CN110070711A - A kind of section travelling speed interval estimation system and method based on intelligent network connection car data - Google Patents

A kind of section travelling speed interval estimation system and method based on intelligent network connection car data Download PDF

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CN110070711A
CN110070711A CN201910284760.2A CN201910284760A CN110070711A CN 110070711 A CN110070711 A CN 110070711A CN 201910284760 A CN201910284760 A CN 201910284760A CN 110070711 A CN110070711 A CN 110070711A
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data
vehicle
section
road
gps
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何书贤
吉海峰
邱志军
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Huali Zhixing (wuhan) Technology Co Ltd
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Huali Zhixing (wuhan) Technology Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P11/00—Measuring average value of speed
    • G01P11/02—Measuring average speed of number of bodies, e.g. of vehicles for traffic control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/025—Services making use of location information using location based information parameters
    • H04W4/027—Services making use of location information using location based information parameters using movement velocity, acceleration information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/42—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for mass transport vehicles, e.g. buses, trains or aircraft

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a kind of section travelling speed interval estimation system and method based on intelligent network connection car data, the application system includes: data reception module, data preprocessing module, data processing module and data storage and release module, it is successively cooperated between each module by interprocess communication realization, it realizes the real-time acquisition and storage to running velocity and GPS track data, publication is realized to section travelling speed and road network operating status.Make full use of car networking V2X vehicle body velocities data, GPS track data, play data message high frequency advantage, by comparing automobile's instant velocity and automobile's instant velocity threshold value, path average speed and path average speed threshold value, the accurate estimation to section runing time, road network operating status is realized.Present system is novel in design, makes full use of car networking V2X in the advantage of data polyphyly, the covering of operation space-time and database completeness, completes the prediction of vehicle section speed, is convenient for effective monitoring urban transportation real-time status.

Description

It is a kind of based on intelligent network connection car data section travelling speed interval estimation system and Method
[technical field]
The present invention relates to intelligent transportation vehicle networking technical field, in particular to a kind of road based on intelligent network connection car data Section travelling speed interval estimation system and method.
[background technique]
Currently, horizontal in order to improve urban traffic status detection and the estimation of road-section average travelling speed, use a variety of sides Formula method.Including fixed detectors such as earth magnetism, coil, microwaves, the imaging sensors such as video vehicle checker, and to carry GPS gathers Instrument is the mobile detection means of commerial vehicle of representative, however above-mentioned various all there is inadequate natural endowment.Wherein, fixed detector equipment It is easy to damage, be difficult to safeguard, data continuity is poor, poor quality；Imaging sensor be laid with it is with high costs, dependent on powerful image Data handling utility system realizes that difficulty is larger；Commerial vehicle GPS data source is single, the data period is longer, reliability Difference.
With the fast development of the 5G communication technology, V2X technology, car networking technology, in the following 3-5 it is contemplated that time range Interior, the rapid businessization landing of intelligent network connection automobile provides new solution with section velocity estimation for urban traffic status detection Thinking.In view of intelligent network connection automobile has the spy of access car CAN data (instrument board speed) and high frequency GPS track record Property, the traffic state data acquisition of covering city system-wide net can be realized in conjunction with V2X technology.Particularly, since intelligent network joins vehicle The nearly all advantage of conventional truck is inherited, is not only possessed in data polyphyly, the covering of operation space-time, database completeness congenital Advantage, and also it is better than traditional detection scheme in terms of construction cost, equipment and application system ease for maintenance.
[summary of the invention]
In view of this, in order to overcome the deficiencies of the prior art, the present invention provides a kind of based on intelligent network connection vehicle V2X and GPS The application system and prediction technique of data make full use of car networking V2X vehicle body velocities data, GPS track data, realize to section Error caused by website is stopped and waited for parking by crossing is effectively reduced in the accurate reckoning of runing time, road network operating status.
To achieve the above object, technical scheme is as follows:
A kind of section travelling speed interval estimation system and method based on intelligent network connection car data, it is characterized in that, The application system include: data reception module, data preprocessing module, data processing module and data storage and release module, It is successively cooperated between each module by interprocess communication realization, realizes the reality to running velocity and GPS track data When acquisition and storage, publication is realized to section travelling speed and road network operating status.
