CN102890869B - Vehicle route predicting and notifying method and mobile intelligent terminal - Google Patents

Vehicle route predicting and notifying method and mobile intelligent terminal Download PDF

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CN102890869B
CN102890869B CN201210361715.0A CN201210361715A CN102890869B CN 102890869 B CN102890869 B CN 102890869B CN 201210361715 A CN201210361715 A CN 201210361715A CN 102890869 B CN102890869 B CN 102890869B
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vehicle
route
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CN102890869A (en
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孙涛
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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

Vehicle driving path prediction Notification Method and mobile intelligent terminal
Technical field
The mobile intelligent terminal the present invention relates to field of mobile communication, especially a kind ofly to predict vehicle driving route, notifying 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 trip of driving.Often also have guider when people drive, as the navigation software etc. be arranged on mobile intelligent terminal realizes navigation feature, identify so that correct the direction travelled.Existing guider is the departure place and the destination that are manually inputted vehicle by driver mostly, guider selects one or more traffic route according to Geographic Information System (GIS), and driver can choose a traffic route wherein and be navigated by guider.
But, existing guider is only to provide optional route and navigates to the route that driver chooses, just simple forecast is carried out to driving duration, and in time the driving duration notice of selected route and prediction can not be needed the other staff obtaining these information.But, in actual life, have relevant specific people in people's trip process to wait in the place of this vehicle approach or arrival, this part specific people often in the urgent need to knowing traffic route and the estimated time of arrival (ETA) of this vehicle, and wishes that estimated time of arrival (ETA) is more accurately better.But existing guider is not well positioned to meet the demand.
In addition, existing guider all needs driver manually to input destination, cannot carry out prediction and judge, make troubles also to the use of driver to destination.Meanwhile, existing guider can not be predicted the situation in traffic route, if there is section that stopping state occurs in the traffic route of required process, driver can not obtain accurately, the traffic congestion information of quantification, also causes inconvenience to people's trip.
In addition, more existing buses are provided with supervising device, supervising device can monitor the traffic route of bus, and the status of implementation that bus travels is sent to background server, background server arrives the time of specific bus stop according to the transport condition prediction bus of bus, and by the time showing of prediction in specific display device.
But, supervising device on bus does not carry out calculating to the traffic route of bus and predicts, because the traffic route of bus is fixing, and background server is the traffic route prestoring bus, this traffic route is input in background server in advance artificially, 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 is to provide a kind of vehicle driving path prediction Notification Method that specific personnel can be allowed to understand traveling state of vehicle in time.
Another object of the present invention is to provide and a kind ofly calculates and the mobile intelligent terminal predicted vehicle driving route.
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 according to driving trace and generate route information that vehicle travels and the vehicle driving duration information at this route running, when mobile intelligent terminal judges that vehicle is positioned at the reference position scope of traffic route after vehicle start-up, using route information and driving duration information as prediction traffic route and driving duration, and the route information of prediction and/or driving duration information are sent to the terminal device of specifying.
From such scheme, the method calculates the route that vehicle travels, and preserve history traffic route, according to the traffic route of history traffic route prediction vehicle, and the information such as traffic route and driving duration are sent to specific terminal device, specific personnel's traveling state of vehicle can be informed in time, be conducive to the travel conditions that related personnel understands vehicle in time.
A preferred scheme is, after mobile intelligent terminal or server generate route information, judges the periodicity of traffic route, generates the cycle information of 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, execution path prediction steps, otherwise, not execution path prediction steps.
As can be seen here, mobile intelligent terminal or background server carry out periodicity analysis to traffic route, more meticulous data can be obtained, for vehicle traffic route and driving duration prediction more accurate, be also conducive to the information that related personnel understands traveling state of vehicle exactly.
Further scheme is, mobile intelligent terminal judges whether to receive block information, as received after sending driving duration information to terminal device, recalculate driving duration information according to block information, and the driving duration information after recalculating is sent to terminal device.
Visible, mobile intelligent terminal recalculates driving duration and is sent to terminal device after reception block information, and related personnel can be allowed to understand the real time status of vehicle traveling in time.
For realizing another above-mentioned object, mobile intelligent terminal provided by the invention comprises the driving trace logging modle of the driving trace for registration of vehicle, according to driving trace calculate generate vehicle travel route information and vehicle the driving duration information of this route running traffic route computing module and prediction notification module, during for judging that vehicle is positioned at the reference position scope of traffic route after vehicle start-up, using route information and driving duration information as prediction traffic route and driving duration, and the route information of prediction and/or driving duration information are sent to the terminal device of specifying.
