CN109118789B - Multi-source data fusion method and device for highway dispatching station - Google Patents
Multi-source data fusion method and device for highway dispatching station Download PDFInfo
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Abstract
The invention provides a multisource data fusion method for a highway cross-dispatching station, which comprises the steps of taking multisource initial data and initial acquisition data of the highway cross-dispatching station as data sources of the highway cross-dispatching station; performing data processing on the data source according to a set data processing model to obtain traffic data; performing data matching according to the space-time characteristics of the traffic data to obtain traffic data included by the intermodulation statistical data indexes with different characteristics; and performing fusion reasoning on the traffic data comprising the road intermodulation statistical data indexes according to the fusion reasoning mode corresponding to each intermodulation statistical data index to obtain each road intermodulation statistical data index. The invention fuses different multi-source data through data fusion to form a required road intermodulation statistical data index, provides a technical basis for constructing a brand-new intermodulation information acquisition scheme and an acquisition system, realizes the effective utilization of the existing resources in the industry, enriches the traffic condition investigation mode, and improves the quality and the working efficiency of the intermodulation data.
Description
Technical Field
The invention relates to the field of road traffic, in particular to a road dispatching station multi-source data fusion method and device.
Background
The highway intermodulation initial data comprises data sources such as automatic intermodulation station data, equipment import data and manual acquisition data, and the multisource initial data acquired by other modes comprises highway toll data, highway monitoring data, radio frequency identification data, satellite positioning data and overload control data.
However, due to different sources of the multiple data, the conversion and the transmission of the multiple source data of the highway cross-dispatching station cannot be directly realized, so that the multiple source data of the highway cross-dispatching station needs to be fused to form a highway cross-dispatching statistical data index, and data support is provided for highway information acquisition, highway traffic planning and highway traffic research. At present, the application of highway charging data and monitoring data in cross-traffic collection is tried, but the application is generally limited to the conversion of a certain type of data. How to comprehensively utilize multi-source data such as the existing highway toll data, monitoring data, radio frequency identification data, satellite positioning data, super-data and the like to form a multi-source data fusion method facing user requirements and sub-indexes is yet to be researched, and key technologies such as a fusion framework, data matching, fusion inference model and the like of highway cross-dispatching multi-source data are still blank at present.
Disclosure of Invention
The invention provides a multisource data fusion method and device for a highway dispatching station, which are used for at least solving at least one of the technical problems in the prior art.
In order to achieve the purpose, the invention provides a multisource data fusion method for a highway dispatching station, which comprises the following steps:
the multisource initial data and the initial acquisition data of the road dispatching station are used as the data source of the road dispatching station;
performing data processing on the data source according to a set data processing model to obtain traffic data;
performing data matching according to the space-time characteristics of the traffic data to obtain traffic data included by the intermodulation statistical data indexes with different characteristics;
and performing fusion reasoning on the traffic data comprising the road intermodulation statistical data indexes according to the fusion reasoning mode corresponding to each intermodulation statistical data index to obtain each road intermodulation statistical data index.
In one embodiment, the data matching is performed according to the space-time characteristics of the traffic data to obtain the traffic data included in the intermodulation statistic data indexes with different characteristics, and the method comprises the following steps:
analyzing the source of the traffic data and determining the range represented by each type of traffic data;
carrying out space calibration and time calibration on the same type of traffic data;
identifying the fusion of the same type of traffic data, and performing time correlation and space correlation on the identified same type of traffic data to screen out the traffic data needing to be fused.
In one embodiment, the performing fusion reasoning on the traffic data including the road traffic dispatching statistical data index according to the fusion reasoning mode corresponding to each traffic dispatching statistical data index to obtain each road traffic dispatching statistical data index includes: according to a fusion reasoning mode corresponding to daily flow, carrying out fusion reasoning on traffic data including the daily flow to obtain the daily flow, and specifically comprising the following steps:
identifying a road network structure of a road and an ambiguous path identifying station provided in the road;
according to the recognition result, adopting a charging data processing model corresponding to the road to obtain the flow of the charging vehicle type of each road section of the road;
and determining the daily traffic volume of each road section according to the number of the traffic regulation automatic survey stations, the traffic volume manual survey stations and the radio frequency identification stations on each road section and the traffic volume of the toll vehicle type of each road section so as to obtain the daily traffic volume of the road.
In one embodiment, the flow of the toll vehicle type obtained by adopting the toll data processing model is used as a road section of daily traffic volume, and the daily traffic volume of the traffic vehicle type is formed by splitting by utilizing a historical data rule method or a near station method;
forming daily traffic volume of the dispatching vehicle type according to the mapping relation of the dispatching vehicle type and the radio frequency identification vehicle type by adopting the road section of the radio frequency identification station data;
and forming the daily traffic volume of the component vehicle type according to the proportion of the traffic dispatching vehicle type by adopting the road section of the traffic volume survey data.
In one embodiment, the inter-modulation statistical data index includes a travel speed, and performing fusion reasoning on the traffic data including each road inter-modulation statistical data index according to a fusion reasoning mode corresponding to each inter-modulation statistical data index to obtain each road inter-modulation statistical data index includes: according to the corresponding fusion reasoning mode of the travel speed, carrying out fusion reasoning on the traffic data including the travel speed to obtain the travel speed, and the method specifically comprises the following steps:
calculating theoretical travel speed between the road entrance and the road exit according to the road toll data;
and determining the travel speed of the road according to the theoretical travel speed calculated according to the charging data of the road at the same time period and the travel speed acquired from the travel speed survey data and the satellite positioning data.
