CN102496279B - 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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- CN102496279B CN102496279B CN201110412813.8A CN201110412813A CN102496279B CN 102496279 B CN102496279 B CN 102496279B CN 201110412813 A CN201110412813 A CN 201110412813A CN 102496279 B CN102496279 B CN 102496279B
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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 intelligent transport technology field, relate in particular to a kind of floating vehicle system collocation method and device.
Background technology
At intelligent transportation field, by floating vehicle system, gathering transport information has become the technical way that obtains Traffic Information.Floating vehicle 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.Floating vehicle system has short, small investment of construction period, wide coverage, the advantage such as real-time.In recent years, along with the widespread use of GPS device in Floating Car is, the fast development of communication transmission technology, the treatment technology of the traffic information data based on floating vehicle system reaches its maturity, and has been applied to some economically developed cities.Because the factors such as each urban geography feature, economic GDP, population are different, cause in the situation that transport information quality index is consistent the quantity of the Floating Car that each city is required, floating vehicle data acquisition cycle difference.
In realizing process of the present invention, inventor finds that in prior art, at least there are the following problems:
At present, in intelligent transportation system, not having a kind of clear and definite computing method can make Floating Car operator, according to traffic-information service quality, floating vehicle system is configured to (mainly comprise Floating Car quantity and data car data collection period are configured), Floating Car operator cannot be managed floating vehicle system, the high cost that causes floating vehicle system, or the service quality of the transport information that provides of floating vehicle 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 vehicle system collocation method and device, can make Floating Car operator weigh between service quality and operation cost, be convenient to Floating Car operator floating vehicle system is managed, be conducive to 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 present invention provides a kind of floating vehicle system collocation method, comprising:
Set up floating vehicle system allocation models;
Obtain the restriction parameter of described floating vehicle system allocation models;
According to described restriction parameter, described floating vehicle system allocation models is processed, obtained the configuration result of floating vehicle system.
On the other hand, the embodiment of the present invention also provides a kind of floating vehicle system inking device, comprising:
Set up unit, for setting up floating vehicle system allocation models;
Acquiring unit, for obtaining the restriction parameter of described floating vehicle system allocation models;
Processing unit, for according to described restriction parameter, described floating vehicle system allocation models being processed, obtains the configuration result of floating vehicle system.
Floating vehicle system collocation method and device that the embodiment of the present invention provides, by setting up floating vehicle system allocation models, the Service Quality Metrics that can set according to user is configured floating vehicle system, obtain configuration result and carry out reference for Floating Car operator, Floating Car operator is weighed between operation cost and service quality, be convenient to operator floating vehicle system is managed, be conducive to the development of intelligent transport technology.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, to the accompanying drawing of required use in embodiment or description of the Prior Art be briefly described below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skills, do not paying under the prerequisite of creative work, can also obtain according to these accompanying drawings other accompanying drawing.
The method flow schematic diagram that Fig. 1 provides for the embodiment of the present invention 1;
The method flow schematic diagram that Fig. 2 provides for the embodiment of the present invention 2;
The structural representation of the device that Fig. 3-Fig. 6 provides for the embodiment of the present invention 3.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the present invention's part embodiment, rather than whole embodiment.Embodiment based in the present invention, those of ordinary skills, not making the every other embodiment obtaining under creative work prerequisite, belong to the scope of protection of the invention.
Embodiment 1
The embodiment of the present invention provides a kind of floating vehicle system collocation method, and as shown in Figure 1, described method comprises:
101, set up floating vehicle system allocation models.
102, obtain the restriction parameter of described floating vehicle system allocation models.
Concrete, the restriction parameter of described floating vehicle system allocation models can be set by Floating Car operator.
103, according to described restriction parameter, described floating vehicle system allocation models is processed, obtained the configuration result of floating vehicle system.
Concrete, described configuration result comprises Floating Car quantity and the floating vehicle data acquisition cycle of floating vehicle system configuration, but is not limited only to this.
