CN100463009C - A traffic information fusion processing method and system - Google Patents

Abstract

The invention discloses a traffic information fusion processing method and system, which solves the problem that the floating car data can only reflect the traffic road condition information of a certain point or a certain section of the driving route of each floating car alone, but cannot calculate the comprehensive road condition information of the entire road The problem. The method includes: cyclically reading the source data collected by all floating vehicles in one cycle; corresponding to the source data of each floating vehicle, calculating the road condition information of the road sections driven by the floating vehicle in different time periods; The link is divided into unit road sections, and the road condition information of the unit road sections is calculated; according to the road condition information of the unit road sections, the road condition information of each road link is calculated, and then the comprehensive road condition information of each road is calculated. The present invention also has scalability, can be compatible with other types of traffic information data, and combines floating car data with other types of data during fusion processing, effectively solving the problem of insufficient coverage of traffic information due to the small number of floating cars question.

Description

A traffic information fusion processing method and system

technical field

The invention relates to information fusion technology, in particular to a traffic information fusion processing method and system.

Background technique

With the deteriorating urban traffic situation, many countries have carried out research and construction on Intelligent Transportation System (ITS). Intelligent transportation system is a general term for traffic systems that use traffic information systems, communication networks, positioning systems, and intelligent analysis and route selection for the purpose of alleviating road congestion, reducing traffic accidents, and improving the convenience and comfort of traffic users. The realization of intelligent transportation includes the collection, analysis and processing of traffic information, and release to the public.

Traditional traffic information collection methods such as induction coils, detectors, video surveillance, etc., all belong to the fixed detection type, and use the "static" method to measure the changes of real-time traffic flow at fixed points. The floating vehicle collection technology is currently the most advanced road traffic information collection technology in the world. It uses the "dynamic" method to measure the characteristics of traffic flow at discrete points in the traffic network. The floating vehicle uses the global positioning system (GPS) vehicle system and wireless communication equipment to transmit vehicle dynamic information (such as time, speed, coordinates, direction, etc.) to the floating vehicle information processing center in real time. Traffic information for the situation.

Because the traffic information data collected by the floating car collection technology only reflects the traffic conditions at a certain point or a certain section of the road on the route of each vehicle alone, and a road may have multiple floating cars driving at the same time, there is a lack of real The overall description of road conditions cannot directly provide road traffic information services to the public. At the same time, the application of floating car technology also has constraints. For example, when the number of floating cars cannot meet the requirements, the road coverage of traffic information will be low, which will affect the accuracy of traffic information.

Contents of the invention

The technical problem to be solved by the present invention is to provide a traffic information fusion processing method and system to solve the problem that the floating car data can only reflect the traffic road condition information of a certain point or a certain section of the driving route of each floating car alone, and cannot calculate the overall traffic information. The problem of comprehensive traffic information of roads.

Another object of the present invention is to solve the problem of insufficient road coverage of traffic information due to the small number of floating cars.

In order to solve the above technical problems, the present invention provides a traffic information fusion processing method, comprising:

Loop read the source data collected by all floating vehicles in one cycle;

Corresponding to each floating car source data, calculate the road condition information of the traveling section of the floating car in different time periods; the traveling section is the road section that the floating car has traveled between two preset time points;

Divide each road link that forms the road into unit road sections, and the unit road sections correspond to one or more floating car driving road sections;

Calculate the road condition information of each unit road section according to the road condition information of the traveling section of the floating vehicle;

calculating the road condition information of each road link according to the road condition information of the unit road section;

According to the road condition information of the road link, the comprehensive road condition information of each road is calculated.

Wherein, calculate the road condition information of each unit road section according to the road condition information of the floating car traveling section according to the following steps: read the road condition information of all floating car traveling sections in the cycle, and the road condition information includes the average driving of the floating car Speed; calculate the road condition information of unit road section, wherein in the road condition information, the calculation method of average driving speed is: described unit road section corresponds to one or more floating car running road sections, judges the number of floating car running road sections across each unit road section, if If it is 1, the average driving speed is equal to the average driving speed of the floating car running section; if it is greater than 1, then the average driving speed is the average driving speed of the floating car running section with the latest travel time and the highest speed.

Preferably, the road condition information of the road link is calculated according to the following steps: if the road link is equally divided according to the distance constant, the average driving speed of the road link is the sum of the road link length divided by the driving time of the included unit road section; wherein, the unit The driving time of a road segment is the length of the unit road segment divided by the average driving speed.

Preferably, the comprehensive road condition information of road is calculated according to the following steps: the average driving speed of road is road length divided by total driving time; The value is calculated.

Preferably, it also includes: calculating the congestion degree coefficient according to the average driving speed of the unit road section, and merging adjacent unit road sections with the same congestion degree coefficient.

Preferably, calculate the road condition information of each floating car traveling section according to the following steps: judge whether each floating car traveling section crosses the road link, if so, supplement two vehicle projection point data at each road link junction, for dividing Said floating car driving section, and adding floating car source data at each vehicle projection point; wherein, said two vehicle projection points are respectively recombined with the start and end projection points of the floating car driving section to form two floating car driving sections; Corresponding to the source data of each floating car, calculate the road condition information of each floating car driving section after the floating car is re-divided, including the average driving speed.

The method also includes: outputting the comprehensive road condition information of the road according to a unified data format.

The present invention also provides another traffic information fusion processing method, comprising:

Convert the source data collected by the fixed detection system into road condition information for each detection section;

Circularly read the source data collected by all floating vehicles in one cycle and the road condition information of the detected road section;

Corresponding to each floating car source data, calculate the road condition information of the traveling section of the floating car in different time periods; the traveling section is the road section that the floating car has traveled between two preset time points;

Divide each link of the road into unit road sections; the unit road sections correspond to one or more floating car driving sections, or correspond to one or more detection road sections;

Combining the road condition information of the traveling section of the floating vehicle with the road condition information of the detected section, calculating the road condition information of each unit section;

calculating the road condition information of each road link according to the road condition information of the unit road section;

According to the road condition information of the road link, the comprehensive road condition information of each road is calculated.

Among them, the link is equally divided according to the distance constant.

Wherein, calculate the road condition information of each unit road section according to the following steps, wherein the method of calculating the average driving speed in the road condition information is: judge the number of the floating car driving section and the detection road section across each unit road section, wherein the unit road section corresponds to One or more floating car travel sections, or corresponding to one or more detection sections: if only the number of floating car travel sections is 0, then the average driving speed is the latest and maximum average speed of the detection section of the detection time; if only If the number of detected road sections is 0, the average driving speed is the average driving speed of the floating car driving section with the latest travel time and the highest speed; One coefficient is multiplied by the average speed of the floating car driving section with the latest travel time and the highest speed, and the second coefficient is multiplied by the average vehicle speed of the detection section with the latest detection time and the highest speed, wherein the first coefficient plus the second coefficient is equal to 1.