In one embodiment, the inter-modulation statistical data indexes include axle loads, and performing fusion reasoning on the traffic data including the road inter-modulation statistical data indexes according to a fusion reasoning mode corresponding to each inter-modulation statistical data index to obtain each road inter-modulation statistical data index includes: according to a fusion reasoning mode corresponding to the axle load, carrying out fusion reasoning on traffic data including the axle load to obtain the axle load, and the method specifically comprises the following steps:
calculating the axle load according to the weight-calculating charge data of the road;
judging the type of the axle load data acquisition station of the road;
when the highway has the axle load investigation station, the axle load is equal to the axle load in the axle load investigation station data;
when the highway has the high-speed pre-detection overtaking station, if the weight-measuring charging data of the highway is acquired by the shortest-path method or the highway of the section has no weight-measuring charging data, the axle load is equal to the axle load in the high-speed pre-detection overtaking station data; if the weighing charge data of the road is not acquired by the shortest-path method, the axle load is equal to the axle load calculated by the weighing charge data;
and when the highway has no axle load data acquisition station, the axle load is equal to the axle load calculated by the weight-calculating charge data.
In one embodiment, the performing fusion reasoning on the traffic data including the road intermodulation statistic data indexes according to a fusion reasoning mode corresponding to each intermodulation statistic data index to obtain each road intermodulation statistic data index includes: according to a fusion reasoning mode corresponding to the real-time flow, the site vehicle speed or the time occupancy, carrying out fusion reasoning on traffic data comprising the real-time flow, the site vehicle speed or the time occupancy so as to obtain the real-time flow, the site vehicle speed or the time occupancy, and specifically comprising the following steps:
judging the distance of the selected road section;
and when the selected road section is less than or equal to the set distance, judging the type of the data acquisition equipment of the same road section, and if the data acquisition equipment of the same road section comprises more than two types, performing data fusion on the data acquired by the data acquisition equipment by adopting a D-S (Dempster/Shafer) evidence fusion algorithm to obtain the real-time flow, the site vehicle speed or the time occupancy of the road section.
In order to achieve the above object, the present invention provides a multisource data fusion device for a highway dispatching station, the fusion device comprising:
the acquisition module is used for taking the initial data of the multiple sources and the initial acquisition data of the road dispatching station as a data source of the road dispatching station;
the processing module is used for carrying out data processing on the data source according to a set data processing model to obtain traffic data;
the matching module is used for carrying out data matching according to the space-time characteristics of the traffic data to obtain traffic data included by the intermodulation statistical data indexes with different characteristics;
and the fusion module is used for performing fusion reasoning on the traffic data comprising the road intermodulation statistical data indexes according to the fusion reasoning mode corresponding to each intermodulation statistical data index so as to obtain each road intermodulation statistical data index.
In one embodiment, the matching module is further configured to analyze the source of the traffic data and determine a range represented by each type of traffic data; carrying out space calibration and time calibration on the same type of traffic data; identifying the fusion of the same type of traffic data, and performing time correlation and space correlation on the identified same type of traffic data to screen out the traffic data needing to be fused.
In one embodiment, the intermodulation statistic indicator includes daily traffic, and the fusion module includes: the daily traffic fusion submodule is used for performing fusion reasoning on the traffic data including the daily traffic according to a fusion reasoning mode corresponding to the daily traffic so as to obtain the daily traffic, and the daily traffic fusion submodule is specifically used for:
identifying a road network structure of a road and an ambiguous path identifying station provided in the road;
according to the recognition result, adopting a charging data processing model corresponding to the road to obtain the flow of the charging vehicle type of each road section of the road;
and determining the daily traffic volume of each road section according to the number of the traffic regulation automatic survey stations, the traffic volume manual survey stations and the radio frequency identification stations on each road section and the traffic volume of the toll vehicle type of each road section so as to obtain the daily traffic volume of the road.
In one embodiment, the flow of the toll vehicle type obtained by adopting the toll data processing model is used as a road section of daily traffic volume, and the daily traffic volume of the traffic vehicle type is formed by splitting by utilizing a historical data rule method or a near station method;
forming daily traffic volume of the dispatching vehicle type according to the mapping relation of the dispatching vehicle type and the radio frequency identification vehicle type by adopting the road section of the radio frequency identification station data;
and forming the daily traffic volume of the component vehicle type according to the proportion of the traffic dispatching vehicle type by adopting the road section of the traffic volume survey data.
In one embodiment, the intermodulation statistical data indicator comprises trip vehicle speed, and the fusion module comprises: the travel speed fusion submodule is used for performing fusion reasoning on traffic data including the travel speed according to a fusion reasoning mode corresponding to the travel speed so as to obtain the travel speed, and the travel speed fusion submodule is specifically used for:
calculating theoretical travel speed between the road entrance and the road exit according to the road toll data;
and determining the travel speed of the road according to the theoretical travel speed calculated according to the charging data of the road at the same time period and the travel speed acquired from the travel speed survey data and the satellite positioning data.
According to the invention, the multi-source data oriented to user requirements and sub-indexes are fused through data fusion to form a required road intermodulation statistical data index, so that a technical basis is provided for constructing a brand-new intermodulation information acquisition scheme and acquisition system, the existing resources of the industry are effectively utilized, the traffic condition investigation mode is enriched, and the quality and the working efficiency of the intermodulation data are improved.
The foregoing summary is for the purpose of description and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and following detailed description.
Drawings
In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict some embodiments in accordance with the disclosure and are not to be considered limiting of its scope.
FIG. 1 is a flow chart of a fusion method according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for performing data matching according to the spatio-temporal characteristics of traffic data to obtain traffic data included in intermodulation statistical data indexes with different characteristics in the embodiment of the present invention.
Fig. 3 is a flowchart illustrating fusion inference performed on traffic data including daily traffic according to a fusion inference mode corresponding to the daily traffic in the embodiment of the present invention to obtain the daily traffic.
