CN104282151A - Real-time floating vehicle path matching method based on high-frequency satellite positioning data - Google Patents
Real-time floating vehicle path matching method based on high-frequency satellite positioning data Download PDFInfo
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- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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Abstract
The invention discloses a real-time floating vehicle path matching method based on high-frequency satellite positioning data. The method includes the following steps that the satellite positioning data collected at high frequency are preprocessed, and abnormal data are filtered out; a map is preprocessed; the satellite positioning data collected in each calculation period are sorted according to time for vehicles so that time sequences can be formed; according to the relationship between projection distances and azimuth angles in different traveling states, alternative path sets of all the satellite positioning data are selected according to a point matching method; according to the matching degree of the satellite positioning data and paths and the adjacent relationship between front and back points of the satellite positioning data collected at high frequency, the paths correctly matched in time sequence and the traveling path are determined according to the time order. According to the method, the requirements for the speed performance of real-time calculation of the large-data-volume high-frequency satellite positioning data can be met, the method can be well suitable for modern complex urban road networks, and the high matching accuracy is acquired.
Description
Technical field
The present invention relates to intelligent transportation applied technical field, particularly relate to a kind of real-time Floating Car route matching method based on high frequency satellite location data.
Background technology
Along with Chinese Urbanization, modernization, vehicularizedly to develop simultaneously and rapidly, and urban population increases rapidly, vehicle guaranteeding organic quantity increases the reasons such as swift and violent, causes traffic congestion day by day serious.Dynamic User-Optimal Route Choice is as an important application of intelligent transportation, be the optimum trip strategy of traveler planning according to the topological relation in section in urban road network and Real-time Traffic Information, thus the delay of vehicle in road network can be reduced, maximally utilise path resource.On the other hand, Dynamic User-Optimal Route Choice is also the important step in transportation planning model, by simulating the travel route choice behavior of passerby, analyzing the spatial and temporal distributions of traffic trip in road network, thus carrying out effective predicting and evaluating to traffic programme, policy.Therefore, choose reasonable is carried out to the dynamic route of vehicle driving in city and accurately generates rational routing set under certain city road network structure and outside environment, have huge theory significance and actual application value to the navigation of traveler dynamic route, Urban Traffic Planning analytical model.
In prior art, the dynamic route adopting floating vehicle system can realize vehicle driving is selected.Floating Car typically refers to the vehicle with location and radio communication device.The position and time data that gather gained are uploaded to data processing centre (DPC) by Floating Car, by data processing centre (DPC), data are processed, then judge the accurate driving path of vehicle in conjunction with operational factor such as road such as map prediction Vehicle Speed, Link Travel Time etc., thus satellite location data is converted into Traffic Information.
Floating car technology is short, real-time by means of its construction period, wide coverage, data precision advantages of higher, has become the important development direction of traffic information collection technology, has especially been widely used in the acquisition of road traffic Flow Velocity.The interval time of existing Floating Car image data is longer, belongs to low frequency satellite location data.And along with reasons such as technical development and application requirements, floating vehicle system enters the epoch gathered by second.This just means that the data of collection are the Floating Car satellite location data that high frequency gathers.For high frequency satellite location data, there is following defect in existing route matching method:
Because the high frequency satellite location data amount of passback is very large, process in real time, higher to the computing velocity performance requirement of system, this just needs the hardware system of more high cost;
Modern city road network structure is complicated, and high building stands in great numbers, and satellite location data often drifts about, even the position data that high frequency gathers, in crossing and ring road region, requires still very high to the coupling serious forgiveness of system.
Summary of the invention
In view of this, the embodiment of the present invention provides a kind of real-time Floating Car route matching method based on high frequency satellite location data, fundamental purpose is, ensure the real-time handling property requirement of large scale floating vehicle high frequency locator data under not increasing the condition of intensive, and improve the coupling fault freedom to complicated city road network.
