CN102496279A - Method and device for system configuration of floating car - Google Patents
Method and device for system configuration of floating car Download PDFInfo
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- CN102496279A CN102496279A CN2011104128138A CN201110412813A CN102496279A CN 102496279 A CN102496279 A CN 102496279A CN 2011104128138 A CN2011104128138 A CN 2011104128138A CN 201110412813 A CN201110412813 A CN 201110412813A CN 102496279 A CN102496279 A CN 102496279A
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Abstract
The invention discloses a method and a device for system configuration of a floating car, which relate to the technical field of intelligent traffic and solve the problem that system configuration of the floating car cannot be realized in the prior art. The method includes: setting up a floating car system configuration model; acquiring limiting parameters of the floating car system configuration model; and processing the floating car system configuration model according to the limiting parameters to obtain the floating car system configuration results. The method and a device for system configuration of the floating car are applicable to the technical field of intelligent traffic and used for system configuration of floating cars.
Description
Technical field
The present invention relates to the intelligent transport technology field, relate in particular to a kind of Floating Car system configuration method and device.
Background technology
At intelligent transportation field, the major technique means of obtaining Traffic Information have been become through Floating Car system acquisition transport information.The Floating Car system with the Floating Car (being generally taxi) that is equipped with GPS (Global Positioning System, GPS) car-mounted device and Wireless Telecom Equipment as sampling instrument, to obtain Real-time Traffic Information.The Floating Car system has construction period weak point, small investment, wide coverage, advantage such as real-time.In recent years, along with the widespread use of GPS device on the Floating Car, the fast development of communication transmission technology, reach its maturity based on the treatment technology of the traffic information data of Floating Car system, and be applied to some economically developed cities.Because factors such as each urban geography characteristic, economic GDP, population are different, cause under the situation of transport information quality index unanimity the quantity of the Floating Car that each city is required, floating car data collection period difference.
In realizing process of the present invention, the inventor finds to exist at least in the prior art following problem:
At present; Not having a kind of definite calculation methods in the intelligent transportation system can make Floating Car operator according to the traffic-information service quality Floating Car system is configured (mainly comprise Floating Car quantity and data car data collection period are configured); Make Floating Car operator to manage to the Floating Car system; Cause the cost of Floating Car system too high, or the service quality of the transport information that provides of Floating Car system is lower, has seriously hindered the development of intelligent transport technology.
Summary of the invention
Embodiments of the invention provide a kind of Floating Car system configuration method and device; Can make Floating Car operator between service quality and operation cost, weigh; Be convenient to Floating Car operator the Floating Car system is managed, help the development of intelligent transport technology.
For achieving the above object, embodiments of the invention adopt following technical scheme:
On the one hand, the embodiment of the invention provides a kind of Floating Car system configuration method, comprising:
Set up Floating Car system configuration model;
Obtain the qualification parameter of said Floating Car system configuration model;
According to said qualification parameter said Floating Car system configuration model is handled, obtained the configuration result of Floating Car system.
On the other hand, the embodiment of the invention also provides a kind of Floating Car system configuration device, comprising:
Set up the unit, be used to set up Floating Car system configuration model;
Acquiring unit is used to obtain the qualification parameter of said Floating Car system configuration model;
Processing unit is used for according to said qualification parameter said Floating Car system configuration model being handled, and obtains the configuration result of Floating Car system.
Floating Car system configuration method and device that the embodiment of the invention provides; Through setting up Floating Car system configuration model; Can the Floating Car system be configured according to the Service Quality Metrics that the user sets, obtain configuration result and carry out reference, make Floating Car operator between operation cost and service quality, weigh for Floating Car operator; Be convenient to operator the Floating Car system is managed, help the development of intelligent transport technology.
Description of drawings
In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art; To do to introduce simply to the accompanying drawing of required use in embodiment or the description of the Prior Art below; Obviously, the accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills; Under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.
