CN114639236A - Method for constructing semantic data model of traffic field based on ontology - Google Patents
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Abstract
The invention provides a method for constructing a semantic data model of a traffic field based on an ontology, and relates to the technical field of traffic. The method for constructing the traffic field semantic data model based on the ontology comprises a data processing center, wherein the data processing center is respectively connected with traffic data, an adjacency matrix, traffic problems and direction detection, the data processing center is respectively connected with space-time dependence and external factors, the data processing center is respectively connected with public data disclosure, route selection and selection, and the public data disclosure, the route selection and the selection are connected with trip personnel. The invention provides a method for constructing a traffic field semantic data model based on an ontology, which can reduce traffic accidents and relieve traffic jam.
Description
Technical Field
The invention relates to the technical field of traffic, in particular to a method for constructing a semantic data model of the traffic field based on an ontology.
Background
The concept of ontology is derived from the philosophy field, and the definition in philosophy is "systematic description of objective things in the world, namely existence theory", the philosophy body is concerned with the abstract nature of objective reality, while in the computer field, ontology can describe knowledge in semantic level, and can be regarded as a general concept model for describing knowledge in a certain subject field, german student Studer gives the related definition of ontology in 1998, "ontology is formal specification of shared concept model", and this definition includes four layers of meanings: namely sharing, conceptualization, definitional and formalization.
With the acceleration of the urbanization process, a large number of people gather in cities rapidly, in many cities, especially in cities of developing countries, the rapid increase of the number of private vehicles and the continuous increase of the demand for public transportation service cause huge pressure on the existing transportation systems, the traffic problems of frequent traffic jam, serious traffic accidents, long commuting time and the like seriously reduce the operation efficiency of the cities, and the travel experience of passengers is reduced.
The prior patent (CN 108628959A) discloses an ontology construction method based on traffic big data, which comprises the following steps: step 1: establishing corresponding relation analysis between the ontology and the traffic information basic information element; step 2: constructing a traffic field ontology hierarchical model based on text big data; and step 3: and (3) constructing a model according to the steps 1 and 2, and performing traffic ontology data perfection, manual inspection and approval based on big data. The method utilizes the traffic text big data, analyzes and cluster-cleans the data through the relation analysis of the ontology and the traffic information basic data elements and the natural language technology, and constructs an ontology construction based on the traffic big data; the invention can help people to realize accurate semantic communication with machines in the intelligent retrieval process through the accurate description of the concept by the ontology, thereby greatly improving the efficiency and the accuracy of information retrieval. However, although the accuracy can be increased, the upcoming travelers in the city cannot quickly and accurately know the road condition and the optimal travel route, so that the time is delayed.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a method for constructing a semantic data model of a traffic field based on an ontology, and solves the problems that the road condition cannot be quickly and accurately known, and the travel is inconvenient and time is wasted.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: the method for constructing the semantic data model of the traffic field based on the ontology comprises a data processing center, wherein the data processing center is respectively connected with traffic data, an adjacency matrix, traffic problems and direction detection, the data processing center is respectively connected with space-time dependence and external factors, the data processing center is respectively connected with public data disclosure and route selection and selection, and the public data disclosure and route selection and selection are connected with trip personnel.
Preferably, the adjacency matrix includes a fixed matrix, a dynamic matrix, and a purification matrix.
Preferably, the fixed matrix includes a connection matrix, a distance matrix, a similarity matrix, and a traffic matrix.
Preferably, the traffic problems include traffic jam, travel demand, traffic safety, traffic monitoring and automatic driving.
Preferably, the traffic data includes sensor data, GPS data, online appointment data and transaction data.
Preferably, the direction detection comprises traffic state prediction, travel demand prediction, traffic signal control, traffic accident detection and human-vehicle trajectory prediction.
Preferably, the method further comprises additional factors, specifically, the factors such as holidays, time attributes, special events and traffic events all affect the direction prediction to a certain extent.
Preferably, the method further comprises time-dependence, in particular that the prediction of traffic conditions at a certain moment in time is typically associated with various historical observations.