Setting external data receives server in the data reception module, and can collect vehicle V2X message information, vehicle GPS track information, website arrival information and urban GIS road network information obtain vehicle data and establish data reception data library, realize Asynchronous collecting and storage to vehicle data.
Data prediction database based on cloud computing, V2X data prediction mould are set in the data preprocessing module Block, GPS data preprocessing module and road net data preprocessing module, by vehicle V2X data, GPS data, urban GIS road Network data, website track data pretreatment, pass through the serial of methods such as data screening, filling, deletion, standardized data formats And content.
Data management database based on cloud computing is set in the data processing module, is capable of handling map match letter Breath carries out car networking V2X Message processing and solves running velocity.
The data storage and setting data storage in release module and access server, for storing and issuing road network letter Breath, including road network operating status, section travelling speed and road network congestion status.
Further, the data reception module, which passes through, accesses city vehicle operation centre, access outside vehicle data-interface, To achieve the purpose that obtain vehicle data.It is to be based respectively on intelligent network connection vehicle V2X and 4G wireless network, obtains vehicle V2X Terminal BSM, PVB message data and GPS terminal data.And data reception data library is established, the asynchronous of vehicle data is adopted in realization Collection and storage.
The operating procedure of the data preprocessing module includes:
(21) it extracts and identifies vehicle V2X data, deletion or erection rate value abnormal data and filled according to historical experience Lack part data.
(22) it extracts and identifies vehicle GPS data, it is abnormal or mixed to delete abnormal GPS coordinate, timestamp exception, line number Random data, and historical data is filled to the big section of time range span between continuous GPS point.
(23) GIS data for arranging covering city system-wide net, edits and verifies section node data, road in geographical grid Section attribute data, nodal community data guarantee that data road network covers comprehensive and accuracy.
(24) the data prediction database based on cloud computing is established, pretreated data are temporally divided into storage value Database constructs vehicle and city road network data set abundant.
Further, the data processing module combination map-matching method, average speed estimation method, data aggregation method Realize that real-time section average speed calculates, comprising the following steps:
(31) vehicle GPS data, GIS road net data are extracted, road network section where solving vehicle using map-matching method Position, and obtain respective stretch subpoint.According to timestamp information, section and vehicle driving locating for continuous two GPS are solved Path, and to acquisite approachs length, as operating range.
(32) it extracts and parses V2X BSM message data, obtain automobile body instantaneous velocity.By the value and GPS track number According to being temporally associated with, compare road-net node instantaneous velocity threshold value, to determine state of motion of vehicle, i.e., at the uniform velocity, even acceleration or even subtracts Speed.
(33) according to continuous GPS point timestamp information, state of motion of vehicle data seek road trip time, in conjunction with road Segment length solves the road-section average travelling speed of single unit vehicle.
(34) according to time interval aggregation vehicle average speed, the speed arithmetic for solving different vehicle in same a road section is average Value, obtains average link speed, as the average link speed estimated value finally issued.
The average link speed estimation value calculating method is as follows:
Then nmWith nm+1Between the Average Travel Speed in section be
Wherein, k is car number, l (nm, nm+1) in nmAnd nm+1Respectively indicate two nodes in section, l (nm, nm+1) table Show the length in section, t (k, nm) indicate bus k in node nmTimestamp, V (Su, l (nm, nm+1)) in indicate SuIndicate the uthAverage Travel Speed estimated value in period.
The data storage and the storage of release module onboard data and access server, for storing and issuing road network letter Breath, including road network operating status, section travelling speed and road network congestion status.The road network Average Travel Speed estimated value that will be obtained It stores to historical data base, for road network history or the storage of real-time running state data and publication.
The invention has the advantages that making full use of V2X message vehicle body velocities data, GPS rail with car networking technology Mark data play data message high frequency advantage, by comparing automobile's instant velocity and automobile's instant velocity threshold value, path average speed and path Average speed threshold value realizes the accurate estimation to section runing time, road network operating status, and website is effectively reduced and stops and passes through Evaluated error caused by crossing waits for parking.It effectively improves to road network real-time running state monitoring efficiency and accuracy rate, for intelligence The rapid businessization landing of net connection automobile provides new resolving ideas with section velocity estimation for urban traffic status detection.