Fig. 4 is a flowchart illustrating fusion inference of traffic data including the travel speed according to a fusion inference mode corresponding to the travel speed in the embodiment of the present invention to obtain the travel speed.
Fig. 5 is a flowchart illustrating fusion reasoning performed on traffic data including axle loads according to a fusion reasoning manner corresponding to the axle loads to obtain the axle loads in the embodiment of the present invention.
Fig. 6 is a schematic connection diagram of a fusion device according to an embodiment of the present invention.
Fig. 7 is a schematic structural diagram of a fusion module in an embodiment of the present invention.
Detailed Description
In the following, certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
The invention provides a multisource data fusion method for a highway dispatching station.
Referring to fig. 1, the fusion method includes:
step S10: and taking the initial data of the multiple sources and the initial acquisition data of the road dispatching station as the data source of the road dispatching station.
The multi-source initial data includes, but is not limited to, highway toll data, highway monitoring data, radio frequency identification data, satellite positioning data and overload control data. The initial acquisition data of the road dispatching station comprises, but is not limited to, dispatching station data, equipment import data and manual acquisition data. (traffic survey for intermodal modulation)
Step S20: and carrying out data processing on the data source according to the set data processing model to obtain traffic data. The data processed by the data processing model can be converted, transmitted and exchanged.
Step S30: and performing data matching according to the space-time characteristics of the traffic data to obtain traffic data included by the intermodulation statistical data indexes with different characteristics. The data matching of the spatio-temporal characteristics realizes the comprehensive processing of the data and the complementation between the data.
Step S40: and performing fusion reasoning on the traffic data comprising the road intermodulation statistical data indexes according to the fusion reasoning mode corresponding to each intermodulation statistical data index to obtain each road intermodulation statistical data index. The data with the same characteristics are traffic data of which one type comprises the indexes of the statistical data of the road intermodulation, and the traffic data of each type comprising the indexes of the statistical data of the road intermodulation are subjected to fusion reasoning to obtain an index of the statistical data of the road intermodulation.
In the embodiment, the multi-source data acquired in different various forms are fused by a data fusion method, so that conversion and comprehensive processing of the data are realized, and a required road traffic dispatching statistical data index is formed, so that a data basis is provided for road traffic planning construction and traffic operation, and a data support is provided for traffic transportation research by using the road traffic dispatching statistical data index.
Further, referring to fig. 2, step S30: performing data matching according to the time-space characteristics of the traffic data to obtain traffic data included in the intermodulation statistical data indexes with different characteristics, wherein the traffic data includes:
step S31: the source of the traffic data is analyzed and the extent to which each type of traffic data represents is determined.
Step S32: and carrying out space calibration and time calibration on the same type of traffic data.
Space calibration ensures that the data to be fused are at the same or similar space positions; time scaling ensures that the fused data are within the same time period.
Step S33: identifying the fusion of the same type of traffic data, and performing time correlation and space correlation on the identified same type of traffic data to screen out the traffic data needing to be fused.
The spatial correlation comprises setting a certain range for various data so that the various data are positioned on the same section for fusion. The time correlation comprises that under the condition of unifying clocks, multiple data form the same section of a set time end for fusion.
In one embodiment, the road cross-dispatching station statistical data index comprises at least one of real-time traffic, daily traffic, site vehicle speed, travel vehicle speed, time occupancy, axle load and traffic volume. The real-time traffic, the daily traffic, the site vehicle speed, the travel vehicle speed, the time occupancy, the axle load and the traffic volume are all statistical data indexes of the highway dispatching station with different characteristics, which are obtained according to a certain set rule.
The invention provides a seven-level index fusion reasoning model of real-time flow, daily flow, travel speed, place speed, time occupancy, axle load and traffic travel amount, which is a key technology of a road acquisition system which takes road intermodulation fixed stations as a main body, takes multisource data as supplement, gives consideration to classification layout of stations comprehensively and typically and surveys abundant and diverse data sources.
In an embodiment, referring to fig. 3, the intermodulation statistic indicator includes daily traffic, and step S40 may include: according to a fusion reasoning mode corresponding to daily flow, carrying out fusion reasoning on traffic data including the daily flow to obtain the daily flow, and specifically comprising the following steps:
a road network structure (301) for identifying roads and an ambiguous path identifying station (302) provided in a road;
according to the recognition result, adopting a charging data processing model corresponding to the road to obtain the flow (303) of the charging vehicle type of each road section of the road;
and determining the daily traffic volume of each road section according to the number of the traffic regulation automatic survey stations, the traffic volume manual survey stations and the radio frequency identification stations on each road section and the traffic volume of the toll vehicle type of each road section so as to obtain the daily traffic volume of the road.
Further, as shown in fig. 3, according to the recognition result, the method for obtaining the flow rate of the toll vehicle type of each road section of the road by using the toll data processing model corresponding to the road may include:
and identifying the number N (304) of the traffic dispatching automatic survey stations, the traffic manual survey stations and the radio frequency identification stations on each road section, and according to the difference of N, specifically calculating the daily traffic of the road section by adopting the following modes:
when N is 0, the daily traffic volume of the road section is equal to the daily traffic volume of the toll vehicle type obtained by the toll data processing model (305).
Determining the daily traffic volume of the road section according to the coverage rate of the radio frequency identification electronic tag of the radio frequency identification station when N is 1; or determining the daily traffic volume of the road section according to the data of the traffic regulation automatic survey station or the traffic volume manual survey station.
For example, when N is 1 and there is an rfid station (306), the coverage of the rfid tag is identified (310). If the coverage rate of the RFID tag is 100%, the daily traffic volume of the road segment is equal to the daily traffic volume formed by the RFID tag data (314). And if the coverage rate of the radio frequency identification electronic tag is less than 100%, the daily traffic volume of the road section is equal to the daily traffic volume of the toll vehicle type formed by the toll data processing model (315).
