CN102890869A - Vehicle route predicting and notifying method and mobile intelligent terminal - Google Patents
Vehicle route predicting and notifying method and mobile intelligent terminal Download PDFInfo
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Abstract
The invention provides a vehicle route predicting and notifying method and a mobile intelligent terminal. The method comprises the following steps that: the mobile intelligent terminal records a traveling track of a vehicle; the mobile intelligent terminal or a server calculates to generate traveling route information and traveling time length information of the vehicle; and when the mobile intelligent terminal judges that the vehicle is positioned in an initial position range of a traveling route, takes the route information and the traveling time length information as a predicted traveling route and a predicted traveling time length, transmits the predicted traveling route or the predicted traveling time length to specified terminal equipment. The mobile intelligent terminal is provided with a traveling track recording module, a traveling route calculating module and a predicting and notifying module, and is used for taking the route information and the traveling time length information as the predicted traveling route and the predicted traveling time length, and transmitting the predicted route information or the predicted traveling time length information to the specified terminal equipment. By the vehicle route predicting and notifying method and the mobile intelligent terminal, traveling condition information of the vehicle can be transmitted to specified personnel in time.
Description
Technical field
The present invention relates to field of mobile communication, the especially a kind of mobile intelligent terminal that can predict, notify the vehicle driving route and this mobile intelligent terminal are to the method for vehicle driving path prediction, notice.
Background technology
Along with popularizing of automobile, people often select the trip of driving.Often also have guider when people drive, as be arranged on the realization navigation features such as navigation software on the mobile intelligent terminal, so that the direction that correct identification is travelled.Existing guider is departure place and the destination of manually being inputted vehicle by the driver mostly, guider selects one or more traffic route, driver can choose traffic route wherein and navigated by guider according to Geographic Information System (GIS).
Yet, existing guider only provides optional route and the route that the driver chooses is navigated, the driving duration is just carried out simple forecast, and can not in time the driving duration notice of selected route and prediction be needed to obtain the other staff of these information.Yet, in actual life, have relevant specific people in people's trip process and wait in the place of this vehicle approach or arrival, this part specific people is traffic route and the estimated time of arrival (ETA) in the urgent need to knowing this vehicle often, and wishes that estimated time of arrival (ETA) is more accurate better.But existing guider can not well satisfy the demand.
In addition, existing guider all needs the driver manually to input the destination, can't predict judgement to the destination, makes troubles also for driver's use.Simultaneously, existing guider can not predict to the situation in the traffic route that if there is the highway section that stopping state occurs in the traffic route of required process, the traffic jam information that the driver can not obtain accurately, quantize also causes inconvenience to people's trip.
In addition, more existing buses are equipped with supervising device, supervising device can be monitored the traffic route of bus, and the status of implementation that bus travels is sent to background server, background server arrives time of specific bus stop according to the transport condition of bus prediction bus, and with the time showing of prediction on specific display device.
But, being used for supervising device on the bus does not calculate the traffic route of bus and predicts, because the traffic route of bus is fixed, and background server is the traffic route of pre-stored bus, this traffic route is to be input to artificially in the background server in advance, for traffic route and unfixed private vehicle, above-mentioned supervising device and system are also inapplicable.
Summary of the invention
Fundamental purpose of the present invention provides a kind of vehicle driving path prediction Notification Method that can allow specific personnel in time understand traveling state of vehicle.
Another object of the present invention provides a kind of mobile intelligent terminal that the vehicle driving route is calculated and predicts.
In order to realize above-mentioned fundamental purpose, vehicle driving path prediction Notification Method provided by the invention comprises the driving trace of mobile intelligent terminal registration of vehicle, mobile intelligent terminal and/or server calculate to generate the route information of Vehicle Driving Cycle and vehicle at the driving duration information of this route running according to driving trace, mobile intelligent terminal is judged behind vehicle launch when vehicle is positioned at the reference position scope of traffic route, with route information and driving duration information traffic route and the driving duration as prediction, and route information and/or the driving duration information of prediction be sent to the terminal device of appointment.
By such scheme as seen, the method is calculated the route of Vehicle Driving Cycle, and keeping track of history traffic route, traffic route according to historical traffic route prediction vehicle, and traffic route and information such as driving duration etc. be sent to specific terminal device, can in time inform specific personnel's traveling state of vehicle, be conducive to the travel conditions that the related personnel in time understands vehicle.
A preferred scheme is after mobile intelligent terminal or server generate route information, to judge the periodicity of traffic route, the cycle information of generation route information; Before mobile intelligent terminal judges whether vehicle is positioned at the reference position scope of traffic route, judge current date and/or time whether in the scope of cycle information corresponding to route information, in this way, the execution path prediction steps, otherwise, execution path prediction steps not.
This shows, mobile intelligent terminal or background server carry out periodicity analysis to traffic route, can obtain more meticulous data, more accurate for the prediction of the traffic route of vehicle and the duration of driving a vehicle, also be conducive to the information that the related personnel understands traveling state of vehicle exactly.
Further scheme is, mobile intelligent terminal judges whether to receive block information, as receives after sending the driving duration information to terminal device, recomputate the driving duration information according to block information, and the driving duration information after will recomputating is sent to terminal device.