Based on the above inventive method, the present invention provides a traffic information fusion processing system, comprising:

The source data receiving unit is used to cyclically read the source data collected by all floating vehicles in one cycle;

The source data processing unit is used to correspond to the source data of each floating car, and calculate the road condition information of the traveling section of the floating car in different time periods; section of the road;

The fusion processing unit is used to divide each road link of the road into unit road sections, and the unit road sections correspond to one or more floating car driving road sections; according to the road condition information of the floating car driving road sections, the road condition information; calculate the road condition information of each road link according to the road condition information of the unit section; calculate the comprehensive road condition information of each road according to the road condition information of the road link.

Wherein, the fusion processing unit includes: a unit road section processing subunit, which is used to read the road condition information of all floating car driving sections in the cycle, and the road condition information includes the average driving speed of the floating car; The road chain is divided into unit road sections; calculate the road condition information of the unit road sections, wherein the calculation method of the average driving speed in the road condition information is: the unit road section corresponds to one or more floating car driving sections, and judges the floating car traveling across each unit road section The number of road sections, if it is 1, the average driving speed is equal to the average driving speed of the floating car running section; if it is greater than 1, then the average driving speed is the latest and the average driving speed of the floating car running section with the latest driving time and the highest speed; fusion The processing subunit is used to calculate the road condition information of each road link according to the road condition information of the unit road section; calculate the comprehensive road condition information of each road according to the road condition information of the road link.

Preferably, the source data processing unit includes: dismantling subunits for judging whether each floating car travel section crosses the road link, if so, supplementing two vehicle projection point data at each road link junction for dividing Floating vehicle driving section, and increase floating vehicle source data at each vehicle projection point; wherein, the two vehicle projection points are recombined with the start and end projection points of the floating vehicle driving section respectively to form two floating vehicle driving sections; The unit is used to correspond to the source data of each floating car, and calculate the road condition information of each floating road section after the floating car is re-divided, including the average driving speed.

The system also includes: an output unit, which is used to output the comprehensive road condition information of the road according to a unified data format.

The present invention also provides another traffic information fusion processing system, comprising:

A pre-processing unit is used to convert the source data collected by the fixed detection system into road condition information for each detection section;

The source data receiving unit is used to cyclically read the source data collected by all floating vehicles in one cycle and detect the road condition information of the road section;

The source data processing unit is used to correspond to the source data of each floating car, and calculate the road condition information of the traveling section of the floating car in different time periods; section of the road;

The fusion processing unit is used to divide each road link of the road into unit road sections; the unit road sections correspond to one or more floating car driving road sections, or correspond to one or more detection road sections; Combining the road condition information with the road condition information of the detected road section, calculating the road condition information of each unit road section; calculating the road condition information of each road link according to the road condition information of the unit road section; Comprehensive traffic information for roads.

Wherein, the fusion processing unit includes: a unit road section processing subunit, which is used to divide each road link forming a road into a unit road section, and calculate the road condition information of each unit road section, wherein the method for calculating the average driving speed in the road condition information Be: described unit road section corresponds to one or more floating car driving road sections, or corresponds to one or more detection road sections, and judges the number of floating car driving road sections and detection road sections across each unit road section: if only the number of floating car driving road sections If the number is 0, the average driving speed is the average speed of the detection road section with the latest detection time and the highest speed; Average driving speed; if the numbers of the floating car driving section and the detection section are both greater than 0, then the average driving speed is equal to the first coefficient multiplied by the average driving speed of the floating car driving section with the latest traveling time and the highest speed, plus the second The coefficient is multiplied by the average vehicle speed of the detection road section with the latest detection time and the maximum speed, wherein the first coefficient plus the second coefficient is equal to 1;

The fusion processing subunit is used to calculate the road condition information of each road link according to the road condition information of the unit road section; calculate the comprehensive road condition information of each road according to the road condition information of the road link.

Compared with prior art, the present invention has the following advantages:

First of all, the present invention proposes a method for efficiently merging the data of multiple floating cars. By analyzing the real-time dynamic floating car data, according to the driving route of each car, it calculates the road conditions of its driving sections in different time periods. And on this basis, the road conditions of all floating vehicles on the roads of the road network are fused, and the comprehensive traffic information of the road is calculated. The method improves the accuracy of traffic information, is applicable to the real-time monitoring and management of urban road traffic information using large-scale floating vehicle collection technology, and provides the public with accurate road congestion information in a timely manner by dynamically releasing comprehensive road traffic information , optimal driving routes and other travel service information, and provide decision-making basis for traffic conditions for relevant traffic management departments. The invention has good practicability.

Secondly, the present invention has scalability and can be compatible with other types of traffic information data. By setting a standard input format and combining floating car data with other types of data during fusion processing, it effectively solves the problem caused by the small number of floating cars. This leads to the problem of insufficient coverage of traffic information. Therefore, the fusion processing of multi-data source data improves the coverage and accuracy of road traffic information.

Again, for the calculation of the road condition information of the floating car section, the present invention divides the traveling section of the cross-road link at the intersection of the link, and supplements the corresponding traffic information data, and then calculates the re-divided floating car traveling section. The re-division of the driving section of the floating vehicle effectively increases the amount of data collected by the floating vehicle, thereby improving the coverage of road traffic information.

Finally, the present invention also defines a road geographic information data format and a road traffic information data format, breaking through the original processing method based on road links, the format and content of traffic information can be defined from a multi-level perspective, and road traffic information can be enhanced. Accuracy and practicality of traffic information description.

Description of drawings

Fig. 1 is the overall flowchart of traffic information fusion processing described in the present invention;

Fig. 2 is the flow chart of road condition information processing of the traveling section of the floating car in the present invention;

Fig. 3 is the calculation flowchart of road comprehensive road condition information among the present invention;

Fig. 4 is a structural diagram of the traffic information fusion processing system of the present invention;

Fig. 5 is the road condition information flowchart of the present invention in combination with other types of data fusion processing unit sections;

Fig. 6 is a structural diagram of the traffic information fusion processing system combined with other types of data according to the present invention.

Detailed ways

In order to make the above objects, features and advantages of the present invention more comprehensible, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

The main goal of intelligent transportation is to realize the modernization of the entire urban transportation system, and the key link in the modernization of the urban transportation system is the high degree of integration and sharing of various traffic information, as well as the provision of real-time dynamic traffic information services to meet the increasingly urgent needs of the public. traffic information needs. Therefore, the real-time dynamic traffic information processing of the road has become an important basis of the entire ITS information system. The real-time dynamic traffic information processing system is used to integrate and analyze the collected traffic information source data, generate real-time dynamic traffic condition information, and finally provide the public with road congestion information, Driving time information and travel assistance information such as the optimal driving route, and at the same time provide some solutions and decision-making basis for the relevant transportation departments to manage transportation.