(III) advantageous effects
The invention provides a method for constructing a semantic data model of a traffic field based on an ontology. The method has the following beneficial effects:
the method provides efficient traffic management, accurate traffic resource allocation and high-quality traffic service, so that traffic accidents can be reduced, traffic jam can be relieved, the safety of public traffic is ensured, a large number of people can be prevented from gathering to cities rapidly, and in many cities, particularly cities in developing countries, the rapid increase of the number of private vehicles and the continuous increase of the demand for the public traffic service cause huge pressure on the existing traffic system, the traffic jam is frequent, the traffic accidents are serious, the commuting time is long, and other traffic problems occur.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic flow chart of a adjacency matrix according to the present invention;
FIG. 3 is a schematic flow chart of the traffic problem of the present invention;
FIG. 4 is a schematic traffic data flow diagram according to the present invention;
FIG. 5 is a schematic view of the direction detection process according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, 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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
as shown in fig. 1-5, the embodiments of the present invention provide a method for constructing a semantic data model of a traffic field based on an ontology, which includes a data processing center, where traffic data, an adjacency matrix, a traffic problem and direction detection are respectively connected, and where the data processing center is respectively connected with a spatio-temporal dependency and an external factor, where the spatio-temporal dependency is a complex spatio-temporal dependency of the traffic data, and may affect a prediction of a traffic task, for example, predicting traffic congestion of an area, where a traffic condition before the area and a traffic condition in a surrounding area are important factors for the prediction, in a vehicle trajectory prediction, a random behavior of a surrounding vehicle and historical information of a self trajectory may affect a prediction performance, and when a taxi calling requirement of an area is predicted, a previous order and orders of other areas having similar functions are important for the prediction, when predicting traffic signals, geometric characteristics of a plurality of intersections are considered, and surrounding conventional traffic flows are considered; external factors such as holidays, weather conditions, extreme events and traffic events, the influence of the external factors on the traffic conditions can be observed in daily life, rainstorms can influence the traffic flow, a large concert or football match can cause traffic congestion and influence surrounding traffic conditions, public data disclosure and route selection and selection are connected to the data processing center respectively, and traveling personnel are connected to the public data disclosure and route selection and selection respectively.
The adjacency matrixes comprise a fixed matrix, a dynamic matrix and a purification matrix, the fixed matrix is designed for many researches, the correlation among nodes is assumed to be fixed and cannot change along with time, and therefore, the fixed matrix is constant in the whole experimental process and various fixed adjacency matrixes are designed to capture various predefined associations among the nodes in the traffic map, such as function similarity, traffic connectivity, semantic connection and time similarity; the dynamic matrix is a new self-adaptive matrix which is not necessarily reflected by a predefined matrix due to the defects of prior knowledge or incomplete data and does not necessarily reflect the real dependency relationship between nodes; the cleansing matrix is such that in some cases the structure of the graph may change over time as some edges may become unavailable, such as a road jam or block, and may be re-available after the congestion has been alleviated.
The fixed matrix comprises a connection matrix, a distance matrix, a similar matrix and a traffic matrix, the connection matrix is used for measuring the connectivity among the nodes, and an entry value in the matrix is defined as 1 or 0; the distance matrix measures the tightness degree between the nodes according to the geometric distance; the similarity matrix is used for judging whether the two nodes are similar in function or not; traffic matrix is a measure of the correlation between regions that are geographically remote but conveniently accessible through highways, highways or subways.
The traffic problems comprise traffic jam, travel demand, traffic safety, traffic monitoring and automatic driving, wherein the traffic jam is that the traffic jam on a road network is relieved by improving traffic efficiency, road strips are controlled by predicting traffic states, traffic signals are controlled, and passenger flow is optimized by predicting the passenger demand of a public transport system; the travel demand is the demand of the predicted crowd angle on the traffic services such as taxies, bicycles, subways, buses and the like, and is beneficial to better distributing resources so as to reduce the commuting time and improve the travel experience; traffic safety is an integral part of public safety, and traffic accidents not only can cause damage to victims, vehicles and road infrastructure, but also can cause traffic congestion and reduce the efficiency of a road network, so that monitoring the traffic accidents is essential to avoid property loss and save lives, wherein the traffic accidents are predicted from social media data by detecting the traffic accidents, and accident prevention is predicted by predicting the injury severity of the traffic accidents; the traffic monitoring is that a monitoring camera is widely deployed on urban roads, a large number of images and videos are generated, the development enhances the traffic monitoring, including a traffic enforcement, automatic charging and traffic monitoring system, and the research directions of the traffic monitoring include license plate detection, vehicle automatic detection and pedestrian identification; the automatic driving is lane vehicle detection, pedestrian detection, traffic sign detection and human-vehicle trajectory prediction.