For achieving the above object, the present invention mainly provides following technical scheme:
Embodiments provide a kind of real-time Floating Car route matching method based on high frequency satellite location data, comprise the steps:
To the satellite location data pre-service that high frequency gathers, filter rejecting abnormalities data;
To map pre-service;
To the satellite location data separating vehicles collected in each computation period according to time sequence formation time sequence;
According to the projector distance under different transport condition and position angle relation, select the alternative section collection of each satellite location data by point match method;
According to the neighbouring relations, the section that sequencing determination time series temporally is correctly mated and the driving path that mate before and after satellite location data that the goodness of fit and high frequency gather between point in satellite location data and section.
As preferably, wherein reject the filtration of abnormal data and comprise geographic position and to control and velocity amplitude controls, geographic position controls will be positioned within this urban geography scope for the latitude and longitude coordinates of the satellite location data received; Velocity amplitude controls as the Instantaneous velocity values of the satellite location data received will between vehicle theoretical velocity minimum value and maximal value.
As preferably, map pre-service comprises the determination of geographic range and the level of detail, is limited within the scope of target area by the geographic range of map, lane class.path in basic road network is carried out filter the number of paths reducing and participate in search.
As preferably, described map pre-service comprises map projection transformation, and under road network layer data in advance is projected to plane coordinate system, the projective transformation of high frequency satellite location data is carried out in real time, under projecting to same plane coordinate system.
As preferably, described map pre-service comprises the foundation of road network topology, the road network layer data participating in coupling comprise section layer and node layer, each node in node layer is made to possess unique ID, line direction, section and reality direction of passing through is consistent, initial period is clear and definite, ensures connectedness and the directivity of road network.
As preferably, described map pre-service comprises the two-way display of road network, and road network twocouese road axis is translated apart 10 meters of intervals, makes it conform to reality and be convenient to two-way display.
As preferably, described map pre-service comprises the graticule mesh layering of road network, target area road network figure layer data is pressed the equidistant lattice of longitude and latitude and stores, to improve section search efficiency according to number order.
As preferably, the selection step of described alternative section collection is as follows:
With satellite location data lateral error and Ordinary Rd half-amplitude duration sum for space radius, this satellite location data is selected the section at place to be alternative section;
If the instantaneous velocity of this satellite location data is 0, then above-mentioned selected whole alternative section is alternative section collection; Otherwise the difference between the anchor point subpoint place section tangential direction comparing this satellite site instantaneous azimuth and above-mentioned alternative section, if be less than direction difference threshold, then alternative section collection is put in this alternative section, otherwise gives up.
As preferably, determine that the step of correctly mating section and driving path is as follows:
If number=0, section is concentrated in alternative section, then it fails to match for this satellite location data, abandons this satellite location data, the matching process of next satellite locator data in direct entry time sequence;
If hop count >=1, set Road, alternative section, then try to achieve this satellite location data one by one to the subpoint on each alternative section, these subpoints are sorted from small to large by projector distance, then previous matching result point is searched for, namely the previous subpoint matched in time series, if unmatched matching result point, then using subpoint minimum for projector distance directly as matching result point; If there is front matching result point, then carry out next step;
If the subpoint that projector distance is minimum and front matching result point are on same section, or the section, subpoint place that projector distance is minimum and section, front matching result point place are adjacent segments, then this subpoint is matching result point, and the match is successful for this satellite location data and the minimum section, subpoint place of this projector distance; If not, then carry out next step;
If the subpoint that projector distance second is little and front matching result point are on same section, or the section, subpoint place that projector distance second is little and section, front matching result point place are adjacent segments, the subpoint that then this projector distance second is little is matching result point, and the match is successful for this satellite location data and the little section, subpoint place of this projector distance second; If be also, then carry out next step;
Shortest path between the subpoint that search projector distance is minimum and front matching result point, if there is path, and point-to-point transmission mistiming and average overall travel speed are all within the scope of reasonable value, this subpoint is matching result point, and the match is successful for this satellite location data and the minimum section, subpoint place of projector distance; Otherwise last route matching terminates, using the matching result point of subpoint minimum for this projector distance as new route, start new route matching according to above step;
Temporally the path that the matching result point in each route matching is connected to form in turn is the correct driving path of target vehicle by the sequencing of sequence.