The method flow synoptic diagram that Fig. 1 provides for the embodiment of the invention 1;
The method flow synoptic diagram that Fig. 2 provides for the embodiment of the invention 2;
The structural representation of the device that Fig. 3-Fig. 6 provides for the embodiment of the invention 3.
Embodiment
To combine the accompanying drawing in the embodiment of the invention below, the technical scheme in the embodiment of the invention is carried out clear, intactly description, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills are not making the every other embodiment that is obtained under the creative work prerequisite, all belong to the scope of the present invention's protection.
Embodiment 1
The embodiment of the invention provides a kind of Floating Car system configuration method, and as shown in Figure 1, said method comprises:
101, set up Floating Car system configuration model.
102, obtain the qualification parameter of said Floating Car system configuration model.
Concrete, the qualification parameter of said Floating Car system configuration model can be set by Floating Car operator.
103, according to said qualification parameter said Floating Car system configuration model is handled, obtained the configuration result of Floating Car system.
Concrete, said configuration result comprises the Floating Car quantity and the floating car data collection period of Floating Car system configuration, but is not limited only to this.
The Floating Car system configuration method that the embodiment of the invention provides; Can the Floating Car system be configured according to the qualification parameter that Floating Car operator sets; Obtain configuration result and carry out reference for Floating Car operator; Make Floating Car operator between operation cost and service quality, weigh, be convenient to operator the Floating Car system is managed, help the development of intelligent transport technology.
Embodiment 2
The embodiment of the invention provides a kind of Floating Car system configuration method, and as shown in Figure 2, said method comprises:
201, road network information is analyzed, confirmed to influence the factor of road network coverage rate and road network fiduciary level, said factor includes but not limited to roading density and each grade road in Floating Car quantity, the road network shared scale-up factor in said road network.
What be worth explanation is that road network coverage rate and road network fiduciary level are to estimate the important indicator of Floating Car system service quality; Because the road information in the road network is comparatively fixing, so Floating Car operator is to improve the prefered method of road network coverage rate and road network fiduciary level through setting Floating Car quantity.
202, set up road network coverage rate model and road network Reliability Model respectively according to the said factor that influences road network coverage rate and road network fiduciary level; Obtain the expression formula of road network coverage rate function and road network Reliability Function, said expression formula is used to characterize the corresponding road network coverage rate function and the road network fiduciary level of different values of Floating Car quantity.
203, according to said road network coverage rate model and road network Reliability Model, and traffic-information service quality and operation cost, said Floating Car quantity allocation models set up.
For the ease of understanding, the embodiment of the invention provides a kind of Floating Car quantity allocation models for your guidance,
Specific as follows:
Wherein, Y1 represents road network coverage rate function, and Y2 represents the road network Reliability Function; MinCoverage is the minimum expectation value of road network coverage rate, and MinReliability is the minimum expectation value of road network fiduciary level, and MinImprove is the minimum value of road network coverage rate degree of improvement, and Nmax is the maximal value of the Floating Car quantity that can move in the road network; Wherein, road network coverage rate degree of improvement is unit with the percentage point, is used to be characterized in the basis that Floating Car quantity is N, the improvement value of road network coverage rate when increasing n Floating Car, and said n is set by Floating Car operator.For example: if Floating Car operator is set at 100 with n, MinImprove is set at 20%, then characterizes Floating Car operator and be desirably in the current Floating Car system, increase by 100 Floating Car, the road network coverage rate will improve 20% at least; If increase by 100 Floating Car, the improvement value of road network coverage rate is lower than 20%, and then Floating Car operator can not be judged to be need increase unsteady vehicle.
Conspicuous, in the Floating Car system, the Floating Car quantity n that exists in the road network is big more, and the road network coverage rate degree of improvement that then every increase N amount Floating Car is brought is more little.
204, obtain the qualification parameter of said Floating Car quantity allocation models, with constraint condition as Floating Car quantity allocation models in the step 203.
