, and a data storage unit 123. The data processing unit 121 can perform information interaction with the data fusion unit 122, and the data fusion unit 122 can perform information interaction with the data storage unit 123.
Specifically, the data processing unit 121 is responsible for receiving real-time data and historical data acquired by the smart sensing subsystem 11, building a massive traffic big data processing framework based on Spark technology by utilizing Hadoop and Spark big data technology ecology, cleaning abnormal data in the massive traffic big data processing framework, eliminating noise, and completing reconstruction and extraction of traffic data.
Specifically, for the traffic flow data, the traffic event data, and the road meteorological data acquired by the traffic detector, the data processing unit 121 performs blank data supplement and noise data smoothing on the traffic flow data, the traffic event data, and the road meteorological data, and finally obtains a high-quality data source. The data with the collection time interval obviously larger than the average data collection time interval is blank data, the data processing unit 121 predicts the numerical value of the blank data based on various different algorithms such as regression analysis, decision trees and the like, and the method can fully utilize the attributes of the data around the blank data, maintain the relation among the data to the maximum extent and improve the prediction precision. The data value is noise data with an excessive difference from the surrounding data value, the data processing unit 121 cleans the noise data based on a moving average algorithm, and the method can consider the attribute of the surrounding data of abnormal data while guaranteeing the characteristics of the abnormal data, reduce the difference between the abnormal data and the surrounding data, and obtain traffic data conforming to the normality. For the mobile phone signaling data, the data processing unit 121 performs switching data extraction to obtain a high-quality data source, and converts the effective sample size and the road section travel speed. The content of the extracted handover data is the handover communication activity information occurring between BTSs managed by the MSC, which includes the numbers of the handover cell and the location area and the time of each handover, and the data sample is shown in the table, where the first column is MSID, the encrypted MS code (i.e. the encrypted mobile phone number, mainly for the purpose of privacy protection), the second column TIMESTAMP, the third column is the time of the handover, the third column is the number of the BTS cell (handover cell), the fourth column is the number of the location area where the handover occurs, and the fifth column is the communication activity event code such as handover, location area update, etc.
Table 1 sample switching data field extracted after parsing mobile phone signaling data
Specifically, the data fusion unit 122 is responsible for summarizing, screening and merging the processed data, and finally realizing multi-source data fusion and corresponding the data to the high-precision map. Fig. 4 is an overall architecture diagram of the data fusion unit 122. As shown in fig. 4, the data fusion unit 122 includes a multi-source data fusion module 1221 and a data map matching module 1222. The multi-source data fusion module 1221 is capable of information interaction with the data map matching module 1222. Further, the multi-source data fusion module 1221 is responsible for summarizing and merging data of traffic detectors (microwave radar, high-definition camera, weather monitor, etc.) and mobile phone signaling data to obtain road fusion traffic data. Firstly, collecting multi-source data, including road traffic flow data, traffic event data and road meteorological data from various traffic detectors of different traffic stations and toll stations, and road section travel speed and effective sample size obtained by extracting mobile phone signaling switching data. Then, the multi-source information is subjected to consistency processing from three dimensions of time, space and semantics based on a neural network technology: the time unification is that the multi-source information is collected in the same time interval; the purpose of spatial unification is to correspond multi-source information to the same spatial interval; the semantics is the meaning of the data acquired by various detection technologies, namely the traffic state quantity represented by the data, taking the traffic flow as an example, the traffic state quantity directly provided by the traffic state extraction technology based on mobile phone switching is converted traffic flow, the traffic state quantity directly provided by the traffic detectors of an interchange station and a toll station is traffic flow, and the semantic unification is to convert the two traffic state quantities into the same representation form, such as fused road flow. The data map matching module 1222 is responsible for matching data at each spatial interval to a high-precision map, so as to realize fusion of data information and geographic information.
Specifically, the data storage unit 123 is responsible for mass data storage, and data processed and fused can be stored in the data storage unit 123 for a long time, so as to provide data support for further application of the data.
Fig. 5 is an overall architecture diagram of the congestion prediction warning subsystem 13. As shown in fig. 5, the congestion prediction and early warning subsystem 13 includes a traffic state estimation unit 131, a traffic state prediction unit 132, and an early warning level matching unit 133. The traffic state estimation unit 131 can perform information interaction with the traffic state prediction unit 132, and the traffic state prediction unit 132 can perform information interaction with the early warning level matching unit 133.