As preferably, according to the concrete locating information of matching result determination satellite site on section, thus the vehicle route average overall travel speed obtained in computation period and road-section average travel speed.
As preferably, the acquisition time of the path distance/two station acquisition point between vehicle route average overall travel speed=former and later two subpoints that the match is successful is poor, this average overall travel speed assignment to be given in this path all sections of process, as in this computation period, the average overall travel speed sample value that this section collects.
Further, in computation period, the average overall travel speed in section is obtained by following formula:
In formula,
---road-section average travel speed;
V
i---the instantaneous velocity that i-th data travels on this section;
N---in this computation period, the data number on this section.
A kind of real-time Floating Car route matching method based on high frequency satellite location data that the embodiment of the present invention proposes compared with prior art tool has the following advantages:
The inventive method can meet the speed ability requirement of the real-time calculating of big data quantity high frequency satellite location data, can be applicable to modern complicated city road network preferably again, obtains higher matching accuracy.
Accompanying drawing explanation
The process flow diagram of the real-time Floating Car route matching method based on high frequency satellite location data that Fig. 1 provides for the embodiment of the present invention.
Fig. 2, Fig. 3 are respectively the matching result figure that the embodiment of the present invention provides.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in further detail, but not as a limitation of the invention.
The process flow diagram of the real-time Floating Car route matching method based on high frequency satellite location data that Fig. 1 provides for the embodiment of the present invention.As shown in Figure 1, based on the real-time Floating Car route matching method of high frequency satellite location data, comprise the steps:
To the satellite location data pre-service that high frequency gathers, filter rejecting abnormalities data;
To map pre-service;
To the satellite location data separating vehicles collected in each computation period according to time sequence formation time sequence;
According to the projector distance under different transport condition and position angle relation, select the alternative section collection of each satellite location data by point match method;
According to the neighbouring relations, the section that sequencing determination time series temporally is correctly mated and the driving path that mate before and after satellite location data that the goodness of fit and high frequency gather between point in satellite location data and section.
The inventive method can meet the speed ability requirement of the real-time calculating of big data quantity high frequency satellite location data, can be applicable to modern complicated city road network preferably again, obtains higher matching accuracy.
Preferred as above-described embodiment, wherein reject the filtration of abnormal data and comprise geographic position and to control and velocity amplitude controls, geographic position controls will be positioned within this urban geography scope for the latitude and longitude coordinates of the high frequency satellite location data received; Velocity amplitude controls as the Instantaneous velocity values of the high frequency satellite location data received will between vehicle theoretical velocity minimum value and maximal value.By the rejecting of abnormal data, the serious data drift error because the reasons such as Floating Car lower-speed state or buildings block cause can be got rid of.Reduce calculated amount, improve matching accuracy.
Preferred as above-described embodiment, map pre-service specifically can comprise following several respects:
1) determination of geographic range and the level of detail, is limited within the scope of target area by the geographic range of map, lane class.path in basic road network is carried out filter the number of paths reducing and participate in search.Reduce workload, improve matching efficiency.
2) map projection transformation, under road network layer data in advance is projected to plane coordinate system, the projective transformation of high frequency satellite location data is carried out in real time, under projecting to same plane coordinate system.Meet the needs of finding range in matching process.
3) foundation of road network topology, the road network layer data participating in coupling comprise section layer and node layer, make each node in node layer possess unique ID, and line direction, section and reality direction of passing through is consistent, initial period is clear and definite, ensures connectedness and the directivity of road network.Road network after process possesses the geometric topo-relationship of clear logic.
4) the two-way display of road network, road network twocouese road axis is translated apart 10 meters of intervals, makes it conform to reality and be convenient to two-way display.
5) the graticule mesh layering of road network, presses the equidistant lattice of longitude and latitude by target area road network figure layer data and stores according to number order.Behind given satellite site to be matched, just can find the grid number at this place fast with binary search, thus improve section search efficiency.