Concrete; Said qualification parameter is set by Floating Car operator; Said qualification parameter comprises: the minimum expectation value of the maximal value of the Floating Car quantity that can move in the road network, the minimum expectation value of road network coverage rate, road network fiduciary level and the minimum value of road network coverage rate degree of improvement; Wherein, the improvement value of road network coverage rate when said road network coverage rate degree of improvement is used for being characterized in N Floating Car of the every increase of road network, said N is set by Floating Car operator.
205, according to the qualification parameter of said Floating Car quantity allocation models, said Floating Car quantity allocation models is calculated, obtain the configuration result of Floating Car quantity, carry out reference for Floating Car operator.Wherein, said configuration result comprises: the upper limit value M axReliability of the upper limit value M axCoverage of road network coverage rate, road network fiduciary level, degree of the improvement scope of road network coverage rate, and optimum Floating Car quantity.
Concrete, can adopt dichotomy that said Floating Car quantity allocation models is calculated with definite optimum Floating Car quantity, but be not limited only to this.For example, can adopt but be not limited to following steps:
1. confirm the interval of Floating Car quantity according to degree of the improvement scope of road network coverage rate, note is made [N1, N2], N1＜N2;
2. make Nmid=(N1+N2)/2, calculate the degree of improvement at Nmid place, note is made MidImprove;
The minimum M inImprove of the road network coverage rate degree of 3. MidImprove and Floating Car operator being set of improvement compares, if MidImprove=MinImprove, Nmid is institute and finds the solution promptly optimum Floating Car quantity so; If MidImprove＞MInImprove then makes N1=Nmid, 2. N2=N2 returns step; If MidImprove＜MinImprove then makes N1=N1,2. N2=Nmid returns step;
4. when satisfying preset precision, or when reaching preset cycle index, with the result who obtains as find the solution, find the solution end.
Preferably, degree of the improvement scope that Floating Car operator can set basis road network coverage rate is confirmed the collocation method of Floating Car quantity.For example, degree of the improvement scope of road network coverage rate is [a, b], a＜b wherein, and then establishing method can be with reference to as follows:
1. think that when operator MinCoverage satisfies the demands, when need not to improve coverage rate again, the value of coverage rate degree of improvement should be at [b-ε; B] interior value, wherein ε is a less value of relative a, b, like [a; B]=[0.05,0.15], then ε desirable 0.02 or 0.01;
2. think when MinCoverage has satisfied that still needs improve coverage rate under the prerequisite of primary demand as the Floating Car operator, can improve two with the road network coverage rate from economy and weigh, finally definite suitable degree of improvement cubic plane.When degree of improvement too hour, though the road network coverage rate still can increase, improve not quite, can cause waste economically this moment.Like [a, b]=[0.0005,0.05], when Floating Car operator select to increase Floating Car quantity, corresponding degree of improvement can be less than 0.005, and for example 0.0004, then representative increases n (for example: 100) Floating Car can only improve 0.04 percentage point, and it is little to improve effect.
206, road network information is analyzed, confirmed to influence the factor of map match accuracy, said factor mainly comprises the floating car data collection period.
What be worth explanation is, the map match accuracy is an important indicator of estimating Floating Car system service quality, and the floating car data collection period is the principal element of decision map match accuracy.
207, according to said factor and traffic-information service quality and the operation cost that influences the map match accuracy, set up said floating car data collection period allocation models, obtain the function expression of map match accuracy.
For the ease of understanding, the embodiment of the invention provides a kind of floating car data collection period allocation models for your guidance, and is specific as follows:
Wherein, Y3 represents map match accuracy function; MinAccuracy is the minimum expectation value of map match precision, and MinImprove2 is the minimum value of map match accuracy degree of improvement, and Tmin is the minimum value of floating car data collection period; Wherein, map match accuracy degree of improvement is unit with the percentage point, is used to be characterized in the improvement value of map match accuracy when reducing t on the basis that the floating car data collection period is T, and said t is set by Floating Car operator.For example: if Floating Car operator is set at 5 seconds with t, MinImprove2 is set at 20%, then characterizes Floating Car operator and be desirably in the current Floating Car system, the floating car data collection period was reduced by 5 seconds, the map match accuracy will improve 20% at least; If the floating car data collection period was reduced by 5 seconds, the improvement value of map match accuracy is lower than 20%, and then Floating Car operator not thinks and need reduce the floating car data collection period.