Specifically, the traffic state estimation unit 131 is responsible for implementing a function of estimating a current road traffic state. Data required by state estimation are retrieved from the data storage unit 123, the data are processed based on an embedded traffic state estimation algorithm, real-time road traffic operation condition data which are uniformly expressed and comprise real-time traffic flow, real-time queuing length, real-time delay time and the like are generated, the real-time traffic operation condition data are evaluated, and the traffic state of the real-time road is obtained through analysis.
Specifically, the traffic state prediction unit 132 is responsible for performing prediction processing on the traffic information in a short period of time (15-30 minutes) in the future, so as to meet the demand of traffic information distribution and forecast. The real-time road traffic operation condition data obtained by calculation is called from the traffic condition estimation unit 131, and based on the current road traffic estimation state, the historical traffic state, the meteorological information and other data, the short-term and medium-term traffic states of the road network are predicted according to a specific state prediction algorithm to generate short-term and medium-term traffic prediction state results including predicted traffic flow, predicted queuing length, predicted delay time and the like, and the future traffic operation condition data is evaluated and analyzed to obtain the short-term and medium-term predicted road traffic states.
Specifically, the early warning level matching unit 133 is responsible for determining congestion levels of a real-time traffic state and a future traffic state, and completing large-flow congestion early warning at different levels. The early warning level matching unit 133 divides the traffic early warning level according to the service level by using the road engineering technical standard as a support, judges whether the current road reaches a congestion state in the present or future by combining the current road service level, the real-time traffic state and the future traffic state, and performs congestion early warning according to the congestion degree.
FIG. 6 is an overall architecture diagram of the online simulation subsystem 14. As shown, the online simulation subsystem 14 includes a traffic data filtering and extracting unit 141, a simulation plan storing unit 142, an intelligent plan testing unit 143, and an optimal plan analyzing unit 144. The traffic data filtering and extracting unit 141 can perform information interaction with the simulation plan storage unit 142, the simulation plan storage unit 142 can perform information interaction with the intelligent plan testing unit 143, and the intelligent plan testing unit 143 can perform information interaction with the optimal plan analyzing unit 144.
Specifically, the online simulation subsystem 14 integrates the travel behavior model into the prediction process, reversely deduces the OD flow of the whole network through the flow monitoring data of part of road sections, can estimate and predict the road network state based on traffic modeling, simulation and analysis, and realizes synchronization of the operation of the online simulation system and real traffic network management, so that a traveler can make better selection on the basis of accurate information notification of upcoming road conditions, and a traffic manager can select an optimal scheme in real time to manage and control a traffic site on the basis of foreseeable effects of multiple sets of management and control schemes, thereby achieving the purpose of helping the traveler and the manager to make a prospective decision.
Specifically, the traffic data filtering and extracting unit 141 is responsible for extracting historical data, actual measurement time and predicted data of traffic congestion required by simulation from the congestion prediction and early warning subsystem, reducing the data computation amount of subsequent simulation, filtering the extracted data, and retaining valid data or performing reliability marking on original data.
Specifically, the simulation plan storage unit 142 is responsible for designing typical traffic diversion plan models of different types in advance, and storing the models in a unit plan library together. Further, the prepared plan model and its function are shown in table 2.
TABLE 2 plan model design
Specifically, the intelligent plan testing unit 143 establishes a basic road network by using traffic simulation model VISSIM software, and calibrates characteristic parameters (road network characteristic parameters, vehicle characteristic parameters, driving behavior characteristic parameters, and the like) closely related to a traffic running state. The parameters are adjusted according to the actual condition of the traffic flow, so that the running modes of people, vehicles and roads in the traffic system can be simulated more accurately, and the influence of special conditions such as special terrain and special weather on the traffic can be simulated indirectly. Further, the intelligent plan testing unit 143 is responsible for calling a single typical traffic plan model in the simulation plan storage unit 142 according to the filtered simulation data, and may also call different typical models to perform permutation and combination to evaluate the comprehensive effect under multiple traffic control measures, thereby implementing test selection of different traffic control measure plans and accurately simulating traffic operation conditions.
