CN105825675A - Link travel time calculating method and link travel time calculating device based on big data - Google Patents
Link travel time calculating method and link travel time calculating device based on big data Download PDFInfo
- Publication number
- CN105825675A CN105825675A CN201610326378.XA CN201610326378A CN105825675A CN 105825675 A CN105825675 A CN 105825675A CN 201610326378 A CN201610326378 A CN 201610326378A CN 105825675 A CN105825675 A CN 105825675A
- Authority
- CN
- China
- Prior art keywords
- cycle
- section
- vehicle
- value
- time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
Landscapes
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
Embodiments of the invention relate to the field of intelligent transportation, and especially relate to a link travel time calculating method and a link travel time calculating device based on big data. After the single-vehicle time taken by each vehicle passing a link i in an mth period is acquired, the initial travel time value of the link i in the mth period is determined according to the single-vehicle time of each vehicle; abnormal vehicles are determined according to the single-vehicle time of each vehicle and the initial travel time value; and finally, the link travel time value of the link i in the mth period is determined according to the single-vehicle time of the vehicles after elimination of abnormal vehicles. Thus, the quality of data is ensured, the link travel time can be calculated out accurately according to collected traffic flow information and based on the big data technology, real-time traffic information service is provided, a decision-making basis is provided for traffic managers to optimize the traffic organization and ease the traffic congestion, and the level of traffic management is raised.
Description
Technical field
The present embodiments relate to intelligent transportation field, particularly relate to a kind of road trip time computational methods based on big data and device.
Background technology
In recent years, continuous propelling along with Urbanization in China, the living standard of people improves day by day, Urban vehicles poputation is skyrocketed through, bring therewith is that urban road traffic congestion problem highlights day by day, frequent accidents occurs, and integrated management level and Information Service Level to vehicle supervision department propose challenge, and with development, urban economy is also brought certain negative effect.
In urban transportation operation conditions is evaluated, road trip time is indispensable pith.Road trip time can provide following traffic and variation tendency for traveler accurately, induction user selects rational travel time, trip mode and trip route, improve the space-time inhomogeneities of urban road network's traffic flow distribution, there is provided reliable characteristic evidences for traffic control and traffic guidance, be also the traffic parameter be concerned about the most of urbanite simultaneously.
In terms of road trip time statistics, the increase of vehicles number so that the amount of calculation exponentially level of vehicle journeys time increases, traditional data processing mode needs just can provide for a long time result, the situation without response even occurs；In terms of real-time road analyses and prediction, estimating task hourage on a large scale in the face of city, traditional data processing mode is the most unable to do what one wishes.Therefore, in today that urbanization develops rapidly, in the urgent need to the most feasible a kind of solution, solve estimation problem hourage of mass data.
The appearance of big data (BigData), solves traditional database and carries out mass data inquiry and analytical performance bottleneck problem, and the analyzing and processing for mass data provides effective solution route.And wherein distributed computing framework based on internal memory, inborn superiority is had especially for complicated interative computation.
The most how according to the telecommunication flow information gathered, calculate road trip time accurately based on big data technique, be just particularly important.
Summary of the invention
The embodiment of the present invention provides a kind of road trip time computational methods based on big data and device, in order to realize according to the telecommunication flow information gathered, calculates road trip time accurately based on big data technique.
The embodiment of the present invention provides a kind of road trip time computational methods based on big data, including:
The bicycle time spent in the m cycle is obtained by each vehicle in i section from big data platform；
The bicycle time according to each vehicle, determine described i section value preliminary hourage within the described m cycle；
Bicycle time according to each vehicle and value described preliminary hourage, determine abnormal vehicle；
According to the bicycle time of the vehicle after the abnormal vehicle of eliminating, determine the described i section road trip time value in the described m cycle；M is positive integer.
The embodiment of the present invention also provides for a kind of road trip time based on big data and calculates device, including:
Acquisition module, for obtaining, from big data platform, the bicycle time spent in the m cycle by each vehicle in i section；
Determine module, for according to bicycle time of each vehicle, determine described i section value preliminary hourage within the described m cycle；
Bicycle time according to each vehicle and value described preliminary hourage, determine abnormal vehicle；
According to the bicycle time of the vehicle after the abnormal vehicle of eliminating, determine the described i section road trip time value in the described m cycle；M is positive integer.
nullThe road trip time computational methods based on big data of above-described embodiment offer and device，Including: the bicycle time spent by each vehicle in i section in first obtaining the m cycle，The bicycle time according to each vehicle，Determine i section value preliminary hourage within the m cycle，Then according to bicycle time and the value preliminary hourage of each vehicle，Determine abnormal vehicle，The bicycle time finally according to the vehicle after the abnormal vehicle of eliminating，Determine the i section road trip time value in the described m cycle，Can be seen that，After passing through the bicycle time that each vehicle in i section is spent in obtaining the m cycle，The bicycle time according to each vehicle，Determine i section value preliminary hourage within the m cycle，Need bicycle time and the value preliminary hourage further according to each vehicle，Determine abnormal vehicle，The bicycle time finally according to the vehicle after the abnormal vehicle of eliminating，Determine the i section road trip time value in the described m cycle，Therefore, it is possible to ensure the quality of data，It is thus possible to realize according to the telecommunication flow information gathered，Road trip time is calculated accurately based on big data technique，May also provide Real-time Traffic Information service simultaneously，Traffic organization optimization is carried out for traffic administration person、Improvement of blocking up provides decision-making foundation，Improve traffic management level.