[Detailed description of the invention]
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below There is attached drawing needed in technical description to be briefly described.It is clear that the accompanying drawings in the following description is only this hair Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root Other attached drawings are obtained according to these attached drawings.
Fig. 1 is principle schematic diagram of the invention.
Fig. 2 is the whole flow process schematic diagram that road average-speed is solved in data processing module of the present invention.
Fig. 2A is the track node schematic diagram present invention determine that target road section.
Fig. 3 is the GPS point schematic diagram of driving scene one of the present invention.
Fig. 4 is the GPS point schematic diagram of driving scene two of the present invention.
Fig. 5 is the GPS point schematic diagram of driving scene three of the present invention.
[specific embodiment]
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other Embodiment shall fall within the protection scope of the present invention.
Referring to Fig.1, a kind of section travelling speed interval estimation system and method based on intelligent network connection car data, it is special Point is that the application includes: data reception module 1, data preprocessing module 2, data processing module 3 and data storage and publication Module 4 is successively cooperated between each module by interprocess communication realization, is realized to running velocity and GPS track number According to real-time acquisition and storage, publication is realized to section travelling speed and road network operating status.
Below using bus as embodiment.
Firstly, setting external data receives server 11 in the data reception module 1, and vehicle V2X message can be collected Information, vehicle GPS trace information, website arrival information and urban GIS road network information.By accessing city vehicle operation centre, Outside vehicle data-interface is accessed, vehicle data is obtained and establishes data reception data library, realize the asynchronous collecting to vehicle data With storage.Mainly realized by following two step:
(1) it is based respectively on intelligent network connection bus V2X and 4G wireless network, obtains bus V2X terminal BSM, PVB report Literary data and GPS terminal data.
(2) data reception data library is established, realizes the asynchronous collecting to public transport car data and storage.
Then: the data prediction database 21 based on cloud computing, V2X data are set in the data preprocessing module 2 Preprocessing module 23, GPS data preprocessing module 24 and road net data preprocessing module 22, the V2X data preprocessing module 23 can carry out the deletion of velocity anomaly value, the reparation of velocity anomaly value, speed Missing Data Filling, velocity amplitude error-tested, and GPS data is pre- Processing module 24 can carry out the deletion of malposition value, the reparation of malposition value, the filling of topagnosis value, location error inspection, road Network data preprocessing module 22 can carry out geographical raster data editor, section data edition, node data editor and road net data Sequence of maneuvers is examined, keeps car networking data processing of information more acurrate, is fed back more timely.
Above-mentioned data preprocessing module 2 passes through to vehicle V2X data, GPS data, urban GIS road net data, website route The pretreatment of data passes through serial of methods, standardized data formats and the contents such as data screening, filling, deletion.
Its operating procedure includes:
(21) it extracts and identifies vehicle V2X data, deletion or erection rate value abnormal data and filled according to historical experience Lack part data.
(22) it extracts and identifies vehicle GPS data, it is abnormal or mixed to delete abnormal GPS coordinate, timestamp exception, line number Random data, and historical data is filled to the big section of time range span between continuous GPS point.
(23) GIS data for arranging covering city system-wide net, edits and verifies section node data, road in geographical grid Section attribute data, nodal community data guarantee that data road network covers comprehensive and accuracy.
(24) the data prediction database based on cloud computing is established, pretreated data are temporally divided into storage value Database constructs vehicle and city road network data set abundant.
Proceed to data processing module 3 again, the data processing data based on cloud computing is set in the data processing module 3 Library 31 is capable of handling map matching information, carries out car networking V2X Message processing and solves running velocity.Data processing mould Block 3 combines map-matching method, average speed estimation method and data aggregation method to realize average link speed estimation, step Include:
(31) vehicle GPS data, GIS road net data are extracted, road network section where solving vehicle using map-matching method Position, and obtain respective stretch subpoint.According to timestamp information, section and vehicle driving locating for continuous two GPS are solved Path, and to acquisite approachs length, as operating range.
(32) it extracts and parses V2X BSM message data, obtain automobile body instantaneous velocity.By the value and GPS track number According to being temporally associated with, compare road-net node instantaneous velocity threshold value, to determine state of motion of vehicle, i.e., at the uniform velocity, even acceleration or even subtracts Speed.