The fusion processing of traffic information refers to the comprehensive analysis and processing of collected traffic information samples to calculate the overall traffic condition information of the road, and finally obtain the real-time traffic condition of the entire urban road network. Traffic information fusion processing is an important part of the real-time dynamic traffic information service system and the key to ensure the quality of traffic information service. The present invention proposes a high-efficiency fusion processing method for large-scale floating car data. By analyzing the real-time dynamic floating car data, according to the driving route of each car, the road conditions of its driving road sections in different time periods are calculated, and hereby On the basis of this, the road conditions of all floating vehicles on the roads of the road network are fused to calculate the comprehensive traffic information of the road.

In order to describe the content of the present invention in detail, it is first necessary to explain the definition of the road geographic information data format and road network topology involved in the invention. Road geographic information data is used to describe the geographic information of an urban road network, which consists of points, road links, and roads, where roads contain road links, and road links contain points. Tables 1, 2, and 3 will be used for illustration below.

serial number content illustrate 1 road ID road number 2 road name road name 3 road grade grade of road 4 road type the type of road

5 road direction direction of the road 6 road length the length of the road 7 Number of links The number of links the road contains

Table 1 road geographic information data table

The attribute information of the road includes road identification (ID), road grade, road type, road name, direction, road length, and the number of road links included. Among them, the road ID is the number assigned to each road by classifying and numbering the entire urban road network; the road name is the standard road name used uniformly in the map field, such as "College Road"; the road grade describes the level of the road. "Level 1", "Level 2" and "Level 3"; road types describe the classification of roads, including ring roads (also divided into main roads and auxiliary roads), trunk roads, expressways, main streets and general roads; roads The direction includes four kinds of information: east, south, west, and north; the road length describes the length of the road in a certain direction, and the unit is meter.

serial number content illustrate 1 road ID road number 2 road name road name 3 road direction direction of the road 4 road length the length of the road 5 link number The sequence number of the link in the direction of the road 6 link number Link ID 7 link length length of link 8 link end distance The distance from the start point of the link to the end point of the road in this direction 9 points The number of nodes contained in the link

Table 2 Link geographic information data table

A link represents the part between two adjacent intersections on the road. The attribute information of the link includes the link number, the link number, the length of the link, the distance from the starting point of the link to the end point in the direction of the road, and the number of points. Among them, the link number is the unique number of the link in the entire road network; the link number describes the position of the link in the link array in a certain direction of the road; the link length describes the length of the link, and the unit is meters; the point is the road The number of nodes on the chain.

serial number content illustrate 1 road ID road number 2 road name road name 3 road direction direction of the road 4 road length the length of the road 5 link number The sequence number of the link in the direction of the road 6 link number Link ID 7 link length length of link 8 point number The sequence number of the road segment on the road link 9 point longitude coordinates the longitude coordinate of the point 10 point latitude coordinates the latitude coordinates of the point 11 end distance The distance from the point to the end of the link

Table 3 point information data table

A point is the most basic element that constitutes a road and a link. The attributes of a point include longitude, latitude, and the distance from the point to the end of the link.

The road network topology is composed of nodes and unit road sections. Among them, the road node describes the information of the connection between the road and the adjacent road. The nodes can be divided into overpasses, important intersections and general intersections according to the type (at the same time, the intersections can be divided into two types: those with traffic lights and those without traffic lights). The node attribute information is determined by the type of node: the overpass node information includes the road information passing through the four directions of the bridge, and the attribute information is the same as the above-mentioned road information; the intersection information includes the type information of the intersection and the coordinate information of the center point, wherein the type information of the intersection describes The level of intersection, each level corresponds to the average time delay to pass through the intersection. A unit road segment is composed of a part or multiple roads on the road between two adjacent nodes. The attribute information of the unit link includes link number, direction, road number, start link number and end link number.

The road network topology describes the compositional relationship between the entire urban road network unit sections and nodes. Each unit road segment has its predecessor road segment and successor road segment, wherein the predecessor road segment represents all adjacent road segments reachable along the direction of the unit road segment, and the successor road segment represents all adjacent road segments accessible to the road segment. Each node has information about unit road segments connected to it in all directions.

The above content clarifies the concepts of road, road link, point and road network, which will be used in the introduction of the invention below. The traffic information fusion processing in the present invention is mainly aimed at floating car data. Because the coordinates of the floating car data are collected in real time by GPS, there are large errors, and it is impossible to ensure that each vehicle coordinate point falls on the correct driving road of the vehicle, and it cannot meet the requirements of traffic information fusion processing. Therefore, it is first necessary to preprocess the initial data of the floating car, map the coordinate data of the floating car, and make it fall on the correct driving road by correcting the coordinate deviation. In the case of a complex road network topology, simple map matching cannot determine the only correct road at one time. It is necessary to select a series of time-continuous data to estimate the driving route according to the driving rules and characteristics of the floating car, so as to find the final road. The correct driving route, and determine the road where the coordinates of each point are located.

It can be seen from the above that the fusion processing can only be performed after the data collected by the floating car is processed by map matching and driving route estimation. Referring to Table 4, it is the estimated data table of the driving route of the floating car, and the preprocessed data is generated according to a unified data format. The estimated data of the floating vehicle’s driving route includes vehicle ID, time, longitude and latitude of the vehicle point (after deviation correction), road number and link number of the vehicle point, etc. The specific content and format are shown in the table below.

serial number content illustrate 1 serial number record number 2 vehicle ID SIM card number of the floating car 3 time year month day hour minute second 4 longitude Vehicle point longitude coordinates after deviation correction 5 latitude Latitude coordinates of the vehicle point after deviation correction 6 road number ID of the road where the vehicle point is located 7 link number The ID of the road link where the vehicle point is located 8 road end distance The distance from the vehicle point to the end of the road 7 link end distance The distance from the vehicle point to the end point of the road link

Table 4 Floating car driving route estimation data table

Preferably, the present invention also defines a road traffic information data format, which divides the entire road network into multiple roads, each road is divided into multiple road links, and each road link is divided into multiple road sections. , link, and link are used to describe the fused data. The present invention breaks through the original road link-based processing method, can define the format and content of traffic information from multi-level perspectives, and enhances the accuracy and practicability of road traffic information description.

serial number content illustrate 1 road ID road number 2 road name road name 3 road grade grade of road 4 road type the type of road 5 road direction direction of the road 6 road length the length of the road 7 road travel time Estimate the time it will take to traverse the road 8 road congestion road traffic congestion 9 Number of links The number of links the road contains 10 The most congested link number The most congested link in the direction of the road