Preferred as above-described embodiment, the selection step of alternative section collection is as follows:
With satellite location data lateral error and Ordinary Rd half-amplitude duration sum for space radius, this satellite location data is selected the section at place to be alternative section;
If the instantaneous velocity in this satellite site is 0, then above-mentioned selected whole alternative section is alternative section collection; Otherwise the difference between the anchor point subpoint place section tangential direction comparing this satellite site instantaneous azimuth and above-mentioned alternative section, if be less than direction difference threshold, then alternative section collection is put in this alternative section, otherwise gives up.
Preferred as above-described embodiment, determine that the step of correctly mating section and driving path is as follows:
If number=0, section is concentrated in alternative section, then it fails to match for this satellite location data, the serious data drift error that this satellite location data may cause because of reasons such as Floating Car lower-speed state or buildings block, therefore this satellite location data is abandoned, the matching process of next satellite locator data in direct entry time sequence;
If hop count >=1, set Road, alternative section, then try to achieve this satellite location data one by one to the subpoint on each alternative section, these subpoints are sorted from small to large by projector distance, then previous matching result point is searched for, namely the previous subpoint matched in time series, if unmatched matching result point, then using subpoint minimum for projector distance directly as matching result point; If there is front matching result point, then carry out next step;
If the subpoint that projector distance is minimum and front matching result point are on same section, or the section, subpoint place that this projector distance is minimum and section, front matching result point place are adjacent segments, then this subpoint is matching result point, and the match is successful for this satellite location data and the minimum section, subpoint place of this projector distance; If not, then carry out next step;
If the subpoint that projector distance second is little and front matching result point are on same section, or the section, subpoint place that projector distance second is little and section, front matching result point place are adjacent segments, the subpoint that then this projector distance second is little is matching result point, and the match is successful for this satellite location data and the little section, subpoint place of this projector distance second; If be also, then carry out next step;
Shortest path between the subpoint that search projector distance is minimum and front matching result point, if there is path, and point-to-point transmission mistiming and average overall travel speed are all within the scope of reasonable value, this subpoint is matching result point, and the match is successful for this satellite location data and the minimum section, subpoint place of projector distance; Otherwise last route matching terminates, using the matching result point of subpoint minimum for this projector distance as new route, start new route matching according to above step;
Temporally the path that the matching result point in each route matching is connected to form in turn is the correct driving path of target vehicle by the sequencing of sequence.
Preferred as above-described embodiment, according to the concrete locating information of matching result determination satellite site on section, thus the road-section average travel speed obtained in computation period and path average overall travel speed.Road-section average travel speed is obtained by following formula:
In formula,
---road-section average travel speed;
Vi---the instantaneous velocity that i-th data travels on this section;
N---in this computation period, the data number on this section.
For Beijing, the inventive method is verified below.Beijing has carried out Real-time Collection and system process to 3232 buses, 2666 taxis, 193 tourism passenger stock energy consumption datas.Wherein bus, taxi and tourism passenger stock all have employed the collection of high frequency satellite location data, turn around time was spaced apart for 1 second, and the high frequency satellite location data content wherein for map match comprises the information such as car number, uplink time, latitude and longitude coordinates, instantaneous velocity, position angle.With 30 seconds for computation period, position data about 180,000 in 30 seconds, need be calculated altogether.Real-time energy consumption of vehicles supervision and analysis has been carried out in conjunction with floor data after map match is carried out to these data.
Concrete matching process is as follows:
The filtration treatment measure of abnormal data comprises geographic position and controls and velocity amplitude control, namely the latitude and longitude coordinates of the satellite location data of high frequency collection will be positioned within the geographic range of areas of Beijing, eliminates the serious data drift error because the reasons such as Floating Car lower-speed state or buildings block cause; In addition, Instantaneous velocity values will, in vehicle theoretical velocity between minimum and maximal value, be got velocity amplitude here and be more than or equal to 0 kilometer/hour, within being less than or equal to areas of Beijing road Maximum speed limit 120 kilometers/hour.