From such scheme, the driving trace of mobile intelligent terminal registration of vehicle also calculates traffic route thus, record simultaneously and preserve history traffic route, and predict according to the traffic route of history traffic route to vehicle, the information of prediction is sent to specific terminal device, so that specific personnel understand the travel conditions of vehicle in time simultaneously.
Further scheme is, traffic route computing module has periodically computing module, for calculating the periodicity of traffic route, generates the cycle information of route information.
As can be seen here, mobile intelligent terminal can predict the traffic route of vehicle more accurately according to cycle information, also more accurate to the prediction of driving duration, is conducive to related personnel and understands traveling state of vehicle more accurately.
Accompanying drawing explanation
Fig. 1 is the schematic diagram that mobile intelligent terminal embodiment of the present invention is connected with server, terminal device.
Fig. 2 is the structural schematic 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 schematic 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 schematic diagram of a driving trace in vehicle driving path prediction Notification Method embodiment of the present invention.
Fig. 6 is the schematic diagram in a driving path in vehicle driving path prediction Notification Method embodiment of the present invention.
Fig. 7 is the schematic diagram in another driving path in vehicle driving path prediction Notification Method embodiment of the present invention.
Fig. 8 is the schematic diagram of vehicle driving path prediction Notification Method embodiment middle rolling car route of the present invention.
Fig. 9 is the schematic diagram of the traffic route after merging in vehicle driving path prediction Notification Method embodiment of the present invention.
Figure 10 is the process flow diagram calculating driving cycle in vehicle driving path prediction Notification Method embodiment of the present invention.
Below in conjunction with drawings and Examples, the invention will be further described.
Embodiment
Vehicle driving path prediction Notification Method of the present invention relates to multiple 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, as panel computer, portable Intelligent music player, intelligent game computer etc.Certainly, can also be on-vehicle navigation apparatus, 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 the mode of radio communication and background server 22, the information that mobile intelligent terminal 10,20,21 records is sent to background server 22, or obtains its data calculating, analyze from background server 22.
And, this method also relates to one or more terminal device, as mobile phone 23,24 or printing device 25 etc., the information of prediction or calculating is sent on terminal device by communication by mobile intelligent terminal 10,20,21, prompting message is sent by terminal device, as shown the traffic route of vehicle or the driving duration of prediction by display screen, or printed by the information of printing device 25 by prediction.Certainly, terminal device can also be the electric equipments such as such as intelligent air condition, and such as, when duration of driving a vehicle remains 20 minutes, mobile intelligent terminal sends information to intelligent air condition, and air-conditioning is opened after receiving information automatically.
See 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 traffic route computing module 12, illustrate the principle of work of above-mentioned modules below in conjunction with vehicle driving path prediction Notification Method.
See Fig. 3, when mobile intelligent terminal 10 works first, need the driving trace of registration of vehicle, as shown in Figure 4, driving trace logging modle 11 sets up two arrays, is Vehicle Run array and driving trace array respectively.When vehicle sets out, driving trace logging modle 11 starts to perform step S 1, the data of record driving trace, and it uses mobile satellite location equipment registration of vehicle driving trace.When after the flame-out parking of vehicle, terminate the record operation travelling track data.
Driving trace array have recorded in vehicle travel process in each time point position, it is made up of four column datas altogether: sequence number, time point, longitude and latitude, wherein latitude and longitude coordinates obtains according to global position system, time point is the time point obtaining latitude and longitude coordinates, sequence number is the index value of array, the self-propagation numerical value started with 1.
Each record in Vehicle Run array represents vehicle travel process and to stop working from beginning to vehicle parking starting position in driving trace array corresponding to this process and end position, the sequence number index of driving trace array that what the first row in Vehicle Run array and secondary series were preserved is, the driving trace array indexing that when numeric representation of first row is set out, vehicle position is corresponding, the driving trace array indexing that when numeric representation of secondary series arrives destination, vehicle position is corresponding.
When travelling track record module 11 and judging to need record driving trace data, in 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 train number array of being simultaneously expert at, the sequence number of the Article 1 record of driving trace array is written to the 1st row of Vehicle Run data corresponding record.
In vehicle travel process, driving trace logging modle 11 regularly obtains current latitude and longitude coordinates, time being increased in driving trace array, and the longitude and latitude obtained each time, time all form a record and is kept in driving trace array.When driving trace logging modle 11 judges that vehicle stops working, read the sequence number of the last item record in driving trace array and this sequence number be written to the 2nd row of Vehicle Run array respective record, forming the record of a complete Vehicle Run array.
