CN106297291B - Urban expressway traffic information acquisition system - Google Patents
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Abstract
The invention discloses an urban expressway traffic information acquisition system which comprises a data acquisition unit, a data management unit, an information processing unit, an integrated interface unit, a user application unit and a decision management unit, wherein the data acquisition unit is used for acquiring, gathering and sorting mass traffic data which are widely distributed and have multi-source isomerism; the integrated interface is used for providing standard information for a requester, the user application unit issues or acquires the standard information through a related interface on a web page, and a decision is made to sort and analyze the past data and provide the decision; the data exchange of national intelligent traffic systems is realized through cloud computing and big data processing, and the systems are intercommunicated, interconnected and interoperated.
Description
Technical Field
The invention relates to the field of intelligent traffic, in particular to urban expressway traffic information acquisition and management in an intelligent traffic system.
Background
The urban expressway is a totally-enclosed and totally-overpass urban road system taking motor vehicles as main service objects, and forms a main road network of urban traffic. Although the express way has the characteristics of good road conditions and running environment, small interference of non-motor vehicles and pedestrians when vehicles run, fast running speed, stronger regularity than that of an ordinary urban road and the like, the distance between the entrance and the exit is small, the entering and exiting frequency of the vehicles is high, the vehicles frequently intersect near the entrance and the exit, the interference on the vehicles running normally on the main road is large, and the vehicles are easy to block. Meanwhile, the express way is closely associated with the urban common road, the express way is communicated with the common road in various modes such as an isolation zone entrance, an interchange overpass, an entrance ramp and an exit ramp, the relationship between an aorta and a microcirculation is formed between the expressway and the common road, and the planning target of the express way can be finally realized only by ensuring the smooth connection between connection points. The urban expressway has the characteristics, so that the traffic information acquisition of the urban expressway is greatly different from the traffic information acquisition of a common road, and the characteristics and the differences of the urban expressway in the target, the range and the side points of the information acquisition are determined.
At present, some products are produced aiming at the problem of expressway traffic information monitoring in China, but as the constructed intelligent traffic information acquisition systems in various regions are developed by different manufacturers based on different technical routes at different periods, the data acquisition and access modes are different and are relatively independent, the constructed systems are in an information isolated island state, the information communication is not smooth, and the data integration and sharing are difficult; the intelligent road traffic information collection is mainly performed on urban ground roads or expressways, and the urban expressway traffic information collection system is few, so that the system has the defects of single function, lack of integration, laggard technology and the like. The method is mainly embodied in the aspects of scattered application system construction, lack of effective integration of mass data, low traffic data utilization rate, incapability of giving full play to data value, limited traffic early warning propagation, difficulty in timely acquiring road condition information and the like. And the specific environment and target determine that the traffic information acquisition method of a common road or an expressway cannot be applied, so that a modern scientific system capable of uniformly fusing and summarizing complicated traffic big data and improving traffic management is the direction of future research.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides urban expressway traffic information acquisition system software which improves the working efficiency of the system by using emerging technologies such as Internet of things, big data, cloud computing and the like and combining system integration service units.
In order to achieve the above object, the present invention provides the following technical solution, including the following service units: the data acquisition unit collects, converges and arranges the multisource heterogeneous traffic big data obtained by diversified, wide and real-time acquisition modes and intelligent comprehensive monitoring and stores the multisource heterogeneous traffic big data into a database; the data management unit is used for realizing distributed storage, query, backup and cleaning of the traffic big data through a distributed file system and a distributed database based on the traffic big data acquired by the data acquisition unit; the information processing unit is used for cleaning, analyzing and filtering the traffic big data in the database and forming an information item meeting the standard requirement; an integrated interface unit for processing application requests from the application system and providing information items meeting the criteria to a requesting party; the user application unit faces to system users and provides system configuration management, a traffic event command and dispatch system, rapid traffic road condition induction, road condition monitoring and information release for the system users; and the decision management unit is used for evaluating the effect implemented by each business unit, setting and correcting the function target of each business unit in a man-machine combination mode, determining the flow of each business processing, specifying the method, the model and the algorithm for generating each business scheme, continuously improving the working condition of the system, and finally converting different interfaces of each business system into WEB services for sharing through SOA architecture design.
Preferably, the traffic big data adopts a data grading storage mechanism and a big data analysis and processing mode to solve the problems of low time delay, high concurrent affairs and high-efficiency information processing and analysis of the system.
Preferably, the data hierarchical storage mechanism includes integrating a plurality of data information into a core model of an existing main data warehouse, and migrating historical data and inventory data in the main data warehouse to the distributed database.