As seen, mobile intelligent terminal recomputates the driving duration and is sent to terminal device after receiving block information, can allow the related personnel in time understand the real time status of Vehicle Driving Cycle.
For realizing another above-mentioned purpose, mobile intelligent terminal provided by the invention comprises the driving trace logging modle for the driving trace of registration of vehicle, calculate to generate the route information of Vehicle Driving Cycle and vehicle at traffic route computing module and the prediction notification module of the driving duration information of this route running according to driving trace, be used for behind vehicle launch, judging when vehicle is positioned at the reference position scope of traffic route, with route information and driving duration information traffic route and the driving duration as prediction, and route information and/or the driving duration information of prediction be sent to the terminal device of appointment.
By such scheme as seen, the driving trace of mobile intelligent terminal registration of vehicle also calculates traffic route thus, record simultaneously and the keeping track of history traffic route, and according to historical traffic route the traffic route of vehicle is predicted, information with prediction is sent to specific terminal device simultaneously, so that specific personnel in time understand the travel conditions of vehicle.
Further scheme is, the traffic route computing module has periodically computing module, is used for calculating the periodicity of traffic route, generates the cycle information of route information.
This shows that mobile intelligent terminal can be predicted more accurately according to cycle information the traffic route of vehicle, also more accurate to the prediction of driving duration, be conducive to the related personnel and understand more accurately traveling state of vehicle.
Description of drawings
Fig. 1 is the synoptic diagram that mobile intelligent terminal embodiment of the present invention is connected with server, terminal device.
Fig. 2 is the structural representation block diagram of mobile intelligent terminal embodiment of the present invention.
Fig. 3 is the process flow diagram of vehicle driving path prediction Notification Method embodiment of the present invention.
Fig. 4 is the synoptic diagram of vehicle driving path prediction Notification Method embodiment middle rolling car number of times array of the present invention and driving trace array.
Fig. 5 is the synoptic diagram of a driving trace among the vehicle driving path prediction Notification Method embodiment of the present invention.
Fig. 6 is the synoptic diagram in a driving path among the vehicle driving path prediction Notification Method embodiment of the present invention.
Fig. 7 is the synoptic diagram in another driving path among the vehicle driving path prediction Notification Method embodiment of the present invention.
Fig. 8 is the synoptic diagram of vehicle driving path prediction Notification Method embodiment middle rolling car route of the present invention.
Fig. 9 is the synoptic diagram of the traffic route after merging among the vehicle driving path prediction Notification Method embodiment of the present invention.
Figure 10 is the process flow diagram that calculates the driving cycle among the vehicle driving path prediction Notification Method embodiment of the present invention.
The invention will be further described below in conjunction with drawings and Examples.
Embodiment
Vehicle driving path prediction Notification Method of the present invention relates to a plurality of equipment, as shown in Figure 1, this method relates to more than one mobile intelligent terminal 10,20,21 etc., mobile intelligent terminal can be smart mobile phone, also can be other portable smart machines, such as panel computer, portable Intelligent music player, intelligent game computer etc.Certainly, can also be on-vehicle navigation apparatus, such as vehicle-mounted GPS navigator etc.
Alternatively, this method also uses a background server 22, mobile intelligent terminal 10,20,21 can carry out radio communication by mode and the background server 22 of radio communication, the information of mobile intelligent terminal 10,20,21 records is sent to background server 22, perhaps obtains the data that it calculates, analyzes from background server 22.
And, this method also relates to one or more terminal devices, such as mobile phone 23,24 or printing device 25 etc., mobile intelligent terminal 10,20,21 information exchanges that will predict or calculate are crossed communication and are sent on the terminal device, send prompting message by terminal device, as showing the traffic route of vehicle or the driving duration of prediction by display screen, perhaps print by the information of printing device 25 with prediction.Certainly, terminal device can also be such as electric equipments such as intelligent air conditions, and for example when the driving duration remained 20 minutes, mobile intelligent terminal sent information to intelligent air condition, and air-conditioning is opened after receiving information automatically.
Referring to Fig. 2, mobile intelligent terminal 10 is provided with driving trace logging modle 11, traffic route computing module 12, prediction notification module 13 and judgment of clogging module 14, wherein be provided with path and duration computing module 15, path merging module 16, periodicity computing module 17 in the traffic route computing module 12, specify the principle of work of above-mentioned modules below in conjunction with vehicle driving path prediction Notification Method.
Referring to Fig. 3, when mobile intelligent terminal 10 is worked first, need the driving trace of registration of vehicle, as shown in Figure 4, driving trace logging modle 11 is set up two arrays, is respectively Vehicle Run array and driving trace array.When vehicle sets out, driving trace logging modle 11 beginning execution in step S 1, the data of record driving trace, it uses mobile satellite location equipment registration of vehicle driving trace.When vehicle is flame-out stop after, the record operation of the track data that finishes to travel.
The driving trace array has recorded in the Vehicle Driving Cycle process in each time point position, it is comprised of four column datas altogether: sequence number, time point, longitude and latitude, wherein latitude and longitude coordinates is to obtain according to global position system, time point is the time point that obtains latitude and longitude coordinates, sequence number is the index value of array, with the self-propagation numerical value of 1 beginning.