Table 5 Road Traffic Information Data Sheet

Referring to Table 5, the road traffic information data mainly describes the traffic condition information of each road in the urban road network, and its attributes include road ID, road name, road grade, road type, direction, road length, road travel time, road The degree of congestion, the number of links included and the number of the most congested link. Among them, the road driving time indicates the time required for the predicted vehicle to pass the road normally, in seconds; the road congestion level indicates the road condition level of the road, which is determined by the average speed of the road, and different road condition levels correspond to different vehicle speeds interval.

serial number content illustrate 1 road ID road number 2 road name road name 3 road direction direction of the road 4 road length the length of the road

5 link number The sequence number of the link in the direction of the road 6 link number Link ID 7 link length length of link 8 Road link travel time Predict the current travel time through this road link 9 Link congestion The congestion level of the current link 10 link end distance The distance from the start point of the link to the end point of the road in this direction 11 Sections The number of traffic segments included in the link 12 Most congested road section number The most congested road section on the road link

Table 6 Road link traffic information data table

Referring to Table 6, the content of link traffic information includes information such as link number, link length, link travel time, link congestion degree, and the number of road sections with road conditions included in the link. Among them, the driving time of the road link indicates the time required for predicting the normal driving of the vehicle through the road link, in seconds; the definition of the congestion degree of the road link is the same as the definition standard of the road where it is located; the end distance of the road link is the distance from the starting point of the road link The distance to the end of the road, in meters; the road link is composed of several road sections representing different road conditions, and each road section contains information on the degree of congestion of the road section, and the definition standard is the same as that of the road link and road.

serial number content illustrate 1 road ID road number 2 road name road name 3 road direction direction of the road 4 road length the length of the road 5 link number The sequence number of the link in the direction of the road 6 link number Link ID 7 link length length of link 8 section number The sequence number of the road segment on the road link 9 section length segment length 10 Section driving time Predict the current travel time through this road link

11 road congestion The congestion level of the current link 12 end point distance The distance from the start point of the link to the end point of the link in this direction

Table 7 Road section traffic information data table

Referring to Table 7, the attributes of a road segment include the distance from the end point of the road segment to the end point of the link link, the length of the road segment, the driving time of the road segment, and the degree of congestion of the road segment. Among them, the driving time of the road segment indicates the time required for predicting the normal driving of the vehicle through the road segment, in seconds; the definition of the congestion degree of the road segment is the same as the definition standard of the road where it is located; the end point distance of the road segment refers to the distance between the starting point of the road segment and the end point Distance, in meters.

The input and output data formats of the traffic information fusion processing have been described above. The input data of the fusion processing is the floating car driving route estimation data shown in Table 4 (hereinafter referred to as the floating car data), and the output data is as shown in Tables 5, 6, and 7. displayed road traffic information data. For the fusion processing process of input data, refer to FIG. 1 , which is an overall flow chart of traffic information fusion processing.

Step 101, read all the floating car data processed by map matching and driving route estimation in one cycle. The fusion processing of traffic information is carried out in a cyclical cycle, and a certain amount of data is read in each processing cycle. The following content is the process of fusion processing of the floating car data in a cycle.

Step 102, calculating the road condition information of the road sections traveled by each floating vehicle in different time periods. Each floating car sets multiple projection points in its own driving route, which is used to collect real-time dynamic traffic condition information in different time periods, and then preprocessed as shown in Table 4. Every two time-adjacent projection points in the driving route of the floating car are called a driving section, and multiple projection points divide the driving route of each vehicle into multiple continuous driving sections according to the time period. The traffic information status reflected by each floating car needs to be obtained by calculating the road condition information of each driving section, wherein the road condition information includes driving time, average driving speed and so on.

Table 4 lists the important parameters needed to calculate the road conditions of the driving section, including the coordinates of each vehicle projection point of the floating car, the number of the road where it is located, and the distance from the point to the end of the road. Let the vehicle projection point information parameters of the floating car in one cycle be respectively: coordinate p _i (x, y), x represents longitude, y represents latitude; point to the distance S _i of the road end; road number n; moment t _i ; Where i=1, 2, ..., k, k is the number of vehicle projection point records in one period. Then calculate the average driving speed, driving time, length of the road section and the distance from the starting point of the road section to the end point of the road where the floating car covers each driving section according to the parameters. Suppose the length of each section is L _i , the driving time is T _i , and the average driving speed is V _i , then the calculation formula is as follows: L _i =S _i -S _i-1 ; T _i =t _i -t _i-1 ; V _i = Li/Ti.

Step 103, according to the road condition information of each driving section of each floating vehicle above, the road condition information of all vehicles on each road is fused to obtain the comprehensive road condition information of the road, thereby obtaining the comprehensive traffic information of the entire road network, as shown in Table 5 , 6, and 7. Although the road condition information above can dynamically reflect the traffic information of the road where the floating car is located in real time, since there may be multiple floating cars driving on the same road at the same time, the traffic information reflected by each car may also be different, so it is necessary to Only when the road condition information reflected by the floating vehicles is fused together can comprehensive and accurate traffic condition information of the entire road be obtained.

It can be seen from the above that the calculation of the comprehensive road condition information of the road is divided into two parts, namely, the calculation of the road condition information of the section where the floating vehicle travels and the calculation of the comprehensive road condition information of the road, which will be described in detail below. Referring to Fig. 2, it is a flow chart of road condition information processing of the traveling section of the floating car in the present invention.

Step 201, read the preprocessed data records of floating vehicles, the data records are estimated driving routes of all floating vehicles within a period, including driving time, average driving speed and so on.

Step 202, group the data records according to vehicle ID, and divide each floating vehicle into a group. Since each floating vehicle can drive on multiple roads and collect data through multiple projection points to form multiple data records, all data records of the same vehicle are divided into one group for processing. Assuming that the number of vehicles is n, the following will start from the first group to calculate the road condition information of each floating vehicle section.

Step 203 , read a set of data records, firstly infer whether the driving route of the corresponding vehicle crosses one or more road links, if so, go to step 204 , otherwise go to step 205 . The inference is based on the link number records in the estimated data of the floating vehicle’s driving route in Table 4. If the link numbers of two time-adjacent projection points are different, the driving section between the two projection points crosses the link. Since the driving section of the floating car is divided according to the time period, a driving section may be included in a certain road link, or may cross one or more road links. Preferably in the present invention, before performing fusion processing on the floating car data, the driving section of the floating car is divided again, which effectively increases the amount of data collected at the projection point of the floating car, thereby improving the coverage of road traffic information.

Step 204, when the traveling section of the floating vehicle crosses one or more road links, add two vehicle projection point data at each road link junction, which belong to two road links, including the time when the vehicle is at this point, the vehicle point The road link number, road number, link end distance and road end distance and other information, and finally add the supplementary vehicle projection point data to the group of the vehicle.