Specifically, the optimal plan analyzing unit 144 is responsible for evaluating the comprehensive traffic implementation effect under different typical management and control measures, and assists the manager to select the optimal plan according to the optimal result of the simulation. According to the traffic jam shunting control requirement, a proper index can be selected from three levels of points, lines and planes to determine a simulation evaluation system of an optimal traffic control plan. The VISSIM outputs various statistical data offline, such as vehicle speed, travel time, delay, queue length and the like as data for decision support.
Fig. 7 is an overall architecture diagram of the visualization application subsystem 15. As shown, the visualization application subsystem 15 includes an effect presentation unit 151 and an information publishing unit 152. The effect presentation unit 151 and the information distribution unit 152 are independent of each other.
Specifically, the visualization application subsystem 15 can utilize a visualization technology to visually display traffic parameter data and simulation results, and push and release dynamic real-time traffic travel information by combining a multimedia means, so that the response time of traffic jam is effectively saved, and the emergency command capability of a road network traffic organization is improved.
Specifically, the effect presenting unit 151 is responsible for providing a display of traffic parameter detection based on various intelligent sensing data such as mobile phone signaling, microwave detection, toll gate, floating car, and the like. On the basis, by establishing a traffic model and fusing multi-source data and utilizing big data analysis technologies such as distributed computing storage and machine learning, the short-time traffic situation is predicted, and early warning service is provided in a visual display system. Meanwhile, a traffic control plan is simulated based on real-time traffic data by utilizing the secondary development function of the VISSIM, and various indexes in an evaluation system are visually displayed in a chart form to assist highway managers in comparing, selecting and deciding the plan. Fig. 8 is an overall architecture diagram of the effect presenting unit 151. As shown in fig. 8, the effect presenting unit 151 includes a real-time traffic parameter displaying module 1511, a short-time predicted traffic situation displaying module 1512, and a simulation result displaying module 1513. The real-time traffic parameter display module 1511, the short-time predicted traffic situation display module 1512, and the simulation result display module 1513 are independent of each other. Further, the real-time traffic parameter display module 1511 is responsible for acquiring more accurate and comprehensive road traffic flow parameters based on the existing multi-source data of the ETC portal data, traffic flow, vehicle speed, traffic accidents, maintenance, meteorological information and the like of the highway based on analyzing the characteristics, advantages and disadvantages of the data acquired by various traffic detectors, and by using data fusion algorithms such as neural networks, combined weighting, deep learning and the like. And displaying the change conditions of the traffic parameters such as real-time flow, average speed of the road section and the like of the appointed release road section along with time by using the time-varying curve, and comparing and analyzing the change conditions with the traffic parameters of a single data source. Meanwhile, as the historical traffic parameter data is stored in the database, the user can inquire the traffic parameters at any time in the past, the system provides download service, and the inquiry result can be stored to the local computer in a chart form. And the traffic parameter data is visually displayed by enriching diversified chart forms such as a pie chart, a bar chart and the like. The data is represented by the collocation and combination among the charts, which helps the system user to better find the rules behind the data, and the expected effect is shown in fig. 9. The short-time predicted traffic situation presentation module 1512 can establish a prediction algorithm model of road section traffic flow and speed based on a traffic flow theory and a K-nearest neighbor non-parameter regression and other technical method, determine a traffic congestion classification threshold value by combining historical traffic conditions, realize prediction of traffic congestion and provide congestion early warning service, and congestion warning information mainly includes a congested road section ID, a congested road section range, a congestion level and the like. And a short-time predicted traffic situation display page mainly displays the predicted traffic flow and the road section speed calculated according to the prediction model, and generally displays the current time 2h later by default. The short-time prediction time range is 0-2h, a user can select any time period in the prediction time range by dragging the time axis, the related predicted traffic parameters can change along with the time period, and the expected effect is shown in fig. 10. The simulation result display module 1513 builds a simulated road network model in the VISSIM in advance, builds a traffic control measure plan model library, determines a simulation evaluation system, calls traffic flow parameters of road sections in real time by using a COM interface of the VISSIM, simulates traffic control measures on predicted traffic jam road sections, and outputs simulated 3D animations and related evaluation indexes in the display system. And respectively carrying out online simulation operation on the alternative measures which are not taken and are drawn up by the user by the system background, comparing and displaying the output result of the simulation and the evaluation index in a chart form, and assisting management personnel in comparing and selecting the traffic control measures. Meanwhile, the implementation start time and the implementation end time of the traffic control plan are adjusted through the online simulation platform, the output result is compared, and the user can select the start time and the end time of the measure, so that the implementation effect is optimized, a solid data support is provided for decision of the traffic control measure, and the expected effect is shown in fig. 11.