The following steps are repeated: when N is 1 and there is a traffic dispatching automation survey station or a traffic volume manual survey station (307), whether the method for estimating the charging data is the shortest-path method or not is judged (311).
If the method of the charging data calculation is the shortest-path method, the daily traffic volume of the road section is equal to the daily traffic volume formed by the data of the traffic dispatching automatic survey station (316).
If the method for estimating the charging data is the label station or the multi-path allocation method, the deviation (317) between the charging data processing model and the daily traffic volume data of the traffic dispatching automatic survey station or the traffic volume manual survey station is calculated. If the calculated deviation is greater than or equal to the set value, the daily traffic volume of the road section is equal to the daily traffic volume of the toll vehicle type formed by the toll data processing model (318). And if the calculated deviation is smaller than the set value, performing data fusion by adopting a D-S evidence fusion algorithm based on data quality evaluation (319).
And thirdly, when N is larger than or equal to 2, calculating the deviation between the observed value and the average value of each intermodulation automatic survey station. If there are survey stations (308) with the same deviation, the daily traffic flow data of the survey stations with the same deviation is selected as the daily traffic volume (312). If there is no survey station (309) with the same deviation, the daily traffic flow data of the survey station with the two values with the smallest deviation is selected as the daily traffic volume (313).
This embodiment is achieved by
Further, referring to fig. 3, determining daily traffic volume of each road segment according to the number of traffic modulation automatic survey stations, traffic volume manual survey stations and radio frequency identification stations on each road segment and the traffic volume of the toll vehicle type of each road segment to obtain the daily traffic volume of the road may include:
the daily traffic volume of the toll vehicle type formed by adopting the toll data processing model is taken as a road section of the daily traffic volume, and the daily traffic volume of the traffic vehicle type is formed by splitting by utilizing a historical data rule method or a near station method (321);
forming daily traffic volume (322) of the dispatching vehicle type according to the mapping relation of the dispatching vehicle type and the radio frequency identification vehicle type by adopting the road section of the radio frequency identification station data;
and forming the daily traffic volume of the sub-vehicle according to the proportion of the cross-dispatching vehicles by adopting the traffic volume survey data and the road section of the D-S evidence fusion algorithm based on the data quality evaluation (320).
The daily traffic is obtained through a fusion reasoning mode, and the rise of the traffic flow is predicted according to the increase and decrease characteristics of the obtained data, so that a basis is provided for traffic planning.
In one embodiment, referring to fig. 4, where the intermodulation statistic indicator includes trip vehicle speed, step S40 includes: according to the corresponding fusion reasoning mode of the travel speed, carrying out fusion reasoning on the traffic data including the travel speed to obtain the travel speed, and the method specifically comprises the following steps:
calculating theoretical travel speed (401) between road entrances and exits according to the road toll data;
and determining the travel speed of the road according to the theoretical travel speed calculated according to the charging data of the road at the same time period and the travel speed acquired from the travel speed survey data and the satellite positioning data.
Further, referring to fig. 4, determining the travel speed of the road according to the theoretical travel speed calculated from the charging data of the road in the same time period and the travel speed obtained from the travel speed survey data and the satellite positioning data includes:
judging the type N (402) of other data sources between the road entrances and exits, and specifically calculating the travel speed by adopting the following method according to the difference of N:
when N is equal to 0, the stroke vehicle speed is equal to the theoretical stroke vehicle speed (403).
In the second method, when N is 1, the travel vehicle speed is calculated from the travel vehicle speed survey data or the satellite positioning data.
For example, when N is 1 and there is trip vehicle speed survey data (404) at the same time, the trip vehicle speed is equal to the trip vehicle speed (407) in the trip vehicle speed survey data;
for another example, when N is 1 and there is satellite positioning data in the same period (405), the trip vehicle speed is equal to the average value of the trip vehicle speed and the theoretical trip vehicle speed in the satellite positioning data (412);
and in the third mode, when the N is 2, the travel vehicle speed is calculated according to the travel vehicle speed survey data and the satellite positioning data.
For example, when N is 2, the trip vehicle speed survey data and the satellite positioning data are simultaneously available (406). A deviation (408) between the theoretical travel speed of the small vehicle and the travel speed survey data of the small vehicle is calculated. If the deviation is greater than or equal to the set value, the travel speed of the small vehicle is equal to the travel speed in the travel speed survey data of the small vehicle (410). If the deviation is smaller than the set value, the travel speed of the small vehicle is equal to the average value of the theoretical travel speed of the small vehicle and the travel speed survey data of the small vehicle (411). The trip vehicle speed of the other vehicle is equal to an average of the trip vehicle speed in the trip vehicle speed survey data and the trip vehicle speed in the satellite positioning data (409).
According to the embodiment, the travel speed is obtained through a fusion reasoning mode, and the intersection control mode and the safety identification position of the road traffic are adjusted according to the obtained data.
In one embodiment, the intermodulation statistic indicator includes an axle load, and step S40 includes: according to the fusion reasoning mode corresponding to the axle load, the traffic data including the axle load is subjected to fusion reasoning to obtain the axle load, and the specific steps can include:
calculating the axle load according to the weight-calculating charge data of the road;
judging the type of the axle load data acquisition station of the road;
when the highway has the axle load investigation station, the axle load is equal to the axle load in the axle load investigation station data;
when the highway has the high-speed pre-detection overtaking station, if the weight-measuring charging data of the highway is acquired by the shortest-path method or the highway of the section has no weight-measuring charging data, the axle load is equal to the axle load in the high-speed pre-detection overtaking station data; if the weighing charge data of the road is not acquired by the shortest-path method, the axle load is equal to the axle load calculated by the weighing charge data;
and when the highway has no axle load data acquisition station, the axle load is equal to the axle load calculated by the weight-calculating charge data.