In step 205, all the projected point data of the vehicles in the group are grouped into a driving road section in pairs according to the time-adjacent order. Therefore, the traveling section of the floating car takes the projected points adjacent in time as the starting and ending points.

Step 206, calculate the road condition information of each driving section according to the formula in step 102, mainly including section length, driving time, average driving speed, link end distance and road end distance.

Step 207, judge whether the group of data is the nth group, if so, end the calculation; otherwise, go to step 203.

After the calculation of the road condition information of each section of the floating vehicle is completed, the calculation of the comprehensive road condition information of the road is performed. Referring to FIG. 3 , it is a flow chart of calculation of road comprehensive road condition information in the present invention.

Step 301, firstly read the road condition information data of the floating vehicle traveling sections of all vehicles in one cycle.

Step 302, group the data records by road number and link number. Assuming that the number of roads is n and the number of links is m, the data can form a two-dimensional array of [n, m]. Wherein, each group of road group data contains one or more groups of road link group data. Therefore, the calculation of road comprehensive road condition information is divided into the calculation of road link conditions and the fusion calculation of all road link conditions on the road.

Step 303, read a set of road grouping data.

Step 304, read a group of road link group data under the road group data.

Step 305, calculating the road condition information of the road link, first setting the value of the fixed distance coefficient S. In the present invention, the road link is divided into unit road sections, and the road condition information of each unit road section is first calculated according to the road condition information of the floating vehicle driving section, and then the road condition information of a road link is generated by fusion. Wherein, the concept of the unit road section is different from that of the floating car driving road section. The former refers to the division of geographic information data, while the latter refers to the division of time segments. The unit road section and the floating car driving road section are not in a one-to-one correspondence. Moreover, for the convenience of calculation, the unit road sections in the present invention are divided according to equidistance, and a distance coefficient S needs to be set. Of course, other processing methods are also possible, such as dividing the links at unequal distances, which need to be selected according to different fusion processing methods.

Step 306: Divide the road link equally from the end point according to the distance factor S, set a node every distance S, the starting point of the road link is the last node, and the last part whose distance is less than S can be used as a separate road section, assuming that it is divided into p road segments. The distance coefficient S can be adjusted according to the specific application. When S is small, it can be considered that the average driving speed at the node is the same as the average driving speed at the previous node.

Step 307, respectively calculate the comprehensive road condition information of each unit road section, mainly including the average driving speed and so on. The calculation method adopted in the present invention is: start from the first node, successively calculate the number of floating car driving sections across each node, if the number of road sections is 0, it means that there is no floating car running through at this point, and the average The driving speed is unknown; if the number of road sections is 1, then the average driving speed of this point is equal to the average driving speed of the section where the floating car travels; if the number of road sections is greater than 1, it means that there are many floating cars passing by this point, Take the average driving speed of the section of the floating car with the latest time and the highest speed as the average driving speed of this point.

Of course, other algorithms can also be used for calculation, such as taking the average value of the average driving speed of multiple driving sections across the node, or considering the speed first, and taking the value of the floating vehicle driving section with the highest speed and the latest time. In addition, the present invention uses the average driving speed at nodes to represent the average driving speed of the entire road section, and other methods can also be used to determine the road condition information of the unit road section.

In the subsequent processing, when some roads have no floating vehicle data in the processing period, the traffic information of the current road can be obtained according to the law of road traffic flow, such as using the traffic information of the road at the same time period in history for calculation.

Step 308, calculating the congestion degree coefficient of each unit road section, and merging adjacent road sections with the same congestion degree coefficient on the link into a new road section. The calculation method of the congestion degree coefficient is: if the average driving speed is less than or equal to 20 km/h, the coefficient value is 1, indicating a congested state; if it is greater than or equal to 40 km/h, the coefficient value is 3, indicating a smooth state; 20 km/h and less than 40 km/h, the coefficient value is 2, indicating the general state.

In the present invention, the purpose of merging adjacent road sections with the same congestion degree coefficient is to facilitate the final release and display of road comprehensive road condition information in the form of urban road traffic maps, and use different colors or charts to identify different road congestion degrees. Moreover, the unit road section in the above-mentioned road network topology description refers to the new unit road section after merging. However, the following calculations are not based on the new unit road section after merging, but are still calculated on the basis of the unit road section before merging.

Step 309, calculating the overall road condition information of the entire road link, including average driving speed, driving time and so on. Among them, the travel time of the entire road link is the sum of the travel time of the included unit road sections, and the travel time of each unit road section is obtained by dividing the unit road section length (that is, the fixed distance coefficient S) by the average driving speed. The average driving speed of the road link is obtained by dividing the length of the road link by the driving time of the road link.

The calculation of the road link condition information may also adopt other calculation methods. In the present invention, because the unit road sections in the road chain are divided according to equidistance, so for the convenience of calculation, the driving time of each unit road section is added to obtain the driving time of the road chain. If the unit road sections are divided by unequal distances, the weight calculation method can be used, which will not be described in detail here.

Step 310, judge whether the road link grouping data is the mth group, if so, it indicates that all the road link groupings under a group of road groupings have been calculated, and you can go to step 311; otherwise, go to step 304, continue to process the same Road link grouping data under the road.

Step 311, calculating comprehensive road condition information of the road in this direction, including average driving speed, driving time and so on. The calculation method is as follows:

Assuming that the number of road links in this direction is n, the length of each road link is L _i , and the travel time is T _i , then the length of the road is $L=\underset{i=1}{\overset{\mathrm{no}}{\mathrm{\Σ}}}{L}_i,$ The total travel time is $T=(\underset{i=1}{\overset{\mathrm{no}}{\mathrm{\Σ}}}{T}_i{L}_i/L)*\mathrm{no},$ The average driving speed of the road is V=L/T.

In the present invention, since the lengths of the links included in the road are not equal, the weight calculation method based on the length is adopted for the above-mentioned total driving time. Moreover, when the coverage of the road traffic information is insufficient and there is a lack of certain road link condition information, the comprehensive road condition information of the entire road can also be calculated according to the above method.

Step 312, determine whether the road grouping is the nth group, if so, it indicates that all road grouping data have been processed, and end the traffic information fusion process of the entire road network; otherwise, go to step 303, and continue to calculate the comprehensive data of other roads traffic information.

The above-mentioned fusion processing of road condition information of all floating vehicle driving sections is the key of the present invention, although those skilled in the art have also realized other fusion methods, the method adopted in the present invention is applicable to urban road traffic information using large-scale floating vehicle collection technology Real-time monitoring and management of the road, through the dynamic release of comprehensive road traffic conditions information, timely provide the public with accurate road congestion information, optimal driving routes and other travel service information, and provide decision-making basis for traffic conditions for relevant traffic management departments. The invention has good practicability and improves the accuracy of traffic information.

