CN112309117A - Traffic incident fusion system and method based on density clustering - Google Patents
Traffic incident fusion system and method based on density clustering Download PDFInfo
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Abstract
The invention relates to a traffic incident fusion system and method based on density clustering, the system receives the data uploaded after the vehicle sensor identifies the incident at first; after map matching and other preprocessing steps, performing spatial clustering based on road network topological distance on the positions of the reported event set; and finally, pushing the fused event to a subscription user. The traffic event data output by the method can be used as beneficial supplement of official release information, and timeliness and coverage rate of short-period traffic event broadcasting are enhanced.
Description
Technical Field
The invention relates to the technical field of dynamic traffic information service, in particular to a traffic event fusion system and method based on density clustering.
Background
The dynamic traffic information service comprises the steps of collecting, processing and releasing real-time traffic information, and is usually integrated on a navigation electronic map to guide a subscriber to select a proper route so as to facilitate the trip. Traffic events belong to a class of dynamic traffic information, and may be classified into short-term traffic events such as traffic accidents, bad weather, etc., and long-term traffic events such as construction work, sporting events, etc., according to duration. At present, long-period traffic events are reliable in source and comprehensive in coverage range; the reliability and coverage rate of the short-period traffic event data source have problems: official information such as accident early warning regularly issued by traffic police on radio stations has the advantages of accurate information and defects of incomplete data coverage and poor timeliness; the User Generated Content (UGC), such as user reported event data collected in a mobile phone map app, has the advantages of being high in timeliness and wide in coverage range, and has the disadvantage of being low in data reliability.
The density-based clustering algorithm belongs to an unsupervised machine learning algorithm, can eliminate noise data interference and effectively distinguish high-density spatial data sets in any shapes.
As mentioned above, the traffic events reported by users are less reliable, because they may contain false reports or malicious reports. Therefore, the density-based clustering algorithm is used for the event data reported by the users, so that the data quality of the reported events is improved, and the clustered traffic event data can be used as a beneficial supplement of official release information.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a traffic incident fusion system and method based on density clustering,
in order to solve the technical problem, the invention is realized as follows:
a traffic incident fusion system based on density clustering comprises a data storage unit, a data preprocessing unit, a data fusion unit, a real-time traffic incident updating interface and a sensor data receiving interface; the sensor data receiving interface is respectively connected with the data storage unit and the data preprocessing unit, the data preprocessing unit is sequentially connected with the data fusion unit and the data storage unit, and the data storage unit is connected with the real-time traffic incident updating interface;
the data storage unit is used for storing the real-time traffic incident related data;
the data fusion unit is used for fusing point, line and plane events uploaded by vehicles;
the real-time traffic event updating interface is used for reading the traffic event data in the data storage unit and issuing the traffic event data to the vehicle subscribed with the service;
the sensor data receiving interface is used for receiving sensor data uploaded by vehicles in real time and synchronously updating the sensor data to the data storage unit, and the data comprises: reporting a timestamp, an event type, an event occurrence timestamp, sensor data and a GPS record in an event occurrence interval;
the data preprocessing unit is used for preprocessing sensor data, dividing events into three types of points, lines and planes according to influence ranges, and performing map matching on the longitude and the latitude of the GPS records during the event.
The traffic incident fusion system based on density clustering is characterized in that: and the traffic event data output by the data fusion unit is stored in a TPEG specification code.
The fusion method of the traffic incident fusion system based on density clustering is characterized by comprising the following steps:
the method comprises the following steps that firstly, a sensor data receiving interface receives sensor data uploaded by vehicle number, safety certification is carried out, invalid data are filtered, and then the sensor data are stored in a data storage unit;
secondly, the data preprocessing unit preprocesses the sensor data and classifies the sensor data into events of three types, namely point, line and surface;
thirdly, the data fusion unit respectively uses a density-based clustering algorithm for the point, line and surface events, and finally codes the event with the fused weight larger than the threshold value into a TPEG specification and stores the TPEG specification into the data storage unit;
and fourthly, monitoring the traffic event data in the data storage unit by the real-time traffic event updating interface, and pulling the updated event data to issue to the vehicle subscribed with the service.
The fusion method of the traffic incident fusion system based on density clustering is characterized in that: the preprocessing step in the second step comprises the steps of decoding the sensor data, dividing the event into three types of points, lines and planes according to the influence range, and performing map matching on the longitude and latitude of the GPS record during the event; the point event refers to an event with an influence range of a certain position on a road section, such as an anchor drop, an accident and the like; the line event refers to an event with an influence range of one or more road sections, such as a wet and slippery road; the surface event refers to an event with an influence range of an area, such as heavy rain, heavy fog and the like.
The fusion method of the traffic incident fusion system based on density clustering is characterized in that: the density-based clustering algorithm in the third step takes a fusion algorithm of line events as an example to explain the specific steps:
5.1 full-scale event fusion:
when the system is started or the map version is updated, executing a full event fusion algorithm;
5.1a, filtering out overdue events, namely events of which the occurrence time is more than a threshold value from the current time;
5.1b calculating the distance between two events, wherein the distance is defined as setting the event E1Event E2The road sections are respectively L1、L2；L1Starting point s1End point e1Length l of1；L2Starting point s2End point e2Length l of2. Event E1Occurrence position to s1A distance of d1Event E2Occurrence position to s2A distance of d2；
dist(E1, E2) = min(l1-d1+dist(e1,s2)+d2, l2-d2+dist(e2, s1)+d1)；
dist(e1, s2) Point e on the road network directed graph1To a point s2Shortest distance, dist (e)2, s1) Represents a point e2To a point s1The shortest distance of;
5.1c, selecting proper parameter values of an epsilon-neighborhood and MinPts, and executing a DBSCAN algorithm on the event set, wherein the epsilon-neighborhood is a distance space with an object O as a center and epsilon as a radius, the parameter epsilon is greater than 0 and is a neighborhood radius value of the object, and the MinPts is a neighborhood density threshold and is the minimum contained point number in the object epsilon-neighborhood;
5.1d, calculating the weight of each cluster in the clustering result, and combining event road sections contained in the clusters into a communication path; the weight of the cluster is determined by comprehensively considering factors such as road grade, vehicle running speed and the like;
5.2 incremental event fusion:
when a report event is newly added or a historical event in a clustering result cluster is expired, executing an incremental event fusion algorithm;
5.2a, calculating the distance between every two newly added events and the distance between the newly added events and the historical events;
5.2b, selecting proper epsilon-neighborhood and MinPts parameter values, and executing a DBSCAN algorithm on the newly added event set;
5.2c, calculating the updated cluster weight, and combining the event road sections contained in the cluster into a communication path;
and when the historical events in a certain cluster are expired, taking other events in the cluster as new events, and executing the steps.
The invention has the beneficial effects that: the system firstly receives data uploaded after a vehicle sensor identifies an event; after map matching and other preprocessing steps, performing spatial clustering based on road network topological distance on the positions of the reported event set; and finally, pushing the fused event to a subscription user. The traffic event data output by the method can be used as beneficial supplement of official release information, and timeliness and coverage rate of short-period traffic event broadcasting are enhanced.
Drawings
The invention is described in further detail below with reference to the following figures and embodiments:
FIG. 1 is a block diagram of a traffic event fusion system based on density clustering according to the present invention.
Detailed Description
As shown in fig. 1: a traffic incident fusion system based on density clustering comprises a data storage unit, a data preprocessing unit, a data fusion unit, a real-time traffic incident updating interface and a sensor data receiving interface; the sensor data receiving interface is respectively connected with the data storage unit and the data preprocessing unit, the data preprocessing unit is sequentially connected with the data fusion unit and the data storage unit, and the data storage unit is connected with the real-time traffic incident updating interface;
the data storage unit is used for storing the real-time traffic incident related data;
the data fusion unit is used for fusing point, line and plane events uploaded by vehicles;
the real-time traffic event updating interface is used for reading the traffic event data in the data storage unit and issuing the traffic event data to the vehicle subscribed with the service;
the sensor data receiving interface is used for receiving sensor data uploaded by vehicles in real time and synchronously updating the sensor data to the data storage unit, and the data comprises: reporting a timestamp, an event type, an event occurrence timestamp, sensor data and a GPS record in an event occurrence interval;
the data preprocessing unit is used for preprocessing sensor data, dividing events into three types of points, lines and planes according to influence ranges, and performing map matching on the longitude and the latitude of the GPS records during the event.
And the traffic event data output by the data fusion unit is stored in a TPEG specification code.
The fusion method of the traffic incident fusion system based on density clustering comprises the following steps:
the method comprises the following steps that firstly, a sensor data receiving interface receives sensor data uploaded by vehicle number, safety certification is carried out, invalid data are filtered, and then the sensor data are stored in a data storage unit;
secondly, the data preprocessing unit preprocesses the sensor data and classifies the sensor data into events of three types, namely point, line and surface;
thirdly, the data fusion unit respectively uses a density-based clustering algorithm for the point, line and surface events, and finally codes the event with the fused weight larger than the threshold value into a TPEG specification and stores the TPEG specification into the data storage unit;
and fourthly, monitoring the traffic event data in the data storage unit by the real-time traffic event updating interface, and pulling the updated event data to issue to the vehicle subscribed with the service.
The preprocessing step in the second step comprises the steps of decoding the sensor data, dividing the event into three types of points, lines and planes according to the influence range, and performing map matching on the longitude and latitude of the GPS record during the event; the point event refers to an event with an influence range of a certain position on a road section, such as an anchor drop, an accident and the like; the line event refers to an event with an influence range of one or more road sections, such as a wet and slippery road; the surface event refers to an event with an influence range of an area, such as heavy rain, heavy fog and the like.
The density-based clustering algorithm in the third step takes a fusion algorithm of line events as an example to explain the specific steps:
5.1 full-scale event fusion:
when the system is started or the map version is updated, executing a full event fusion algorithm;
5.1a, filtering out overdue events, namely events of which the occurrence time is more than a threshold value from the current time;
5.1b calculating the distance between two events, wherein the distance is defined as setting the event E1Event E2The road sections are respectively L1、L2；L1Starting point s1End point e1Length l of1；L2Starting point s2End point e2Length l of2. Event E1Occurrence position to s1A distance of d1Event E2Occurrence position to s2A distance of d2；
dist(E1, E2) = min(l1-d1+dist(e1,s2)+d2, l2-d2+dist(e2, s1)+d1)；
dist(e1, s2) Point e on the road network directed graph1To a point s2Shortest distance, dist (e)2, s1) Represents a point e2To a point s1The shortest distance of;
5.1c, selecting proper parameter values of an epsilon-neighborhood and MinPts, and executing a DBSCAN algorithm on the event set, wherein the epsilon-neighborhood is a distance space with an object O as a center and epsilon as a radius, the parameter epsilon is greater than 0 and is a neighborhood radius value of the object, and the MinPts is a neighborhood density threshold and is the minimum contained point number in the object epsilon-neighborhood;
5.1d, calculating the weight of each cluster in the clustering result, and combining event road sections contained in the clusters into a communication path; the weight of the cluster is determined by comprehensively considering factors such as road grade, vehicle running speed and the like;
5.2 incremental event fusion:
when a report event is newly added or a historical event in a clustering result cluster is expired, executing an incremental event fusion algorithm;
5.2a, calculating the distance between every two newly added events and the distance between the newly added events and the historical events;
5.2b, selecting proper epsilon-neighborhood and MinPts parameter values, and executing a DBSCAN algorithm on the newly added event set;
5.2c, calculating the updated cluster weight, and combining the event road sections contained in the cluster into a communication path;
and when the historical events in a certain cluster are expired, taking other events in the cluster as new events, and executing the steps.
Claims (5)
1. A traffic incident fusion system based on density clustering comprises a data storage unit, a data preprocessing unit, a data fusion unit, a real-time traffic incident updating interface and a sensor data receiving interface; the sensor data receiving interface is respectively connected with the data storage unit and the data preprocessing unit, the data preprocessing unit is sequentially connected with the data fusion unit and the data storage unit, and the data storage unit is connected with the real-time traffic incident updating interface;
the data storage unit is used for storing the real-time traffic incident related data;
the data fusion unit is used for fusing point, line and plane events uploaded by vehicles;
the real-time traffic event updating interface is used for reading the traffic event data in the data storage unit and issuing the traffic event data to the vehicle subscribed with the service;
the sensor data receiving interface is used for receiving sensor data uploaded by vehicles in real time and synchronously updating the sensor data to the data storage unit, and the data comprises: reporting a timestamp, an event type, an event occurrence timestamp, sensor data and a GPS record in an event occurrence interval;
the data preprocessing unit is used for preprocessing sensor data, dividing events into three types of points, lines and planes according to influence ranges, and performing map matching on the longitude and the latitude of the GPS records during the event.
2. The density-cluster-based traffic event fusion system of claim 1, wherein: and the traffic event data output by the data fusion unit is stored in a TPEG specification code.
3. The fusion method of the traffic event fusion system based on density clustering according to claim 1, characterized by comprising the steps of:
the method comprises the following steps that firstly, a sensor data receiving interface receives sensor data uploaded by vehicle number, safety certification is carried out, invalid data are filtered, and then the sensor data are stored in a data storage unit;
secondly, the data preprocessing unit preprocesses the sensor data and classifies the sensor data into events of three types, namely point, line and surface;
thirdly, the data fusion unit respectively uses a density-based clustering algorithm for the point, line and surface events, and finally codes the event with the fused weight larger than the threshold value into a TPEG specification and stores the TPEG specification into the data storage unit;
and fourthly, monitoring the traffic event data in the data storage unit by the real-time traffic event updating interface, and pulling the updated event data to issue to the vehicle subscribed with the service.
4. The fusion method of the traffic event fusion system based on density clustering of claim 3, wherein: the preprocessing step in the second step comprises the steps of decoding the sensor data, dividing the event into three types of points, lines and planes according to the influence range, and performing map matching on the longitude and latitude of the GPS record during the event; the point event refers to an event with an influence range of a certain position on a road section, such as an anchor drop, an accident and the like; the line event refers to an event with an influence range of one or more road sections, such as a wet and slippery road; the surface event refers to an event with an influence range of an area, such as heavy rain, heavy fog and the like.
5. The fusion method of the traffic event fusion system based on density clustering of claim 3, wherein: the density-based clustering algorithm in the third step takes a fusion algorithm of line events as an example to explain the specific steps:
5.1 full-scale event fusion:
when the system is started or the map version is updated, executing a full event fusion algorithm;
5.1a, filtering out overdue events, namely events of which the occurrence time is more than a threshold value from the current time;
5.1b calculating the distance between two events, wherein the distance is defined as setting the event E1Event E2The road sections are respectively L1、L2；L1Starting point s1End point e1Length l of1；L2Starting point s2End point e2Length l of2. Event E1Occurrence position to s1A distance of d1Event E2Occurrence position to s2A distance of d2；
dist(E1, E2) = min(l1-d1+dist(e1,s2)+d2, l2-d2+dist(e2, s1)+d1)；
dist(e1, s2) Point e on the road network directed graph1To a point s2Shortest distance, dist (e)2, s1) Watch (A)Indication point e2To a point s1The shortest distance of;
5.1c, selecting proper parameter values of an epsilon-neighborhood and MinPts, and executing a DBSCAN algorithm on the event set, wherein the epsilon-neighborhood is a distance space with an object O as a center and epsilon as a radius, the parameter epsilon is greater than 0 and is a neighborhood radius value of the object, and the MinPts is a neighborhood density threshold and is the minimum contained point number in the object epsilon-neighborhood;
5.1d, calculating the weight of each cluster in the clustering result, and combining event road sections contained in the clusters into a communication path; the weight of the cluster is determined by comprehensively considering factors such as road grade, vehicle running speed and the like;
5.2 incremental event fusion:
when a report event is newly added or a historical event in a clustering result cluster is expired, executing an incremental event fusion algorithm;
5.2a, calculating the distance between every two newly added events and the distance between the newly added events and the historical events;
5.2b, selecting proper epsilon-neighborhood and MinPts parameter values, and executing a DBSCAN algorithm on the newly added event set;
5.2c, calculating the updated cluster weight, and combining the event road sections contained in the cluster into a communication path;
and when the historical events in a certain cluster are expired, taking other events in the cluster as new events, and executing the steps.
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