In a specific embodiment of the foregoing embodiment, referring to fig. 5, the intermodulation statistic indicator includes an axle load, and step S40 includes: according to a fusion reasoning mode corresponding to the axle load, carrying out fusion reasoning on traffic data including the axle load to obtain the axle load, and the method specifically comprises the following steps:
judging whether a road has a weight-calculating toll station or not and calculating the axle load according to the weight-calculating toll data (501);
if the highway has the weight-calculating toll station, judging whether other axle load data acquisition stations exist on the highway or not (502);
when the highway has an axle load survey station (504), the axle load of the highway is equal to the axle load in the axle load survey station data (509);
when the highway has the highway pre-inspection overtaking station (505), judging whether the weighing charge data of the weighing charge station of the highway is acquired by the shortest-path method (510);
if the weight-calculating charging data is obtained by the shortest-path method, the axle load of the highway is equal to the axle load calculated by the high-speed pre-detection and over-station data (513);
if the weight-based charging data is not obtained by the shortest-path method, the axle load of the road is equal to the axle load calculated by the weight-based charging data (514);
when the highway has no other axle load data acquisition station (506), the axle load of the highway is equal to the axle load calculated by the weight charge data (514);
if the highway has no weight-calculating toll station, judging whether other axle load data acquisition stations exist on the highway or not (503);
when the highway has an axle load survey station (507), the axle load of the highway is equal to the axle load in the axle load survey station data (511);
when the highway has the high-speed pre-detection and over-station (508), the axle load of the highway is equal to the axle load calculated by the data of the high-speed pre-detection and over-station (512).
The embodiment obtains the axle load through a fusion reasoning mode, knows the loss of each road according to the obtained data, and makes a proper road surface repair plan.
In one embodiment, the intermodulation statistic indicator includes real-time traffic, site vehicle speed or time occupancy, and step S40 includes: according to a fusion reasoning mode corresponding to the real-time flow, the site vehicle speed or the time occupancy, carrying out fusion reasoning on traffic data comprising the real-time flow, the site vehicle speed or the time occupancy so as to obtain the real-time flow, the site vehicle speed or the time occupancy, and specifically comprising the following steps:
judging the distance of the selected road section;
and when the selected road section is less than or equal to the set distance, judging the type of the data acquisition equipment of the same road section, and if the data acquisition equipment of the same road section comprises more than two types, performing data fusion on the data acquired by the data acquisition equipment by adopting a D-S evidence fusion algorithm to obtain the real-time flow, the place vehicle speed or the time occupancy of the road section.
In the fusion reasoning, if the road is a common road, the reasoning process does not contain various charging data, the reasoning of the step is skipped, and the next fusion reasoning in the reasoning process is continued.
In one embodiment, the intermodulation statistic indicator includes traffic volume, and step S40 includes: according to a fusion reasoning mode corresponding to the traffic volume, carrying out fusion reasoning on traffic data including the traffic volume to obtain the traffic volume, and the method specifically comprises the following steps:
judging whether the highway has traffic volume survey data or not;
if the road has the traffic volume survey data, the traffic volume is equal to the traffic volume in the traffic volume survey data;
if the road has no traffic volume survey data, the traffic volume is equal to the traffic volume pushed in the road toll data.
The embodiment obtains the traffic volume through a fusion reasoning mode, and is convenient to master traffic volume distribution and highway peak time according to data, so as to strengthen traffic management.
In one embodiment, the fusion of multi-source data may refer to the following principles:
principle 1: the necessity of data fusion is identified, and unnecessary fusion data acquired in a single mode is removed.
Principle 2: the consistency of the connotation features of the fused data is ensured, and the connotation features in the multi-source data are identified and extracted, so that the concepts of the quality change and the functional attributes contained in the fusion of different data are consistent.
Principle 3: ensuring that granularity characteristics of various source data indexes are consistent, wherein granularity of a time dimension is fused in the same dimension by taking an index with coarse time granularity as a reference; and the index parameters of the source data characteristics of the salient space regions are based on indexes with coarse space granularity, and the index parameters of the source data characteristics of the salient space nodes are based on indexes with fine space granularity.
Principle 4: ensuring the representativeness of multi-source data samples to be consistent, when the representativeness of the samples of various multi-source data is consistent, directly fusing the multi-source data, and when the representativeness of the samples of various multi-source data is similar, if fused data indexes have the attribute characteristics of sample complementation and superposition, fusing the fused data indexes serving as supplements for check and recheck; and if the fused data indexes do not have the attribute characteristics of sample complementation and superposition, not fusing.
Principle 5: the data fusion of different levels can be mutually converted and progressed, the multi-source data fusion is divided into a data layer, a feature layer and a decision layer, the three layers are mutually related and are sequentially progressed, a data fusion result formed by the data layer can be used by the feature layer and the decision layer, and the fusion can be performed on the fusion layer when the multi-source data is fused.
The invention fuses the multi-source data acquired in different multi-forms through a data fusion method, realizes the conversion and comprehensive processing of the data, forms the required road traffic dispatching statistical data index, provides data basis for road traffic planning construction and traffic operation, and analyzes the traffic transportation economy by using the road traffic dispatching statistical data index.
The second embodiment of the invention provides a multisource data fusion device for a road dispatching station.
Referring to fig. 6, the fusion apparatus includes an acquisition module 610, a processing module 620, a matching module 630, and a fusion module 640.
The acquisition module 640 is configured to use the initial data of the multiple sources and the initial acquired data of the road dispatching station as a data source of the road dispatching station.
The processing module 620 is configured to perform data processing on the data source according to the set data processing model to obtain traffic data.
The matching module 630 is configured to perform data matching according to the time-space characteristics of the traffic data to obtain traffic data included in the inter-modulation statistical data indexes with different characteristics.