The traffic data comprises sensor data, GPS data, network appointment data and transaction data, the sensor data are traffic measurement data which are usually collected by sensors on a large-city road network within a short time interval, the sensor data set is the most popular data set in the existing work, and generally, the road network comprises traffic objects such as sensors, road sections and the like; the GPS data is a GPS track data set which is usually generated by the number of taxis in a city within a period of time, each taxi provides a large number of GPS records each day, the GPS records comprise time, position and speed information, each GPS record is installed on the nearest road on a city road map, and all roads are divided into a plurality of road sections through road intersections; the network appointment data is that the demand orders of automobiles, taxis and bicycles in the city within a period of time are recorded, a target city with an OpenStreetMap is divided into grid areas with equal size, each area is defined as a node in the graph, and each node is characterized by the number of orders in the corresponding area within a given time interval; the transaction data is collected by an automatic charging system deployed in a public transportation network such as a subway, a public transportation network and the like, a subway map is constructed, each station in the subway system is regarded as a node, the characteristics of one station generally comprise the number of passengers departing from the station and the number of passengers arriving at the station in a given time interval, and the data are obtained according to transaction records collected by the subway afc system.
The direction detection comprises traffic state prediction, travel demand prediction, traffic signal control, traffic accident detection and human-vehicle trajectory prediction, wherein the traffic state prediction is traffic flow, traffic speed, travel time, traffic density and the like; travel demand forecasting, which is to estimate the number of users who need 4 kinds of traffic services in the future, can be divided into two categories, namely area-level demand forecasting and departure-destination travel demand forecasting, the former aiming at forecasting a city of future travel demands in each area, and the latter aiming at forecasting the number of travel demands from one area to another; the traffic signal control is to properly control the traffic lights so as to reduce the time that the vehicles stay at the intersection for a long time, and can optimize the traffic flow and reduce traffic jam and vehicle emission; the traffic accident detection is that a major accident may cause fatal damage to passengers and cause long-time delay on a road network, so that the understanding of the main reasons of the accident and the influence of the accident on the traffic network is crucial to a modern traffic management system; the human-vehicle trajectory prediction is the position of a dynamic intelligent agent in the future, and the accurate human-vehicle trajectory prediction is very important for downstream tasks such as automatic driving, traffic monitoring and the like.
The method further comprises additional factors, in particular holidays, time attributes, special events and traffic events, which all influence the direction prediction to a certain extent, and also comprises time dependencies, in particular the prediction of traffic conditions at a certain moment is usually associated with various historical observations.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. The method for constructing the semantic data model of the traffic field based on the ontology comprises a data processing center and is characterized in that: the system comprises a data processing center, a data processing center and a control center, wherein the data processing center is respectively connected with traffic data, an adjacency matrix, traffic problems and direction detection, the data processing center is respectively connected with space-time dependence and external factors, the data processing center is respectively connected with public data disclosure and route selection and selection, and the public data disclosure and route selection and selection are connected with trip personnel.
2. The ontology-based method for building a traffic domain semantic data model according to claim 1, wherein: the adjacency matrix comprises a fixed matrix, a dynamic matrix and a purification matrix.
3. The ontology-based method for building a traffic domain semantic data model according to claim 2, wherein: the fixed matrix comprises a connection matrix, a distance matrix, a similarity matrix and a traffic matrix.
4. The ontology-based method for building a traffic domain semantic data model according to claim 1, wherein: the traffic problems include traffic congestion, travel demand, traffic safety, traffic monitoring, and autopilot.
5. The ontology-based method for building a traffic domain semantic data model according to claim 1, wherein: the traffic data includes sensor data, GPS data, online car appointment data, and transaction data.
6. The ontology-based method for building a traffic domain semantic data model according to claim 1, wherein: the direction detection comprises traffic state prediction, travel demand prediction, traffic signal control, traffic accident detection and human-vehicle trajectory prediction.
7. The ontology-based method for building a traffic domain semantic data model according to claim 1, wherein: the method also includes additional factors, particularly holidays, time attributes, special events, traffic events and the like, which all affect the direction prediction to a certain extent.
8. The ontology-based method for building a traffic domain semantic data model according to claim 1, wherein: the method also includes time dependence, in particular that predictions of traffic conditions at a certain moment in time are typically associated with various historical observations.
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