Accompanying drawing explanation
For the technical scheme being illustrated more clearly that in the embodiment of the present invention, in describing embodiment below, the required accompanying drawing used is briefly introduced.
A kind of based on big data the road trip time computational methods flow charts that Fig. 1 provides for the embodiment of the present invention；
A kind of based on big data road trip time computational methods that Fig. 2 provides for the embodiment of the present invention and the flow chart determining road congestion level；
A kind of based on big data the road trip time computing device structure schematic diagrams that Fig. 3 provides for the embodiment of the present invention.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and beneficial effect clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.
It should be noted that the big data technique that the embodiment of the present invention is applied is same as the prior art, do not repeat them here.
Fig. 1 illustrates a kind of based on big data the road trip time computational methods flow charts that the embodiment of the present invention provides, as it is shown in figure 1, the method comprises the steps that
S101, obtain, from big data platform, the bicycle time spent in the m cycle by each vehicle in the i-th section.
S102, bicycle time according to each vehicle, determine i section value preliminary hourage within the m cycle.
S103, according to bicycle time of each vehicle and i section the value preliminary hourage within the m cycle, determine abnormal vehicle.
S104, according to getting rid of bicycle time of the vehicle after abnormal vehicle, determine the i section road trip time value in the m cycle.
Wherein, m is positive integer.
In above-mentioned steps S101, first electronic police, traffic block port, electronic license plate, RFID (RadioFrequencyIdentification can be passed through, radio frequency identification) etc. equipment gather in the m cycle by the car plate data in i section, then the car plate data obtained are carried out pretreatment, car plate data normalization, reject wrong data, reject the data that transfinite after can calculate the m cycle in bicycle time of being spent by each vehicle in i section.
In the specific implementation, according to traffic rules, wrong data, the data that transfinite can be rejected.For example, it is possible to be principle according to entering statistics section by normal traffic custom, mask the bicycle time of the vehicle entering statistics section under improper traffic is accustomed to.Such as, as a example by orthogonal crossroad, Through Lane can be used to cross car data to road, and for turning left to enter the vehicle in statistics section, then choose roadmarking and indicate the car data excessively of left turn lane, for the vehicle in entrance statistics section of turning right, if if vehicle contains right-hand rotation lamp control when turning right and entering statistics crossing, upstream, section, then that chooses right-turn lane crosses car data.
Owing to the velocity amplitude of vehicle should be in a rational travel speed interval range, therefore when wrong data, the data that transfinite being rejected according to traffic rules, also according to the velocity amplitude of vehicle, wrong data, the data that transfinite can be rejected.Such as, using urban road as analyzing object, it is assumed that the city road travel speed scope of acquiescence is [5km/h, 80km/h], then the feasible interval of the rational travel speed of corresponding vehicle is [L/80km/h, L/5km/h].For expressway, the more common urban road of travel speed is high, and principle is the same, and only value is different, repeats no more here.
If hourage less than L/80km/h, and such data in total sample size accounting less than 20% time, then be considered abnormal data and removed.
If the hourage collected more than L/5km/h, and such data in total sample size accounting less than 20% time, then be considered abnormal data and removed.
Transfinited data by rejecting, reasonable can mask the isolated datas such as excessive or too small, provide the quality of data to ensure for follow-up statistical analysis.
After obtaining the bicycle time spent in the m cycle by each vehicle in i section, i section value preliminary hourage within the m cycle can be determined according to following equation one.
In above-mentioned formula one, ωkFor regression coefficient；N is the vehicle fleet within the m cycle through i section；Ti kThe time spent through i section within the m cycle by kth vehicle；T1 is i section value preliminary hourage within the m cycle.
Preferably, ωkValue can be 1/n, it is also possible to be 1/ (n-2), naturally it is also possible to being other parameter, the embodiment of the present invention is to ωkSpan do not carry out any restriction.
I-th section preliminary hourage within the m cycle after value is being determined according to above-mentioned formula one,
Eliminating coefficient can be determined further according to following equation two,
In above-mentioned formula two, σ is for getting rid of coefficient；N is the sum of the m cycle interior vehicle through i section；Ti kThe time spent through i section within the m cycle by kth vehicle；T1 is i section value preliminary hourage within the m cycle.
After calculate eliminating coefficient according to above-mentioned formula two, abnormal vehicle can be determined according to following equation three.
|Ti k-T1 | ＞ 3 σ (formula three)
In above-mentioned formula three, σ is for getting rid of coefficient；Ti kBy i-th time that vehicle was spent through i section within the m cycle；T1 is i section value preliminary hourage within the m cycle.
In the specific implementation, the bicycle time of the vehicles that the absolute value of the difference between the bicycle time collected in the m cycle and described preliminary hourage value can be got rid of coefficients more than 3 times excludes, the bicycle time of the vehicles that the absolute value of the difference between the bicycle time collected in the m cycle and described preliminary hourage value can be got rid of coefficients more than 2 times can also be excluded, directly the absolute value of the difference between the bicycle time collected in the m cycle and described preliminary hourage value can also be excluded more than the bicycle time of the vehicle getting rid of coefficient, the most in the specific implementation, above-mentioned formula three can be applied flexibly, above-mentioned formula three can be carried out various deformation, then the bicycle time of abnormal vehicle is got rid of according to the formula after deformation, thus can be that follow-up statistical analysis provides the quality of data to ensure.