The fusion module 640 is configured to perform fusion reasoning on the traffic data including the road intermodulation statistical data indexes according to a fusion reasoning manner corresponding to each intermodulation statistical data index to obtain each road intermodulation statistical data index.
Further, the matching module 630 is further configured to analyze the source of the traffic data and determine a range represented by each type of traffic data; carrying out space calibration and time calibration on the same type of traffic data; identifying the fusion of the same type of traffic data, and performing time correlation and space correlation on the identified same type of traffic data to screen out the traffic data needing to be fused.
In an embodiment, referring to fig. 7, the inter-modulation statistical data index includes daily traffic, the fusion module 640 includes a daily traffic fusion sub-module 641 configured to perform fusion inference on traffic data including the daily traffic according to a fusion inference manner corresponding to the daily traffic to obtain the daily traffic, and the daily traffic fusion sub-module 641 is specifically configured to:
identifying a road network structure of a road and an ambiguous path identifying station provided in the road;
according to the recognition result, adopting a charging data processing model corresponding to the road to obtain the flow of the charging vehicle type of each road section of the road;
and determining the daily traffic volume of each road section according to the number of the traffic regulation automatic survey stations, the traffic volume manual survey stations and the radio frequency identification stations on each road section and the traffic volume of the toll vehicle type of each road section so as to obtain the daily traffic volume of the road.
In one embodiment, the traffic flow of the toll vehicle type obtained by adopting the toll data processing model is taken as a road section of daily traffic volume, and the daily traffic volume of the traffic vehicle type is formed by splitting by utilizing a historical data rule method or a near station method;
forming daily traffic volume of the dispatching vehicle type according to the mapping relation of the dispatching vehicle type and the radio frequency identification vehicle type by adopting the road section of the radio frequency identification station data;
and forming the daily traffic volume of the component vehicle type according to the proportion of the traffic dispatching vehicle type by adopting the road section of the traffic volume survey data.
In one embodiment, referring to fig. 7, the inter-modulation statistical data index includes a trip vehicle speed, the fusion module 640 includes a trip vehicle speed fusion submodule 642 configured to perform fusion inference on traffic data including the trip vehicle speed according to a fusion inference mode corresponding to the trip vehicle speed to obtain the trip vehicle speed, and the trip vehicle speed fusion submodule 642 is specifically configured to:
calculating theoretical travel speed between the road entrance and the road exit according to the road toll data;
and determining the travel speed of the road according to the theoretical travel speed calculated according to the charging data of the road at the same time period and the travel speed acquired from the travel speed survey data and the satellite positioning data.
In one embodiment, referring to fig. 7, the intermodulation statistic indicator includes an axle load, and the fusion module includes: the axle load fusion submodule 643 is configured to perform fusion reasoning on traffic data including axle loads according to a fusion reasoning mode corresponding to the axle loads to obtain the axle loads, and the axle load fusion submodule 643 is specifically configured to:
calculating the axle load according to the weight-calculating charge data of the road;
judging the type of the axle load data acquisition station of the road;
when the highway has the axle load investigation station, the axle load is equal to the axle load in the axle load investigation station data;
when the highway has the high-speed pre-detection overtaking station, if the weight-measuring charging data of the highway is acquired by the shortest-path method or the highway of the section has no weight-measuring charging data, the axle load is equal to the axle load in the high-speed pre-detection overtaking station data; if the weighing charge data of the road is not acquired by the shortest-path method, the axle load is equal to the axle load calculated by the weighing charge data;
and when the highway has no axle load data acquisition station, the axle load is equal to the axle load calculated by the weight-calculating charge data.
In one embodiment, referring to fig. 7, the intermodulation statistic data indicator includes real-time traffic, location vehicle speed, or time occupancy, the fusion module 640 includes a real-time traffic fusion submodule 644, a location vehicle speed fusion submodule 645, or a time occupancy fusion submodule 646, and is configured to perform fusion inference on traffic data including the real-time traffic, the location vehicle speed, or the time occupancy according to a fusion inference manner corresponding to the real-time traffic, the location vehicle speed, or the time occupancy, so as to obtain the real-time traffic, the location vehicle speed, or the time occupancy, and the real-time traffic fusion submodule 644, the location vehicle speed fusion submodule 645, or the time occupancy fusion submodule 646 is specifically configured to:
judging the distance of the selected road section;
and when the selected road section is less than or equal to the set distance, judging the type of the data acquisition equipment of the same road section, and if the data acquisition equipment of the same road section comprises more than two types, performing data fusion on the data acquired by the data acquisition equipment by adopting a D-S evidence fusion algorithm to obtain the real-time flow, the place vehicle speed or the time occupancy of the road section.
In an embodiment, referring to fig. 7, the intermodulation statistical data indicator includes traffic travel volume, the fusion module 640 includes a traffic travel volume fusion sub-module 647, configured to perform fusion reasoning on the traffic data including the traffic travel volume according to a fusion reasoning manner corresponding to the traffic travel volume to obtain the traffic travel volume, and the traffic travel volume fusion sub-module 647 is specifically configured to:
judging whether the highway has traffic volume survey data or not;
if the road has the traffic volume survey data, the traffic volume is equal to the traffic volume in the traffic volume survey data;
if the road has no traffic volume survey data, the traffic volume is equal to the traffic volume pushed in the road toll data.