208, obtain the qualification parameter of said floating car data collection period allocation models, with constraint condition as floating car data collection period allocation models in the step 207.
Concrete; The qualification parameter of said floating car data collection period allocation models is set by Floating Car operator; Comprise: the minimum value of the minimum expectation value of the minimum value of floating car data collection period, map match accuracy and map match accuracy degree of improvement; Wherein, the improvement value of map match accuracy when said map match accuracy degree of improvement is used to characterize with the every reduction of said floating car data collection period t, said t value is set by Floating Car operator.
209, according to the qualification parameter of said floating car data collection period allocation models said floating car data collection period allocation models is calculated, obtain the configuration result of floating car data collection period, carry out reference for Floating Car operator.Wherein, said configuration result comprises: degree of the improvement scope of the upper limit value M axAccuracy of map match accuracy, map match accuracy, and optimum floating car data collection period etc.
Concrete, can adopt dichotomy that said floating car data collection period allocation models is calculated, concrete implementation method can adopt dichotomy that Floating Car quantity allocation models is carried out Calculation Method in the refer step 205, repeats no more here.
210, the configuration result of the Floating Car quantity that obtains and the configuration result of floating car data collection period are shown, carry out reference for Floating Car operator.
The Floating Car system configuration method that the embodiment of the invention provides can be configured Floating Car quantity and floating car data collection period, carries out reference for Floating Car operator.Compared with prior art, the method that the embodiment of the invention provides can make Floating Car operator between operation cost and service quality, weigh, and is convenient to Floating Car operator the Floating Car system is managed, and helps the development of intelligent transport technology.
The embodiment of the invention also provides a kind of Floating Car system configuration device, and is as shown in Figure 3, and said device comprises:
Set up unit 31, be used to set up Floating Car system configuration model;
Acquiring unit 32 is used to obtain the qualification parameter of said Floating Car system configuration model;
Processing unit 33 is used for according to said qualification parameter said Floating Car system configuration model being handled, and obtains the configuration result of Floating Car system.
Further, as shown in Figure 4, the said unit 31 of setting up comprises:
First sets up subelement 311, is used to set up Floating Car quantity allocation models, and said Floating Car quantity allocation models is used for the Floating Car quantity of Floating Car system is configured;
Second sets up subelement 312, is used to set up floating car data collection period allocation models, and said floating car data collection period allocation models is used for the floating car data collection period of Floating Car system is configured.
Further, as shown in Figure 5, said first sets up subelement 311 comprises:
First sets up module 3113; Be used for the corresponding relation that influences the factor and the road network coverage rate of road network coverage rate according to said; And the said corresponding relation that influences the factor and the road network fiduciary level of road network fiduciary level; And traffic-information service quality and operation cost, set up said Floating Car quantity allocation models, said Floating Car quantity allocation models is used to characterize different Floating Car quantity corresponding road network coverage rate and road network fiduciary level.
Said acquiring unit 32 comprises:
First obtains subelement 321, is used to obtain the qualification parameter of said Floating Car quantity allocation models;
Wherein, The qualification parameter of said Floating Car quantity allocation models comprises: the minimum expectation value of the maximal value of the Floating Car quantity that can move in the road network, the minimum expectation value of road network coverage rate, road network fiduciary level and the minimum value of road network coverage rate degree of improvement; Wherein, The improvement value of road network coverage rate when said road network coverage rate degree of improvement is used for being characterized in N Floating Car of road network increase, said n value is set by Floating Car operator.
Said processing unit 33 comprises:
First handles subelement 331; Be used for said Floating Car quantity allocation models being handled according to the qualification parameter of said Floating Car quantity allocation models; Obtain the configuration result of said Floating Car quantity allocation models; Said configuration result comprises: the higher limit of the higher limit of road network coverage rate, road network fiduciary level, degree of the improvement scope of road network coverage rate, and optimum Floating Car quantity.
Concrete, said first handles subelement 331 also is used to adopt dichotomy that degree of the improvement scope of said road network coverage rate and the minimum value of road network coverage rate degree of improvement are calculated, to obtain optimum Floating Car quantity.Concrete computing method can be with reference to said method embodiment.
Further, as shown in Figure 6, said second sets up subelement 312 comprises:
The 3rd analysis module 3121 is used for road network information is analyzed, and confirms to influence the factor of map match accuracy, and said factor comprises the floating car data collection period;
Second sets up module 3122; Be used for the factor and traffic-information service quality and the operation cost that influence the map match accuracy according to said; Set up said floating car data collection period allocation models, said floating car data collection period allocation models is used to characterize different floating car data collection period corresponding map matching accuracies.
Said acquiring unit 32 comprises:
Second obtains subelement 322, is used to obtain the qualification parameter of said floating car data collection period allocation models;
Wherein, The qualification parameter of said floating car data collection period allocation models comprises: the minimum value of the minimum expectation value of the minimum value of floating car data collection period, map match accuracy and map match accuracy degree of improvement; Wherein, Said map match accuracy degree of improvement is used to characterize the improvement value of map match accuracy when said floating car data collection period reduced t, and said t value is set by Floating Car operator.
Said processing unit 33 comprises:
Second handles subelement 332; Be used for said floating car data collection period allocation models being handled according to the qualification parameter of said floating car data collection period allocation models; Obtain the configuration result of said floating car data collection period allocation models; Said configuration result comprises: the higher limit of map match accuracy, map match accuracy degree of improvement scope, and optimum floating car data collection period value.
Concrete, said second handles subelement 332 also is used to adopt dichotomy that the minimum value of said map match accuracy degree of improvement scope and map match accuracy degree of improvement is calculated, and obtains optimum floating car data collection period value.
The Floating Car system configuration device that the embodiment of the invention provides can be configured Floating Car quantity and floating car data collection period, carries out reference for Floating Car operator.Compared with prior art, the method that the embodiment of the invention provides can make Floating Car operator between operation cost and service quality, weigh, and is convenient to Floating Car operator the Floating Car system is managed, and helps the development of intelligent transport technology.
Through the description of above embodiment, the those skilled in the art can be well understood to the present invention and can realize by the mode that software adds essential common hardware, can certainly pass through hardware, but the former is better embodiment under a lot of situation.Based on such understanding; The part that technical scheme of the present invention contributes to prior art in essence in other words can be come out with the embodied of software product, and this computer software product is stored in the storage medium that can read, like the floppy disk of computing machine; Hard disk or CD etc.; Comprise some instructions with so that computer equipment (can be personal computer, server, the perhaps network equipment etc.) carry out the described method of each embodiment of the present invention.
The above; Be merely embodiment of the present invention, but protection scope of the present invention is not limited thereto, any technician who is familiar with the present technique field is in the technical scope that the present invention discloses; Can expect easily changing or replacement, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of said claim.
Claims (20)
1. a Floating Car system configuration method is characterized in that, comprising:
Set up Floating Car system configuration model;
Obtain the qualification parameter of said Floating Car system configuration model;
According to said qualification parameter said Floating Car system configuration model is handled, obtained the configuration result of Floating Car system.
2. method according to claim 1 is characterized in that, the said Floating Car system configuration model of setting up comprises:
Set up Floating Car quantity allocation models, said Floating Car quantity allocation models is used for the Floating Car quantity of Floating Car system is configured;
Set up floating car data collection period allocation models, said floating car data collection period allocation models is used for the floating car data collection period of Floating Car system is configured.
3. method according to claim 2 is characterized in that, the said Floating Car quantity allocation models of setting up comprises:
Road network information is analyzed, confirmed to influence the factor of road network coverage rate and road network fiduciary level, said factor comprises roading density and each grade road in Floating Car quantity, the road network shared scale-up factor in said road network;
The said factor that influences road network coverage rate and road network fiduciary level is analyzed, confirmed the said corresponding relation that influences the factor and the road network coverage rate of road network coverage rate, and confirm the said corresponding relation that influences the factor and the road network fiduciary level of road network fiduciary level;
According to the said corresponding relation that influences the factor and the road network coverage rate of road network coverage rate; And the said corresponding relation that influences the factor and the road network fiduciary level of road network fiduciary level; And traffic-information service quality and operation cost; Set up said Floating Car quantity allocation models, said Floating Car quantity allocation models is used to characterize different Floating Car quantity corresponding road network coverage rate and road network fiduciary level.
4. method according to claim 3 is characterized in that, the said qualification parameter of obtaining said Floating Car system configuration model comprises:
Obtain the qualification parameter of said Floating Car quantity allocation models;
Wherein, The qualification parameter of said Floating Car quantity allocation models comprises: the minimum expectation value of the maximal value of the Floating Car quantity that can move in the road network, the minimum expectation value of road network coverage rate, road network fiduciary level and the minimum value of road network coverage rate degree of improvement; Wherein, The improvement value of road network coverage rate when said road network coverage rate degree of improvement is used for being characterized in n Floating Car of road network increase, said n value is set by Floating Car operator.
5. method according to claim 4 is characterized in that, saidly according to said qualification parameter said Floating Car system configuration model is handled, and the configuration result that obtains the Floating Car system comprises:
Qualification parameter according to said Floating Car quantity allocation models is handled said Floating Car quantity allocation models; Obtain the configuration result of said Floating Car quantity allocation models; Said configuration result comprises: the higher limit of the higher limit of road network coverage rate, road network fiduciary level, degree of the improvement scope of road network coverage rate, and optimum Floating Car quantity.
6. method according to claim 5 is characterized in that, saidly according to said qualification parameter said Floating Car system configuration model is handled, and the configuration result that obtains the Floating Car system comprises:
Adopt dichotomy that degree of the improvement scope of said road network coverage rate and the minimum value of road network coverage rate degree of improvement are calculated, to obtain optimum Floating Car quantity.
7. method according to claim 2 is characterized in that, the said floating car data collection period allocation models of setting up comprises:
Road network information is analyzed, confirmed to influence the factor of map match accuracy, said factor comprises the floating car data collection period;
According to saidly influencing the factor of map match accuracy, combining traffic-information service quality and operation cost simultaneously; Set up said floating car data collection period allocation models, said floating car data collection period allocation models is used to characterize different floating car data collection period corresponding map matching accuracies.
8. method according to claim 7 is characterized in that, the said qualification parameter of obtaining said Floating Car system configuration model comprises:
Obtain the qualification parameter of said floating car data collection period allocation models;
Wherein, The qualification parameter of said floating car data collection period allocation models comprises: the minimum value of the minimum expectation value of the minimum value of floating car data collection period, map match accuracy and map match accuracy degree of improvement; Wherein, Said map match accuracy degree of improvement is used to characterize the improvement value of map match accuracy when said floating car data collection period reduced t, and said t value is set by Floating Car operator.
9. method according to claim 8 is characterized in that, saidly according to said qualification parameter said Floating Car system configuration model is handled, and the configuration result that obtains the floating car data collection period comprises:
Qualification parameter according to said floating car data collection period allocation models is handled said floating car data collection period allocation models; Obtain the configuration result of said floating car data collection period allocation models; Said configuration result comprises: the higher limit of map match accuracy, map match accuracy degree of improvement scope, and optimum floating car data collection period value.
10. method according to claim 9 is characterized in that, saidly according to said qualification parameter said Floating Car system configuration model is handled, and the configuration result that obtains the Floating Car system comprises:
Adopt dichotomy that the minimum value of said map match accuracy degree of improvement scope and map match accuracy degree of improvement is calculated, obtain optimum floating car data collection period value.