Preferably, after the bicycle time getting rid of abnormal vehicle according to above-mentioned formula three, in also can adding up the m cycle, the bicycle time of vehicle is less than the bicycle time of the vehicle of value described preliminary hourage, and determines i section normal travelling time value within the m cycle according to adding up obtain all less than the bicycle time of the vehicle of value described preliminary hourage.
Preferably, also congestion level can be determined according to the absolute value of the difference between above-mentioned road trip time value and above-mentioned normal travelling time value.
Such as, in the specific implementation, when the absolute value of the difference between the road trip time value and normal travelling time value in i section is more than first threshold, yellow early warning can be provided；And the absolute value of the difference between the road trip time value and normal travelling time value in i section more than Second Threshold time, orange early warning can be given.Wherein, first threshold is more than Second Threshold.
In the specific implementation, it is also possible to according to the absolute value of the difference between above-mentioned road trip time value and above-mentioned normal travelling time value, simply determine whether road blocks up.Such as, the value the biggest expression road of the absolute value of the difference between the road trip time value and normal travelling time value in i section more blocks up, and the value the least expression road of the absolute value of the difference between the road trip time value and normal travelling time value in i section is the most unimpeded.
Preferably, determining that i section, after the road trip time value in m cycle, also can be carried out preserving to historical data base, compensating for the follow-up data to disappearance.
Determining that i section is after the road trip time value in m cycle, except being carried out preserving to historical data base, also calculated i section can be issued by modes such as microblogging, wechat, website, mobile client, induced screens in the road trip time value in m cycle, therefore Real-time Traffic Information service can be provided, thus carry out traffic organization optimization, improvement offer decision-making foundation of blocking up for traffic administration person.
Preferably, less than threshold value, then i section missing data within the m cycle it is judged to when i section vehicle fleet size of process within the m cycle.Such as, at non-offpeak period when detecting that through i section the vehicle fleet size of process is less than 5 within the m cycle, then can determine that into shortage of data, shortage of data in this case is likely to belong to unit exception, crossing stopping state is caused.
In peak period, if continuous p the cycle that i section is before the m cycle is all judged to shortage of data, then according to formula four, the data in m cycle are compensated.Wherein, p >=4 and p are positive integer.
HTi(r)=α Ti e(r)+(1-α)HTi e-x(r) (formula four)
In formula four, α is smoothing factor；R is cycle corresponding with the m cycle on described i section in history；Ti eR () is the most described i section road trip time value in the r cycle；HTi e-xR () is in history based on Ti eR x days described i sections are in the road trip time value in described r cycle before ()；E is the time segment value in distance m cycle.
After the data in m cycle being compensated according to above-mentioned formula four, also the offset calculated according to formula four can be preserved, and using the offset of preservation as history value.
When the i section continuous p the cycle before the m cycle is all judged to shortage of data, owing to using history synperiodic smooth historical data that the data of disappearance are compensated, therefore there is the advantage that change is steady, undulatory property is little.It addition, preserved as history value after the compensation data to disappearance, thus historical traffic state variation characteristic can also well be described.
Preferably, if continuous v the cycle that i section is before the m cycle is all judged as shortage of data, the most never determine v distance m cycle nearest road trip time in the cycle of missing data, and the data lacked the m cycle according to the meansigma methods of v distance m cycle nearest road trip time carry out compensation data.Wherein, v ＜ 4 and v is positive integer.
It should be noted that the α in above-mentioned formula four can rule of thumb take 0.8, naturally it is also possible to take other parameter value, the embodiment of the present invention does not carry out any restriction to the value of α.
When offpeak period produces the situation of shortage of data, according to road trip time corresponding under the free flow velocity in section, the data of disappearance can be compensated.In the specific implementation, the section corresponding road trip time under free flow velocity can be gone out with the free flow relocity calculation in section according to the length in section.
Below by an example, above-mentioned method flow is carried out detailed explanation.
nullThe Cycle Length assuming initially that i section is 5 minutes，It is further assumed that i section is in this cycle of 17:00 17:05 in the afternoon within April 25th, 2016，Through the car plate data gathered are carried out pretreatment、Car plate data normalization、Reject wrong data、Get, after rejecting the data that transfinite, the bicycle time that 50 vehicles are spent through i section altogether，The bicycle time spent through i section in these 50 vehicles on April 25th, 2016 is respectively T1 in the 17:00 17:05 cycle in the afternoon、T2、T3、T4、T5、T6、T7、T8、T9、T10……T50，It is further assumed that the value of regression coefficient is 1/n in this example embodiment，Regression coefficient will be set to 1/50，Then calculate i section road trip time within the 17:00 17:05 cycle and the method determining i road congestion level after calculating i section road trip time within the 17:00 17:05 cycle，Can be found in Fig. 2.
S201, bicycle time T1, T2, T3, T4, T5, T6, T7, T8, T9, T10 of being spent respectively by 50 cars in i section in the acquisition 17:00 17:05 cycle ... T50.
S202, it is calculated i section value preliminary hourage within April 25th, 2016 according to formula five in the 17:00 17:05 cycle in the afternoon.
In formula five, T1, T2, T3, T4, T5 ... T50 be i section within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon, through the car plate data gathered being carried out pretreatment, car plate data normalization, rejecting wrong data, reject the bicycle time that 50 vehicles got after data that transfinite are spent respectively through i section；For i section value preliminary hourage within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon.
S203, it is calculated eliminating coefficient according to formula six.