The embodiment provides a seven-level index fusion reasoning model with real-time flow, daily flow, travel speed, place speed, time occupancy, axle load and traffic trip amount, and is a key technology of a road acquisition system which takes road intermodulation fixed stations as a main body, takes multi-source data as supplement, gives consideration to classification layout of stations comprehensively and typically, and has abundant and diverse data sources.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various changes and substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for the convenience of describing the invention and for the simplicity of description, and are not intended to indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered as limiting.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; the connection can be mechanical connection, electrical connection or communication; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, "above" or "below" a first feature means that the first and second features are in direct contact, or that the first and second features are not in direct contact but are in contact with each other via another feature therebetween. Also, the first feature being "on," "square," and "over" the second feature includes the first feature being directly above and obliquely above the second feature, or indicating that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature includes the first feature being directly above and obliquely above the second feature, or meaning that the first feature is at a lesser level than the second feature.
The above disclosure provides many different embodiments, or examples, for implementing different features of the invention. The components and arrangements of the specific examples are described above to simplify the present disclosure. Of course, they are examples and are not intended to limit the present invention. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples, such repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. In addition, the present invention provides examples of various specific processes and materials, but one of ordinary skill in the art may recognize applications of other processes and/or uses of other materials.
Claims (8)
1. A multi-source data fusion method for a road dispatching station is characterized by comprising the following steps:
the multisource initial data and the initial acquisition data of the road dispatching station are used as the data source of the road dispatching station;
performing data processing on the data source according to a set data processing model to obtain traffic data;
performing data matching according to the space-time characteristics of the traffic data to obtain traffic data included by the intermodulation statistical data indexes with different characteristics;
performing fusion reasoning on the traffic data comprising the road intermodulation statistical data indexes according to the fusion reasoning mode corresponding to each intermodulation statistical data index to obtain each road intermodulation statistical data index;
the traffic statistic data indexes comprise daily flow, and the traffic data comprising the daily flow are subjected to fusion reasoning according to a fusion reasoning mode corresponding to the daily flow so as to obtain the daily flow, and the method comprises the following specific steps of:
identifying a road network structure of a road and an ambiguous path identifying station provided in the road;
according to the recognition result, adopting a charging data processing model corresponding to the road to obtain the flow of the charging vehicle type of each road section of the road;
determining daily traffic volume of each road section according to the number of traffic dispatching automatic survey stations, traffic volume manual survey stations and radio frequency identification stations on each road section and the traffic volume of the toll vehicle type of each road section so as to obtain the daily traffic volume of the road;
wherein, obtaining the daily traffic of the highway comprises:
adopting the flow of the toll car type obtained by the toll data processing model as a road section of daily traffic, and splitting by utilizing a historical data rule method or a near station method to form the daily traffic of the traffic and dispatching car type;
forming daily traffic volume of the dispatching vehicle type according to the mapping relation of the dispatching vehicle type and the radio frequency identification vehicle type by adopting the road section of the radio frequency identification station data;
and forming the daily traffic volume of the component vehicle type according to the proportion of the traffic dispatching vehicle type by adopting the road section of the traffic volume survey data.
2. The fusion method of claim 1, wherein performing data matching according to spatio-temporal features of traffic data to obtain traffic data included in inter-modulation statistical data indexes of different features comprises:
analyzing the source of the traffic data and determining the range represented by each type of traffic data;
carrying out space calibration and time calibration on the same type of traffic data;
identifying the fusion of the same type of traffic data, and performing time correlation and space correlation on the identified same type of traffic data to screen out the traffic data needing to be fused.
3. The fusion method of claim 1, wherein the intermodulation statistic indicators comprise trip speed, and the fusion reasoning of the traffic data comprising the road intermodulation statistic indicators according to the fusion reasoning way corresponding to each intermodulation statistic indicator to obtain each road intermodulation statistic indicator comprises: according to the corresponding fusion reasoning mode of the travel speed, carrying out fusion reasoning on the traffic data including the travel speed to obtain the travel speed, and the method specifically comprises the following steps:
calculating theoretical travel speed between the road entrance and the road exit according to the road toll data;
and determining the travel speed of the road according to the theoretical travel speed calculated according to the charging data of the road at the same time period and the travel speed acquired from the travel speed survey data and the satellite positioning data.
4. The fusion method of claim 1, wherein the intermodulation statistic indicators comprise axle loads, and the fusion reasoning of the traffic data comprising the road intermodulation statistic indicators according to the fusion reasoning way corresponding to each intermodulation statistic indicator to obtain each road intermodulation statistic indicator comprises: according to a fusion reasoning mode corresponding to the axle load, carrying out fusion reasoning on traffic data including the axle load to obtain the axle load, and the method specifically comprises the following steps:
calculating the axle load according to the weight-calculating charge data of the road;
judging the type of the axle load data acquisition station of the road;
when the highway has the axle load investigation station, the axle load is equal to the axle load in the axle load investigation station data;
when the highway has the high-speed pre-detection overtaking station, if the weight-measuring charging data of the highway is acquired by the shortest-path method or the highway of the section has no weight-measuring charging data, the axle load is equal to the axle load in the high-speed pre-detection overtaking station data; if the weighing charge data of the road is not acquired by the shortest-path method, the axle load is equal to the axle load calculated by the weighing charge data;
and when the highway has no axle load data acquisition station, the axle load is equal to the axle load calculated by the weight-calculating charge data.
5. The fusion method of claim 1, wherein the inter-modulation statistic data indexes comprise real-time flow, location vehicle speed or time occupancy, and the fusion reasoning of the traffic data comprising the road inter-modulation statistic data indexes according to the fusion reasoning manner corresponding to each inter-modulation statistic data index to obtain each road inter-modulation statistic data index comprises: according to a fusion reasoning mode corresponding to the real-time flow, the site vehicle speed or the time occupancy, carrying out fusion reasoning on traffic data comprising the real-time flow, the site vehicle speed or the time occupancy so as to obtain the real-time flow, the site vehicle speed or the time occupancy, and specifically comprising the following steps:
judging the distance of the selected road section;
and when the selected road section is less than or equal to the set distance, judging the type of the data acquisition equipment of the same road section, and if the data acquisition equipment of the same road section comprises more than two types, performing data fusion on the data acquired by the data acquisition equipment by adopting a D-S evidence fusion algorithm to obtain the real-time flow, the place vehicle speed or the time occupancy of the road section.