11. a Floating Car system configuration device is characterized in that, comprising:
Set up the unit, be used to set up Floating Car system configuration model;
Acquiring unit is used to obtain the qualification parameter of said Floating Car system configuration model;
Processing unit is used for according to said qualification parameter said Floating Car system configuration model being handled, and obtains the configuration result of Floating Car system.
12. device according to claim 11 is characterized in that, the said unit of setting up comprises:
First sets up subelement, is used to set up Floating Car quantity allocation models, and said Floating Car quantity allocation models is used for the Floating Car quantity of Floating Car system is configured;
Second sets up subelement, is used to set up floating car data collection period allocation models, and said floating car data collection period allocation models is used for the floating car data collection period of Floating Car system is configured.
13. device according to claim 12 is characterized in that, said first sets up subelement comprises:
First analysis module is used for road network information is analyzed, and confirms to influence the factor of road network coverage rate and road network fiduciary level, and said factor comprises roading density and each grade road in Floating Car quantity, the road network shared scale-up factor in said road network;
Second analysis module; Be used for the said factor that influences road network coverage rate and road network fiduciary level is analyzed; Confirm the said corresponding relation that influences the factor and the road network coverage rate of road network coverage rate, and confirm the said corresponding relation that influences the factor and the road network fiduciary level of road network fiduciary level;
First sets up module; Be used for the corresponding relation that influences the factor and the road network coverage rate of road network coverage rate according to said; And the said corresponding relation that influences the factor and the road network fiduciary level of road network fiduciary level; And traffic-information service quality and operation cost, set up said Floating Car quantity allocation models, said Floating Car quantity allocation models is used to characterize different Floating Car quantity corresponding road network coverage rate and road network fiduciary level.
14. device according to claim 13 is characterized in that, said acquiring unit comprises:
First obtains subelement, is used to obtain the qualification parameter of said Floating Car quantity allocation models;
Wherein, The qualification parameter of said Floating Car quantity allocation models comprises: the minimum expectation value of the maximal value of the Floating Car quantity that can move in the road network, the minimum expectation value of road network coverage rate, road network fiduciary level and the minimum value of road network coverage rate degree of improvement; Wherein, The improvement value of road network coverage rate when said road network coverage rate degree of improvement is used for being characterized in N Floating Car of road network increase, said n value is set by Floating Car operator.
15. device according to claim 12 is characterized in that, said processing unit comprises:
First handles subelement; Be used for said Floating Car quantity allocation models being handled according to the qualification parameter of said Floating Car quantity allocation models; Obtain the configuration result of said Floating Car quantity allocation models; Said configuration result comprises: the higher limit of the higher limit of road network coverage rate, road network fiduciary level, degree of the improvement scope of road network coverage rate, and optimum Floating Car quantity.
16. device according to claim 15; It is characterized in that; Said first handles subelement specifically is used to adopt dichotomy that degree of the improvement scope of said road network coverage rate and the minimum value of road network coverage rate degree of improvement are calculated, to obtain optimum Floating Car quantity.
17. device according to claim 12 is characterized in that, said second sets up subelement comprises:
The 3rd analysis module is used for road network information is analyzed, and confirms to influence the factor of map match accuracy, and said factor comprises the floating car data collection period;
Second sets up module; Be used for the factor and traffic-information service quality and the operation cost that influence the map match accuracy according to said; Set up said floating car data collection period allocation models, said floating car data collection period allocation models is used to characterize different floating car data collection period corresponding map matching accuracies.
18. device according to claim 17 is characterized in that, said acquiring unit comprises:
Second obtains subelement, is used to obtain the qualification parameter of said floating car data collection period allocation models;
Wherein, The qualification parameter of said floating car data collection period allocation models comprises: the minimum value of the minimum expectation value of the minimum value of floating car data collection period, map match accuracy and map match accuracy degree of improvement; Wherein, Said map match accuracy degree of improvement is used to characterize the improvement value of map match accuracy when said floating car data collection period reduced t, and said t value is set by Floating Car operator.