In formula six, σ50For i section eliminating coefficient within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon；50 is the sum of on April 25th, the 2016 interior vehicle through i section in the 17:00 17:05 cycle in the afternoon；Ti kThe bicycle time spent through i section within April 25th, 2016 by kth vehicle in this cycle of 17:00 17:05 in the afternoon；For i section value preliminary hourage within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon.
S204, get rid of bicycle time of abnormal vehicle according to formula seven.
In formula seven, σ is for getting rid of coefficient；Ti kThe time spent through i section within April 25th, 2016 by kth vehicle in the 17:00 17:05 cycle in the afternoon；For i section value preliminary hourage within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon.
It is further assumed that according to above-mentioned formula sixThe quantity determining abnormal vehicle is 5, and it is further assumed that within April 25th, 2016 in this cycle of 17:00 17:05 in the afternoon the 1st vehicle be abnormal data to bicycle time T1, T2, T3, T4, T5 that the 5th vehicle is spent on i section.Therefore when calculating the road trip time on April 25th, 2016 in the 17:00 17:05 cycle in the afternoon, in order to increase accuracy, do not use bicycle time T1, T2, T3, T4, T5 of abnormal vehicle, i.e. when calculating 17:00 17:05 cycle road trip time in afternoon on April 25th, 2016, only use remaining 45 data.
S205, the road trip time calculated according to formula eight on April 25th, 2016 in the 17:00 17:05 cycle in the afternoon.
In formula eight, T6, T7, T8, T9, T10 ... T50 be i section within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon, through according to getting rid of the bicycle time that remaining 45 vehicles got after coefficient gets rid of the bicycle time of abnormal vehicle spend respectively through i section；It it is the road trip time on April 25th, 2016 in the 17:00 17:05 cycle in the afternoon.
It is further assumed that in the bicycle time data of remaining 45 vehicles, the bicycle time data of only 20 vehicles is less than i section road trip time within April 25th, 2016 in the 17:00 17:05 cycle in the afternoonAnd it is further assumed that T30, T31, T32, T33, T34 ... T50 i.e. from the bicycle time data of these 20 vehicles of T30 to T50 less than i section road trip time within April 25th, 2016 in the 17:00 17:05 cycle in the afternoonThat is within April 25th, 2016, the 30th vehicle is less than i section road trip time within April 25th, 2016 to the bicycle time that the 50th vehicle is spent on i section in this cycle of 17:00 17:05 in the afternoon in the 17:00 17:05 cycle in the afternoon.
S206, calculate the i section normal travelling time within April 25th, 2016 according to formula nine in the 17:00 17:05 cycle in the afternoon.
In formula nine,For the i section normal travelling time within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon；T30, T31, T32, T33, T34 ... the bicycle time that T50 is spent on i section to the 50th vehicle in the 17:00 17:05 cycle in the afternoon by the 30th vehicle within April 25th, 2016.
S207, i section road trip time within April 25th, 2016 was compared with the normal travelling time in the 17:00 17:05 cycle in the afternoon, determine congestion level.
In the specific implementation, i section road trip time within April 25th, 2016 and normal travelling time can be sought difference in the 17:00 17:05 cycle in the afternoon, according to i section road trip time within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon and between the normal travelling time absolute value of difference provide congestion level.Such as, when i section road trip time within April 25th, 2016 and time the absolute value of difference is the biggest between the normal travelling time, represent that i section more blocked up within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon in the 17:00 17:05 cycle in the afternoon；When i section road trip time within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon and between the normal travelling time absolute value of difference more hour, represent that i section the most unimpeded within April 25th, 2016 in the 17:00 17:05 cycle in the afternoon.
On the basis of the example above, the most by way of example the situation of missing data is carried out detailed explanation.
Continue to assume i section afternoon on April 25th, 2016 16:45 16:50,16:50 16:55,16:55 17:00,17:00 17:05 be judged as missing data in these four cycles, and the periodicity of missing data meets the condition compensated by formula four, it is further assumed that the time segment value in formula four be 1 day, smoothing factor α value be 0.8, x value is after 7 days, then according to formula ten, the missing data in 16:45 16:50 cycle can be compensated.
HTi(r)=0.8Ti e(r)+0.2HTi e-7(r) (formula ten)
In formula ten, 0.8 is smoothing factor；R is i section and on 04 25th, 2016 16:45 corresponding cycles in 16:50 cycle, i.e. r is that i section was 04 month 24 days these cycles of 16:45 16:50 in 2016；Ti eR () is the road trip time value in 04 month 24 days 16:45 16:50 cycles in 2016；HTi e-7R before () 04 month 24 days 16:45 16:50 cycles in 2016,7 days described i sections are at the road trip time value in described r cycle, i.e. HTi e-7R () i section was on 04 17th, 2016 16:45 16:50 cycle road trip time values.