The pre-service of electronics road network base map mainly comprises following several respects: the 1) determination of geographic range and the level of detail: current Beijing utilizes high frequency floating car technology to carry out slow stifled guarantor target zone that is smooth, energy-saving and emission-reduction support performance and is generally heart of Beijing city, therefore determines with the inner city of 1086 square kilometres of Beijing's general plan delimitation for research range.In addition, also contains lane class.path in basic road network, these paths often stretch into Intra-cell, neither the road section scope be concerned about of people, therefore lane class.path in basic road network are also filtered.This kind of filtration decreases the number of paths participating in search, decreases unnecessary workload, substantially increases matching efficiency; 2) map projection transformation: original GPS receives data and geographical base map road net data is all WGS84 latitude and longitude coordinates data, under considering that the demand of finding range in map match will transform to same plane coordinate system these GPS point data to be matched and geographical base map data projection, under road network base map being projected to WGS84 UTM planimetric coordinates in advance in native system, the projective transformation of GPS point data is carried out in real time; 3) foundation of road network topology: the connectedness and the directivity that ensure that road network.The base map road network finally participating in mating comprises section layer and node layer, and in node layer, each node possesses unique ID, and line direction, section and reality direction of passing through is consistent, and initial period clearly, processes the geometric topo-relationship of way of escape netting gear for clear logic; 4) the two-way display of road network: road network twocouese road axis is translated apart 10 meters of intervals, and road network is more tallied with the actual situation, also facilitates the two-way display of last road network velocity diagram.5) the graticule mesh layering of road network: goals research field road network layer is pressed the equidistant lattice of longitude and latitude and stores according to number order, behind given satellite site to be matched, just can find the grid number at this place with binary search fast, thus improve section recall precision.
To the high frequency satellite location data separating vehicles received in each computation period according to time sequence;
Point matching selects alternative section collection specific as follows:
A. with 40 meters for space radius, select this satellite location data may the alternative section at place;
If b. this satellite location data instantaneous velocity is 0, be then all alternative section collection selected by above-mentioned a.; Otherwise the difference between the anchor point subpoint place section tangential direction comparing alternative section in this satellite location data instantaneous azimuth and above-mentioned a., if be less than 60 ° (the high frequency satellite location data actual error situation according to Beijing is determined), then put into alternative section collection, otherwise give up.
Determine correctly to mate section and driving path is specific as follows:
If hop count=0, set Road, alternative section, it fails to match, directly enters next Point matching process;
If hop count >=1, set Road, alternative section, then try to achieve this satellite location data one by one to the subpoint on each alternative section, these subpoints are sorted from small to large by projector distance, then point before search, front point refers to previous matching result point, namely the previous subpoint matched in time series, if unmatched point, then using subpoint minimum for projector distance directly as matching result point; If there is front point, then carry out next step;
If the subpoint that projector distance is minimum and front point are on same section, or the minimum section, subpoint place of projector distance and section, front some place are adjacent segments, then the subpoint that this projector distance is minimum is matching result point; If not, then carry out next step;
If the subpoint that projector distance second is little and front point are on same section, or the little section, subpoint place of projector distance second and section, front some place are adjacent segments, then the subpoint that this projector distance second is little is matching result point; If be also, then carry out next step;
Shortest path between the subpoint that search projector distance is minimum and front point, if there is path, and point-to-point transmission mistiming and average overall travel speed all within the scope of reasonable value, (in this example, the point-to-point transmission mistiming is separated by and is less than 1 minute, operating range is more than or equal to 0 kilometer/hour divided by the average overall travel speed of running time, be less than or equal to 100 kilometers/hour), then route matching success, this subpoint is matching result point; Otherwise this satellite location data can not form continuous path with the satellite location data that the match is successful before, then last route matching terminates, using the matching result point of subpoint minimum for this projector distance as new route, start new route matching according to above step;
Temporally the path that the matching result point in each route matching is connected to form in turn is the correct driving path of target vehicle by the sequencing of sequence.
Determine the concrete locating information of satellite site on section, output matching result.Specific as follows:
According to matching result, the timing path information of output satellite anchor point;
Obtain the information such as road-section average travel speed and path average overall travel speed in computation period.
In order to verify feasibility and the actual operating efficiency of the inventive method, tested by the high frequency CAN data acquisition Floating Car twice actual line that runs having bound Big Dipper locating module, respectively from the route matching accuracy of operation efficiency and satellite location data, performance the present invention being applied to the complicated city road network in heart of Beijing city is described further.