Specifically, the information publishing unit 152 is responsible for combining the traffic control implementation plan finally selected by the user with the push publishing by using the multimedia means such as the road variable information board, the broadcast, the mobile phone and the like, so as to provide a dynamic real-time traffic information service for public travel and improve the emergency command capability of the road network traffic organization. Further, the information issuing unit 152 recommends, according to the implementation control plan finally selected by the user and in combination with the distribution situation of the variable information boards on the road segment, the variable situation boards for issuing the control measures, including the contents of the location, the device number, the issuing time, the issuing state, and the like, for the user by the system, and is helpful for dynamically inducing the public to perform the travel behavior.
In conclusion, the multisource data-based highway congestion control system provided by the invention can establish comprehensive traffic operation situation monitoring, analyzing, studying and judging and situation prediction, and can automatically predict traffic operation states and perform congestion early warning; establishing an intelligent simulation platform, simulating the control effect of each plan, and assisting a manager to select an optimal plan through simulation effect comparison; and establishing a visual system to assist the decision of a manager.
Details not described in this specification are within the skill of the art that are well known to those skilled in the art.
Claims (13)
1. The multi-source data-based highway congestion control system is characterized in that the multi-source data-based highway congestion control system (1) comprises an intelligent perception subsystem (11), a data management subsystem (12), a congestion prediction early warning subsystem (13), an online simulation subsystem (14) and a visual application subsystem (15), wherein the intelligent perception subsystem (11) is interconnected with the data management subsystem (12), the data management subsystem (12) is interconnected with the congestion prediction early warning subsystem (13), the congestion prediction early warning subsystem (13) is interconnected with the online simulation subsystem (14) and the visual application subsystem (15), and the online simulation subsystem (14) is interconnected with the visual application subsystem (15).
2. The multisource data-based highway congestion management and control system according to claim 1, wherein the intelligent perception subsystem (11) comprises a real-time data acquisition unit (111) and a historical data acquisition unit (112), and the real-time data acquisition unit (111) and the historical data acquisition unit (112) are independent from each other.
3. The multisource data-based highway congestion management and control system according to claim 2, wherein the real-time data acquisition unit (111) acquires data of traffic detectors (such as microwave radars, high-definition cameras, weather monitors and the like) and mobile phone signaling data, specifically real-time traffic flow information (flow, average speed, vehicle types, headway, time occupancy), real-time traffic event information (road traffic accidents, cargo spills, traffic violations, construction maintenance), real-time road weather information (rainfall, snowfall, heavy fog, sand and dust) and the like, and the historical data acquisition unit (112) collects and records historical data including workday thematic data, holiday thematic data, large-scale activity thematic data and the like.
4. The multisource data-based highway congestion management and control system according to claim 1, wherein the data management subsystem (12) comprises a data processing unit (121), a data fusion unit (122) and a data storage unit (123), the data processing unit (121) is interconnected with the data fusion unit (122), and the data fusion unit (122) is interconnected with the data storage unit (123).
5. The multisource data-based highway congestion management and control system according to claim 4, wherein the data processing unit (121) can clean, filter and eliminate noise of incomplete, false, repeated and missing abnormal data, and finally complete reconstruction and extraction of traffic data, the data fusion unit (122) comprises a multisource data fusion module (1221) and a data map matching module (1222), and the data storage unit (123) is used for storing mass data, storing the data after data processing and data fusion in the data storage unit, and providing support for further application of the data.