Aiming at the above traffic information fusion processing method, the present invention also provides a traffic information fusion processing system. 4 is a structural diagram of the traffic information fusion processing system of the present invention, including: a source data receiving unit 401, a source data processing unit 402, a fusion processing unit 403, and an output unit 404.

The source data receiving unit 401 is used to receive the data collected by the floating vehicle. In the above method, the preprocessed data is received by cyclically reading the driving route estimation data of all floating vehicles within one cycle.

The source data processing unit 402 is configured to correspond to the data of each floating car and calculate the road condition information of the road sections traveled by the floating car in different time periods. Preferably in the present invention, in order to improve the coverage of road traffic information, before performing fusion processing on the floating car data, the traveling section of the floating car across the road chain in the data is divided again, effectively increasing the amount of collected data of the floating car projection points. Therefore, the source data processing unit 402 includes a splitting subunit 405 and a computing subunit 406 . in,

Dismantling the subunit 405 is used to judge whether each floating car travel section crosses the road link according to the driving route estimation data received by the source data receiving unit 401, and if so, divide the floating car travel section at each road link junction, and supplement two The projected point data of a vehicle belongs to two road links, including information such as the time of the vehicle at the point, the link number of the vehicle point, the road number, the distance to the end point of the road link, and the distance to the end point of the road;

Calculation subunit 406 is used for the data processed by splitting subunit 405, according to the formula in the above-mentioned step 102, calculates the road condition information of each driving road section after each floating car is re-divided, mainly including road section length, driving time, Average driving speed, link end distance and road end distance.

The fusion processing unit 403 is configured to perform fusion processing on the road condition information of all the driving sections of the floating vehicles on the same road to obtain comprehensive road condition information of the road. The processing process is to first fuse and process the data of multiple floating vehicles on each road link, and then perform fusion processing on the data of multiple road links contained in each road. The fusion processing unit 403 includes: a unit section processing subunit 407 and a fusion processing subunit 408, wherein,

The unit section processing subunit 407 firstly divides each link into unit sections. For the convenience of calculation, the present invention divides them according to equidistance. Then, read the road condition information of all floating car driving sections in the above-mentioned cycle, calculate the road condition information of each unit section, wherein the calculation method of average driving speed is as described in the above-mentioned step 307: start from the first node of the road chain, and then Estimate the number of floating car driving sections across each node. If the number of road sections is 0, it means that there is no floating car passing by at this point, and its average driving speed is unknown; if the number of road sections is 1, then the point’s The average driving speed is equal to the average driving speed of the floating car driving section; if the number of road sections is greater than 1, it means that there are multiple floating cars passing by this point, and the average driving speed of the floating car driving section with the latest time and the highest speed is taken The speed is taken as the average driving speed at that point.

In the present invention, in order to finally release and display the comprehensive road condition information in the form of urban road traffic map, different colors or graphs are used to mark different road congestion degrees, and the congestion degree coefficient of each unit section needs to be calculated. The calculation method of the congestion degree coefficient is: if the average driving speed is less than or equal to 20 km/h, the coefficient value is 1, indicating a congested state; if it is greater than or equal to 40 km/h, the coefficient value is 3, indicating a smooth state; 20 km/h and less than 40 km/h, the coefficient value is 2, indicating the general state. Merge adjacent road segments with the same congestion degree coefficient on the road link, and then publish the entire traffic congestion situation in the form of a graph. However, the following calculations are not based on the new unit road section after merging, but are still calculated on the basis of the unit road section before merging.

The fusion processing subunit 408 is configured to calculate the road condition information of each road link according to the processing result of the unit road section processing subunit 407, and calculate the comprehensive road condition information of each road. The calculation method used in the present invention is as described above in step 309 and step 311, and will not be described in detail here.

The output unit 404 is configured to output the comprehensive road condition information obtained by the fusion processing unit 403 in a unified data format. According to different display requirements, different data content can be output, such as the traffic information of a certain road link, or the traffic information of a certain road, or the overall traffic information status of the city. The present invention can define the format and content of traffic information from multi-level perspectives, and enhance the accuracy and practicability of road traffic information description.

Preferably in the present invention, while fusing and processing floating car data, other types of traffic information data, such as data collected by fixed detection systems such as induction coils, monitors, and video surveillance, can also be processed compatiblely. The method of obtaining and processing data from multiple data sources effectively solves the problem of insufficient coverage of traffic information due to the small number of floating cars, and can also improve the accuracy of traffic information. The traffic information fusion processing method provided by the invention has good scalability.

Although many traffic information data collected by non-floating vehicles are quite different from floating vehicle data collection methods and collection content, they can all be processed and converted to describe one or several road condition information for the detection section, such as average vehicle speed, etc. Wherein, the detection road section is used to describe other types of data, which is different from the concepts of the floating car driving road section and the unit road section above. The following table is the specific requirements for the data content format that meets the fusion processing requirements.

serial number content illustrate 1 serial number record number 2 time year month day hour minute second 3 origin longitude Longitude coordinates of the starting point of the road segment described 4 starting latitude The latitude coordinates of the starting point of the road segment described

5 origin longitude Longitude coordinates of the end point of the road segment being described 6 starting latitude The latitude coordinates of the end point of the road segment described 7 average speed The average driving speed of the road segment 8 road number ID of the road where the vehicle point is located 9 link number The ID of the road link where the vehicle point is located 10 road end distance The distance from the vehicle point to the end of the road 11 link end distance The distance from the vehicle point to the end point of the road link

Table 8 Data sheets for other types of traffic information

Taking the magnetic induction coil collection method as an example, the collected initial data is to describe the traffic flow of the cross-section of each point where the coil is buried on the road. Through the processing of some existing models, the traffic flow of the road section between two adjacent coils can be calculated. At the same time, the coordinates of each coil embedding point and the road and road link where it is located can be obtained, so that it can be converted into the data content format required above. Based on the information and combined with the floating car data, traffic information fusion processing can be performed, making the generated road traffic conditions more accurate and comprehensive.

In the case of processing floating car data and other types of data at the same time, the overall fusion processing process is basically the same, and it is also performed according to the following steps: loop reading the driving route estimation data and other types of traffic information data of all floating vehicles in a cycle; According to the driving route estimation data of each floating car, calculate the road condition information of the road section that the floating car travels in different time periods, and the other types of traffic information data have shown the road condition information of the detected road section; Fusion processing, dividing each road link of the road into unit road sections according to the constant distance; combining the road condition information of the floating car driving section with the road condition information of other types of detected road sections, and calculating the road condition information of each unit road section; The road condition information of the unit road section is used to calculate the road condition information of each road link; and then the comprehensive road condition information of the entire road is calculated according to the road condition information of the road link.