In the test Floating Car of energy consumption monitoring, be loaded with CAN data acquisition front equipment, one of them important module is the real-time passback of high frequency Big Dipper locator data together with engine operating condition data, and data acquisition and turn around time were spaced apart for 1 second.Twice actual line that runs is tested as test carriage is from the Zao Junmiao near Xizhimen, westwards detoured respectively about 42 kilometers once longer distance trip, detoured eastwards about 9 kilometers once comparatively short distance trip, the matching primitives of road network map has been carried out by gathering the high frequency locator data of returning, the matching result westwards detoured is shown in as Fig. 2, and the matching result detoured eastwards as shown in Figure 3.
Matching accuracy
The satellite site that vehicle high frequency gathers is a little in Fig. 2.Find out from matching result, the point that the match is successful has two classes: a class is positioned at Intra-cell without base map road network part, and another kind of is that therefore the match is successful because the position angle of low speed satellite location data there occurs serious drift near crossing.Through statistics, except not having base map road network part, in the high frequency satellite location data coupling that this longer distance trip gathers, the matching rate of the inventive method reaches 99.1%.
The satellite site that vehicle high frequency gathers is a little in Fig. 3.Find out from matching result, the point that the match is successful is the same with Fig. 2, is also mainly because the reason of Intra-cell without base map road network caused, and remaining is near crossing or corner is due to azimuthal drift or uncertain and it fails to match.Through statistics, except not having base map road network part, in the high frequency satellite location data coupling that this gathers compared with short distance trip, the matching rate of the inventive method also reaches 98.8%.
Operation efficiency
The inventive method adopts general IBM X3850 M2 six core server to realize, and 23443 points, have calculated within 10 seconds altogether.Therefore, according to the data volume of Beijing's current energy consumption monitoring platform 6091 cars passback in every 30 seconds about 180,000 points, complete the real-time calculation requirement in 30 seconds, 3 are carried out parallel computation with performance server and just can reach requirement, and the Feasible degree of system Construction is very high.
Therefore, the inventive method meets at present to high frequency collection from practice significance, the performance requirement of the real-time map matching primitives system of the big data quantity satellite location data of passback in real time.
Through the above description of the embodiments, those skilled in the art can be well understood to the mode that the present invention can add required common hardware by software and realize, and can certainly pass through hardware, but in a lot of situation, the former is better embodiment.Based on such understanding, technical scheme of the present invention can embody with the form of software product the part that prior art contributes in essence in other words, this computer software product is stored in the storage medium that can read, as the floppy disk of computing machine, hard disk or CD etc., comprise some instructions and perform method described in each embodiment of the present invention in order to make a computer equipment (can be personal computer, server, or the network equipment etc.).
Above embodiment is only exemplary embodiment of the present invention, and be not used in restriction the present invention, protection scope of the present invention is defined by the claims.Those skilled in the art can in essence of the present invention and protection domain, and make various amendment or equivalent replacement to the present invention, this amendment or equivalent replacement also should be considered as dropping in protection scope of the present invention.
Claims (10)
1., based on the real-time Floating Car route matching method of high frequency satellite location data, it is characterized in that, comprise the steps:
To the satellite location data pre-service that high frequency gathers, filter rejecting abnormalities data;
To map pre-service;
To the satellite location data separating vehicles collected in each computation period according to time sequence formation time sequence;
According to the projector distance under different transport condition and position angle relation, select the alternative section collection of each satellite location data by point match method;
According to the neighbouring relations, the section that sequencing determination time series temporally is correctly mated and the driving path that mate before and after satellite location data that the goodness of fit and high frequency gather between point in satellite location data and section.
2. the real-time Floating Car route matching method based on high frequency satellite location data according to claim 1, it is characterized in that, wherein reject the filtration of abnormal data and comprise geographic position control and velocity amplitude control, geographic position controls will be positioned within this urban geography scope for the latitude and longitude coordinates of the satellite location data received; Velocity amplitude controls as the Instantaneous velocity values of the satellite location data received will between vehicle theoretical velocity minimum value and maximal value.