The floating vehicle system collocation method that the embodiment of the present invention provides, the restriction parameter that can set according to Floating Car operator is configured floating vehicle system, obtain configuration result and carry out reference for Floating Car operator, Floating Car operator is weighed between operation cost and service quality, be convenient to operator floating vehicle system is managed, be conducive to the development of intelligent transport technology.
Embodiment 2
The embodiment of the present invention provides a kind of floating vehicle system collocation method, and as shown in Figure 2, described method comprises:
201, road network information is analyzed, determined the factor affect road network coverage rate and road network fiduciary level, described factor includes but not limited to roading density in Floating Car quantity, road network and each grade road shared scale-up factor in described road network.
What deserves to be explained is, road network coverage rate and road network fiduciary level are to evaluate the important indicator of floating vehicle system service quality; Because the road information in 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 by setting Floating Car quantity.
202, according to the described factor that affects road network coverage rate and road network fiduciary level, set up respectively road network coverage rate model and road network Reliability Model, obtain the expression formula of road network coverage rate function and road network Reliability Function, described expression formula is for characterizing corresponding road network coverage rate function and the road network fiduciary level of different values of Floating Car quantity.
203, according to described road network coverage rate model and road network Reliability Model, and traffic-information service quality and operation cost, described Floating Car quantity allocation models set up.
For the ease of understanding, the embodiment of the present 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 road network Reliability Function; MinCoverage is the minimization expected value of road network coverage rate, and MinReliability is the minimization expected 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 road network; Wherein, road network coverage rate degree of improvement be take percentage point as unit, for being characterized in the basis that Floating Car quantity is N, and the improvement value of road network coverage rate while increasing n Floating Car, described n is set by Floating Car operator.For example: if Floating Car operator is set as 100 by n, MinImprove is set as to 20%, characterizes Floating Car operator and be desirably in current floating vehicle system, increase by 100 Floating Car, road network coverage rate at least will improve 20%; If increase by 100 Floating Car, the improvement value of road network coverage rate is lower than 20%, and Floating Car operator can be judged to be does not need to increase unsteady vehicle.
Apparent, in floating vehicle system, the Floating Car quantity n existing in road network is larger, and the road network coverage rate degree of improvement that every increase N amount Floating Car is brought is less.
204, obtain the restriction parameter of described Floating Car quantity allocation models, the constraint condition of usining as Floating Car quantity allocation models in step 203.
Concrete, described restriction parameter is set by Floating Car operator, described restriction parameter comprises: the minimization expected value of the maximal value of the Floating Car quantity that can move in road network, the minimization expected 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 described road network coverage rate degree of improvement road network coverage rate when being characterized in N Floating Car of the every increase of road network, described N is set by Floating Car operator.
205, according to the restriction parameter of described Floating Car quantity allocation models, described Floating Car quantity allocation models is calculated, obtain the configuration result of Floating Car quantity, for Floating Car operator, carry out reference.Wherein, described 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 to calculate to determine optimum Floating Car quantity to described Floating Car quantity allocation models, but be not limited only to this.For example, can adopt but be not limited to following steps:
1. according to degree of the improvement scope of road network coverage rate, determine the interval of Floating Car quantity, be denoted as [N1, N2], N1 < N2;
2. make Nmid=(N1+N2)/2, calculate the degree of improvement at Nmid place, be denoted as MidImprove;
The minimum M inImprove of the road network coverage rate degree of improvement of 3. MidImprove and Floating Car operator being set compares, if MidImprove=MinImprove, so Nmid be solve, i.e. optimum Floating Car quantity; If MidImprove > is MInImprove, make N1=Nmid, 2. N2=N2, return to step; If MidImprove < is MinImprove, make N1=N1,2. N2=Nmid, return to step;
4. when meeting default precision, or while reaching default cycle index, using the result obtaining as solve, solve end.
Preferably, degree of the improvement scope that Floating Car operator can set basis road network coverage rate is determined 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 establishing method can be with reference to as follows:
①Dang operator thinks that MinCoverage satisfies the demands, and when improving coverage rate again, the value of coverage rate degree of improvement should be at [b-ε, b] interior value, wherein ε is the value that relative a, b are less, as [a, b]=[0.05,0.15], ε desirable 0.02 or 0.01;
2. when Floating Car operator thinks that still needs improve coverage rate under MinCoverage has met the prerequisite of primary demand, can improve two from economy and road network coverage rate and weigh cubic plane, finally determine suitable degree of improvement.When degree of improvement too hour, although road network coverage rate still can increase, improve not quite, now can cause waste economically.As [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, representative (for example: 100) Floating Car can only improve 0.04 percentage point, improves effect little increases n.
206, road network information is analyzed, determined the factor that affects map match accuracy, described factor mainly comprises the floating vehicle data acquisition cycle.
What deserves to be explained is, map match accuracy is the important indicator of evaluating floating vehicle system service quality, and the floating vehicle data acquisition cycle is the principal element that determines map match accuracy.
207, according to described factor and traffic-information service quality and the operation cost that affects map match accuracy, set up described floating vehicle data acquisition cycle allocation models, obtain the function expression of map match accuracy.
For the ease of understanding, the embodiment of the present invention provides a kind of floating vehicle data acquisition cycle allocation models for your guidance, specific as follows:
Wherein, Y3 represents map match accuracy function; MinAccuracy is the minimization expected value of map match precision, and MinImprove2 is the minimum value of map match accuracy degree of improvement, and Tmin is the minimum value in floating vehicle data acquisition cycle; Wherein, map match accuracy degree of improvement be take percentage point as unit, the improvement value of map match accuracy while reducing t for being characterized on the basis that the floating vehicle data acquisition cycle is T, and described t is set by Floating Car operator.For example: if Floating Car operator is set as 5 seconds by t, MinImprove2 is set as to 20%, characterizes Floating Car operator and be desirably in current floating vehicle system, the floating vehicle data acquisition cycle is reduced by 5 seconds, map match accuracy at least will improve 20%; If the floating vehicle data acquisition cycle is reduced by 5 seconds, the improvement value of map match accuracy is lower than 20%, and Floating Car operator is thought does not need to reduce the floating vehicle data acquisition cycle.
208, obtain the restriction parameter of described floating vehicle data acquisition cycle allocation models, the constraint condition of usining as floating vehicle data acquisition cycle allocation models in step 207.
Concrete, the restriction parameter of described floating vehicle data acquisition cycle allocation models is set by Floating Car operator, comprise: the minimum value of the minimization expected value of the minimum value in floating vehicle data acquisition cycle, map match accuracy and map match accuracy degree of improvement, wherein, the improvement value of described map match accuracy degree of improvement map match accuracy when characterizing every reduction of described floating vehicle data acquisition cycle t, described t Zhi You Floating Car operator sets.
209, according to the restriction parameter of described floating vehicle data acquisition cycle allocation models, described floating vehicle data acquisition cycle allocation models is calculated, obtain the configuration result in floating vehicle data acquisition cycle, for Floating Car operator, carry out reference.Wherein, described configuration result comprises: degree of the improvement scope of the upper limit value M axAccuracy of map match accuracy, map match accuracy, and the optimum floating vehicle data acquisition cycle etc.
Concrete, can adopt dichotomy to calculate described floating vehicle data acquisition cycle allocation models, the method that concrete methods of realizing adopts dichotomy to calculate Floating Car quantity allocation models in can refer step 205 repeats no more herein.
210, the configuration result of the Floating Car quantity obtaining and the configuration result in floating vehicle data acquisition cycle are shown, for Floating Car operator, carry out reference.
The floating vehicle system collocation method that the embodiment of the present invention provides, can be configured Floating Car quantity and floating vehicle data acquisition cycle, for Floating Car operator, carries out reference.Compared with prior art, the method that the embodiment of the present invention provides can make Floating Car operator weigh between operation cost and service quality, is convenient to Floating Car operator floating vehicle system is managed, and is conducive to the development of intelligent transport technology.
The embodiment of the present invention also provides a kind of floating vehicle system inking device, and as shown in Figure 3, described device comprises:
Set up unit 31, for setting up floating vehicle system allocation models;
Acquiring unit 32, for obtaining the restriction parameter of described floating vehicle system allocation models;
Processing unit 33, for according to described restriction parameter, described floating vehicle system allocation models being processed, obtains the configuration result of floating vehicle system.
Further, as shown in Figure 4, the described unit 31 of setting up comprises:
First sets up subelement 311, and for setting up Floating Car quantity allocation models, described Floating Car quantity allocation models is for being configured the Floating Car quantity of floating vehicle system;
Second sets up subelement 312, and for setting up floating vehicle data acquisition cycle allocation models, described floating vehicle data acquisition cycle allocation models is for being configured the floating vehicle data acquisition cycle of floating vehicle system.
Further, as shown in Figure 5, described first sets up subelement 311 comprises:
The first analysis module 3111, for road network information is analyzed, determines the factor affect road network coverage rate and road network fiduciary level, and described factor comprises roading density in Floating Car quantity, road network and each grade road shared scale-up factor in described road network;
The second analysis module 3112, for the described factor that affects road network coverage rate and road network fiduciary level is analyzed, determine the described corresponding relation that affects factor and the road network coverage rate of road network coverage rate, and determine the described corresponding relation that affects factor and the road network fiduciary level of road network fiduciary level;
First sets up module 3113, be used for according to the described corresponding relation that affects factor and the road network coverage rate of road network coverage rate, and the described corresponding relation that affects factor and the road network fiduciary level of road network fiduciary level, and traffic-information service quality and operation cost, set up described Floating Car quantity allocation models, described Floating Car quantity allocation models is for characterizing road network coverage rate corresponding to different Floating Car quantity and road network fiduciary level.
Described acquiring unit 32 comprises:
First obtains subelement 321, for obtaining the restriction parameter of described Floating Car quantity allocation models;
Wherein, the restriction parameter of described Floating Car quantity allocation models comprises: the minimization expected value of the maximal value of the Floating Car quantity that can move in road network, the minimization expected 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 described road network coverage rate degree of improvement increases N Floating Car for being characterized in road network, described n Zhi You Floating Car operator sets.
Described processing unit 33 comprises:
First processes subelement 331, for described Floating Car quantity allocation models being processed according to the restriction parameter of described Floating Car quantity allocation models, obtain the configuration result of described Floating Car quantity allocation models, described 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, described first processes subelement 331 also for adopting dichotomy to calculate the minimum value of degree of the improvement scope of described road network coverage rate and road network coverage rate degree of improvement, to obtain optimum Floating Car quantity.Concrete computing method can be with reference to said method embodiment.
Further, as shown in Figure 6, described second sets up subelement 312 comprises:
The 3rd analysis module 3121, for road network information is analyzed, determines the factor that affects map match accuracy, and described factor comprises the floating vehicle data acquisition cycle;
Second sets up module 3122, be used for according to described factor and traffic-information service quality and the operation cost that affects map match accuracy, set up described floating vehicle data acquisition cycle allocation models, described floating vehicle data acquisition cycle allocation models is for characterizing map match accuracy corresponding to different floating vehicle data acquisition cycles.
Described acquiring unit 32 comprises:
Second obtains subelement 322, for obtaining the restriction parameter of described floating vehicle data acquisition cycle allocation models;
Wherein, the restriction parameter of described floating vehicle data acquisition cycle allocation models comprises: the minimum value of the minimization expected value of the minimum value in floating vehicle data acquisition cycle, map match accuracy and map match accuracy degree of improvement, wherein, the improvement value of map match accuracy when described map match accuracy degree of improvement reduces t for characterizing by the described floating vehicle data acquisition cycle, described t Zhi You Floating Car operator sets.
Described processing unit 33 comprises:
Second processes subelement 332, for described floating vehicle data acquisition cycle allocation models being processed according to the restriction parameter of described floating vehicle data acquisition cycle allocation models, obtain the configuration result of described floating vehicle data acquisition cycle allocation models, described configuration result comprises: the higher limit of map match accuracy, map match accuracy degree of improvement scope, and optimum floating vehicle data acquisition periodic quantity.
Concrete, described second processes subelement 332 also for adopting dichotomy to calculate the minimum value of described map match accuracy degree of improvement scope and map match accuracy degree of improvement, obtains optimum floating vehicle data acquisition periodic quantity.
The floating vehicle system inking device that the embodiment of the present invention provides, can be configured Floating Car quantity and floating vehicle data acquisition cycle, for Floating Car operator, carries out reference.Compared with prior art, the method that the embodiment of the present invention provides can make Floating Car operator weigh between operation cost and service quality, is convenient to Floating Car operator floating vehicle system is managed, and is conducive to the development of intelligent transport technology.
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 essential common hardware by software and realize, and can certainly pass through hardware, but in a lot of situation, the former is better embodiment.Understanding based on such, the part that technical scheme of the present invention contributes to prior art in essence in other words can embody with the form of software product, this computer software product is stored in the storage medium can read, as the floppy disk of computing machine, hard disk or CD etc., comprise some instructions with so that computer equipment (can be personal computer, server, or the network equipment etc.) carry out the method described in each embodiment of the present invention.
The above; be only the specific embodiment of the present invention, but protection scope of the present invention is not limited to this, is anyly familiar with those skilled in the art in the technical scope that the present invention discloses; can expect easily changing or replacing, within all should being encompassed in protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection domain of described claim.
Claims (18)
1. a floating vehicle system collocation method, is characterized in that, comprising:
Set up floating vehicle system allocation models, comprising: set up Floating Car quantity allocation models, described Floating Car quantity allocation models is for being configured the Floating Car quantity of floating vehicle system; Set up floating vehicle data acquisition cycle allocation models, described floating vehicle data acquisition cycle allocation models is for being configured the floating vehicle data acquisition cycle of floating vehicle system;
Obtain the restriction parameter of described floating vehicle system allocation models;
According to described restriction parameter, described floating vehicle system allocation models is processed, obtained the configuration result of floating vehicle system;
Wherein, the described Floating Car quantity allocation models of setting up comprises: road network information is analyzed, determine the factor affect road network coverage rate and road network fiduciary level, described factor comprises roading density in Floating Car quantity, road network and each grade road shared scale-up factor in described road network; The described factor that affects road network coverage rate and road network fiduciary level is analyzed, determined the described corresponding relation that affects factor and the road network coverage rate of road network coverage rate, and determine the described corresponding relation that affects factor and the road network fiduciary level of road network fiduciary level; According to the described corresponding relation that affects factor and the road network coverage rate of road network coverage rate, and the described corresponding relation that affects factor and the road network fiduciary level of road network fiduciary level, and traffic-information service quality and operation cost, set up described Floating Car quantity allocation models, described Floating Car quantity allocation models is for characterizing road network coverage rate corresponding to different Floating Car quantity and road network fiduciary level.
2. method according to claim 1, is characterized in that, described in obtain described floating vehicle system allocation models restriction parameter comprise:
Obtain the restriction parameter of described Floating Car quantity allocation models;
Wherein, the restriction parameter of described Floating Car quantity allocation models comprises: the minimization expected value of the maximal value of the Floating Car quantity that can move in road network, the minimization expected 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 described road network coverage rate degree of improvement increases n Floating Car for being characterized in road network, described n Zhi You Floating Car operator sets.
3. method according to claim 2, is characterized in that, describedly according to described restriction parameter, described floating vehicle system allocation models is processed, and the configuration result that obtains floating vehicle system comprises:
According to the restriction parameter of described Floating Car quantity allocation models, described Floating Car quantity allocation models is processed, obtain the configuration result of described Floating Car quantity allocation models, described 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.
4. method according to claim 3, is characterized in that, describedly according to described restriction parameter, described floating vehicle system allocation models is processed, and the configuration result that obtains floating vehicle system comprises:
Adopt dichotomy to calculate the minimum value of degree of the improvement scope of described road network coverage rate and road network coverage rate degree of improvement, to obtain optimum Floating Car quantity.
5. method according to claim 1, is characterized in that, the described floating vehicle data acquisition cycle allocation models of setting up comprises:
Road network information is analyzed, determined the factor that affects map match accuracy, the described factor that affects map match accuracy comprises the floating vehicle data acquisition cycle;
According to the described factor that affects map match accuracy, simultaneously in conjunction with traffic-information service quality and operation cost, set up described floating vehicle data acquisition cycle allocation models, described floating vehicle data acquisition cycle allocation models is for characterizing map match accuracy corresponding to different floating vehicle data acquisition cycles.
6. method according to claim 5, is characterized in that, described in obtain described floating vehicle system allocation models restriction parameter comprise:
Obtain the restriction parameter of described floating vehicle data acquisition cycle allocation models;
Wherein, the restriction parameter of described floating vehicle data acquisition cycle allocation models comprises: the minimum value of the minimization expected value of the minimum value in floating vehicle data acquisition cycle, map match accuracy and map match accuracy degree of improvement, wherein, the improvement value of map match accuracy when described map match accuracy degree of improvement reduces t for characterizing by the described floating vehicle data acquisition cycle, described t Zhi You Floating Car operator sets.
7. method according to claim 6, is characterized in that, describedly according to described restriction parameter, described floating vehicle system allocation models is processed, and the configuration result that obtains floating vehicle system comprises:
According to the restriction parameter of described floating vehicle data acquisition cycle allocation models, described floating vehicle data acquisition cycle allocation models is processed, obtain the configuration result of described floating vehicle data acquisition cycle allocation models, the configuration result of described floating vehicle data acquisition cycle allocation models comprises: the higher limit of map match accuracy, map match accuracy degree of improvement scope, and optimum floating vehicle data acquisition periodic quantity.
8. method according to claim 7, is characterized in that, describedly according to described restriction parameter, described floating vehicle system allocation models is processed, and the configuration result that obtains floating vehicle system comprises:
Adopt dichotomy to calculate the minimum value of described map match accuracy degree of improvement scope and map match accuracy degree of improvement, obtain optimum floating vehicle data acquisition periodic quantity.
9. a floating vehicle system inking device, is characterized in that, comprising:
Set up unit, for setting up floating vehicle system allocation models;
Acquiring unit, for obtaining the restriction parameter of described floating vehicle system allocation models;
Processing unit, for according to described restriction parameter, described floating vehicle system allocation models being processed, obtains the configuration result of floating vehicle system.
10. device according to claim 9, is characterized in that, the described unit of setting up comprises:
First sets up subelement, and for setting up Floating Car quantity allocation models, described Floating Car quantity allocation models is for being configured the Floating Car quantity of floating vehicle system;
Second sets up subelement, and for setting up floating vehicle data acquisition cycle allocation models, described floating vehicle data acquisition cycle allocation models is for being configured the floating vehicle data acquisition cycle of floating vehicle system.
11. devices according to claim 10, is characterized in that, described first sets up subelement comprises:
The first analysis module, for road network information is analyzed, determines the factor affect road network coverage rate and road network fiduciary level, and described factor comprises roading density in Floating Car quantity, road network and each grade road shared scale-up factor in described road network;
The second analysis module, for the described factor that affects road network coverage rate and road network fiduciary level is analyzed, determine the described corresponding relation that affects factor and the road network coverage rate of road network coverage rate, and determine the described corresponding relation that affects factor and the road network fiduciary level of road network fiduciary level;
First sets up module, be used for according to the described corresponding relation that affects factor and the road network coverage rate of road network coverage rate, and the described corresponding relation that affects factor and the road network fiduciary level of road network fiduciary level, and traffic-information service quality and operation cost, set up described Floating Car quantity allocation models, described Floating Car quantity allocation models is for characterizing road network coverage rate corresponding to different Floating Car quantity and road network fiduciary level.
12. devices according to claim 11, is characterized in that, described acquiring unit comprises:
First obtains subelement, for obtaining the restriction parameter of described Floating Car quantity allocation models;
Wherein, the restriction parameter of described Floating Car quantity allocation models comprises: the minimization expected value of the maximal value of the Floating Car quantity that can move in road network, the minimization expected 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 described road network coverage rate degree of improvement increases n Floating Car for being characterized in road network, described n Zhi You Floating Car operator sets.
13. devices according to claim 10, is characterized in that, described processing unit comprises:
First processes subelement, for described Floating Car quantity allocation models being processed according to the restriction parameter of described Floating Car quantity allocation models, obtain the configuration result of described Floating Car quantity allocation models, the configuration result of described Floating Car quantity allocation models 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.
14. devices according to claim 13, it is characterized in that, described first processes subelement specifically for adopting dichotomy to calculate the minimum value of degree of the improvement scope of described road network coverage rate and road network coverage rate degree of improvement, to obtain optimum Floating Car quantity.
15. devices according to claim 10, is characterized in that, described second sets up subelement comprises:
The 3rd analysis module, for road network information is analyzed, determines the factor that affects map match accuracy, and described factor comprises the floating vehicle data acquisition cycle;
Second sets up module, be used for according to described factor and traffic-information service quality and the operation cost that affects map match accuracy, set up described floating vehicle data acquisition cycle allocation models, described floating vehicle data acquisition cycle allocation models is for characterizing map match accuracy corresponding to different floating vehicle data acquisition cycles.
16. devices according to claim 15, is characterized in that, described acquiring unit comprises:
Second obtains subelement, for obtaining the restriction parameter of described floating vehicle data acquisition cycle allocation models;
Wherein, the restriction parameter of described floating vehicle data acquisition cycle allocation models comprises: the minimum value of the minimization expected value of the minimum value in floating vehicle data acquisition cycle, map match accuracy and map match accuracy degree of improvement, wherein, the improvement value of map match accuracy when described map match accuracy degree of improvement reduces t for characterizing by the described floating vehicle data acquisition cycle, described t Zhi You Floating Car operator sets.
17. devices according to claim 16, is characterized in that, described processing unit comprises:
Second processes subelement, for described floating vehicle data acquisition cycle allocation models being processed according to the restriction parameter of described floating vehicle data acquisition cycle allocation models, obtain the configuration result of described floating vehicle data acquisition cycle allocation models, the configuration result of described floating vehicle data acquisition cycle allocation models comprises: the higher limit of map match accuracy, map match accuracy degree of improvement scope, and optimum floating vehicle data acquisition periodic quantity.
18. devices according to claim 17, it is characterized in that, described second processes subelement specifically for adopting dichotomy to calculate the minimum value of described map match accuracy degree of improvement scope and map match accuracy degree of improvement, obtains optimum floating vehicle data acquisition periodic quantity.
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| JP2002208094A (en) * | 2001-01-09 | 2002-07-26 | Matsushita Electric Ind Co Ltd | Traffic information collection/service system |
| US7188026B2 (en) * | 2003-05-12 | 2007-03-06 | Dash Navigation, Inc. | Hierarchical floating car data network |
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2011
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