Preferably, the big data analysis and processing firstly constructs a big data analysis and processing platform and analyzes and processes the traffic big data by adopting a distributed parallel processing technology and a real-time data stream processing technology.
Preferably, the big data analysis and processing platform comprises a distributed storage layer, a distributed processing layer, a metadata service layer, a processing analysis layer and a traffic big data processing application layer.
Preferably, the processing and analyzing layer comprises complex event processing, real-time analysis processing, online analysis processing and deep analysis.
Preferably, the big data analysis and processing platform comprises a Hadoop platform, the raw collected data is processed by a data warehouse technology (ETL) and then loaded to the Hadoop platform, and the processed data is stored in a distributed database and a main database through Hadoop, wherein the Hadoop is a distributed system infrastructure.
Preferably, the traffic management unit comprises a traffic guidance system, a traffic event monitoring system, a traffic guidance system and a traffic decision analysis system.
Preferably, the traffic decision analysis system comprises a traffic flow statistical analysis system, a congestion multi-road section analysis system and a traffic prediction system.
The invention has the beneficial effects that:
1. a universal data interface is established on the basis of system modularization, so that the compatibility problem of a new system and an old system is solved;
2. a plurality of data analysis models are established, and results beneficial to traffic management and control are analyzed from limited data to the maximum extent, so that the working efficiency of the system is improved;
3. providing a universal external service interface to provide service for secondary development;
4. a space and service data sharing mechanism is realized, and the problem of multi-center data intercommunication and interconnection is solved;
5. the problem of multi-level control is solved, and different-level authorization of the urban traffic management command center is realized;
6. the application of cloud computing and big data can realize real-time network transmission and rapid persistent storage of a large amount of pictures, vehicle data and video data under the conditions of mass data, severe network environment and complex service processing.
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FIG. 1 is a schematic diagram of the software of the urban expressway traffic information collection system.
Detailed Description
The technical solution of the embodiment of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention.
Referring to fig. 1, the urban expressway traffic information collection system software disclosed by the invention comprises a data collection unit, a data management unit, an information processing unit, an integrated interface unit, a user application unit and a decision management unit.
The data acquisition unit collects, gathers and arranges the acquired traffic big data and stores the collected traffic big data in a database, wherein the information collection of the traffic big data mainly comprises the collection of information such as traffic flow information, traffic condition information, vehicle violation information, weather information, vehicle quantity, vehicle speed, vehicle type, vehicle headway and the like, wherein the traffic flow is the number of running vehicles passing a certain point in a certain specific time period, and the unit is vehicle/hour; average speed: the arithmetic mean value of the speed of each vehicle in a certain length is driven along a road in a certain specific time period, and the unit is kilometers per hour; occupancy is the time the detector is occupied divided by the total time observed multiplied by 100%. The data can be collected by various means, such as detector collection, manual collection, floating car real-time information (FCD) collection, air traffic remote sensing monitoring, and traffic environment and weather detection. There are many ways for the detector to pick up signals such as microwave radar, video, infrared and ground sensing coils. Meanwhile, the data of the light traffic accident are rapidly collected through intelligent comprehensive monitoring and are positioned through a GIS. The intelligent comprehensive monitoring comprises a video docking video monitoring system, an illegal detection system, a mobile phone client snapshot system and the like which are docked, and a GIS system and a 110 receiving and processing alarm system are matched with each other, so that a traffic police can be quickly locked in position and notified in the shortest time and traffic maintenance can be carried out.
The data management unit classifies and arranges the traffic big data into the distributed database through the distributed file system, and forms a main database and the distributed database, so that the burden of the main database is reduced, the file information is divided into small blocks to be stored in the distributed database, the management and the query are facilitated, and unnecessary information is eliminated. The distributed file system can be independently operated or embedded into any Java-supporting Web container to operate, is a core part of Hadoop, and is characterized in that original collected data are processed by a data warehouse technology (ETL) through a Hadoop platform and then loaded to the Hadoop platform, and the processed data are stored in a distributed database and a main database through the Hadoop. The Hadoop has the characteristic of high fault tolerance, can be deployed on low-cost hardware, provides high throughput to access data of an application program, and is high in flexibility and low in cost.
Specifically, the traffic big data adopts a data grading storage mechanism and a big data analysis and processing mode to solve the problems of low time delay, high concurrency and high-efficiency information processing and analysis of the system. The data hierarchical storage mechanism comprises the steps of integrating various data information into a core model of an existing main data warehouse through modification of the core model, and migrating historical data and inventory data in the main data warehouse to the distributed database.