After the above-mentioned driving trace data that mobile intelligent terminal 10 generates, 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 traveling track record and can be realized by the manual input command of driver with stopping driving trace record, also can request for utilization number be 201210288884.6, the method that provides of the denomination of invention application for a patent for invention that is " starting method of mobile intelligent terminal and vehicle management module thereof " realizes, and automatically sends corresponding order according to current vehicle condition.
After mobile intelligent terminal 10 records driving trace data, step S2 is performed by the path of mobile intelligent terminal 10 and duration computing module 15 or background server 22, according to driving trace data genaration driving trace, and generate according to driving trace path of driving a vehicle accordingly.Such as, a record is taken out from Vehicle Run array, numerical value in arranging according to the 1st row and the 2nd of this driving recording, determine the driving trace array element set that this driving process is corresponding, then according to the longitude and latitude data in driving trace array, connect each latitude and longitude coordinates and form driving curve, and mark goes out the binding site between each line segment, namely the tie point between two line segments, as shown in Figure 5, wherein LS represents the starting point of driving, and LD is the end point of driving, and L1 to L5 is the binding site of each line segment respectively.
When mobile intelligent terminal 10 have recorded repeatedly driving trace data, need to calculate each driving trace data and generate many driving curves, the driving curve of generation i.e. driving trace.
Then, all driving curves of the above-mentioned generation of searching loop, generate driving path.Such as, take out a driving curve, and take out the coordinate of the starting point LS of driving curve, from all driving curves generated, search all driving collection of curves being departure place with LS coordinate.If the set found is null set, before illustrating, also do not generate driving path corresponding to curve, then generate driving path according to this driving curve.
The step generating driving path is: centered by the LS coordinate of curve of driving a vehicle, using 100 meters of round regions that are radius as the departure place in path of driving a vehicle, represent departure place with PS.In like manner, centered by the LD coordinate of curve of driving a vehicle, using 100 meters of border circular areas that are radius as the destination in driving path, represent destination with PD.Then, along driving curve starting point to end point direction, each straight-line segment of traversal driving curve, does translation to each straight-line segment and has gone out, to left 40 meters successively, and form a rectangular region to right translation 20 meters, each rectangular region along direction of traffic horizontal-extending, calculate 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 driving path.As shown in Figure 6, the departure place in Fig. 6 represents with PS, and destination PD represents, and P1, P2, P3 etc. represent the binding site of regional in the driving path generated.Finally, the driving path of above-mentioned generation is preserved and history of forming is driven a vehicle path.
To the straight-line segment of driving curve carry out translation be because travel the road of process itself have certain width, and the precision of global position system also has certain deviation.Meanwhile, adopting the method for left-right asymmetry translation during generation pass region, is consider that travelling rule in China is that right side travels rule.
Certainly, the more excellent scheme generating driving path is combined with Geographic Information System (GIS), searches the road data in Geographic Information System according to each straight-line segment of driving curve, finds the coordinate data of corresponding road as driving path.Complete because road net coordinate data in Geographic Information System is through actual mapping, the degree of accuracy in the driving path generated accordingly can be higher.
If searching all set with the LS coordinate driving curve that is departure place from all driving curves generated is nonempty set, having from LS before illustrating is the driving path of departure place, then judge that whether current driving curve is complete in current line car passage zone successively, if so, then preserve driving path and driving trace record corresponding to curve of driving a vehicle to be associated relation.If not, then generate a new driving path according to above-mentioned steps.
But, in actual life, many driving paths may be had from identical departure place to identical destination, therefore need identical departure place and the path of identical destination to be merged into a traffic route.Therefore, merging module 16 in path will merge driving path.Drive a vehicle the in the present invention departure place in path is a border circular areas, judge two departure places whether identical can adopt following method: if two border circular areas in satellite positioning coordinate planimetric map have common factor, then think that this departure place is identical, otherwise think two different departure places.In like manner, be also like this for the determination methods whether destination is identical.Perform above-mentioned algorithm successively, many of identical departure place and identical destination driving paths are merged into a traffic route, therefore this traffic route is optionally traffic route.
Suppose that Fig. 6 and Fig. 7 is two different driving paths respectively, and PS and PS' is same departure place, PD and PD' is same destination, and the traffic route after merging as shown in Figure 8.
On the basis of the above, continue merging rows bus or train route footpath by the following method: if two the driving passage zone of path more than 90% are identical, and air line distance is within 800 meters between the departure place in path of two driving a vehicle, and between two destinations air line distance also within 800 meters, then these two driving paths are merged into a new traffic route, perform the driving path after above-mentioned merging successively and can there is multiple departure place and/or multiple destination.Traffic route after a merging as shown in Figure 9.Why merging, is that driver drives a vehicle at every turn to be needed according to circumstances to dock to different parking lots because departure place or destination may exist multiple Public Parking in reality.
Preferably, " the driving path " of above-mentioned generation can allow driver to merge by hand according to actual conditions, and many driving paths are merged into a traffic route.
Then, periodicity computing module 17 or the background server 22 of mobile intelligent terminal 10 perform step S3, calculate the periodicity of each traffic route.Driving cycle is divided into two parts in the present invention: diurnal periodicity and hours period.Refer to cycle period and the probability of happening of sailing date diurnal periodicity, hours period refers to that, at the cycle period of different time sections and probability of happening within same diurnal periodicity, hours period can be as accurate as 0.5 hour.
Such as, 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 Citing
HC1 On a time period From 1 to the time of time 2 Such as drive to set out at 7:30-8:30
Table 2
Driving cycle data structure is as shown in table 3.
Table 3
Wherein, type such as " daily cycle ", " by cycle " of type diurnal periodicity in a few days period type table, the content in driving cycle data structure is type list diurnal periodicity " code name ".Diurnal periodicity, parameter referred to the parameter needed for particular decision type diurnal periodicity, and such as diurnal periodicity, type was " by cycle " (i.e. code name DC3), so diurnal periodicity parameter content be then " Monday, Tuesday, Wednesday ..., Sunday ".Diurnal periodicity, probability referred to the probability set out in the particular day existence driving of specific " type diurnal periodicity ", and such as diurnal periodicity, type was " by cycle ", and " Monday ", diurnal periodicity, probability was 92%.Hours period type refers to " code name " in hours period type list, i.e. " HC1 ".Hours period parameter refers to the parameter needed for the judgement of specific hours period type, such as " 7:30-8:30 ".Vehicle Run refers to which time driving in a day.Hours period probability refers in the particular day of particular day period type, and the number of days meeting hours period type accounts for driving and to set out the number percent of number of days of record.Such as nearest 3 middle of the month have 13 " Mondays ", wherein have 12 " Mondays " to there is driving to set out record, wherein 10 Mondays set out in the 7:30-8:00 period, 2 Mondays set out within the 8:00-8:30 period, so the hours period probability of these two times is 10 ÷ 12 × 100%=83% respectively, 10 ÷ 12 × 100%=14%.
Article one, the driving cycle table that driving path is corresponding is as shown in table 4.
Table 4
Periodically computing module 17 calculates the periodic flow process of traffic route as shown in Figure 10.First, periodically computing module 17 calculates the traffic route daily probability of cycle information and parameter, namely performs step S11.Daily computation of Period formula is: number of days ÷ driving sailing date leap number of days × 100% that sets out of driving a vehicle.Meanwhile, use DCP1 to represent this of probability diurnal periodicity.Driving set out number of days refer to exist driving sailing date number of days, it is the earliest date and the number of days the latest between the date in driving sailing date that driving sailing date crosses over number of days, such as driving recording contain on June 1st, 2012 and on August 31st, 2012 trimestral driving trace, wherein have within 66 days, there is driving recording, so the driving number of days that sets out is 66 days, it is 92 days that driving sailing date crosses over number of days, and diurnal periodicity, probability was 66 ÷ 92 × 100%=72%.
Then, perform step S12, calculate the probability press cycle information of traffic route and parameter, computing formula is: the particular day driving number of days ÷ that sets out drives a vehicle particular day number of days × 100% of sailing date leap.Such as, nearest 3 middle of the month have 13 " Mondays ", wherein have 12 " Mondays " to there is driving to set out record, so the driving of particular day " Monday " number of days that sets out is 12 days, the particular day number of days that driving sailing date crosses over is 13 days, then probability diurnal periodicity of " Monday " is 12 ÷ 13 × 100%=92%.In like manner, calculate successively Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday by cycle probability.
In above-mentioned 7 diurnal periodicity probability, screening is more than or equal to the particular day corresponding to probability diurnal periodicity of 60%, calculate driving corresponding to these particular day to set out number of days total amount, represent as used w1, and calculate the particular day number of days total amount of all driving sailing date leaps, as used w2 to represent, and by calculating w1 ÷ w2 × 100% as probability diurnal periodicity pressing cycle, use DCP2 represents this of probability diurnal periodicity.Such as 3 middle of the month have 13 " Mondays ", " Tuesday ", " Saturday " respectively, wherein there is driving recording 12 Mondays, 11 Tuesdays there is driving recording, there are driving recording 2 Saturdays, 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 to be more than or equal to the date of 60% be Monday and Tuesday, Monday and Tuesday corresponding driving number of days total amount be 12+11=23 days, total driving number of days is 12+11+2=25 days, and so probability diurnal periodicity of this period type DC2 is 23 ÷ 25 × 100%=92%.
Probability above-mentioned diurnal periodicity is more than or equal to the particular day of the correspondence of 0 as parameter diurnal periodicity by cycle type, and such as, in above-mentioned example, diurnal periodicity, parameter was Monday, Tuesday and Saturday.
Then, perform step S13, calculate probability and the parameter of daily gap periods information.Such as searching loop driving trace array, finds out corresponding sailing date, generates sailing date array, and the date in sailing date array does not repeat.For there being the multiple record that sets out in one day, then only get a sailing date as sailing date array element.To above-mentioned sailing date array by ascending sort.
Calculate the number of days of being separated by between above-mentioned sailing date array adjacent element successively, generation is separated by number of days array, calculates identical number of days of being separated by the proportion in number of days array of being separated by, such as, is being separated by number of days array, 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 of daily gap periods, use DCP3 to represent.If what proportion maximal value was corresponding be separated by, number of days is 0 day, and explanation is driving event is occur every day, then DCP3 is reset to 0%.Getting diurnal periodicity probability is the diurnal periodicity parameter of number of days as this period type of being separated by that DCP3 is corresponding, such as, be separated by 1 day.
Then, perform step S14, calculate the probability by the section cycle and parameter, such as searching loop driving trace array, find out corresponding sailing date, generate sailing date array.Date in sailing date array does not repeat, and for there being multiple record that sets out in one day, then only gets a sailing date as sailing date array element.To above-mentioned sailing date array by ascending sort.Then, the above-mentioned date array of searching loop, finds out the set that continuous date number of days is greater than 1 day, is defined as set 1, and finds out the set that number of days of being separated by between date array adjacent element is more than or equal to 1 day, is defined as set 2.From set 1, get a numerical value, as m, and get a numerical value from set 2, as n, in date array, search the number of elements meeting continuous m days and be separated by n days, calculate the number percent that this number of elements accounts for sailing date array total quantity.Such as array content is [2012-1-1,2012-1-2,2012-1-5 sailing date, 2012-1-6,2012-1-9,2012-1-10,2012-1-13,2012-1-14], then the number percent that the number of elements meeting " being separated by 2 days for continuous 2 days " accounts for total quantity is 100%.By that analogy, take out set 1 and set 2 all combinations, the number of elements calculating and meet " continuous m days be separated by n days " all combinations accounts for the number percent of total quantity, selects number percent the maximum as probability diurnal periodicity of this period type, uses DCP4 to represent.Finally, get variable m and n corresponding to probability DCP4 diurnal periodicity as parameter diurnal periodicity by spacer segment period type, such as, " within continuous 2 days, be separated by 2 days ", now m=2, and n=2.
Periodically computing module 17 then performs step S15, judge daily cycle information, by cycle information, daily gap periods information and by whether there being probability to be more than or equal to the numerical value of 60% in spacer segment cycle information, in this way, then perform step S17, calculate the interval probability by the hour of traffic route.If be not more than or equal to the numerical value of 60% in above-mentioned cycle information, then perform step S16, calculate probability and the parameter in monthly cycle.
In step S16, periodically computing module 17 uses DCP5 to represent monthly cycle information, its initialization DCP5=0%.Then, searching loop driving trace array, finds out corresponding sailing date, generates sailing date array, and the date in sailing date array does not repeat, and for there being multiple record that sets out in one day, then only gets a sailing date as sailing date array element.To above-mentioned sailing date array by ascending sort.According to sailing date array calculate respectively monthly in the driving probability of the 1st to the 31st, the present invention is referred to as " moon subsists " driving probability.Its computing formula is: total number of days × 100% of subsisting the number of days ÷ month of subsisting by the moon that there is driving recording, such as array contains the driving date on March 31st, 1 day 1 January in 2012 sailing date, wherein monthly the 1st day totally 3 days, wherein on January 1st, 2012 and on February 1st, 2012 there is driving recording, but on March 1st, 2012 does not have driving recording, then the driving probability of " monthly 1st day " is 2 ÷ 3 × 100%=67%.
Calculate monthly 2nd day, monthly 3rd day until the driving probability of monthly 31st day by that analogy respectively, screen above-mentioned driving probability be more than or equal to 60% subsist by the moon as parameter diurnal periodicity, calculated probability diurnal periodicity of monthly period type by formula " subsist by the moon Vehicle Run total amount ÷ drive a vehicle total degree × 100% ".
Probability of such as driving a vehicle be more than or equal to 60% to subsist by the moon be monthly 1st day, monthly 8th day, monthly 15th day, this Vehicle Run total amount of subsisting for three months is 18 times, the driving total degree going through 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 1st day, monthly 8th day, monthly 15th day.
More above-mentioned generation 5 diurnal periodicity probability numbers, get period type corresponding to greatest measure as driving path type diurnal periodicity.Such as, be 72% by the probability in the daily cycle of above-mentioned calculating, be 92% by the probability of cycle, daily the probability of gap periods is 10%, 0 is by the probability in spacer segment cycle and monthly cycle, then maximum probability value is the probability by cycle, and so type diurnal periodicity of traffic route is by cycle.
Above-mentioned result of calculation is written in driving cycle table, as shown in table 5.
Table 5
Periodically computing module 17 determines all after dates of stroke route, performs step S17, calculates hour period probability in interval.Such as, from 00:00, with 0.5 hour for interval, being divided into 48 intervals one day 24 hours, is 00:00-00:30,00:30-01:00 respectively ... 23:30-24:00, calculates day part according to the departure time in driving trace and to set out number of times and probability of happening.
Such as, have 50 driving recordings 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 interval probability of happening of continuous print two is greater than 0, an interval is merged in these two intervals, such as above-mentioned example, an interval 07:30-08:30 is merged in 07:30-08:00 and 08:00-08:30 two intervals, and probability of happening is 100%.
Certainly, the departure time in the driving trace in above-mentioned computation process is the departure time after parameter diurnal periodicity corresponding date per diem in period type and Vehicle Run divide into groups, and then calculates interval probability of happening of each group according to these groupings.Such as, if the departure time of this traffic route is by cycle, press " Monday " so respectively to " Sunday " for grouped element is divided into 7 groups the departure time in driving trace, if the interior on the same day driving of a grouping is set out, number of times has 2 times, 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 fractionation to meeting 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, after calculating the interval probability of each group departure time, interval probability is filled up in driving cycle table respectively, as shown in table 6.
Table 6
Preferably, " the driving cycle table " of above-mentioned generation can allow driver to adjust by hand according to actual needs, also can increase or deletion record at " driving cycle table " by hand.
Finally, periodically computing module 17 generates the cycle information of traffic route according to above-mentioned information, namely performs step S17.The cycle information generated is exactly information as shown in table 6, and these information can be kept in mobile intelligent terminal 10 or background server 22.
Review Fig. 3, after calculating the periodicity of traffic route, path and duration computing module 15 perform step S4, calculate the driving duration of each bar traffic route, driving duration can be corresponding with destination according to the time that departure place in driving trace array corresponding to each path of driving a vehicle is corresponding Time Calculation acquisition, when there is many driving traces, calculate average driving duration, driving duration information can be kept in mobile intelligent terminal 10 or background server 22.
Such as, from traffic route, get a concrete driving path, retrieve the driving trace historical record that this driving path is corresponding, by driving cycle diurnal periodicity parameter and hours period parameter these driving trace records are divided into groups.Then for the driving trace record in each grouping, the time that driving recording each time spends is calculated respectively according to driving departure time and time of arrival, preferably by minute in units of, result of calculation is merged the integer array generated, such as [30 minutes, 35 minutes, 32 minutes, 33 minutes].Certainly, some the long or too short times in integer array can be deleted, existing to prevent the improper running time caused because of some accidentalia from counting, the accuracy that impact calculates.
Calculate the average running time of integer array running time, the numerical value of all elements in the integer array that namely adds up, divided by integer array length, then look into calculating knot and round, the running time data structure in a particular row bus or train route footpath is as shown in table 7.
Particular row bus or train route footpath Diurnal periodicity parameter Hours period parameter Average driving time span
Driving path 1 Monday 7:30-08:00 35 minutes
Driving path 1 Tuesday 7:30-08:00 30 minutes
Driving path 1 Friday 7:30-08:00 50 minutes
Table 7
Then, according to the traffic intersection existed in driving path as separation mark, above-mentioned particular row bus or train route footpath is divided into multiple section, if single road section length is more than 1 km, then is that unit divides by 1 km, until be split as satisfactory multiple section.Retrieve the driving trace that above-mentioned integer array is corresponding, according in driving trace through latitude coordinates calculate each section in special time period spend average running time, the data structure of a concrete road-section average running time is as shown in table 8.
Section reference position Section end position Average driving time span
Longitude 11, dimension 11 Longitude 12, dimension 12 5 minutes
Longitude 21, dimension 21 Longitude 22, dimension 22 3 minutes
... ... ...
Table 8
Circulation performs above-mentioned steps until the average running time in average running time under having calculated all diurnal periodicities in a particular row bus or train route footpath and hours period and each section.If one corresponding many driving paths in traffic route, are also the driving durations calculating every bar driving path in the manner described above respectively.For other traffic route, also calculate according to above-mentioned steps.
Perform the running time information generated after above-mentioned steps, comprise the driving average length of time in the driving path that traffic route comprises, road-section average running time length that driving path comprises.Certainly, the running time length data calculated can carry out manual amendment by driver according to actual conditions.
Mobile intelligent terminal 10, after judging vehicle launch, performs step S5, judges vehicle whether in the initial range of the traffic route recorded, and initial range is the place within the scope of the starting point certain radius that calculated by above-mentioned steps.Its concrete calculation procedure is the initial point position traveling through many traffic routes of having preserved, judge current vehicle position whether in multiple initial point position scopes of many traffic routes one, in this way, represent that vehicle is within the scope of the initial point position of a traffic route, obtains the information of this traffic route.If judge within the scope of the initial point position of a vehicle traffic route not in office, then not perform the step of prediction, return and perform step S1, record this driving trace.
In step S6, the traffic route of prediction notification module 13 using vehicle position as the traffic route of starting point as prediction, in this traffic route of preserving, the average driving duration in the last driving path is as the driving duration predicted, the traffic route of prediction and driving duration are automatically sent on the terminal device of specifying, as equipment such as the mobile phone of specific people or the air-conditionings of family.
Certainly, driving duration is not singly the time span of driving, can be the time of the arrival destination calculated according to current time, comprises the time arriving a certain specified point in traffic route yet.Due to calculate driving duration step in multiple different sections of highway averaging time of exercising as calculated, therefore, it is possible to accurate Calculation arrives the time in wherein some section.
In vehicle travel process, judgment of clogging module 14 judges whether vehicle is in blocked state.Such as, vehicle is when specific road section travels, if do not arrive the end position in section within the time of 1.2 times of the average driving time span in this section yet, namely vehicle is greater than the predetermined value of average driving duration at the driving duration in this section, this predetermined value is 20%, and current line vehicle speed in 1 minute continue lower than this road-section average road speed 50% time, illustrate that this section may exist traffic jam point.Now, 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 that continues is more than or equal to 80% of the average speeds in this section, illustrate that vehicle has left blocking point, mobile intelligent terminal 10 find out from driving trace 1 minute vehicle in front position corresponding send to background server 22 through latitude coordinates data and current congestion section institute's spended time.
If the block information that the mobile intelligent terminal 10 that background server 22 received more than 3 in 10 minutes sends, and the latitude and longitude coordinates of these blocking points belongs to same traffic route route and travel direction is consistent, and have at least 3 longitude and latitude positions between two air line distance be no more than 100 meters, then confirm that specific road section exists blocking point, and the mobile intelligent terminal to other issues block information.If do not meet above-mentioned condition, then illustrate to there is not blocking point.
Note, above-mentionedly judge whether to exist the precondition mobile intelligent terminal that has been many vehicle configuration of blocking point and employ judgment of clogging module 14, by means of mobile internet, blocking dot information notice is shared to other associated vehicles.
After there is blocking point in confirmation, the latitude and longitude coordinates left when blocking point of each mobile intelligent terminal 10 transmission of background server 22 real-time reception, the central point of these latitude and longitude coordinates is calculated in real time as blocking point end position according to these longitudes and latitudes, the blocking section institute spended time sent according to each mobile intelligent terminal 10 again calculates blocking road-section average running time in real time, issues above-mentioned information to mobile intelligent terminal 10.
Therefore, in vehicle travel process, mobile intelligent terminal performs step S7, judge whether to receive block information, in this way, perform step S8, if when the predicted travel path of Current vehicle comprises this blocking section and travelling or do not driving to congested link, then according to blocking road-section average running time dynamic conditioning estimated time of arrival (ETA), and the result of calculating is sent to the terminal device of specifying.
Certainly, if Current vehicle is on blocking section, according to current vehicle location and the air line distance of calculating Current vehicle to blocking point blocking some end position, and calculate according to blocking road-section average running time and Current vehicle blocking section running time and arrive a blocking point end position also needed wait time, to driver, prompting is described to blocking section relevant information by the screen of mobile intelligent terminal 10 or speech form.
When vehicle is after blocking point section, place is driving through, if complete within the road-section average running time of driving time span table, illustrate that the blocking point in this section is eliminated, mobile intelligent terminal 10 sends the notice blocking point and eliminated to background server 22.If the information that the blocking point that background server 22 received the transmission of at least 2 mobile intelligent terminals 10 in 10 minutes has been eliminated, then confirm that blocking point is eliminated.Or, if background server 22 did not receive any information put about this blocking that any mobile intelligent terminal 10 sends in 30 minutes, be then also considered as blocking point and eliminate.When background server 22 confirms that a blocking point is eliminated, send to multiple mobile intelligent terminal 10 information that blocking point eliminated, each mobile intelligent terminal 10 re-executes prediction time of arrival according to current traffic route and sends information to specific terminal device in time.
When above-mentioned prediction notification module 13 carries out transmission notice, only send to the specific people be associated with prediction traffic route and current time or remote equipment, therefore can accomplish to send as required, accurately, can effectively prevent from being known Current vehicle driving trace by other unrelated persons and the individual privacy caused is leaked.
Finally, mobile intelligent terminal 10 judges whether vehicle arrives destination, namely performs step S9, in this way, terminates prediction steps, if do not arrive destination, then returns step S7, continue to judge whether to receive block information.
From above-mentioned scheme, the driving trace of mobile intelligent terminal 10 registration of vehicle also calculates the traffic route of vehicle, the periodicity of each traffic route and driving duration are calculated simultaneously, traffic route and the driving duration of vehicle can be predicted in time after each vehicle launch, and relevant information is sent to specific terminal device, to related personnel with prompting, be conducive to the travel conditions that related personnel understands vehicle in time.
In addition, because mobile intelligent terminal 10 calculates traffic route according to the history driving trace of vehicle, there is self-learning function, can be applied on the unfixed private vehicle of traffic route.
Certainly, above-described embodiment is only the present invention's preferably embodiment, during practical application, can also have more change, and such as, mobile intelligent terminal only includes the information of driving duration to the information that terminal device sends, and does not comprise the information of traffic route; Or increase new period type etc. in date period type, such change also can realize object of the present invention.
Finally it is emphasized that and the invention is not restricted to above-mentioned embodiment, as the change of date period type, path merging method change, judge that the change such as change of obstruction method also should be included in the protection domain of the claims in the present invention.

Claims (8)

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 according to described driving trace and generate route information that vehicle travels and the vehicle driving duration information at this route running;
When mobile intelligent terminal judges that vehicle is positioned at the reference position scope of described traffic route after vehicle start-up, using described route information and driving duration information as the traffic route of prediction and driving duration, and described route information and/or described driving duration information are sent to the terminal device of specifying being different from described mobile intelligent terminal;
After described mobile intelligent terminal sends described driving duration information to described terminal device, judge whether to receive block information, as received, recalculate driving duration information according to described block information, and the driving duration information after recalculating is sent to described terminal device.
2. vehicle driving path prediction Notification Method according to claim 1, is characterized in that:
The step calculating the route information that generation vehicle travels comprises: calculate driving path according to described driving trace, and many described driving paths with identical driving start-stop position are merged the described traffic route information of formation.
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. the vehicle driving path prediction Notification Method according to any one of claims 1 to 3, is characterized in that:
After described mobile intelligent terminal or server generate described route information, judge the periodicity 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 current date and/or time whether in the scope of described cycle information corresponding to described route information, in this way, execution path prediction steps, otherwise, do not perform described path prediction step.
5. vehicle driving path prediction Notification Method according to claim 4, is characterized in that:
Described cycle information at least comprises daily cycle information or by cycle information or daily gap periods information or by section cycle information or monthly cycle information.
6. mobile intelligent terminal, is characterized in that: comprise
Driving trace logging modle, for the driving trace of registration of vehicle;
Traffic route computing module, calculates according to described driving trace and generates route information that vehicle travels and the vehicle driving duration information at this route running;
Prediction notification module, when judging that vehicle is positioned at the reference position scope of described traffic route after vehicle start-up, using described route information and driving duration information as the traffic route of prediction and driving duration, and described route information and/or described driving duration information are sent to the terminal device of specifying being different from described mobile intelligent terminal;
Judgment of clogging module, calculates vehicle and exports block information when the driving duration at least part of section of traffic route is greater than the predetermined value of average driving duration.
7. mobile intelligent terminal according to claim 6, is characterized in that:
Described traffic route computing module has path and merges module, for calculating driving path according to described driving trace, and many described driving paths with identical driving start-stop position is merged the described traffic route information of formation.
8. the mobile intelligent terminal according to claim 6 or 7, is characterized in that:
Described traffic route computing module has periodically computing module, for calculating the periodicity of traffic route, generates the cycle information of described route information.
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