19. device according to claim 18 is characterized in that, said processing unit comprises:
Second handles subelement; Be used for said floating car data collection period allocation models being handled according to the qualification parameter of said floating car data collection period allocation models; Obtain the configuration result of said floating car data collection period allocation models; Said configuration result comprises: the higher limit of map match accuracy, map match accuracy degree of improvement scope, and optimum floating car data collection period value.
20. method according to claim 19; It is characterized in that; Said second handles subelement specifically is used to adopt dichotomy that the minimum value of said map match accuracy degree of improvement scope and map match accuracy degree of improvement is calculated, and obtains optimum floating car data collection period value.
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| CN104282149A (en) * | 2014-09-29 | 2015-01-14 | 同济大学 | Road network probe vehicle arrangement method based on traffic state accuracy index evaluation |
| CN104809871A (en) * | 2015-04-10 | 2015-07-29 | 安徽四创电子股份有限公司 | Data compensation method of different kinds of networked vehicles on basis of global positioning system (GPS) |
| CN105721238A (en) * | 2014-11-03 | 2016-06-29 | 通用汽车环球科技运作有限责任公司 | Method and apparatus of adaptive sampling for vehicular crowd sensing applications |
| WO2020020260A1 (en) * | 2017-07-29 | 2020-01-30 | 司书春 | Method for improving monitoring coverage rate when taxi is used to perform air monitoring |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002208094A (en) * | 2001-01-09 | 2002-07-26 | Matsushita Electric Ind Co Ltd | Traffic information collection/service system |
| US20070271029A1 (en) * | 2003-05-12 | 2007-11-22 | Assimakis Tzamaloukas | Hierarchical floating car data network |
-
2011
- 2011-12-12 CN CN201110412813.8A patent/CN102496279B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002208094A (en) * | 2001-01-09 | 2002-07-26 | Matsushita Electric Ind Co Ltd | Traffic information collection/service system |
| US20070271029A1 (en) * | 2003-05-12 | 2007-11-22 | Assimakis Tzamaloukas | Hierarchical floating car data network |
Non-Patent Citations (5)
| Title |
|---|
| JEAB-LUC YGNACE,CHRIS DRANE: "CELLULAR TELECOMMUNICATION AND TRANSPORTATION CONVERGENCE:a Case Study of a Research Conducted in California and in France on Cellular Positioning Techniques and Transportation Issues", 《2001 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE PROCEEDINGS》 * |
| 刘丽娜，吴建平，左兴权，王春露: "浮动车最小样本数量确定方法综述", 《公路交通科技》 * |
| 姜桂艳，牛世峰，常安德，丛玉良: "GPS浮动车数据时间间隔优化方法", 《吉林大学学报（工学版）》 * |
| 涂智，李昊，姚琛，袁理: "基于最小浮动车样本数量的道路覆盖率与交通信息更新周期研究", 《中国铁道科学》 * |
| 高玲玲，李志鹏，陈宜开: "城市交通流状态估计中探测车采样量的计算方法", 《上海交通大学学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104282149A (en) * | 2014-09-29 | 2015-01-14 | 同济大学 | Road network probe vehicle arrangement method based on traffic state accuracy index evaluation |
| CN105721238A (en) * | 2014-11-03 | 2016-06-29 | 通用汽车环球科技运作有限责任公司 | Method and apparatus of adaptive sampling for vehicular crowd sensing applications |
| CN105721238B (en) * | 2014-11-03 | 2019-03-22 | 通用汽车环球科技运作有限责任公司 | Adaptively sampled method and system for the sensing application of vehicle cluster |
| CN104809871A (en) * | 2015-04-10 | 2015-07-29 | 安徽四创电子股份有限公司 | Data compensation method of different kinds of networked vehicles on basis of global positioning system (GPS) |
| WO2020020260A1 (en) * | 2017-07-29 | 2020-01-30 | 司书春 | Method for improving monitoring coverage rate when taxi is used to perform air monitoring |
| WO2020020259A1 (en) * | 2017-07-29 | 2020-01-30 | 司书春 | Method for improving monitoring coverage when using public service vehicle to perform atmospheric monitoring |
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