Each bar record in the Vehicle Run array represent Vehicle Driving Cycle process stop working from beginning to vehicle parking starting position and end position driving trace array corresponding to this process, what the first row in the Vehicle Run array and secondary series were preserved is the sequence number index of driving trace array, driving trace array indexing corresponding to vehicle position when the numeric representation of first row is set out, driving trace array indexing corresponding to vehicle position when the numeric representation of secondary series arrives the destination.
When track record module 11 judgements of travelling need record driving trace data, in the driving trace array, increase a record, current latitude and longitude coordinates, current time are filled up in the record of record driving trace array.Also correspondingly increase a record in the train number array of being expert at simultaneously, the sequence number of article one of driving trace array record is written to the 1st row of Vehicle Run data corresponding record.
In the Vehicle Driving Cycle process, driving trace logging modle 11 is regularly obtained current latitude and longitude coordinates, time and is increased in the driving trace array, and the longitude and latitude that obtains each time, time all form a record and is kept in the driving trace array.When driving trace logging modle 11 judged that vehicle is flame-out, the sequence number that reads the last item record in the driving trace array also was written to this sequence number the 2nd row of Vehicle Run array respective record, forms the record of a complete Vehicle Run array.
After the above-mentioned driving trace data that mobile intelligent terminal 10 generates, it can be kept in the magnetic disk media of mobile intelligent terminal 10, also can upload to background server 22 by the asynchronous timing of mobile internet and preserve.
Driving trace logging modle 11 starts the driving trace record and stops the driving trace record and can be realized by the manual input command of driver, also can request for utilization number be 201210288884.6, denomination of invention is for the method that the application for a patent for invention of " starting method of mobile intelligent terminal and vehicle management module thereof " provides realizes, automatically sends corresponding order according to current vehicle-state.
After the mobile intelligent terminal 10 record driving trace data, path and duration computing module 15 or background server 22 execution in step S2 by mobile intelligent terminal 10, generate driving trace according to the driving trace data, and generate corresponding driving path according to driving trace.For example, from the Vehicle Run array, take out a record, according to the 1st row of this driving recording and the numerical value in the 2nd row, determine the driving trace array element set that this driving process is corresponding, then according to the longitude and latitude data in the driving trace array, connect each latitude and longitude coordinates and form the driving curve, and indicate binding site between each line segment, i.e. tie point between two line segments, as shown in Figure 5, wherein LS represents the starting point of driving a vehicle, and LD is the end point of driving, and L1 to L5 is respectively the binding site of each line segment.
Recorded repeatedly in the situation of driving trace data at mobile intelligent terminal 10, need to calculate and generate many driving curves to each driving trace data, the driving curve of generation is driving trace namely.
Then, all driving curves of the above-mentioned generation of searching loop generate the driving path.For example, take out a driving curve, and take out the coordinate of the starting point LS of driving curve, from all driving curves that generate, search all driving collection of curves take the LS coordinate as the departure place.If the set that finds is null set, also do not generate driving path corresponding to curve before illustrating, then generate the driving path according to this driving curve.
The step that generates the driving path is: centered by the LS coordinate of driving curve, take 100 meters regional departure places as the driving path of the circle as radius, represent the departure place with PS.In like manner, centered by the LD coordinate of driving curve, take 100 meters as the border circular areas of the radius destination as the driving path, represent the destination with PD.Then, along the driving curve starting point to the end point direction, each straight line line segment of traversal driving curve is done translation to each bar straight line line segment and has been gone out, to 40 meters of lefts successively, and form a rectangular region to 20 meters of right translations, each rectangular region along the direction of traffic horizontal-extending, is calculated the intersecting area of adjacent two rectangular region, i.e. the position of binding site, according to each binding site adjacent rectangular region is coupled together successively, generate the driving path.The driving path that generates as shown in Figure 6, the departure place among Fig. 6 represents with PS, the destination represents with PD, and P1, P2, P3 etc. represent the binding site of regional.At last, also history of forming driving path is preserved in the driving path of above-mentioned generation.
To the straight line line segment of driving curve carry out translation be because travel the road of process itself certain width is arranged, and the precision of global position system also has certain deviation.Simultaneously, in generation pass when zone, adopted the method for left-right asymmetry translation, is to consider that in China's rule of travelling be the right side rule of travelling.
Certainly, the more excellent scheme that generates the driving path is to be combined with Geographic Information System (GIS), searches road data in the Geographic Information System according to each straight line line segment of driving curve, finds the coordinate data of corresponding road as the driving path.Because the road net coordinate data is that mapping is finished through reality in the Geographic Information System, the degree of accuracy in the driving path that generates accordingly can be higher.
If searching the set of all driving curves take the LS coordinate as the departure place from all driving curves that generate is nonempty sets, having from LS before illustrating is the driving path of departure place, judge successively that then current driving curve is whether fully in zone, current line bus or train route footpath, if so, then preserve the driving path driving trace record corresponding with the driving curve and set up incidence relation.If not, then generate a new driving path according to above-mentioned steps.
Yet, in actual life, may have many driving paths from identical departure place to identical destination, therefore need to be merged into a traffic route to the path of identical departure place and identical destination.Therefore, merging module 16 in path will merge the driving path.Drive a vehicle the in the present invention departure place in path is a border circular areas, judge that two departure places are whether identical and can adopt following method: if two border circular areas in the satellite positioning coordinate planimetric map have common factor, think that then this departure place is identical, otherwise think two different departure places.In like manner, whether identical determination methods also is like this for the destination.Carry out successively above-mentioned algorithm, identical departure place and identical destination many driving paths are merged into a traffic route, so this traffic route is traffic route optionally.