In like manner, identical with the method for the missing data compensation to the 16:45 16:50 cycle to the method for the missing data compensation in 16:50 16:55 cycle, 16:55 17:00 cycle and 17:00 17:05 cycle, do not repeat them here.
nullIn the specific implementation，If i section is only at 16:45 16:50 in afternoon on April 25th, 2016、16:50—16:55、16:55 is judged as shortage of data in the 17:00 these three cycle，Therefore the periodicity of missing data is unsatisfactory for the condition compensated by formula four，It is further assumed that 16:30 16:35 in afternoon on April 25th, 2016、16:35—16:40、Road trip time data in the 16:40 16:45 these three cycle do not lack，Then can be by 16:30 16:35 in afternoon on April 25th, 2016、16:35—16:40、The road trip time in 16:40 16:45 these three cycle is carried out averaging computing，And according to the result after computing of averaging to 16:45 16:50 in afternoon on April 25th, 2016、16:50—16:55、In the 16:55 17:00 these three cycle, the data of disappearance compensate.
nullIn the specific implementation，If i section is only at 16:50 16:55 in afternoon on April 25th, 2016、16:55 is judged as shortage of data in the 17:00 the two cycle，Therefore the periodicity of missing data is unsatisfactory for the condition compensated by formula four，It is further assumed that 16:40 16:45 in afternoon on April 25th, 2016、Road trip time data in the 16:45 16:50 the two cycle do not lack，Then can be by 16:40 16:45 in afternoon on April 25th, 2016、The road trip time in 16:45 16:50 the two cycle is carried out averaging computing，And according to the result after computing of averaging, 16:50 16:55 in afternoon on April 25th, 2016 and the data lacked in the 16:55 17:00 the two cycle are compensated.
In the specific implementation, if i section is only judged as shortage of data within April 25th, 2016 in this cycle of 16:55 17:00 in the afternoon, therefore the periodicity of missing data is unsatisfactory for the condition compensated by formula four, it is further assumed that the road trip time data on April 25th, 2016 do not lack in this cycle of 16:50 16:55 in the afternoon, then according to the road trip time on April 25th, 2016, the data of disappearance on April 25th, 2016 can be compensated in this cycle of 16:40 16:45 in the afternoon in this cycle of 16:50 16:55 in the afternoon.
It should be noted that, either road is in peak period or low peak period, when road is in large sample amount, said method all can be used to carry out calculating the road trip time of road, and combining road determines congestion level hourage, but when isolated point occur in the data gathered, according to road trip time corresponding under the free flow velocity of road, the data of disappearance can be compensated.In the specific implementation, the section corresponding road trip time under free flow velocity can be gone out with the free flow relocity calculation in section according to the length in section.
It can be seen from the above, after passing through the bicycle time that each vehicle in i section is spent in obtaining the m cycle, the bicycle time according to each vehicle, determine i section value preliminary hourage within the m cycle, need bicycle time and the value preliminary hourage further according to each vehicle, determine abnormal vehicle, the bicycle time finally according to the vehicle after the abnormal vehicle of eliminating, determine the i section road trip time value in the described m cycle, therefore, it is possible to ensure the quality of data, it is thus possible to realize according to the telecommunication flow information gathered, road trip time is calculated fast and accurately based on big data technique.
Additionally, also can be carried out preserving to historical data base after calculating road trip time, for follow-up, the situation of change of road trip time in historical time section is carried out statistical analysis, by the statistical analysis to historical data, obtain feature hourage and rule of conversion thereof, carry out traffic organization optimization for traffic administration person, improvement of blocking up provides decision-making foundation, the most also can be according to historical data and current road conditions, provide congestion warning, traffic administration person is reminded to take corresponding measure in time, so that it is guaranteed that the unimpeded operation of road.Additionally, also can be according to the situation of change of road trip time in the historical time section counted, Comprehensive Correlation, with cycle history data and current data, provides decision-making foundation for signal timing dial, traffic organization optimization.
Based on same idea, the embodiment of the present invention provides and also provides for a kind of road trip time based on big data calculating device.
Fig. 3 illustrates the structural representation of the road trip time calculating device that the present invention implements to provide.As it is shown on figure 3, this device comprises the steps that acquisition module 301 and determines module 302.
Acquisition module 301, for obtaining, from big data platform, the bicycle time spent in the m cycle by each vehicle in i section；
Determine module 302, for according to bicycle time of each vehicle, determine described i section value preliminary hourage within the described m cycle；
Bicycle time according to each vehicle and value described preliminary hourage, determine abnormal vehicle；
According to the bicycle time of the vehicle after the abnormal vehicle of eliminating, determine the described i section road trip time value in the described m cycle；M is positive integer.
Preferably determine module 302 specifically for:
According to formulaDetermine described r section value preliminary hourage within the described m cycle；
Wherein, ωkFor regression coefficient；N is the vehicle fleet within the described m cycle through described r section；Ti kThe time spent through described i section within the described m cycle by kth vehicle；T1 is described i section value preliminary hourage within the described m cycle.
Preferably determine module 302 specifically for:
According to formula (1), determine eliminating coefficient；
According to formula (2), determine abnormal vehicle；Wherein:
Formula (1)Wherein, σ is described i section eliminating coefficient within the m cycle；N is the sum of the interior vehicle through described i section of described m cycle；Ti kThe time spent through described i section within the described m cycle by kth vehicle；T1 is described i section value preliminary hourage within the described m cycle；
Formula (2) | Ti k-T1 | ＞ 3 σ；Wherein, σ is for getting rid of coefficient；Ti kThe time spent through described i section within the described m cycle by kth vehicle；T1 is described i section value preliminary hourage within the described m cycle.
Preferably determine that module 302 is additionally operable to:
Statistics bicycle time of vehicle within the described m cycle less than bicycle time of vehicle of value described preliminary hourage, all determines described i section normal travelling time value within the described m cycle less than the bicycle time of the vehicle of value described preliminary hourage according to what statistics obtained；
According to the absolute value of the difference between described road trip time value and described normal travelling time value, determine congestion level.