3. the real-time Floating Car route matching method based on high frequency satellite location data according to claim 1, it is characterized in that, map pre-service comprises the determination of geographic range and the level of detail, the geographic range of map is limited within the scope of target area, lane class.path in basic road network is carried out filter the number of paths reducing and participate in search.
4. the real-time Floating Car route matching method based on high frequency satellite location data according to claim 1, it is characterized in that, described map pre-service comprises map projection transformation, under road network layer data in advance is projected to plane coordinate system, the projective transformation of high frequency satellite location data is carried out in real time, under projecting to same plane coordinate system.
5. the real-time Floating Car route matching method based on high frequency satellite location data according to claim 1, it is characterized in that, described map pre-service comprises the foundation of road network topology, the road network layer data participating in coupling comprise section layer and node layer, each node in node layer is made to possess unique ID, line direction, section and reality direction of passing through is consistent, and initial period is clear and definite, ensures connectedness and the directivity of road network.
6. the real-time Floating Car route matching method based on high frequency satellite location data according to claim 1, it is characterized in that, described map pre-service comprises the two-way display of road network, and road network twocouese road axis is translated apart 10 meters of intervals, makes it conform to reality and be convenient to two-way display.
7. the real-time Floating Car route matching method based on high frequency satellite location data according to claim 1, it is characterized in that, described map pre-service comprises the graticule mesh layering of road network, target area road network figure layer data is pressed the equidistant lattice of longitude and latitude and stores, to improve section search efficiency according to number order.
8. the real-time Floating Car route matching method based on high frequency satellite location data according to claim 1, it is characterized in that, the selection step of described alternative section collection is as follows:
With satellite location data lateral error and Ordinary Rd half-amplitude duration sum for space radius, this satnav point data is selected the section at place to be alternative section;
If the instantaneous velocity in this satellite site is 0, then above-mentioned selected whole alternative section is alternative section collection; Otherwise the difference between the anchor point subpoint place section tangential direction comparing this satellite site instantaneous azimuth and above-mentioned alternative section, if be less than direction difference threshold, then alternative section collection is put in this alternative section, otherwise gives up.
9. the real-time Floating Car route matching method based on high frequency satellite location data according to claim 1, is characterized in that, determine that the step of correctly mating section and driving path is as follows:
If number=0, section is concentrated in alternative section, then it fails to match for this satellite location data, abandons this satellite location data, the matching process of next satellite locator data in direct entry time sequence;
If hop count >=1, set Road, alternative section, then try to achieve this satellite location data one by one to the subpoint on each alternative section, these subpoints are sorted from small to large by projector distance, then previous matching result point is searched for, namely the previous subpoint matched in time series, if unmatched matching result point, then using subpoint minimum for projector distance directly as matching result point; If there is front matching result point, then carry out next step;
If the subpoint that projector distance is minimum and front matching result point are on same section, or the section, subpoint place that projector distance is minimum and section, front matching result point place are adjacent segments, then this subpoint is matching result point, and the match is successful for this satellite location data and the minimum section, subpoint place of this projector distance; If not, then carry out next step;
If the subpoint that projector distance second is little and front matching result point are on same section, or the section, subpoint place that projector distance second is little and section, front matching result point place are adjacent segments, the subpoint that then this projector distance second is little is matching result point, and the match is successful for this satellite location data and the little section, subpoint place of this projector distance second; If be also, then carry out next step;
Shortest path between the subpoint that search projector distance is minimum and front matching result point, if there is path, and point-to-point transmission mistiming and average overall travel speed are all within the scope of reasonable value, this subpoint is matching result point, and the match is successful for this satellite location data and the minimum section, subpoint place of projector distance; Otherwise last route matching terminates, using the matching result point of subpoint minimum for this projector distance as new route, start new route matching according to above step;
Temporally the path that the matching result point in each route matching is connected to form in turn is the correct driving path of target vehicle by the sequencing of sequence.