In the processing steps of the above-mentioned multi-data source data, the difference with the processing of single data source (floating car data) is:

First, it is first necessary to convert other types of raw data processing into the standard data format shown in Table 8 before subsequent fusion processing can be performed;

Second, when calculating the road condition information of each unit road section, it is necessary to comprehensively consider the road condition information of the floating car driving section and the road condition information of other types of detection road sections. The number of floating car driving sections and detection sections at the node. When the number of floating car driving sections is 0, take the one with the closest time and the highest speed among other types of data as the average driving speed of this point; when the floating car driving section When the number is greater than or equal to 1, the average speed of this point is v=α*v ₁ +β*v ₂ , where v ₁ is the average driving speed of the section with the latest time and the highest speed in the floating car driving section, and v ₂ is the other The average vehicle speed of the section with the shortest time and maximum speed in the type data, and α+β=1. After calculating the average driving speed of all nodes in sequence according to the above process, the average driving speed of all road sections on the road is obtained. The specific processing process is shown in FIG. 5 , which is a flow chart of road condition information of road sections combined with other types of data fusion processing units in the present invention.

Step 501, starting from the first node, get a unit link in the link.

Step 502, judging whether the number of traveling sections of the floating vehicle is 0, if so, go to step 503, otherwise go to step 506.

Step 503, judging whether the number of detected road sections of other types of data is 0, if so, go to step 504, otherwise go to step 505.

In step 504, when the number of traveling sections of the floating vehicle and the number of detected sections are both 0, the average driving speed of this section is unknown, and the process continues to step 509.

Step 505, when the number of road sections driven by the floating car is 0 and the number of detected road sections is not 0, the average speed of this road section is the average vehicle speed of the road section with the closest time and the highest speed in other types of data, and continue to step 509.

Step 506, judge whether the number of detected road sections of other types is 0, if so, go to step 507, otherwise go to step 508.

Step 507, when the number of road sections the floating car travels is not 0 and the number of detected road sections is 0, the average speed of the road section is the average speed of the road section with the latest time and the highest speed among the road sections where the floating car travels, and continues to step 509.

Step 508, when the number of the floating car traveling section and the number of detected sections are not 0, the average driving speed of this section is v=α*v ₁ +β*v ₂ , where v ₁ is the latest and most recent time in the floating car traveling section The average speed of the section with the highest speed, v ₂ is the average speed of the section with the shortest time and the highest speed in other types of data, and α+β=1. Continue to step 509.

In step 509, it is judged whether the road condition calculation of all road sections of the road link is completed, if so, proceed to the next step, otherwise go to step 501. Wherein, the next step of processing refers to calculating the comprehensive road condition of the entire road link according to the road condition information of the unit road section, and the calculation method is the same as above.

For the above-mentioned fusion processing method of other types of data, the present invention also provides a corresponding traffic information fusion processing system, as shown in FIG. , a source data receiving unit 602, a source data processing unit 603, a fusion processing unit 604 and an output unit.

The preprocessing unit 601 is configured to convert the traffic information data collected by the fixed detection system into road condition information for each detected road section, as shown in Table 8.

The source data receiving unit 602 receives the source data that needs fusion processing by cyclically reading the estimated driving route data collected by all floating cars in one cycle and the road condition information of the detected road section obtained by the preprocessing unit 601 .

The source data processing unit 603 has the same function as the above-mentioned source data processing unit 402, and is used to correspond to the data of each floating car, and calculate the road condition information of the driving section of the floating car in different time periods, and also includes splitting subunits and calculation subunits, No more details.

The fusion processing unit 604 is used to combine the road condition information of the driving section of the floating car with the road condition information of the detected section to calculate the traffic information status of the entire road, and also includes a unit section processing subunit 605 and fusion processing subunit.

Among them, the unit road section processing subunit 605 is used to divide each link of the road into unit road sections. For the convenience of calculation, the present invention divides according to equidistance, and then calculates the road condition information of each unit road section. Different from the unit section processing subunit 407 in the system shown in Figure 4, the method of the unit section processing subunit 605 estimating the average driving speed is:

Calculate the number of floating car driving sections and detection sections across each node from the first node of the road chain. When the number of floating car driving sections is 0, take the one with the closest time and the largest speed among other types of data as The average driving speed of this point; when the number of floating car driving sections is greater than or equal to 1, the average speed of this point is v=α*v ₁ +β*v ₂ , where v ₁ is the latest time in the floating car driving section And the average driving speed of the road section with the highest speed, v ₂ is the average speed of the road section with the shortest time and the highest speed in other types of data, and α+β=1.

As mentioned above, it is also necessary to calculate the congestion degree coefficient of the unit road section and merge the adjacent unit road sections with the same congestion degree coefficient.

The fusion processing subunit has the same function as the above fusion processing subunit 408, which is used to calculate the road condition information of each road link according to the road condition information of the unit road section; then calculate the comprehensive road condition information of each road according to the road condition information of the road link traffic information. The calculation method is as mentioned above,

Finally, it is also necessary to use the output unit to output the comprehensive road condition information obtained by the fusion processing unit 403 according to different display requirements.

The above is a detailed introduction to the traffic information fusion processing method and system provided by the present invention. In this paper, specific examples are used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only used to help understand the present invention. The method of the invention and its core idea; at the same time, for those of ordinary skill in the art, according to the idea of the invention, there will be changes in the specific implementation and application range. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims (16)