Suppose that Fig. 6 and Fig. 7 are respectively two different driving paths, and PS and PS' be same departure place, PD and PD' are same destinations, and the traffic route after the merging as shown in Figure 8.
On the basis of the above, continue by the following method to merge the driving path: if two driving paths are identical above 90% zone, path, and air line distance is in 800 meters between the departure place in the path of two driving a vehicle, and air line distance is also in 800 meters between two destinations, then these two driving paths are merged into a new traffic route, the driving path of carrying out successively after the above-mentioned merging can exist a plurality of departure places and/or a plurality of destination.Traffic route after merging as shown in Figure 9.Why merging, is may have a plurality of Public Parkings because of departure place in reality or destination, and the driver drives a vehicle at every turn and need to according to circumstances dock to different parking lots.
Preferably, " the driving path " of above-mentioned generation can allow the driver to merge by hand according to actual conditions, and many driving paths are merged into a traffic route.
Then, the periodicity computing module 17 of mobile intelligent terminal 10 or background server 22 execution in step S3 calculate the periodicity of each bar traffic route.The in the present invention driving cycle is divided into two parts: diurnal periodicity and hours period.Refer to cycle period and the probability of happening of sailing date diurnal periodicity, hours period refers to that within same diurnal periodicity at cycle period and the probability of happening of different time sections, hours period can be as accurate as 0.5 hour.
For example, shown in the period type table 1 of diurnal periodicity of the present invention.
Table 1
The period type of hours period is as shown in table 2.
| Code name | Type | Explanation | For example |
| HC1 | On a time period | From the | For example drive to set out at 7:30-8:30 |
Table 2
Driving cycle data structure is as shown in table 3.
Table 3
Wherein, the type diurnal periodicity types such as " by day cycle ", " press cycle " of period type table in a few days, the content in the cycle data structure of driving a vehicle is type list diurnal periodicity " code name ".Diurnal periodicity, parameter referred to the required parameter of particular decision type diurnal periodicity, and for example diurnal periodicity, type was " by cycle " (being code name DC3), so diurnal periodicity parameter content then be " Monday, Tuesday, Wednesday ..., Sunday ".Diurnal periodicity, probability referred to have the probability of driving a vehicle and setting out in the particular day of specific " type diurnal periodicity ", and for example diurnal periodicity, type was " pressing cycle ", and " Monday ", diurnal periodicity, probability was 92%.The hours period type refers to " code name " in the hours period type list, i.e. " HC1 ".The hours period parameter refers to the required parameter of specific hours period type judgement, for example " 7:30-8:30 ".Vehicle Run refers to which time driving in a day.The hours period probability refers to that in the particular day of particular day period type the fate that meets the hours period type accounts for the number percent of the fate that driving a vehicle sets out records.For example have nearest 3 middle of the month 13 " Mondays ", wherein there be 12 " Monday " to have a driving record that sets out, wherein be to set out in the 7:30-8:00 period 10 Mondays, be to set out in the period at 8:00-8:30 2 Mondays, the hours period probability of these two times is respectively 10 ÷, 12 * 100%=83% so, 10 ÷, 12 * 100%=14%.
Article one, driving periodic table corresponding to path of driving a vehicle is as shown in table 4.
Table 4
Periodically the periodic flow process of computing module 17 calculating traffic routes as shown in figure 10.At first, periodically computing module 17 calculates traffic route by probability and the parameter of day cycle information, i.e. execution in step S11.By sky computation of Period formula be: fate ÷ driving sailing date leap fate * 100% that sets out of driving a vehicle.Simultaneously, use DCP1 to represent this of probability diurnal periodicity.The driving fate that sets out refers to exist the fate of sailing date of driving a vehicle, it is the earliest date and the fate between the date the latest in driving sailing date that driving sailing date crosses over fate, for example driving recording comprised on June 1st, 2012 and on August 31st, 2012 trimestral driving trace, wherein there are 66 days and have driving recording, the driving fate that sets out is 66 days so, driving sailing date leap fate is 92 days, and diurnal periodicity, probability was 66 ÷, 92 * 100%=72%.
Then, execution in step S12 calculates the probability of press cycle information and the parameter of traffic route, and computing formula is: the particular day driving particular day fate of fate ÷ driving sailing date leap * 100% that sets out.For example, have nearest 3 middle of the month 13 " Mondays ", wherein there be 12 " Monday " to have a driving record that sets out, the driving of particular day " Monday " fate that sets out is 12 days so, the particular day fate of driving sailing date leap is 13 days, and then probability diurnal periodicity of " Monday " is 12 ÷, 13 * 100%=92%.In like manner, calculate successively Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday by the cycle probability.
In above-mentioned 7 diurnal periodicity probability, screening is more than or equal to particular day corresponding to 60% probability diurnal periodicity, calculate driving corresponding to these particular day fate total amount that sets out, as use w1 to represent, and calculate the particular day fate total amount that all drivings sailing dates cross over, as use w2 to represent, and by calculating w1 ÷ w2 * 100% as probability diurnal periodicity of press cycle, use DCP2 represents this of probability diurnal periodicity.13 " Mondays ", " Tuesday ", " Saturday " are for example arranged respectively 3 middle of the month, driving recording is wherein arranged 12 Mondays, have driving recording is arranged driving recording, 2 Saturdays 11 Tuesday, then probability diurnal periodicity of Monday is 92%, probability diurnal periodicity of Tuesday is 85%, probability diurnal periodicity of Saturday is 15%, diurnal periodicity, probability was Monday and Tuesday more than or equal to date of 60%, Monday and Tuesday, corresponding driving fate total amount was 12+11=23 days, total driving fate is 12+11+2=25 days, and probability diurnal periodicity of this cycle type DC2 is 23 ÷, 25 * 100%=92% so.
Above-mentioned diurnal periodicity probability more than or equal to the particular day of 0 correspondence as parameter diurnal periodicity of pressing the cycle type, for example in the above-mentioned example, diurnal periodicity, parameter was Monday, Tuesday and Saturday.
Then, execution in step S13 calculates by day probability and the parameter of gap periods information.For example searching loop driving trace array is found out corresponding sailing date, generates the sailing date array, and sailing date on the interior date of array does not repeat.For a plurality of records that set out were arranged in one day, then only get a sailing date as the sailing date array element.To above-mentioned sailing date array by ascending sort.
Calculate successively the fate of being separated by between above-mentioned sailing date array adjacent element, the generation fate array of being separated by is calculated the identical proportion of fate in the fate array of being separated by of being separated by, for example in the fate array of being separated by, the number of elements of being separated by 1 day accounts for 80%, and the number of elements of being separated by 2 days accounts for 10%.
Select above-mentioned proportion maximal value as probability diurnal periodicity by the sky gap periods, use DCP3 to represent.If the fate of being separated by that the proportion maximal value is corresponding is 0 day, explanation is that the driving event is to occur every day, then DCP3 is reset to 0%.Get diurnal periodicity probability be the fate of being separated by corresponding to DCP3 as parameter diurnal periodicity of this cycle type, for example be separated by 1 day.
Then, execution in step S14 calculates probability and parameter by the section cycle, and for example searching loop driving trace array is found out corresponding sailing date, generates the sailing date array.Date in the sailing date array does not repeat, and for a plurality of records that set out were arranged in one day, then only gets a sailing date as the sailing date array element.To above-mentioned sailing date array by ascending sort.Then, the above-mentioned date array of searching loop is found out continuous date fate greater than 1 day set, is defined as set 1, and finds out the fate of being separated by between the date array adjacent element more than or equal to 1 day set, is defined as set 2.Get a numerical value 1 from gathering, such as m, and get a numerical value from gathering 2, such as n, in the date array, search and meet the number of elements of being separated by n days in continuous m days, calculate the number percent that this number of elements accounts for sailing date array total quantity.For example the array content is [2012-1-1,2012-1-2,2012-1-5,2012-1-6,2012-1-9,2012-1-10,2012-1-13,2012-1-14] sailing date, and the number percent that the number of elements that then meets " being separated by 2 days in continuous 2 days " accounts for total quantity is 100%.By that analogy, take out set 1 and set 2 all combinations, calculate the number of elements that meets " being separated by n days in m days continuously " all combinations and account for the number percent of total quantity, select number percent the maximum as probability diurnal periodicity of this cycle type, use DCP4 to represent.At last, get variable m corresponding to probability DCP4 diurnal periodicity and n as parameter diurnal periodicity by the spacer segment period type, for example " be separated by 2 days in continuous 2 days ", this moment m=2, and n=2.
Periodically computing module 17 is followed execution in step S15, judgement is by the sky cycle information, by cycle information, by sky gap periods information and by the numerical value that whether has probability more than or equal to 60% in the spacer segment cycle information, in this way, execution in step S17 then calculates the by the hour interval probability of traffic route.If there is not the numerical value more than or equal to 60% in the above-mentioned cycle information, then execution in step S16 calculates monthly probability and the parameter in cycle.
Among the step S16, periodically computing module 17 uses DCP5 to represent monthly cycle information, its initialization DCP5=0%.Then, searching loop driving trace array is found out corresponding sailing date, generates the sailing date array, and the date in the sailing date array does not repeat, and for a plurality of records that set out were arranged in one day, then only gets a sailing date as the sailing date array element.To above-mentioned sailing date array by ascending sort.According to sailing date array calculate respectively monthly in the 1st to the 31st driving probability, the present invention is referred to as " month subsist " driving probability.Its computing formula is: total fate * 100% of subsisting the fate ÷ month of subsisting by the moon that has driving recording, for example array has comprised driving date on March 31st, 1 day 1 January in 2012 sailing date, wherein monthly the 1st day totally 3 days, driving recording is wherein arranged on January 1st, 2012 and on February 1st, 2012, but do not have driving recording on March 1st, 2012, then the driving probability of " monthly the 1st day " is 2 ÷, 3 * 100%=67%.
Calculate respectively by that analogy monthly the 2nd day, monthly the 3rd day until monthly the 31st day driving probability, screen above-mentioned driving probability and subsist as parameter diurnal periodicity more than or equal to 60% the moon, calculate monthly probability diurnal periodicity of period type by formula " Vehicle Run total amount ÷ driving total degree * 100% of subsisting by the moon ".
It is monthly the 1st day, monthly the 8th day, monthly the 15th day that the probability of for example driving a vehicle was subsisted more than or equal to 60% the moon, the Vehicle Run total amount of subsisting in these three months is 18 times, the driving total degree of going through the driving trace record is 20 times, so monthly probability diurnal periodicity of period type is 18 ÷, 20 * 100%=90%, and diurnal periodicity, parameter was monthly the 1st day, monthly the 8th day, monthly the 15th day.
More above-mentioned generation 5 diurnal periodicity probability numbers, get period type corresponding to greatest measure as driving path type diurnal periodicity.For example, the probability by the sky cycle by above-mentioned calculating is 72%, probability by cycle is 92%, probability by the sky gap periods is 10%, by the spacer segment cycle and monthly the probability in cycle be 0, then the maximum probability value is the probability by cycle, and type diurnal periodicity of traffic route is by cycle so.
Above-mentioned result of calculation is written in the driving periodic table, as shown in table 5.
Table 5
Periodically computing module 17 is determined all after dates of stroke route, and execution in step S17 calculates a hour interval cycle probability.For example, from 00:00, take 0.5 hour as the interval, be divided into 48 intervals to one day 24 hours, be respectively 00:00-00:30,00:30-01:00 ... 23:30-24:00, calculate day part set out number of times and probability of happening according to the departure time in the driving trace.
For example have driving recording 50 times in a traffic route, wherein 07:30-08:00 has 40 times, and probability of happening accounts for 80%, 08:00-08:30 totally 10 times, probability of happening 20%.In above-mentioned 48 intervals, if the probability of happening in two continuous intervals greater than 0, is merged into an interval, for example above-mentioned example to these two intervals, 07:30-08:00 and two intervals of 08:00-08:30 are merged into an interval 07:30-08:30, and probability of happening is 100%.
Certainly, the departure time in the driving trace in the above-mentioned computation process is the departure time after per diem the corresponding date of parameter diurnal periodicity in the period type and Vehicle Run divide into groups, and then calculates the interval probability of happening of each group according to these groupings.For example, if the departure time of this traffic route is by cycle, for grouped element the departure time in the driving trace is divided into 7 groups by " Monday " to " Sunday " so respectively, number of times has 2 times if the on the same day interior driving of a grouping is set out, so the departure time of this group is split as two groups again by the number of times that sets out, by that analogy, until split till meet above-mentioned rule, and calculate the interval probability of happening of each group.According to the method described above the departure time is divided into groups, calculate each the group departure time interval probability after, interval probability be filled up to respectively the driving periodic table in, as shown in table 6.
Table 6
Preferably, " the driving periodic table " of above-mentioned generation can allow the driver to adjust by hand according to actual needs, also can increase or deletion record at " driving periodic table " by hand.
At last, periodically computing module 17 is according to the cycle information of above-mentioned Information generation traffic route, i.e. execution in step S17.The cycle information that generates is exactly information as shown in table 6, and these information can be kept in mobile intelligent terminal 10 or the background server 22.
Review Fig. 3, after calculating the periodicity of traffic route, path and duration computing module 15 execution in step S4, calculate the driving duration of each bar traffic route, the driving duration can obtain according to time corresponding to departure place in driving trace array corresponding to each path of the driving a vehicle Time Calculation corresponding with the destination, exist in the situation of many driving traces, calculate average driving duration, the driving duration information can be kept in mobile intelligent terminal 10 or the background server 22.
For example, from traffic route, get a concrete driving path, retrieve driving trace historical record corresponding to this driving path, by in the driving cycle diurnal periodicity parameter and the hours period parameter these driving traces record is divided into groups.Then for the driving trace record in each grouping, departure time and time of arrival calculate respectively the time that driving recording each time spends according to driving, preferably take minute as unit, result of calculation is merged an integer array that generates, for example [30 minutes, 35 minutes, 32 minutes, 33 minutes].Certainly, can delete some the long or too short times in the integer array, with the improper running time number appearance that prevents from causing because of some accidentalia, the accuracy that impact is calculated.
Calculate integer array average running time of running time, i.e. the numerical value of all elements in the cumulative integer array divided by integer array length, is then looked into and is rounded calculating knot, and the running time data structure in a concrete driving path is as shown in table 7.
| Concrete driving path | Diurnal periodicity parameter | The hours period parameter | Average driving time span |
| Driving |
Monday | 7:30-08:00 | 35 |
| Driving path | |||
| 1 | Tuesday | 7:30-08:00 | 30 |
| Driving path | |||
| 1 | Friday | 7:30-08:00 | 50 minutes |
Table 7
Then, as separating sign, above-mentioned concrete driving path being divided into a plurality of highway sections according to the traffic intersection that exists in the driving path, if single road section length surpasses 1 km, is that unit divides by 1 km, until be split as satisfactory a plurality of highway section again.Retrieve driving trace corresponding to above-mentioned integer array, according to spending average running time through each highway section of dimension coordinate Calculation in special time period in the driving trace, the data structure of concrete road-section average running time is as shown in table 8.
| The highway section reference position | The highway section end position | Average driving time span |
| Longitude 11, dimension 11 | Longitude 12, dimension 12 | 5 |
| Longitude | ||
| 21, | | 3 minutes |
| ... | ... | ... |
Table 8
Above-mentioned steps is carried out in circulation until all diurnal periodicities in a concrete driving path and the average running time in the average running time under the hours period and each highway section are finished in calculating.If one corresponding many driving paths in the traffic route also are the driving durations that calculates respectively in the manner described above every driving path.For other traffic route, also calculate according to above-mentioned steps.
Carry out the running time information that generates after the above-mentioned steps, comprise the driving average length of time in the driving path that traffic route comprises, the road-section average running time length that the driving path comprises.Certainly, the running time length data that calculates can be carried out manual modification according to actual conditions by the driver.
Mobile intelligent terminal 10 is after judging vehicle launch, and execution in step S5 judges vehicle whether in the initial scope of the traffic route that has recorded, and initial scope is by the place in the starting point certain radius scope of above-mentioned steps calculating.Its concrete calculation procedure is the initial point position that travels through many traffic routes of having preserved, judge whether in a plurality of initial point position scopes of many traffic routes one of vehicle current location, in this way, the expression vehicle obtains the information of this traffic route in the initial point position scope of a traffic route.If judge in the initial point position scope of a vehicle traffic route not in office, then do not carry out the step of prediction, return execution in step S1, record this time driving trace.
Among the step S6, prediction notification module 13 with the vehicle position as the traffic route of the starting point traffic route as prediction, with the average driving duration in the last driving path in this traffic route of the preserving driving duration as prediction, traffic route and the driving duration of prediction automatically are sent on the terminal device of appointment, such as specific people's mobile phone or the equipment such as air-conditioning of family.
Certainly, the driving duration singly is not the time span of driving, can be the time of the arrival destination that calculates according to the current time, comprises the time that arrives a certain specified point on the traffic route yet.Owing to calculate in the step of driving duration the averaging time that a plurality of different sections of highways are as calculated exercised, therefore can accurate Calculation arrive the wherein time in some highway section.
In the Vehicle Driving Cycle process, judgment of clogging module 14 judges whether vehicle is in blocked state.For example, when vehicle travels in specific road section, if within 1.2 times time of the average driving time span in this highway section, do not arrive yet the end position in highway section, be vehicle in the driving duration in this highway section predetermined value greater than average driving duration, this predetermined value is 20%, and the current line vehicle speed in 1 minute, continue to be lower than this road-section average road speed 50% the time, illustrate that may there be traffic jam point in this highway section.At this moment, mobile intelligent terminal 10 sends block information, and automatically by wireless network current location is sent to background server 22.
When the road speed of Vehicle Speed in 1 minute of continuing more than or equal to the average speeds in this highway section 80% the time, illustrate that vehicle has left the obstruction point, mobile intelligent terminal 10 from driving trace, find out 1 minute vehicle in front position corresponding through dimension coordinate data and current obstruction highway section institute's spended time and send to background server 22.
If background server 22 received the block information that the mobile intelligent terminal 10 more than 3 sends in 10 minutes, and these latitude and longitude coordinates of stopping up point belong to same traffic route route and travel direction is consistent, and have at least 3 longitude and latitude positions in twos air line distance be no more than 100 meters, confirm that then there is the obstruction point in specific road section, and issue block information to other mobile intelligent terminal.If do not meet above-mentioned condition, then there is not the obstruction point in explanation.
Note, above-mentionedly judge whether there is the precondition of stopping up point the mobile intelligent terminal that has been many vehicle configuration and used judgment of clogging module 14, share to other associated vehicles by means of mobile internet stopping up the dot information notice.
After confirming there is the obstruction point, background server 22 receives in real time that each mobile intelligent terminal 10 sends leaves latitude and longitude coordinates when stopping up point, calculate in real time the central point of these latitude and longitude coordinates according to these longitudes and latitudes as stopping up the some end position, the obstruction highway section institute spended time that sends according to each mobile intelligent terminal 10 again calculates in real time and stops up road-section average running time, to the above-mentioned information of mobile intelligent terminal 10 issues.
Therefore, in the Vehicle Driving Cycle process, mobile intelligent terminal execution in step S7, judge whether to receive block information, in this way, execution in step S8 is if stop up highway section and travelling or when not driving to congested link when the expectation driving path of vehicle in front comprises this, then dynamically adjust estimated time of arrival (ETA) according to stopping up road-section average running time, and the result that will calculate is sent to the terminal device of appointment.
Certainly, if when vehicle in front has been stopped up on the highway section, arrive the air line distance of obstruction point when vehicle in front according to current vehicle location and the calculating of stopping up the some end position, and according to stop up road-section average running time and current congested with cars highway section running time calculate to arrive and stop up also needed wait time of some end position, screen or speech form by mobile intelligent terminal 10 describe prompting to the driver to stopping up the highway section relevant information.
When vehicle stopping up some highway section, place drive pass through after, if finish in running time at the road-section average of driving time span table, the obstruction point that this highway section is described is eliminated, 10 notices of having eliminated to background server 22 transmission obstruction points of mobile intelligent terminal.If background server 22 was received the information that the obstruction point of the transmission of at least 2 mobile intelligent terminals 10 has been eliminated in 10 minutes, then confirm to stop up point and eliminate.Perhaps, if background server 22 did not receive any information about this obstruction point that any mobile intelligent terminal 10 sends in 30 minutes, then also be considered as stopping up point and eliminate.When background server 22 confirms that an obstruction point is eliminated, send the information that point has been eliminated of stopping up to a plurality of mobile intelligent terminals 10, each mobile intelligent terminal 10 re-executes prediction time of arrival and in time sends information to specific terminal device according to current traffic route.
When above-mentioned prediction notification module 13 sends notice, only send to the specific people or the remote equipment that are associated with prediction traffic route and current time, therefore can accomplish as required, accurately send, can effectively prevent from being known current vehicle driving trace by other unrelated persons and the individual privacy that causes is leaked.
At last, mobile intelligent terminal 10 judges whether vehicle arrives the destination, and namely execution in step S9 in this way, finishes prediction steps, if do not arrive the destination, then returns step S7, continues to judge whether to receive block information.
By above-mentioned scheme as seen, the driving trace of mobile intelligent terminal 10 registration of vehicle also calculates the traffic route of vehicle, simultaneously periodicity and the driving duration of each traffic route are calculated, behind each vehicle launch, can in time predict traffic route and the driving duration of vehicle, and relevant information is sent to specific terminal device, give the related personnel with prompting, be conducive to the travel conditions that the related personnel in time understands vehicle.
In addition, because mobile intelligent terminal 10 is the historical driving trace calculating traffic routes according to vehicle, have self-learning function, can be applied on the unfixed private vehicle of traffic route.
Certainly, above-described embodiment only is the better embodiment of the present invention, during practical application, more change can also be arranged, and for example, the information that mobile intelligent terminal sends to terminal device includes only the information of driving duration, does not comprise the information of traffic route; Perhaps, increase new period type etc. in the date period type, such change also can realize purpose of the present invention.
It is emphasized that at last to the invention is not restricted to above-mentioned embodiment, also should be included in the protection domain of claim of the present invention such as the change of date period type, the change of path merging method, the variations such as change of judgement obstruction method.
Claims (10)
1. vehicle driving path prediction Notification Method is characterized in that: comprise
The driving trace of mobile intelligent terminal registration of vehicle;
Mobile intelligent terminal and/or server calculate the route information that generates Vehicle Driving Cycle and vehicle at the driving duration information of this route running according to described driving trace;
Mobile intelligent terminal is judged behind vehicle launch when vehicle is positioned at the reference position scope of described traffic route, with described route information and driving duration information traffic route and the driving duration as prediction, and described route information and/or described driving duration information are sent to the terminal device of appointment.
2. vehicle driving path prediction Notification Method according to claim 1 is characterized in that:
The step that calculating generates the route information of Vehicle Driving Cycle comprises: calculate the driving path according to described driving trace, and will have many described traffic route information of described driving paths merging formation of identical driving start-stop position.
3. vehicle driving path prediction Notification Method according to claim 2 is characterized in that:
Described traffic route information is selectivity traffic route information.
4. according to claim 1 to 3 each described vehicle driving path prediction Notification Methods, it is characterized in that:
After described mobile intelligent terminal or server generate described route information, judge the periodicity of calculating traffic route, generate the cycle information of described route information;
Before described mobile intelligent terminal judges whether vehicle is positioned at the reference position scope of described traffic route, judge that current date and/or time are whether in the scope of described cycle information corresponding to described route information, in this way, the execution path prediction steps, otherwise, do not carry out described path prediction step.
5. vehicle driving path prediction Notification Method according to claim 4 is characterized in that:
Described cycle information comprises at least by the sky cycle information or by cycle information or by sky gap periods information or by section cycle information or cycle information monthly.
6. according to claim 1 to 3 each described vehicle driving path prediction Notification Methods, it is characterized in that:
After described mobile intelligent terminal sends described driving duration information to described terminal device, judge whether to receive block information, as receive, recomputate the driving duration information according to described block information, and the driving duration information after will recomputating is sent to described terminal device.
7. mobile intelligent terminal is characterized in that: comprise
The driving trace logging modle is for the driving trace of registration of vehicle;
The traffic route computing module calculates the route information that generates Vehicle Driving Cycle and vehicle at the driving duration information of this route running according to described driving trace;
The prediction notification module, behind vehicle launch, judge when vehicle is positioned at the reference position scope of described traffic route, with described route information and driving duration information traffic route and the driving duration as prediction, and described route information and/or described driving duration information are sent to the terminal device of appointment.
8. mobile intelligent terminal according to claim 7 is characterized in that:
Described traffic route computing module has path merging module, is used for calculating the driving path according to described driving trace, and will has many described traffic route information of described driving paths merging formation of identical driving start-stop position.
9. it is characterized in that according to claim 7 or 8 described mobile intelligent terminals:
Described traffic route computing module has periodically computing module, is used for calculating the periodicity of traffic route, generates the cycle information of described route information.
10. it is characterized in that according to claim 7 or 8 described mobile intelligent terminals:
Described mobile intelligent terminal also is provided with the judgment of clogging module, calculates vehicle at the driving duration at least part of highway section of traffic route output block information during greater than the predetermined value of average driving duration.
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