6. The multisource data-based highway congestion control system according to claim 5, wherein the multisource data fusion module (1221) firstly collects and merges traffic detector (microwave radar, high-definition camera, weather monitor, etc.) data and mobile phone signaling data to obtain road fusion traffic data, then carries out consistency processing on multisource information from three dimensions of time, space and semantics based on a neural network technology, and the data map matching module (1222) matches data at each space interval onto a high-precision map to realize fusion of data information and geographic information. And finally, sending the data fusion result to a fusion database for storage.
7. The multisource data-based highway congestion management and control system according to claim 1, wherein the congestion prediction and early warning subsystem (13) comprises a traffic state estimation unit (131), a traffic state prediction unit (132) and an early warning level matching unit (133), wherein the traffic state estimation unit (131) is interconnected with the traffic state prediction unit (132), and the traffic state prediction unit (132) is interconnected with the early warning level matching unit (133).
8. The multisource data-based highway congestion management and control system of claim 7, the traffic state estimation unit (131) firstly acquires data required by state estimation, processes the data based on a state estimation algorithm to generate real-time road traffic operation condition data which are uniformly expressed, and the real-time traffic running condition data is evaluated to realize the estimation of the traffic state of the real-time road, the traffic state prediction unit (132) realizes the prediction processing of the road condition information in the short term (15-30 minutes) in the future on the basis of processing and generating the current real-time road condition, the early warning grade matching unit (133) judges the congestion degree of the real-time traffic state and the future traffic state so as to meet the demand of traffic information publishing and forecasting, completes large-flow congestion early warning under different grades, and matches corresponding early warning grades based on the congestion grades.
9. The multisource data-based highway congestion management and control system according to claim 1, wherein the online simulation subsystem (14) comprises a traffic data filtering and extracting unit (141), a simulation plan storage unit (142), an intelligent plan testing unit (143) and an optimal plan analyzing unit (144), the traffic data filtering and extracting unit (141) is interconnected with the simulation plan storage unit (142), the simulation plan storage unit (142) is interconnected with the intelligent plan testing unit (143), and the intelligent plan testing unit (143) is interconnected with the optimal plan analyzing unit (144).
10. The multisource data-based highway congestion control system according to claim 9, wherein the traffic data filtering and extracting unit (141) is configured to extract historical data, real-time data and predicted data of traffic congestion required by simulation from the congestion prediction and early warning subsystem, filter the extracted data, and retain valid data, the simulation plan storage unit (142) is configured to design typical traffic plan models of different types and store the typical traffic plan models in the unit plan library together, the intelligent plan testing unit (143) is configured to calibrate characteristic parameters of actual road traffic flow according to the filtered simulated data, perform test comparison and selection on different measure plans, and accurately simulate traffic operating conditions, and the optimal plan analyzing unit (144) is configured to evaluate comprehensive traffic implementation effects under different typical control measures, and assisting a manager to select an optimal plan according to the optimal result of the simulation.
11. The multisource data-based highway congestion management and control system according to claim 1, wherein the visualization application subsystem (15) comprises an effect presenting unit (151) and an information issuing unit (152), and the effect presenting unit (151) and the information issuing unit (152) are independent from each other.
12. The multisource data-based highway congestion management and control system according to claim 11, wherein the effect presenting unit (151) comprises a real-time traffic parameter display module (1511), a short-time predicted traffic situation display module (1512) and a simulation result display module (1513), and the information issuing unit (152) is configured to combine a traffic management and control implementation plan finally selected by a user with a push and issue by using multimedia means such as a road variable information board, a broadcast, a mobile phone and the like, so as to provide a dynamic real-time traffic information service for public travel and improve the emergency command capability of a road network traffic organization.
13. The multisource data-based highway congestion management and control system according to claim 12, wherein the real-time traffic parameter display module (1511) is configured to visually display road traffic flow parameter data such as real-time flow, vehicle speed and traffic accidents by using various chart combinations for users to inquire and download, the short-time predicted traffic situation display module (1512) is configured to determine a traffic congestion grading threshold according to a prediction algorithm model of road traffic flow and speed and combine historical traffic conditions to determine a traffic congestion grading threshold, so as to realize traffic congestion prediction and early warning service, and the simulation result display module (1513) is configured to compare and display different alternative plan traffic simulation 3D animation results and evaluation indexes in a typical management and control measure plan model library in a chart form, so as to realize intuitive and three-dimensional presentation of schemes.
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