1. A traffic information fusion processing method, characterized in that, comprising: Loop read the source data collected by all floating vehicles in one cycle; Corresponding to each floating car source data, calculate the road condition information of the traveling section of the floating car in different time periods; the traveling section is the road section that the floating car has traveled between two preset time points; Divide each road link that forms the road into unit road sections, and the unit road sections correspond to one or more floating car driving road sections; Calculate the road condition information of each unit road section according to the road condition information of the traveling section of the floating vehicle; calculating the road condition information of each road link according to the road condition information of the unit road section; According to the road condition information of the road link, the comprehensive road condition information of each road is calculated. 2. The method according to claim 1, characterized in that, according to the following steps, according to the road condition information of the traveling section of the floating vehicle, the road condition information of each unit road section is calculated: Read the road condition information of all floating car driving sections in the cycle, the road condition information includes the average driving speed of the floating car; Estimate the road condition information of the unit road section, wherein the method for calculating the average driving speed in the road condition information is: the unit road section corresponds to one or more floating vehicle driving sections, and the number of floating vehicle driving sections across each unit road section is judged, if it is 1 , then the average driving speed is equal to the average driving speed of the floating car traveling section; if it is greater than 1, the average driving speed is the average driving speed of the floating car traveling section with the latest traveling time and the highest speed. 3. The method according to claim 2, wherein the road condition information of the road link is calculated according to the following steps: if the road link is equally divided according to the distance constant, then the average driving speed of the road link is divided by the length of the road link by the included unit The sum of the driving time of the road section; wherein, the driving time of the unit road section is the length of the unit road section divided by the average driving speed. 4. method according to claim 2, it is characterized in that, calculate the comprehensive road condition information of road according to the following steps: the average driving speed of road is road length divided by total driving time; Wherein, described total driving time is the The driving time including the link is calculated according to the weight of the link length. 5. The method according to claim 2, further comprising: calculating the congestion degree coefficient according to the average driving speed of the unit road section, and merging adjacent unit road sections with the same congestion degree coefficient. 6. method according to claim 1, is characterized in that, calculates the road condition information of every floating car traveling road section according to the following steps: Determine whether each floating car travel section crosses the road link, and if so, add two vehicle projection point data at each road link junction to divide the floating car driving section, and add a floating car at each vehicle projection point Source data; wherein, the two vehicle projection points are respectively recombined with the start and end projection points of the floating vehicle driving section to form two floating vehicle driving sections; Corresponding to the source data of each floating car, calculate the road condition information of each floating car driving section after the floating car is re-divided, including the average driving speed. 7. The method according to claim 1, further comprising: outputting the comprehensive road condition information of the road in a unified data format. 8. A traffic information fusion processing method, characterized in that, comprising: Convert the source data collected by the fixed detection system into road condition information for each detection section; Circularly read the source data collected by all floating vehicles in one cycle and the road condition information of the detected road section; Corresponding to each floating car source data, calculate the road condition information of the traveling section of the floating car in different time periods; the traveling section is the road section that the floating car has traveled between two preset time points; Divide each link of the road into unit road sections; the unit road sections correspond to one or more floating car driving sections, or correspond to one or more detection road sections; Combining the road condition information of the traveling section of the floating vehicle with the road condition information of the detected section, calculating the road condition information of each unit section; calculating the road condition information of each road link according to the road condition information of the unit road section; According to the road condition information of the road link, the comprehensive road condition information of each road is calculated. 9. The method according to claim 8, characterized in that, according to the following steps, the road condition information of each unit road section is estimated, wherein the method for calculating the average driving speed in the road condition information is: judging the floating car driving section across each unit road section and the number of detection road sections, wherein the unit road section corresponds to one or more floating car driving road sections, or corresponds to one or more detection road sections: If only the number of floating car driving sections is 0, the average driving speed is the average speed of the detection section with the latest detection time and the highest speed; If only the number of detected road sections is 0, then the average driving speed is the average driving speed of the floating car driving road section with the latest travel time and the highest speed; If the numbers of the floating car driving section and the detection section are both greater than 0, the average driving speed is equal to the first coefficient multiplied by the average driving speed of the floating car driving section with the latest travel time and the highest speed, plus the second coefficient multiplied by the detection The average speed of the detected road section with the latest time and the highest speed, where the first coefficient plus the second coefficient is equal to 1. 10. The method according to claim 8, characterized in that: the link is equally divided according to a distance constant. 11. A traffic information fusion processing system, characterized in that it comprises: The source data receiving unit is used to cyclically read the source data collected by all floating vehicles in one cycle; The source data processing unit is used to correspond to the source data of each floating car, and calculate the road condition information of the traveling section of the floating car in different time periods; section of the road; The fusion processing unit is used to divide each road link of the road into unit road sections, and the unit road sections correspond to one or more floating car driving road sections; according to the road condition information of the floating car driving road sections, the road condition information; calculate the road condition information of each road link according to the road condition information of the unit section; calculate the comprehensive road condition information of each road according to the road condition information of the road link. 12. The system according to claim 11, wherein the fusion processing unit comprises: The unit road section processing subunit is used to read the road condition information of all floating car driving sections in the cycle, and the road condition information includes the average driving speed of the floating car; each road link forming the road is divided into unit road sections; the calculation unit The road condition information of the road section, wherein the method for calculating the average driving speed in the road condition information is: the unit road section corresponds to one or more floating vehicle driving sections, and the number of floating vehicle driving sections across each unit road section is judged. If it is 1, then The average driving speed is equal to the average driving speed of the floating car running section; if it is greater than 1, then the average driving speed is the latest and the average driving speed of the floating car running section whose driving time is the latest and the speed is the largest; The fusion processing subunit is used to calculate the road condition information of each road link according to the road condition information of the unit road section; calculate the comprehensive road condition information of each road according to the road condition information of the road link. 13. The system according to claim 11, wherein the source data processing unit comprises: Split the sub-units to determine whether each floating car travel section crosses the road link. If so, add two vehicle projection point data at each road link junction to divide the floating car travel section, and project each vehicle Points increase the floating vehicle source data; wherein, the two vehicle projection points recombine with the start and end projection points of the floating vehicle driving section respectively to form two floating vehicle driving sections; The calculation subunit is used to calculate the road condition information of each floating vehicle section after the floating vehicle is re-divided corresponding to the source data of each floating vehicle, including the average driving speed. 14. The system according to claim 11, further comprising: an output unit, configured to output the comprehensive road condition information of the road in a unified data format. 15. A traffic information fusion processing system, characterized in that it comprises: A pre-processing unit is used to convert the source data collected by the fixed detection system into road condition information for each detection section; The source data receiving unit is used to cyclically read the source data collected by all floating vehicles in one cycle and detect the road condition information of the road section; The source data processing unit is used to correspond to the source data of each floating car, and calculate the road condition information of the traveling section of the floating car in different time periods; section of the road; The fusion processing unit is used to divide each road link of the road into unit road sections; the unit road sections correspond to one or more floating car driving road sections, or correspond to one or more detection road sections; Combining the road condition information with the road condition information of the detected road section, calculating the road condition information of each unit road section; calculating the road condition information of each road link according to the road condition information of the unit road section; Comprehensive traffic information for roads. 16. The system according to claim 15, wherein the fusion processing unit comprises: The unit section processing sub-unit is used to divide each link of the road into unit sections, and calculate the road condition information of each unit section, wherein the calculation method of the average driving speed in the road condition information is: the unit section corresponds to one or Multiple floating car driving sections, or corresponding to one or more detection road sections, determine the number of floating car driving sections and detection sections across each unit road section: if only the number of floating car driving sections is 0, the average driving speed is the average vehicle speed of the detection section with the latest detection time and the highest speed; if only the number of detection sections is 0, the average vehicle speed is the average vehicle speed of the floating vehicle section with the latest travel time and the maximum speed; if the floating vehicle travels the section and the number of detected road sections are both greater than 0, then the average driving speed is equal to the first coefficient multiplied by the average driving speed of the floating car driving section with the latest driving time and the highest speed, plus the second coefficient multiplied by the latest detection time and the highest speed The average vehicle speed of the detected road section, wherein the first coefficient plus the second coefficient is equal to 1; The fusion processing subunit is used to calculate the road condition information of each road link according to the road condition information of the unit road section; calculate the comprehensive road condition information of each road according to the road condition information of the road link.

Images