The most also include: compensation data module (not shown)；
Described compensation data module in described i section within the described m cycle vehicle fleet size of process less than threshold value, it is determined that for described i section missing data within the described m cycle；
If continuous p the cycle that described i section is before the described m cycle is all judged to shortage of data, then according to formula (3), the data in m cycle are compensated；
Formula (3) HTi(r)=α Ti e(r)+(1-α)HTi e-x(r)；Wherein, α is smoothing factor；R is cycle corresponding with the m cycle on described i section in history；Ti eR () is the most described i section road trip time value in the r cycle；HTi e-xK () is in history based on Ti eK x days described i sections are in the road trip time value in described r cycle before ()；E is the time segment value in distance m cycle.
It is also preferred that the left described compensation data module is additionally operable to:
If continuous v the cycle that described i section is before the described m cycle is all judged as shortage of data, the most never in the cycle of missing data, determine the individual road trip time nearest apart from the described m cycle of v；
Meansigma methods according to the individual road trip time nearest apart from the described m cycle of described v carries out compensation data to the data that the described m cycle lacks；V ＜ 4 and v is positive integer.
Can be seen that according to the above, after passing through the bicycle time that each vehicle in i section is spent in obtaining the m cycle, the bicycle time according to each vehicle, determine i section value preliminary hourage within the m cycle, need bicycle time and the value preliminary hourage further according to each vehicle, determine abnormal vehicle, the bicycle time finally according to the vehicle after the abnormal vehicle of eliminating, determine the i section road trip time value in the described m cycle, therefore, it is possible to ensure the quality of data, it is thus possible to realize according to the telecommunication flow information gathered, road trip time is calculated fast and accurately based on big data technique.
Additionally, calculate section calculate road trip time after also can be carried out preserving to historical data base, statistical analysis is carried out for the situation of change of road trip time in follow-up historical time section, by the statistical analysis to historical data, obtain feature hourage and rule of conversion thereof, carry out traffic organization optimization for traffic administration person, improvement of blocking up provides decision-making foundation, the most also can be according to historical data and current road conditions, provide congestion warning, traffic administration person is reminded to take corresponding measure in time, so that it is guaranteed that the unimpeded operation of road.Additionally, also can be according to the situation of change of road trip time in the historical time section counted, Comprehensive Correlation, with cycle history data and current data, provides decision-making foundation for signal timing dial, traffic organization optimization.
Those skilled in the art are it should be appreciated that embodiments of the invention can be provided as method or computer program.Therefore, the form of the embodiment in terms of the present invention can use complete hardware embodiment, complete software implementation or combine software and hardware.And, the present invention can use the form at one or more upper computer programs implemented of computer-usable storage medium (including but not limited to disk memory, CD-ROM, optical memory etc.) wherein including computer usable program code.
The present invention is to describe with reference to method, equipment (system) and the flow chart of computer program according to embodiments of the present invention and/or block diagram.It should be understood that can be by the flow process in each flow process in computer program instructions flowchart and/or block diagram and/or square frame and flow chart and/or block diagram and/or the combination of square frame.These computer program instructions can be provided to produce a machine to the processor of general purpose computer, special-purpose computer, Embedded Processor or other programmable data processing device so that the instruction performed by the processor of computer or other programmable data processing device is produced for realizing the device of function specified in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame.
These computer program instructions may be alternatively stored in and can guide in the computer-readable memory that computer or other programmable data processing device work in a specific way, the instruction making to be stored in this computer-readable memory produces the manufacture including command device, and this command device realizes the function specified in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame.
These computer program instructions also can be loaded in computer or other programmable data processing device, make to perform sequence of operations step on computer or other programmable devices to produce computer implemented process, thus the instruction performed on computer or other programmable devices provides the step of the function specified in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame for realization.
Although preferred embodiments of the present invention have been described, but those skilled in the art once know basic creative concept, then these embodiments can be made other change and amendment.So, claims are intended to be construed to include preferred embodiment and fall into all changes and the amendment of the scope of the invention.
Obviously, those skilled in the art can carry out various change and modification without departing from the spirit and scope of the present invention to the present invention.So, if these amendments of the present invention and modification belong within the scope of the claims in the present invention and equivalent technologies thereof, then the present invention is also intended to comprise these change and modification.
Claims (12)
1. road trip time computational methods based on big data, it is characterised in that including:
The bicycle time spent in the m cycle is obtained by each vehicle in i section from big data platform；
The bicycle time according to each vehicle, determine described i section value preliminary hourage within the described m cycle；
Bicycle time according to each vehicle and value described preliminary hourage, determine abnormal vehicle；
According to the bicycle time of the vehicle after the abnormal vehicle of eliminating, determine the described i section road trip time value in the described m cycle；M is positive integer.
2. the method for claim 1, it is characterised in that the described bicycle time according to each vehicle, determines described i section value preliminary hourage within the described m cycle, including:
According to formulaDetermine described r section value preliminary hourage within the described m cycle；
Wherein, ωkFor regression coefficient；N is the vehicle fleet within the described m cycle through described r section；Ti kThe time spent through described i section within the described m cycle by kth vehicle；T1 is described i section value preliminary hourage within the described m cycle.
3. the method for claim 1, it is characterised in that the described bicycle time according to each vehicle and value described preliminary hourage, determines abnormal vehicle, including:
According to formula (1), determine eliminating coefficient；
According to formula (2), determine abnormal vehicle；Wherein:
Formula (1)Wherein, σ is described i section eliminating coefficient within the m cycle；N is the sum of the interior vehicle through described i section of described m cycle；Ti kThe time spent through described i section within the described m cycle by kth vehicle；T1 is described i section value preliminary hourage within the described m cycle；
Formula (2) | Ti k-T1 | ＞ 3 σ；Wherein, σ is for getting rid of coefficient；Ti kThe time spent through described i section within the described m cycle by kth vehicle；T1 is described i section value preliminary hourage within the described m cycle.