(33) according to continuous GPS point timestamp information, state of motion of vehicle data seek road trip time, in conjunction with road Segment length solves the road-section average travelling speed of single unit vehicle.
(34) according to time interval aggregation vehicle average speed, the speed arithmetic for solving different vehicle in same a road section is average Value, obtains average link speed, as the average link speed estimated value finally issued.
The average link speed estimation value calculating method is as follows:
Then nmWith nm+1Between the Average Travel Speed in section be
Wherein, k is car number, l (nm, nm+1) in nmAnd nm+1Respectively indicate two nodes in section, l (nm, nm+1) table Show the length in section, t (k, nm) indicate bus k in node nmTimestamp, V (Su, l (nm, nm+1)) in indicate SuIndicate the uthAverage Travel Speed estimated value in period.
Finally, data storage and release module 4: for storing and issue road network information, including road network operating status, Section travelling speed and road network congestion status.Obtained road network Average Travel Speed estimated value is stored to historical data base, is used In to road network history or the storage of real-time running state data and publication.
Wherein, above-mentioned third step, for data processing module 3 when carrying out map-matching method, concrete scheme is as follows:
(4) vehicle GPS data are extracted: extracting the vehicle GPS website track data handled well in data reception module 1.
Shown in data form such as the following table 1 " ETSGTFS_TU ".
Table 1:ETSGTFS_TU
Detailed data extraction method:
(41) intraday all public transport data are chosen according to timestamp information (TIMESTAMP), and in chronological sequence arranged Sequence；
(42) the public transport car data that (ROUTE_ID) extracts identical number is numbered according to bus routes
(43) data of same same bus of route are extracted according to bus number (VEH_ID)
(44) the bus GPS track data divided by public bus network number, vehicle number and time sequencing, data format are obtained As shown in the following table 2 " BUS ROUTE DATA ".
Table 2:BUS ROUTE DATA
(5) for data processing module 3 when carrying out GIS data road network editor, concrete scheme is as follows:
(51) in order to make full use of road net data, system-computed efficiency is improved, road section information (information is made Of links) attribute list, as shown in the following table 3 " LINK_ATTRIBUTE ".
Table 3:LINK_ATTRIBUTE
(52) in order to improve map match efficiency, geographical grid table is made.With grid division city road network, each grid packet Containing spatially associated road-net node number, as shown in the following table 4 " GRID_ATTRIBUTE ".
Table 4:GRID_ATTRIBUTE
(53) road-net node attribute list is made, arbitrary node attribute information in road-net node is indicated, such as the following table 5 " REF_ Shown in POINT_ATTRIBUTE ".
Table 5:REF_POINT_ATTRIBUTE
(6) section matching step: it is based on projective analysis method, in conjunction with road network grid, section, nodal community table, according to a certain Moment GPS track data determine the section where bus.
(61) grid locating for GPS point is determined.Solve grid geographic center point coordinate respectively with GPS coordinate residual error:
Wherein, nlonWith nlatRespectively indicate the longitude and latitude of GPS point n；LonNWith LatNIt respectively indicates in the grid that number is N Heart point longitude and latitude；εlonWith εlatRespectively indicate threshold residual value；S constitutes all grid sets for meeting above formula condition.In view of GPS Point precision problem, the allowable range of error of 20 meters of setting.
(62) destination node is determined.After determining grid locating for GPS point, include with grid respectively and allowable range of error Interior node seeks earth surface distance, and selected distance is worth the destination node that the smallest point is successful match.
n*=min { dN, N, n ∈ S } and (6)
Wherein, dN, NIndicate the earth surface distance in current GPS point and n-th of grid between n-th node；
(63) road section to be matched is screened.Table 5 is searched for according to destination node, all sections that this includes the point is obtained and compiles Number set L.Search table 3 is numbered further according to section, as road section to be matched.
(64) target road section is determined.Projected triangle is constructed with section each in L respectively, as shown in Figure 2 A.
Hypotenuse and:
li=ai+bi, i ∈ L (7)
Meet:
Ltarget=min { li, i ∈ L } and (8)
Wherein, aiWith biIndicate the length in triangle between GPS point and node；LtargetIndicate the target road road chosen Section.
(7) Path Recognition: in view of the driving path that continuous two GPS points of the same bus are constituted may cover it is multiple Section, it is therefore desirable to which sexual intercourse is connected to identify bus driving path according to section.
(71) determine that section connects sexual intercourse.It is numbered according to 3 road-net node of table, the connection sexual intercourse in section is determined, with table Show the possible driving path of bus.
(72) optimal path identifies.In the case where lacking public bus network data, it is based on shortest path thought, determines public transport The driving path of vehicle.
Above-mentioned third step, data processing module 3 is when specifically solving travelling speed, the tool that carries out on the basis of above-mentioned Body scheme is as follows:
Front fix road network section (LINK) and continuous two GPS point of the route bus locating for particular moment it Between the path passed through.In order to realize the Average Travel Speed estimation to road section, this method combination high frequency V2X message includes Vehicle body instantaneous velocity data realize the estimation to section travelling speed.
(8) driving scene: since GPS data frequency is smaller, it is understood that there may be the case where be the traveling of continuous two GPS point It path can be there are many situation:
(81) Case1: road network section node is not included, as shown in Figure 3.
(82) Case2: including a road network section node, as shown in Figure 4.
(83) Case3: including two or more road network section nodes, as shown in Figure 5.
Wherein,
gK, i: number (ROUTE_ID) is the bus of k, i-th of GPS track point data of extraction；
Number the bus that (ROUTE_ID) is k, the position data of i-th of GPS point projection of extraction；
nm: m-th of road-net node；
(9) hourage is estimated
(91) for Case3, it can be considered and slowly driven at a constant speed under free flow transportation condition or saturation state, seek bus K reaches n-thm+1At the time of a node:
Bus k is asked to reach n-thmAt the time of a node:
Wherein, t (k, nm) indicate that bus k reaches nmAt the time of node；tK, iIndicate that i-th of GPS point of bus k is corresponding Moment；Indicate i-th of subpoint of bus k to the distance of current road segment starting point；Indicate public Hand over the i+1 subpoint of vehicle k to the distance of current road segment terminal；length(pK, i) indicate i-th of subpoint of bus k with The distance between (i-1)-th subpoint；
(92) Case2 --- it only include a road-net node between continuous two GPS points.
(93) Case1 --- do not include any road-net node between continuous two GPS points, respectively along front and back GPS point twice It searches for outward, until including road-net node number between other GPS points and current GPS point.
(94) calculating logic process is stabbed by node time:
1. obtaining adjacent GPS point path pK, i-1, pK, i, pK, i+1；
2. calculating pK, iThe number of nodes for including is calculated as c (pK, i), if c (pK, i) > cthres(i), then step 9 is gone to, otherwise Go to step 3；
3. calculating average path travelling speed v (pK, i), if v (pK, i) > pvthres, then step 9 is gone to, step is otherwise gone to 4；
4. extracting V2X BSM message vehicle body instantaneous velocity vK, i-1And vK, i, GPS subpointWith
If vK, i-1≤vthresAnd vK, i> vthres, step 5 is gone to, if vK, i-1> vthresAnd vK, i≤vthres, go to Step 7, step 9 is otherwise gone to；
5. calculating c (pK, i-1), if c (pK, i-1) < cthres(i-1), step 6 is gone to, step 9 is otherwise gone to；
6. calculating v (pK, i-1), if v (pK, i-1) > pvthres, using formula (10), otherwise go to step 9.
7. calculating c (pK, i+1), if c (pK, i+1) < cthres(i+1), step 8 is gone to, step 9 is otherwise gone to.
8. calculating v (pK, i+1), if v (pK, i+1) > pvthres, use formula (11).
9. using formula (7).
(10) travelling speed is estimated
By calculating the timestamp by two nodes on section, combining road length, can to Lv Duan travelling speed into Row estimation:
V (k, l (nm, nm+1))=length (l (nm, nm+1))/(t (k, nm+1)-t (k, nm)) (14)
Then nmWith nm+The Average Travel Speed in section between 1 are as follows:
Wherein, length (l (nm, nm+1)) indicate nmWith nm+1Between section distance；V (k, l (nm, nm+1)) indicate kth Bus is in node nmWith nm+1Between section between travelling speed.
Fig. 2 and above-mentioned principle explanation in conjunction with Figure of description, are finally obtained by above-mentioned entire solution process and formula Road average-speed, to realize accurate estimation to section runing time, road network operating status, be effectively reduced website stop and Evaluated error caused by being waited for parking by crossing effectively increases the real-time monitoring efficiency of intelligent network connection automobile road network and accurate Rate.
Illustrate herein, however not excluded that average value, but whole systems technology side is calculated using other mathematical physics formula Case system and process are our core protection points, therefore using similar section travelling speed estimation method still ours Within protection scope.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention Within mind and principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.

Claims (4)

1. a kind of section travelling speed interval estimation system and method based on intelligent network connection car data, which is characterized in that should Application system include: data reception module, data preprocessing module, data processing module and data storage and release module, respectively It is successively cooperated, is realized to the real-time of running velocity and GPS track data by interprocess communication realization between module Acquisition and storage realize publication to section travelling speed and road network operating status；
Setting external data receives server in the data reception module, and can collect vehicle V2X message information, vehicle GPS Trace information, website arrival information and urban GIS road network information obtain vehicle data and establish data reception data library, realization pair The asynchronous collecting of vehicle data and storage；
Data prediction database of the setting based on cloud computing in the data preprocessing module, V2X data preprocessing module, GPS data preprocessing module and road net data preprocessing module, by vehicle V2X data, GPS data, urban GIS road network number According to the pretreatment of, website track data, by data screening, filling, deletion, standardized data formats and content；
Data management database based on cloud computing is set in the data processing module, be capable of handling map matching information, into Driving networking V2X Message processing and solution running velocity；
The data storage and setting data storage in release module and access server, for storing and issuing road network information, Including road network operating status, section travelling speed and road network congestion status.
2. a kind of section travelling speed interval estimation system and side based on intelligent network connection car data as described in claim 1 Method, which is characterized in that the data reception module accesses outside vehicle data-interface by access city vehicle operation centre, It is to be based respectively on intelligent network connection vehicle V2X and 4G wireless network, obtains vehicle V2X terminal BSM, PVB message data and GPS is whole End data.
3. a kind of section travelling speed interval estimation system and side based on intelligent network connection car data as described in claim 1 Method, which is characterized in that the operating procedure of the data preprocessing module includes:
(1) it extracts and identifies vehicle V2X data, deletion or erection rate value abnormal data and filled according to historical experience and lacked Partial data.
(2) it extracts and identifies vehicle GPS data, delete that GPS coordinate is abnormal, timestamp is abnormal, line number is abnormal or chaotic Data, and historical data is filled to the big section of time range span between continuous GPS point.
(3) GIS data for arranging covering city system-wide net, edits and verifies section node data, road section in geographical grid Attribute data, nodal community data guarantee that data road network covers comprehensive and accuracy.
(4) the data prediction database based on cloud computing is established, pretreated data are temporally divided to storage Value Data Library constructs vehicle and city road network data set abundant.
4. a kind of section travelling speed interval estimation system and side based on intelligent network connection car data as described in claim 1 Method, which is characterized in that the data processing module combination map-matching method, average speed estimation method, data aggregation method Realize that real-time section average speed calculates, comprising the following steps:
(21) vehicle GPS data, GIS road net data are extracted, road network section position where solving vehicle using map-matching method, And obtain respective stretch subpoint.According to timestamp information, section and vehicle running path locating for continuous two GPS are solved, And to acquisite approachs length, as operating range.
(22) it extracts and parses V2X BSM message data, obtain automobile body instantaneous velocity.The value and GPS track data are pressed Association in time simultaneously fills to table 1, compares road-net node instantaneous velocity threshold value, to determine state of motion of vehicle, i.e., at the uniform velocity, even acceleration Or even deceleration.
(23) according to continuous GPS point timestamp information, state of motion of vehicle data seek road trip time, and combining road is long Degree, solves the road-section average travelling speed of single unit vehicle.
(24) according to time interval aggregation vehicle average speed, different vehicle is solved in speed arithmetic's average value of same a road section, Average link speed is obtained, as the average link speed value finally issued.
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