10. the real-time Floating Car route matching method based on high frequency satellite location data according to claim 1, it is characterized in that, according to the concrete locating information of matching result determination satellite site on section, thus the road-section average travel speed obtained in computation period and path average overall travel speed.In computation period, the average overall travel speed in section is obtained by following formula:
In formula,
---road-section average travel speed;
V
i---the instantaneous velocity that i-th data travels on this section;
N---in this computation period, the data number on this section.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105137462A (en) * | 2015-09-22 | 2015-12-09 | 中山大学 | Research method on impacts of environmental factors on vehicle-mounted GPS positioning errors |
| CN105575120A (en) * | 2015-12-31 | 2016-05-11 | 银江股份有限公司 | Floating car data parking behavior mode cleaning method specific to road real time speed calculation |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040068369A1 (en) * | 2002-10-04 | 2004-04-08 | Lg Electronics, Inc. | Vehicle navigation system and method for guiding path of complex intersection |
| CN101270997A (en) * | 2007-03-21 | 2008-09-24 | 北京交通发展研究中心 | Floating car dynamic real-time traffic information processing method based on GPS data |
| CN102063793A (en) * | 2011-01-12 | 2011-05-18 | 上海炬宏信息技术有限公司 | Road condition information detection method and system |
| CN103177585A (en) * | 2013-02-27 | 2013-06-26 | 上海美慧软件有限公司 | Road turning average travel speed calculating method based on floating car data |
-
2014
- 2014-09-30 CN CN201410522515.8A patent/CN104282151A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040068369A1 (en) * | 2002-10-04 | 2004-04-08 | Lg Electronics, Inc. | Vehicle navigation system and method for guiding path of complex intersection |
| CN101270997A (en) * | 2007-03-21 | 2008-09-24 | 北京交通发展研究中心 | Floating car dynamic real-time traffic information processing method based on GPS data |
| CN102063793A (en) * | 2011-01-12 | 2011-05-18 | 上海炬宏信息技术有限公司 | Road condition information detection method and system |
| CN103177585A (en) * | 2013-02-27 | 2013-06-26 | 上海美慧软件有限公司 | Road turning average travel speed calculating method based on floating car data |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105137462A (en) * | 2015-09-22 | 2015-12-09 | 中山大学 | Research method on impacts of environmental factors on vehicle-mounted GPS positioning errors |
| CN106571042A (en) * | 2015-10-12 | 2017-04-19 | 深圳市赛格导航科技股份有限公司 | Variable speed limit vehicle overspeed determining method and system |
| CN105575120B (en) * | 2015-12-31 | 2017-11-07 | 银江股份有限公司 | The floating car data parking behavior pattern cleaning method calculated towards road real-time speed |
| CN105575120A (en) * | 2015-12-31 | 2016-05-11 | 银江股份有限公司 | Floating car data parking behavior mode cleaning method specific to road real time speed calculation |
| CN106988588B (en) * | 2016-12-12 | 2019-07-26 | 蔚来汽车有限公司 | Parking stall floor recognition methods based on two dimensional motion track |
| CN106988588A (en) * | 2016-12-12 | 2017-07-28 | 蔚来汽车有限公司 | Parking stall floor recognition methods based on two dimensional motion track |
| CN109844832A (en) * | 2016-12-30 | 2019-06-04 | 同济大学 | A kind of multi-modal accident detection method based on journey time distribution |
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| CN107992579A (en) * | 2017-12-06 | 2018-05-04 | 上海评驾科技有限公司 | A kind of filter method of car networking data |
| CN108253974A (en) * | 2017-12-29 | 2018-07-06 | 深圳市城市交通规划设计研究中心有限公司 | Floating Car location data automatic adaptation cushion route matching system and method |
| CN110901705A (en) * | 2019-10-30 | 2020-03-24 | 北京全路通信信号研究设计院集团有限公司 | Train initial positioning method and system |
| CN112017429A (en) * | 2020-07-10 | 2020-12-01 | 中山大学 | Overload control monitoring stationing method based on truck GPS data |
| CN112017429B (en) * | 2020-07-10 | 2021-11-09 | 中山大学 | Overload control monitoring stationing method based on truck GPS data |
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