4. the method as described in any one of claims 1 to 3, it is characterised in that also include:
Statistics bicycle time of vehicle within the described m cycle less than bicycle time of vehicle of value described preliminary hourage, all determines described i section normal travelling time value within the described m cycle less than the bicycle time of the vehicle of value described preliminary hourage according to what statistics obtained；
According to the absolute value of the difference between described road trip time value and described normal travelling time value, determine congestion level.
5. the method as described in any one of claims 1 to 3, it is characterised in that also include:
When described i section, the vehicle fleet size of process within the described m cycle less than threshold value, is then judged to described i section missing data within the described m cycle；
If continuous p the cycle that described i section is before the described m cycle is all judged to shortage of data, then according to formula (3), the data in m cycle are compensated；P >=4 and p are positive integer；
Formula (3) HTi(r)=α Ti e(r)+(1-α)HTi e-x(r)；Wherein, α is smoothing factor；R is cycle corresponding with the m cycle on described i section in history；Ti eR () is the most described i section road trip time value in the r cycle；HTi e-xK () is in history based on Ti eK x days described i sections are in the road trip time value in described r cycle before ()；E is the time segment value in distance m cycle.
6. the method for claim 1, it is characterised in that also include:
When described i section, the vehicle fleet size of process within the described m cycle less than threshold value, is then judged to described i section missing data within the described m cycle；
If continuous v the cycle that described i section is before the described m cycle is all judged as shortage of data, the most never in the cycle of missing data, determine the individual road trip time nearest apart from the described m cycle of v；
Meansigma methods according to the individual road trip time nearest apart from the described m cycle of described v carries out compensation data to the data that the described m cycle lacks；V ＜ 4 and v is positive integer.
7. road trip time based on big data calculate device, it is characterised in that including:
Acquisition module, for obtaining, from big data platform, the bicycle time spent in the m cycle by each vehicle in i section；
Determine module, for according to bicycle time of each vehicle, determine described i section value preliminary hourage within the described m cycle；
Bicycle time according to each vehicle and value described preliminary hourage, determine abnormal vehicle；
According to the bicycle time of the vehicle after the abnormal vehicle of eliminating, determine the described i section road trip time value in the described m cycle；M is positive integer.
8. device as claimed in claim 7, it is characterised in that described determine module specifically for:
According to formulaDetermine described r section value preliminary hourage within the described m cycle；
Wherein, ωkFor regression coefficient；N is the vehicle fleet within the described m cycle through described r section；Ti kThe time spent through described i section within the described m cycle by kth vehicle；T1 is described i section value preliminary hourage within the described m cycle.
9. device as claimed in claim 7, it is characterised in that described determine module specifically for:
According to formula (1), determine eliminating coefficient；
According to formula (2), determine abnormal vehicle；Wherein:
Formula (1)Wherein, σ is described i section eliminating coefficient within the m cycle；N is the sum of the interior vehicle through described i section of described m cycle；Ti kThe time spent through described i section within the described m cycle by kth vehicle；T1 is described i section value preliminary hourage within the described m cycle；
Formula (2) | Ti k-T1 | ＞ 3 σ；Wherein, σ is for getting rid of coefficient；Ti kThe time spent through described i section within the described m cycle by kth vehicle；T1 is described i section value preliminary hourage within the described m cycle.
10. the device as described in any one of claim 7 to 9, it is characterised in that described determine that module is additionally operable to:
Statistics bicycle time of vehicle within the described m cycle less than bicycle time of vehicle of value described preliminary hourage, all determines described i section normal travelling time value within the described m cycle less than the bicycle time of the vehicle of value described preliminary hourage according to what statistics obtained；
According to the absolute value of the difference between described road trip time value and described normal travelling time value, determine congestion level.
11. devices as described in any one of claim 7 to 9, it is characterised in that also include: compensation data module；
Described compensation data module in described i section within the described m cycle vehicle fleet size of process less than threshold value, it is determined that for described i section missing data within the described m cycle；
If continuous p the cycle that described i section is before the described m cycle is all judged to shortage of data, then according to formula (3), the data in m cycle are compensated；
Formula (3) HTi(r)=α Ti e(r)+(1-α)HTi e-x(r)；Wherein, α is smoothing factor；R is cycle corresponding with the m cycle on described i section in history；Ti eR () is the most described i section road trip time value in the r cycle；HTi e-xK () is in history based on Ti eK x days described i sections are in the road trip time value in described r cycle before ()；E is the time segment value in distance m cycle.