The big data analysis and processing firstly constructs a big data analysis and processing platform and analyzes and processes the traffic big data by combining a distributed parallel processing technology and a real-time data stream processing technology, wherein the distributed parallel processing technology can connect a plurality of computers in different places or with different functions or with different data through a communication network, so that the plurality of processors work simultaneously and are managed in a unified way in a control system. The real-time data stream processing technology is used for storing the traffic big data in real time, and calculating the data in real time, so that the quick response of the data is guaranteed, and the real-time requirements of users are met.
The big data analysis and processing platform comprises a distributed storage layer, a distributed processing layer, a metadata service layer, a processing and analysis layer and a traffic big data processing application layer, and the storage, processing and analysis of traffic big data are completed step by step through 5 layers. The processing analysis comprises Complex Event Processing (CEP), real-time analysis processing (RTAP), online analysis processing (OLAP) and deep analysis (OLAM), and the structure is recorded and saved to provide reliable data for subsequent analysis.
The information processing unit extracts the data stored in the distributed database and the data stored in the main database for processing including cleaning, analysis and filtering, rechecks and verifies the data, filters out incomplete data, repeats and wrong information, removes labels from the texts with the labels, and unifies standard information.
The integrated interface unit finally converts different interfaces of each unit into a Web server to share through an SOA service-oriented architecture, processes application requests to a user application layer or other application systems in the future, extracts data from a database linked to the server and provides the data to a requester, and the requester can click a webpage through the Web at any position to obtain required information.
The user unit is a client-oriented terminal and provides functions of user authority management, traffic command and scheduling, guidance information editing and publishing, data statistical analysis, mobile terminal data pushing and the like. The system provides a large amount of situation analysis and traffic research and judgment, thereby providing powerful technical support for traffic jam management, public traffic scheduling and the like. The system can perform statistical analysis on the express traffic flow data and provide convenient data printout functions such as line graphs, pie charts, bar charts, and the like. The user can select any time period and any shape of graphics to print out according to the needs of the user. Such as:
1. road conditions situation
The road condition situation mainly realizes the statistics of the current road condition state in each district and the distribution display of the road conditions in different districts on the map.
2. Traffic congestion tendency
(1) The method can select different statistical modes and statistical time to carry out district, road section congestion data statistics and congestion trend display, and mainly comprises a congestion high-rise area, a congestion high-rise road section, an area congestion peak time period and a road section congestion peak time period.
(2) The statistical display of district and road section peak (early peak and late peak) time periods can be carried out in different statistical modes and statistical times.
The decision analysis unit is a base data acquisition unit, a data processing unit, an information processing unit, an integrated interface unit and a user application unit, performs summary analysis and new indication or prediction, evaluates the effect of each service unit, sets and corrects the function target of each service unit in a man-machine combination mode, determines the flow of each service processing, specifies the method, the model and the algorithm for generating each service scheme, and continuously improves the working condition of the system. The traffic management unit comprises a traffic command system, a traffic event monitoring system, a traffic guidance system and a traffic decision analysis system.
The traffic command system is characterized in that the traffic command and dispatch takes the actual combat requirement of a traffic police as the core, and an integrated, dynamic and planned command and dispatch integrated platform is built by effectively integrating the built and newly-built systems on the basis of the GIS, so that the functions of daily duty management, traffic plan and simulation management, command and dispatch management, emergency command and disposition management, police force management, traffic rescue and GIS geographic information in a normal state are met.
The traffic incident detection system is in butt joint with a 110 alarm receiving and processing system, a GIS map, a high-definition video monitoring system and the like, video image information is obtained, the system automatically processes and analyzes, and the collection of traffic incidents and the automatic detection of various traffic incidents are realized, wherein the traffic incidents comprise parking, vehicle retrograde motion, traffic jam, vehicle slow motion, pedestrians, sprinkles, smoke incident detection and the like.
The event generation mode mainly comprises the following steps: the method comprises three steps of automatically generating an event, and manually confirming and manually inputting after the event is generated.
When a road traffic emergency or a key event occurs, the system carries out automatic and intelligent event early warning and multi-system linkage:
(1) automatically triggering a traffic command system and starting a corresponding traffic plan;
(2) issuing guidance information through a traffic guidance system;
(3) punishment and the like through a traffic violation system.
The traffic guidance system provides road conditions and warning information for the traffic participants through a certain information media, reminds, suggests or controls the traffic participants to select the optimal walking route, and avoids and reduces travel delay and loss. The traffic information guidance and release method can be divided into: an induction screen, a broadcast, a mobile phone, a vehicle navigation, etc.
The traffic decision analysis system deeply excavates various traffic management information resources (traffic of different time periods, peak periods and the like of roads) based on traffic research and judgment analysis, analyzes and researches and provides powerful technical support for maintenance and planning of urban expressways.
1. Statistical analysis of traffic flow
Counting the passing amount of the card entrance, and carrying out classified statistics according to the card entrance, the vehicle type and the number plate type; and classifying and counting according to the day, month and year by time segments. The statistical results are presented in tabular and graphical form.
2. Congested multi-occurrence road segment analysis
And counting congested and multi-road sections of each road in the urban area according to the days, ranking in a descending order, and simultaneously identifying the congested and multi-road sections on the GIS map by using red. Carrying out traffic index definition:
sketched out in a GIS map, wherein 'unblocked' is depicted by dark green, and 'basically unblocked' is depicted by light green; "light congestion" is depicted in yellow; "moderate congestion" is depicted in brown; "Severe congestion" is depicted in red.
3. Traffic volume prediction
Traffic volume prediction is the fundamental work necessary for making a plan for expressway system planning and for conducting feasibility studies. Directly influences the layout of the cross section of the road and the selection of the overpass type.
Traffic volume prediction is based on analysis and arrangement of collected traffic information, besides a correct prediction mathematical model is established for prediction, the method also needs to consider that the attraction to traffic is greatly enhanced after an expressway system is built, so that abnormal growth of traffic volume occurs in a certain part, and therefore a series of negative effects such as the saturation age of a road is greatly advanced, and necessary solutions are provided from the aspects of construction and operation management in a planning and design stage.
Therefore, the scope of the present invention should not be limited to the disclosure of the embodiments, but includes various alternatives and modifications without departing from the scope of the present invention, which is defined by the claims of the present patent application.
Claims (6)
1. An urban expressway traffic information acquisition system is characterized by comprising the following service units:
the data acquisition unit is used for collecting, gathering and arranging multi-source heterogeneous traffic big data obtained by an acquisition mode and intelligent comprehensive monitoring and storing the collected, gathered and arranged data in a database;
the data management unit is used for realizing distributed storage, query, backup and cleaning on the traffic big data through a distributed file system and a distributed database based on the traffic big data collected by the data collection unit, and the traffic big data adopts a data grading storage mechanism and a big data analysis and processing mode to solve the problems of low time delay, high concurrency and high-efficiency information processing and analysis of the system;
the information processing unit is used for cleaning, analyzing and filtering the heterogeneous traffic big data in the database and forming an information item meeting the standard requirement;
an integrated interface unit for processing application requests from the application system and providing information items meeting the criteria to a requesting party;
the user application unit faces to system users and provides system configuration management, a traffic event command and dispatch system, rapid traffic road condition induction, road condition monitoring and information release for the system users; and
the decision management unit is used for evaluating the effect implemented by each service unit, setting and correcting the function target of each service unit, determining the flow of each service processing, appointing a method, a model and an algorithm for generating each service scheme, and continuously improving the working condition of the system in a man-machine combination mode, and comprises a traffic management unit, wherein the traffic management unit comprises a traffic command system, a traffic incident monitoring system, a traffic guidance system and a traffic decision analysis system, and the traffic decision analysis system comprises a traffic flow statistical analysis system, a congestion multi-road-section analysis system and a traffic volume prediction system.
2. The traffic information collection system of claim 1, wherein the data staging mechanism includes integrating a plurality of data information into a core model of an existing primary data warehouse to transform the core model into a core model of the primary data warehouse and migrating historical and inventory data in the primary data warehouse to the distributed database.
3. The traffic information collection system of claim 2, wherein the big data analysis and processing first constructs a big data analysis and processing platform and analyzes and processes the traffic big data by combining a distributed parallel processing technique and a real-time data stream processing technique.
4. The traffic information collection system of claim 3, wherein the big data analysis and processing platform comprises a distributed storage layer, a distributed processing layer, a metadata service layer, a processing analysis layer and a traffic big data processing application layer.
5. The traffic information collection system of claim 4, wherein the process analysis layer comprises complex event processing, real-time analysis processing, online analysis processing, and deep analysis.
6. The traffic information collection system of claim 4, wherein the big data analysis and processing platform comprises a Hadoop platform, wherein the Hadoop platform loads raw collected data after being processed by data warehouse technology (ETL), and stores the processed data in a distributed database and a main database through Hadoop, and the Hadoop is a distributed system infrastructure.
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