6. A multisource data fusion device for a highway dispatch station, the fusion device comprising:
the acquisition module is used for taking the initial data of the multiple sources and the initial acquisition data of the road dispatching station as a data source of the road dispatching station;
the processing module is used for carrying out data processing on the data source according to a set data processing model to obtain traffic data;
the matching module is used for carrying out data matching according to the space-time characteristics of the traffic data to obtain traffic data included by the intermodulation statistical data indexes with different characteristics;
the fusion module is used for performing fusion reasoning on the traffic data comprising the road intermodulation statistical data indexes according to a fusion reasoning mode corresponding to each intermodulation statistical data index so as to obtain each road intermodulation statistical data index;
wherein, the intermodulation statistic data index includes daily flow, the fusion module includes:
the daily traffic fusion submodule is used for performing fusion reasoning on the traffic data including the daily traffic according to a fusion reasoning mode corresponding to the daily traffic so as to obtain the daily traffic, and the daily traffic fusion submodule is specifically used for:
identifying a road network structure of a road and an ambiguous path identifying station provided in the road;
according to the recognition result, adopting a charging data processing model corresponding to the road to obtain the flow of the charging vehicle type of each road section of the road;
determining daily traffic volume of each road section according to the number of traffic dispatching automatic survey stations, traffic volume manual survey stations and radio frequency identification stations on each road section and the traffic volume of the toll vehicle type of each road section so as to obtain the daily traffic volume of the road;
wherein, obtaining the daily traffic of the highway comprises:
adopting the flow of the toll car type obtained by the toll data processing model as a road section of daily traffic, and splitting by utilizing a historical data rule method or a near station method to form the daily traffic of the traffic and dispatching car type;
forming daily traffic volume of the dispatching vehicle type according to the mapping relation of the dispatching vehicle type and the radio frequency identification vehicle type by adopting the road section of the radio frequency identification station data;
and forming the daily traffic volume of the component vehicle type according to the proportion of the traffic dispatching vehicle type by adopting the road section of the traffic volume survey data.
7. The fusion device of claim 6, wherein the matching module is further configured to analyze a source of the traffic data and determine a range represented by each type of traffic data; carrying out space calibration and time calibration on the same type of traffic data; identifying the fusion of the same type of traffic data, and performing time correlation and space correlation on the identified same type of traffic data to screen out the traffic data needing to be fused.
8. The fusion device of claim 6 wherein the intermodulation statistical data indicator comprises trip vehicle speed, the fusion module comprising: the travel speed fusion submodule is used for performing fusion reasoning on traffic data including the travel speed according to a fusion reasoning mode corresponding to the travel speed so as to obtain the travel speed, and the travel speed fusion submodule is specifically used for:
calculating theoretical travel speed between the road entrance and the road exit according to the road toll data;
and determining the travel speed of the road according to the theoretical travel speed calculated according to the charging data of the road at the same time period and the travel speed acquired from the travel speed survey data and the satellite positioning data.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101694706A (en) * | 2009-09-28 | 2010-04-14 | 深圳先进技术研究院 | Modeling method of characteristics of population space-time dynamic moving based on multisource data fusion |
| CN104408919A (en) * | 2014-11-25 | 2015-03-11 | 深圳大学 | Manual road traffic parameter investigation system based on mobile intelligent terminal |
| CN106327871A (en) * | 2016-09-06 | 2017-01-11 | 华南理工大学 | Highway congestion forecasting method based on historical data and reservation data |
| CA3027485A1 (en) * | 2016-06-24 | 2017-12-28 | Freeport-Mcmoran Inc. | Systems and methods of correlating satellite position data with terrestrial features |
-
2018
- 2018-08-24 CN CN201810972470.2A patent/CN109118789B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101694706A (en) * | 2009-09-28 | 2010-04-14 | 深圳先进技术研究院 | Modeling method of characteristics of population space-time dynamic moving based on multisource data fusion |
| CN104408919A (en) * | 2014-11-25 | 2015-03-11 | 深圳大学 | Manual road traffic parameter investigation system based on mobile intelligent terminal |
| CA3027485A1 (en) * | 2016-06-24 | 2017-12-28 | Freeport-Mcmoran Inc. | Systems and methods of correlating satellite position data with terrestrial features |
| CN106327871A (en) * | 2016-09-06 | 2017-01-11 | 华南理工大学 | Highway congestion forecasting method based on historical data and reservation data |
Non-Patent Citations (7)
| Title |
|---|
| 公路交通情况多元化调查体系与数据融合框架研究;王英平;《国际道路联盟中国研讨会暨中国公路学会2015年学术年会论文集》;20150930;第45-51页 * |
| 关于贵州高速公路交调系统得分析与设计浅论;马启志;《黑龙江交通科技》;20171115(第11期);第210-212页 * |
| 基于多源数据的交通状态监测与预测方法研究;李琦;《中国博士学位论文全文数据库信息科技辑》;20130815(第 08 期);I140-171 * |
| 基于大数据分析的交调数据应用趋势探讨;康云霞;《中国交通信息化》;20160215(第2期);第87-89、96页 * |
| 基于空间网络分析的高速公路网一类调查观测站布设技术;刘浩;《测绘工程》;20170831;第26卷(第8期);第45-49页 * |
| 基于组合赋权理论的高速公路多源信息融合算法;杨彬彬;《交通信息与安全》;20150131;第33卷(第1期);第100-104、132页 * |
| 高速公路交通量调查技术体系研究;吴雪梅;《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》;20081015(第10期);C034-204 * |
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