12. devices as claimed in claim 7, it is characterised in that described compensation data module is additionally operable to:
When described i section, the vehicle fleet size of process within the described m cycle is less than threshold value, it is determined that for described i section missing data within the described m cycle；
If continuous v the cycle that described i section is before the described m cycle is all judged as shortage of data, the most never in the cycle of missing data, determine the individual road trip time nearest apart from the described m cycle of v；
The meansigma methods of individual for the described v road trip time nearest apart from the described m cycle is carried out compensation data to the data that the described m cycle lacks；V ＜ 4 and v is positive integer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610326378.XA CN105825675B (en) | 2016-05-17 | 2016-05-17 | A kind of road trip time calculation method and device based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610326378.XA CN105825675B (en) | 2016-05-17 | 2016-05-17 | A kind of road trip time calculation method and device based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105825675A true CN105825675A (en) | 2016-08-03 |
| CN105825675B CN105825675B (en) | 2019-09-17 |
Family
ID=56529765
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610326378.XA Active CN105825675B (en) | 2016-05-17 | 2016-05-17 | A kind of road trip time calculation method and device based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105825675B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106506226A (en) * | 2016-11-29 | 2017-03-15 | 青岛海信网络科技股份有限公司 | A kind of startup method and device of fault detect |
| CN107045791A (en) * | 2017-03-16 | 2017-08-15 | 王德旺 | The implementation method of automobile and motorcycle Intelligent traffic management systems |
| CN111141950A (en) * | 2019-12-03 | 2020-05-12 | 深圳供电局有限公司 | Method for judging and repairing time-sharing code abnormality of electric meter of metering automation system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009259158A (en) * | 2008-04-21 | 2009-11-05 | Toyota Central R&D Labs Inc | Traffic state simulation device, and program |
| CN101976305A (en) * | 2010-10-25 | 2011-02-16 | 北京航空航天大学 | Travel time filtering method based on probability statistics |
| CN103258430A (en) * | 2013-04-26 | 2013-08-21 | 青岛海信网络科技股份有限公司 | Road traveling time calculating and traffic road condition judging method and road traveling time calculating and traffic road condition judging device |
| CN105405293A (en) * | 2015-12-23 | 2016-03-16 | 青岛海信网络科技股份有限公司 | Short-term prediction method of road travel time and system |
-
2016
- 2016-05-17 CN CN201610326378.XA patent/CN105825675B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009259158A (en) * | 2008-04-21 | 2009-11-05 | Toyota Central R&D Labs Inc | Traffic state simulation device, and program |
| CN101976305A (en) * | 2010-10-25 | 2011-02-16 | 北京航空航天大学 | Travel time filtering method based on probability statistics |
| CN103258430A (en) * | 2013-04-26 | 2013-08-21 | 青岛海信网络科技股份有限公司 | Road traveling time calculating and traffic road condition judging method and road traveling time calculating and traffic road condition judging device |
| CN105405293A (en) * | 2015-12-23 | 2016-03-16 | 青岛海信网络科技股份有限公司 | Short-term prediction method of road travel time and system |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106506226A (en) * | 2016-11-29 | 2017-03-15 | 青岛海信网络科技股份有限公司 | A kind of startup method and device of fault detect |
| CN106506226B (en) * | 2016-11-29 | 2019-07-02 | 青岛海信网络科技股份有限公司 | A kind of starting method and device of fault detection |
| CN107045791A (en) * | 2017-03-16 | 2017-08-15 | 王德旺 | The implementation method of automobile and motorcycle Intelligent traffic management systems |
| CN111141950A (en) * | 2019-12-03 | 2020-05-12 | 深圳供电局有限公司 | Method for judging and repairing time-sharing code abnormality of electric meter of metering automation system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105825675B (en) | 2019-09-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hellinga et al. | Decomposing travel times measured by probe-based traffic monitoring systems to individual road segments | |
| EP2354762B1 (en) | Navigation system and method for determining parameters in a navigation system | |
| CN105513337B (en) | The Forecasting Methodology and device of a kind of volume of the flow of passengers | |
| CN112700072A (en) | Traffic condition prediction method, electronic device, and storage medium | |
| CN102324128A (en) | Method for predicting OD (Origin-Destination) passenger flow among bus stations on basis of IC (Integrated Circuit)-card record and device | |
| CN109215350A (en) | A kind of short-term traffic status prediction method based on RFID electronic license plate data | |
| CN106097730A (en) | The method of estimation of a kind of section vehicle queue length, Apparatus and system | |
| CN105868861A (en) | Bus passenger flow evolution analysis method based on time-space data fusion | |
| CN104318781B (en) | Based on the travel speed acquisition methods of RFID technique | |
| Xinghao et al. | Predicting bus real-time travel time basing on both GPS and RFID data | |
| CN108335483B (en) | Method and system for inferring traffic jam diffusion path | |
| CN106682812A (en) | Comprehensive transport system passenger transport mode sharing rate-distance transfer curve determination method | |
| CN110633558B (en) | Urban traffic system modeling system | |
| CN110851490B (en) | Vehicle travel common stay point mining method and device based on vehicle passing data | |
| CN106651728A (en) | Determination method for advantageous haul distances of passenger transport modes in comprehensive transport system | |
| CN107240264A (en) | A kind of non-effective driving trace recognition methods of vehicle and urban road facility planing method | |
| CN111583641A (en) | Road congestion analysis method, device, equipment and storage medium | |
| CN108573604A (en) | Public transport OD data capture methods based on mobile terminal location and storage medium | |
| CN104700630A (en) | Method and system for monitoring vehicle flow of highway | |
| CN106251628B (en) | A kind of method and device of the traffic trip amount of determining motor vehicle | |
| CN105825675A (en) | Link travel time calculating method and link travel time calculating device based on big data | |
| CN109410576A (en) | Road condition analyzing method, apparatus, storage medium and the system of multisource data fusion | |
| CN107230350A (en) | A kind of urban transportation amount acquisition methods based on bayonet socket Yu mobile phone flow call bill data | |
| CN114509081B (en) | Method and device for calculating vehicle passing cost based on differential charging | |
| Anusha et al. | Dynamical systems approach for queue and delay estimation at signalized intersections under mixed traffic conditions |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |