CN105679021B  Journey time fusion forecasting and querying method based on traffic big data  Google Patents
Journey time fusion forecasting and querying method based on traffic big data Download PDFInfo
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 CN105679021B CN105679021B CN201610075689.3A CN201610075689A CN105679021B CN 105679021 B CN105679021 B CN 105679021B CN 201610075689 A CN201610075689 A CN 201610075689A CN 105679021 B CN105679021 B CN 105679021B
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Abstract
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Technical field
The present invention relates to the traffic status prediction technologies of road network or bicycle, are related specifically to a kind of based on traffic big data Journey time fusion forecasting and querying method.
Background technology
Existing road network or bicycle traffic status prediction method mainly utilize the traffic number that Floating Car or roadside device provide According to, or even the traffic data of roadside device offer is only relied on to predict the traffic behavior of road network or bicycle.Such methods are mainly led to It crosses roadside device and directly acquires the traffic informations such as road network flow, average speed, or by carrying out secondary add to floating car data Work obtains certain class traffic information, and is based on road network topology, is predicted using model recursion mode, the traffic information master of prediction Traffic three elements are concentrated on, cause popularization face wideless, promotional value is not also high, that is, there is general sex chromosome mosaicism, this expands to business Exhibition causes larger puzzlement.Also, existing method usually from up time to go consider floating car data influence, it will usually neglect floating The time factor of motorcar data, and linear mathematical computations mode is used, it such as sums, ask impartial operation, departing from road grid traffic Primitive character, cause the extraction accuracy of traffic information and stability not high, practicability is not also strong.It is especially multiple in face of structure When miscellaneous magnanimity floating car data, existing road network or bicycle traffic status prediction method usually seem helpless.Obviously, existing Some road networks or bicycle traffic status prediction technology there is realtimes it is poor, versatility is poor and practicability is not strong the problems such as.
Invention content
To solve, realtime is poor existing for existing road network or bicycle traffic status prediction technology, versatility is poor and real The problems such as not strong with property, the present invention propose a kind of journey time fusion forecasting and querying method based on traffic big data.
The present invention is based on the journey time fusion forecasting method of traffic big data, data that all online vehicles are uploaded into Row offline calculation is trained to obtain all kinds of prediction models and parameter, is established according to all kinds of prediction models and parameter and dynamic updates Data dictionary；All kinds of prediction models and parameter are called, and combines the travel time data of realtime section and path, to road network or list The traffic behavior of vehicle is predicted；The data dictionary includes vehicle data dictionary, section data dictionary and path data dictionary； The online vehicle refers to login network access and uploads the vehicle of positioning and speed data automatically.
Further, the present invention is based on the journey time fusion forecasting methods of traffic big data, on all online vehicles The data of biography carry out offline calculation or training, to obtain all kinds of prediction models and parameter, including, it sets daily from 0:It is arrived when 00 24:It it is a complete cycle period when 00, when according to the trip mode of working day, Saturday, Sunday or festivals or holidays to stroke Between data carry out trip Type division, obtain trip type goes out row number；The travel time data in each period is according to the time Sequencing be arranged in a journey time sequence.
Further, the present invention is based on the journey time fusion forecasting method of traffic big data, all kinds of prediction models And parameter includes section or path forms time data periods rules model, the section or statistical rules mould of path forms time Type, section or path forms time longterm prediction model, section or path forms time shorttime forecasting model, section or path row The journey time grows fusion forecasting model and intersection delay relation analysis model in shortterm；Wherein,
Using periodic law series approximation PLSA（Periodic Law Series Approximation, PLSA）Algorithm obtains Section or path forms time data periods rules model are taken, and using least square method LSM（Least Square Method, LSM）Solve approximate model parameter；
SRE is extracted using statistical law（Statistical Rule Extraction, SRE）Algorithm obtains section or road Diameter journey time statistical rules model, and using Density Estimator KDE（Kernel Density Estimation, KDE）It obtains The probability density changing rule of section or path forms time；
Using it is long when roll correction prediction LRCF（Longtime Rolling Correction Forecast, LRCF） Algorithm obtains section or path forms time longterm prediction model, and obtains longterm prediction model parameter by offline calculation, leads to It crosses and fast implements section or path forms time prediction in line computation；
SRFF is predicted using fitting is rolled in shortterm（Shorttime Rolling Fitting Forecast, SRFF）It calculates Method obtains section or path forms time shorttime forecasting model, and using time series autoregressive moving average ARMA (Auto Regressive and Moving Average, ARMA) algorithm construction shorttime forecasting model, and using least square method LSM （Least Square Method, LSM）Solving model parameter；
Using sievelike fusion forecasting SFF（Sieve Fusion Forecast, SFF）Algorithm obtains section or path forms Time grows fusion forecasting model in shortterm, and by offline training, using GaussianNewton method GNIM（GuassianNewton Iterative Method, GNIM）Obtain fusion forecasting model parameter；
Using intersection delay association analysis IDCA（Intersection Delay Correlation Analysis, IDCA）Algorithm construction intersection delay relation analysis model, and pass through least square method LSM（Least Square Method, LSM）Solving model parameter obtains model parameter, by the quick of line computation realizing route journey time by offline training Compensation；
Wherein, the longterm prediction refers to obtaining section or the longterm prediction value of path forms time by LRCF algorithms, The value range of longterm prediction duration is set as 0 minute to 3 months, and specific longterm prediction duration can be in the prediction that user selectes It is obtained on the basis of duration by linearly converting；The shortterm prediction refers to when obtaining section or path forms by SRFF algorithms Between shortterm prediction value, the value range of shortterm prediction duration is set as 0 minute to 3 hour, specific shortterm prediction duration It can be obtained on the basis of the prediction duration that user selectes by linearly converting.
Further, the present invention is based on the journey time fusion forecasting method of traffic big data, the section data dictionaries Periodic sequence, probability sequence and various models for storing journey time and parameter, data storage and update include following Step：
S101, historical data is read, historical data is read from the data dictionary of section, including section number, even time interval are adopted Link Travel Time data, the date and hour data of sample；
S102, section is chosen, a untreated section is chosen according to the sequencing of section number；
S103, the classification of Link Travel Time data, it is theoretical based on road network tidal current, it sets daily from 0:To 24 when 00:00 The Shi Weiyi complete cycle period, according to the trip mode on working day, Saturday, Sunday, festivals or holidays, to travel time data Trip Type division and number are carried out, that is, goes out row number, the travel time data in each period is arranged in a journey time sequence Row；
S104, Link Travel Time data are chosen, a kind of untreated section is chosen according to the sequencing for going out row number Travel time data；
S105, travel time data periods rules model and parameter are obtained, section or path is obtained using PLSA algorithms Travel time data periods rules model, and approximate model parameter is solved using LSM；Including：
S1051, degree of correlation clustering is carried out to the journey time sequence of any two different cycles, the extraction degree of correlation is big Travel time data form collection and be merged into row and ask and calculate, obtain average travel time sequence；
S1052, take " 4 π/hour " as basis pi, one Fourier space model of construction is come when approaching average stroke Between sequence, equation is approached by LSM solutions and obtains model parameter；
S1053, the sequence according to energy from high to low, the interception model parameter of gross energy >=98%, remaining parameter zero setting, from And obtain PLSA model parameters；
S1054, a cycle sequence that Link Travel Time is generated by PLSA algorithms, by the periodic sequence and PLSA models In the information storages such as parameter to section data dictionary；
S106, Link Travel Time statistical law model and parameter are obtained, section or path forms is obtained using SRE algorithms The statistical rules model of time, and section or the probability density changing rule of path forms time are obtained using KDE；Including：
Sometime, the travel time data for selecting all periods at the moment forms data set, uses KDE for S1061, calibration Probability density function is obtained, the corresponding journey time of probability density maximum value, i.e. maximum probability journey time are found；
S1062, corresponding maximum probability journey time of all moment is solved, is chronologically arranged in the general of Link Travel Time Rate sequence is simultaneously stored into section data dictionary；
S107, Link Travel Time longterm prediction model and parameter are obtained, Link Travel Time is obtained using LRCF algorithms Longterm prediction model and parameter, and longterm prediction model parameter is obtained by offline calculation；Including：
S1071, by the periodic sequence of journey time and probability sequence sumaverage arithmetic, obtain journey time it is long when initial value sequence Row
S1072, calibration sometime, calculate all periods the moment corresponding journey time with it is long when initial value compared Compared with, acquisition difference, and chronologically it is arranged in a sequence of differences；
S1073, in view of the number of days spacing between adjacent difference, construction one multiple multinomial model of binary approaches this Sequence of differences adaptively adjusts polynomial item number, finds the corresponding multinomial model of minimum fitness bias；
S1074, the process for solving multinomial model are LRCF algorithms, obtain corresponding LRCF model parameters of all moment And it is stored in the data dictionary of section；
S108, the shorttime forecasting model and parameter for obtaining Link Travel Time, when obtaining link travel using SRFF algorithms Between shorttime forecasting model, and use ARMA algorithm construction shorttime forecasting models, and use LSM solving model parameters；Including：
S1081, chronologically by the rhythmic journey time series arrangement of institute at a long sequence；
S1082, assume that the time interval of the long sequence is impartial, with ARMA algorithm constructions oneNMultinomial model is intended After conjunctionNA journey time asks its parameter and error of fitting with LSM；
S1083, by adjustingNThe size of error of fitting is adjusted, chooses corresponding multinomial model when error minimum；
S1084, the process for solving the multinomial model are SRFF algorithms, and SRFF model parameters are stored in section data word In allusion quotation；
S109, Link Travel Time length fusion forecasting model and parameter in shortterm are obtained, link travel is obtained using SFF algorithms Time grows fusion forecasting model in shortterm, and by offline training, and fusion forecasting model parameter is obtained using GNIM；Including：
S1091, calibration initial time, obtain the item number of SRFF modelsN, construction one 2 ×NSievelike coefficient matrix, wherein It is 1 that each element, which is the sum of the element of nonnegative and each row perseverance, and element value is unknown；
S1092, journey time is predicted since initial time, by LRCF algorithms come initial value sequence when compensating long Row obtain futureNA longterm prediction value obtains future by SRFF algorithmsNA shortterm prediction value, by both prediction value sequences Composition one 2 ×NPrediction matrix；
S1093, coefficient matrix is added to processing with row after prediction matrix dot product, obtain one 1 ×NFusion vector, with melting Resultant vector approaches corresponding journey time sequence, obtains corresponding related coefficient equation；
S1094, initial time is gradually adjusted backward, corresponding related coefficient equation is obtained with same method, by these Equation constitutes a related coefficient equation group；
S1095, equation group is solved with GNIM, obtains the element value namely SFF model parameters of sievelike coefficient matrix, it will SFF model parameters are stored in the data dictionary of section；
S110, judge whether that all Link Travel Times are disposed？It is that then, sequence executes step S111, otherwise, returns Execute step S104；
S111, judge whether all sections are disposed？It is that then, sequence executes step S112 and otherwise returns to step S102；
S112, the data storage and update for terminating this section data dictionary；
Wherein, the longterm prediction refers to obtaining section or the longterm prediction value of path forms time by LRCF algorithms, The value range of longterm prediction duration is set as 0 minute to 3 months, and specific longterm prediction duration can be in the prediction that user selectes It is obtained on the basis of duration by linearly converting；The shortterm prediction refers to when obtaining section or path forms by SRFF algorithms Between shortterm prediction value, the value range of shortterm prediction duration is set as 0 minute to 3 hour, specific shortterm prediction duration It can be obtained on the basis of the prediction duration that user selectes by linearly converting.
Further, the present invention is based on the journey time fusion forecasting method of traffic big data, the path data dictionaries Periodic sequence, probability sequence and various models for storing journey time and parameter, data storage and update include following Step：
S201, historical data is read, historical data, including roadnet node, road is read from path and section data dictionary Segment number, path number, the section of even time interval sampling and path forms time data, date and hour data；
S202, combination of nodes is chosen, according to the permutation and combination relationship that roadnet node is numbered, chooses one group of untreated two section Point combination；
S203, path selection choose a untreated path according to two combination of nodes；
S204, the classification of path forms time data, it is theoretical based on road network tidal current, it sets daily from 0:To 24 when 00:00 The Shi Weiyi complete cycle period, according to the trip mode on working day, Saturday, Sunday, festivals or holidays, to travel time data Trip Type division and number are carried out, that is, goes out row number, the travel time data in each period is arranged in a journey time sequence Row；
S205, path selection travel time data choose a kind of untreated path according to the sequencing for going out row number Travel time data；
S206, travel time data periods rules model and parameter are obtained, section or path is obtained using PLSA algorithms Travel time data periods rules model, and approximate model parameter is solved using LSM；Including：
S2061, degree of correlation clustering is carried out to the journey time sequence of any two different cycles, the extraction degree of correlation is big Travel time data form collection and be merged into row and ask and calculate, obtain average travel time sequence；
S2062, take " 4 π/hour " as basis pi, one Fourier space model of construction is come when approaching average stroke Between sequence, equation is approached by LSM solutions and obtains model parameter；
S2063, the sequence according to energy from high to low, the interception model parameter of gross energy >=98%, remaining parameter zero setting, from And obtain PLSA model parameters；
S2064, a cycle sequence that the path forms time is generated by PLSA algorithms, by the periodic sequence and PLSA models In the information storages such as parameter to path data dictionary；
S207, Link Travel Time statistical law model and parameter are obtained, section or path forms is obtained using SRE algorithms The statistical rules model of time, and section or the probability density changing rule of path forms time are obtained using KDE；Including：
Sometime, the travel time data for choosing all periods at the moment forms data set, uses KDE for S2071, calibration Probability density function is obtained, the corresponding journey time of probability density maximum value, i.e. maximum probability journey time are found；
S2072, corresponding maximum probability journey time of all moment is solved, is chronologically arranged in the general of path forms time Rate sequence is simultaneously stored into path data dictionary；
S208, intersection delay relation analysis model and parameter are obtained, is associated with using IDCA algorithm construction intersection delays Analysis model, and by LSM solving model parameters, and model parameter is obtained by offline training；Including：
S2081, it demarcates some period sometime, calculates the sum of all Link Travel Times in the path, Yi Jilu Diameter journey time and Link Travel Time and its difference, the difference are intersection total delay；
S2082, all periods and all moment are traversed, calculates all intersection total delays, path forms is established by LSM Incidence relation between time and corresponding intersection total delay and solution obtain LSM model parameters；
S2083, all intersection total delays are chronologically arranged, it is contemplated that the number of days spacing between consecutive value, construction one A multiple multinomial of binary approaches the sequence, adaptively adjusts polynomial item number, it is corresponding more to find minimum fitness bias Item formula model；
S2084, LSM model of fit and multinomial model are subjected to equal weight merging, pooled model is IDCA models, will In the storage to path data dictionary of IDCA model parameters；
S209, acquisition approach journey time longterm prediction model and parameter obtain section or path row using LRCF algorithms Journey time longterm prediction model, and longterm prediction model parameter is obtained by offline calculation；Including：
S2091, by the periodic sequence of journey time and probability sequence sumaverage arithmetic, obtain journey time it is long when initial value sequence Row
S2092, calibration sometime, calculate all periods the moment corresponding journey time with it is long when initial value compared Compared with, acquisition difference, and chronologically it is arranged in a sequence of differences；
S2093, in view of the number of days spacing between adjacent difference, construction one multiple multinomial model of binary approaches this Sequence of differences adaptively adjusts polynomial item number, finds the corresponding multinomial model of minimum fitness bias；
S2094, the process for solving multinomial model are LRCF algorithms, obtain corresponding LRCF model parameters of all moment And it is stored in path data dictionary；
S210, acquisition approach journey time shorttime forecasting model and parameter, using SRFF algorithm acquisition approach journey times Shorttime forecasting model, and ARMA algorithm construction shorttime forecasting models are used, and use LSM solving model parameters；Including：
S2101, chronologically by the rhythmic journey time series arrangement of institute at a long sequence；
S2102, assume that the time interval of the long sequence is impartial, with ARMA algorithm constructions oneNMultinomial model is intended After conjunctionNA journey time asks its parameter and error of fitting with LSM；
S2103, by adjustingNThe size of error of fitting is adjusted, chooses corresponding multinomial model when error minimum；
S2104, the process for solving the multinomial model are SRFF algorithms, and SRFF model parameters are stored in path data word In allusion quotation；
S211, acquisition approach journey time grow fusion forecasting model and parameter in shortterm, using SFF algorithm acquisition approach strokes Time grows fusion forecasting model in shortterm, and by offline training, and fusion forecasting model parameter is obtained using GNIM；Including：
S2111, calibration initial time, obtain the item number of SRFF modelsN, construction one 2 ×NSievelike coefficient matrix, wherein It is 1 that each element, which is the sum of the element of nonnegative and each row perseverance, and element value is unknown；
S2112, journey time is predicted since initial time, by LRCF algorithms come initial value sequence when compensating long Row obtain futureNA longterm prediction value obtains future by SRFF algorithmsNA shortterm prediction value, by both prediction value sequences Composition one 2 ×NPrediction matrix；
S2112, coefficient matrix is added to processing with row after prediction matrix dot product, obtain one 1 ×NFusion vector, with melting Resultant vector approaches corresponding journey time sequence, obtains corresponding related coefficient equation；
S2114, initial time is gradually adjusted backward, corresponding related coefficient equation is obtained with same method, by these Equation constitutes a related coefficient equation group；
S2115, equation group is solved with GNIM, obtains the element value namely SFF model parameters of sievelike coefficient matrix, it will SFF model parameters are stored in path data dictionary；
S212, judge whether all path forms time datas are disposed？It is that then, sequence executes step S213, otherwise, Return to step S205；
S213, judge whether all paths are disposed, be that then, sequence executes step S214 and otherwise returns to step S203；
S214, judge whether all combination of nodes are disposed, be that then, sequence executes step S215, otherwise, return and execute Step S202；
S215, the data storage and update for terminating this path data dictionary；
Wherein, the longterm prediction refers to obtaining section or the longterm prediction value of path forms time by LRCF algorithms, The value range of longterm prediction duration is set as 0 minute to 3 months, and specific longterm prediction duration can be in the prediction that user selectes It is obtained on the basis of duration by linearly converting；The shortterm prediction refers to when obtaining section or path forms by SRFF algorithms Between shortterm prediction value, the value range of shortterm prediction duration is set as 0 minute to 3 hour, specific shortterm prediction duration It can be obtained on the basis of the prediction duration that user selectes by linearly converting.
The present invention is based on the journey time fusion query methods of traffic big data, using the present invention is based on traffic big datas Journey time fusion forecasting method is to the query path of setting and goes out the journey time variation of row number and carries out prediction and will prediction As a result it is supplied to user, including, the data uploaded using all online vehicles obtain all kinds of predictions by offline calculation or training Model and parameter are simultaneously established according to all kinds of prediction models and parameter and dynamically update the data dictionary；According to inquiry road set by user Diameter and go out row number, all kinds of prediction models and parameter is called to combine the realtime travel time data of the path and respective stretch to this The journey time of path and respective stretch predict and prediction result is supplied to user；The data dictionary includes vehicle number According to dictionary, section data dictionary and path data dictionary；The online vehicle refers to login network access and uploads positioning and speed automatically The vehicle of degrees of data.
Further, the present invention is based on the journey time fusion query method of traffic big data, it is described go out row number refer to It is theoretical based on road network tidal current, it sets daily from 0:To 24 when 00:It it is a complete cycle period when 00, according to working day, star The trip mode of phase six, Sunday, festivals or holidays carry out trip Type division and number to travel time data, that is, go out row number, The travel time data in each period is arranged in a journey time sequence.
Further, the present invention is based on the journey time fusion query method of traffic big data, all kinds of prediction models And parameter includes section or path forms time data periods rules model, the section or statistical rules mould of path forms time Type, section or path forms time longterm prediction model, section or path forms time shorttime forecasting model, section or path row The journey time grows fusion forecasting model and intersection delay relation analysis model in shortterm；Wherein,
Using periodic law series approximation PLSA（Periodic Law Series Approximation, PLSA）Algorithm obtains Section or path forms time data periods rules model are taken, and using least square method LSM（Least Square Method, LSM）Solve approximate model parameter；
SRE is extracted using statistical law（Statistical Rule Extraction, SRE）Algorithm obtains section or road Diameter journey time statistical rules model, and using Density Estimator KDE（Kernel Density Estimation, KDE）It obtains The probability density changing rule of section or path forms time；
Using it is long when roll correction prediction LRCF（Longtime Rolling Correction Forecast, LRCF） Algorithm obtains section or path forms time longterm prediction model, and obtains longterm prediction model parameter by offline calculation, leads to It crosses and fast implements section or path forms time prediction in line computation；
SRFF is predicted using fitting is rolled in shortterm（Shorttime Rolling Fitting Forecast, SRFF）It calculates Method obtains section or path forms time shorttime forecasting model, and using time series autoregressive moving average ARMA (Auto Regressive and Moving Average, ARMA) algorithm construction shorttime forecasting model, and using least square method LSM （Least Square Method, LSM）Solving model parameter；
Using sievelike fusion forecasting SFF（Sieve Fusion Forecast, SFF）Algorithm obtains section or path forms Time grows fusion forecasting model in shortterm, and by offline training, using GaussianNewton method GNIM（GuassianNewton Iterative Method, GNIM）Obtain fusion forecasting model parameter；
Using intersection delay association analysis IDCA（Intersection Delay Correlation Analysis, IDCA）Algorithm construction intersection delay relation analysis model, and pass through least square method LSM（Least Square Method, LSM）Solving model parameter obtains model parameter, by the quick of line computation realizing route journey time by offline training Compensation；
Wherein, the longterm prediction refers to obtaining section or the longterm prediction value of path forms time by LRCF algorithms, The value range of longterm prediction duration is set as 0 minute to 3 months, and specific longterm prediction duration can be in the prediction that user selectes It is obtained on the basis of duration by linearly converting；The shortterm prediction refers to when obtaining section or path forms by SRFF algorithms Between shortterm prediction value, the value range of shortterm prediction duration is set as 0 minute to 3 hour, specific shortterm prediction duration It can be obtained on the basis of the prediction duration that user selectes by linearly converting.
Further, the present invention is based on the journey time fusion query method of traffic big data, include the following steps：
S301, user's inquiry；
S302, acquisition history and real time traffic data, including roadnet node number, section number, path number, even time interval The section of sampling and path forms time, all kinds of Forecasting of Travel Time data, all kinds of model parameters, date and hour, data point Class is numbered；
S303, setting path forms time change whether is inquired？It is then, to continue to execute step S304, otherwise, executes step S314；
S304, querying condition setting, user according to the demand of oneself set inquiry content, including the duration of shortterm prediction and The period of longterm prediction, according to even time interval principle, the corresponding starting point and ending point with longterm prediction steplength in shortterm of calibration, and Go out row number accordingly according to calendar setting；Meanwhile user sets the starting point and ending point of roadnet node, passes through searching route Data dictionary demarcates the path between the two roadnet nodes and path number；
S305, path selection travel time data choose a kind of untreated path according to the sequencing for going out row number Travel time data；
S306, path selection choose a untreated path, passage path data according to the sequencing of path number It numbers in the section that dictionary search is included to the path；
S307, section is chosen, a untreated section is chosen according to the sequencing of section number；
S308, Link Travel Time fusion forecasting are read accordingly according to user's search request from the data dictionary of section Model and parameter are as follows by step process in conjunction with realtime travel time data：
S3081, by LRCF algorithms obtain journey time it is long when at the beginning of value sequence and sequence of differences, the two, which is added, is gone The longterm prediction sequence of journey time；
S3082, by user choose shortterm prediction steplength starting point and ending point on the basis of, obtained by SRFF algorithms The shortterm prediction sequence of journey time, then fusion forecasting sequence in shortterm is obtained by SFF algorithms；
The starting point and ending point for the longterm prediction steplength that S3083, user of being subject to choose, it is short with longterm prediction sequence pair When fusion forecasting sequence expanded, obtain the final fusion forecasting sequence of Link Travel Time；
S309, judge whether all sections are disposed？It is that then, sequence executes step S310 and otherwise returns to step S307；
S310, the prediction of path forms Fusion in Time, set according to user in predicting, as follows by step process：
S3101, the forecasting sequence that intersection total delay is obtained by IDCA algorithms, it is according to temporal order, intersection is total The forecasting sequence of delay is added with the final fusion forecasting sequence in the included section in the path, obtains the compensation pre of journey time Sequencing row；
S3102, by LRCF algorithms obtain journey time it is long when at the beginning of value sequence and sequence of differences, the two be added i.e. obtains The longterm prediction sequence of journey time；
S3103, by user choose shortterm prediction steplength starting point and ending point on the basis of, obtained by SRFF algorithms The shortterm prediction sequence of journey time, then fusion forecasting sequence in shortterm is obtained by SFF algorithms；
The starting point and ending point for the longterm prediction steplength that S3104, user of being subject to choose, it is short with longterm prediction sequence pair When fusion forecasting sequence expanded, obtain the expansion type forecasting sequence of journey time；
S3105, expansion type forecasting sequence is added averaging with compensation forecasting sequence, obtains the path forms time most Whole fusion forecasting sequence；
S311, judge whether all paths are disposed？It is that then, sequence executes step S312 and otherwise returns to step S306；
S312, judge whether all trips are disposed？It is that then, sequence executes step S313 and otherwise returns to step S305；
S313, path forms time prediction recombination and Dynamic Announce, by all path forms time final fusion forecastings Sequence, by path, the logical order of classifying and numbering and sequential carry out recombination arrangement, obtain the final forecasting sequence of each path, And the chronologically Dynamic Announce on road network；
S314, terminate this path query；
Wherein, the longterm prediction refers to obtaining section or the longterm prediction value of path forms time by LRCF algorithms, The value range of longterm prediction duration is set as 0 minute to 3 months, and specific longterm prediction duration can be in the prediction that user selectes It is obtained on the basis of duration by linearly converting；The shortterm prediction refers to when obtaining section or path forms by SRFF algorithms Between shortterm prediction value, the value range of shortterm prediction duration is set as 0 minute to 3 hour, specific shortterm prediction duration It can be obtained on the basis of the prediction duration that user selectes by linearly converting.
Advantageous effects the present invention is based on the journey time fusion forecasting of traffic big data and querying method are to overcome Realtime is poor, versatility is poor and practicability is not strong existing for the traffic status prediction method of prior art road network or bicycle The problems such as, survey precision greatly improved, while the speed of online prediction greatly improved, ensure that realtime in engineer application Property, practicability and versatility.
Description of the drawings
Attached drawing 1 is that data dictionary data in section of the present invention store and update flow diagram；
Attached drawing 2 is that path data dictionary data of the present invention stores and update flow diagram；
Attached drawing 3 is path forms time change querying flow schematic diagram of the present invention.
In the following with reference to the drawings and specific embodiments to the present invention is based on the journey time fusion forecasting of traffic big data and looking into Inquiry method is further described.
Specific implementation mode
The present invention is based on the journey time fusion forecasting method of traffic big data, the data uploaded using all online vehicles Obtain all kinds of prediction models and parameter by offline calculation or training, and establish according to all kinds of prediction models and parameter and dynamic more New data dictionary；Call all kinds of prediction models and parameter combination realtime section and path forms time data to road network or bicycle Traffic behavior is predicted；The data dictionary includes vehicle data dictionary, section data dictionary and path data dictionary；It is described Online vehicle refers to login network access and uploads the vehicle of positioning and speed data automatically.Compared with prior art prediction technique, this Inventive method combines big data technology, and each data dictionary contains multiple indexes, and data storage has preferable systematicness, can be very Meet the requirement of realtime of data storage and readwrite well.It of particular concern is, fully take into account in engineer application to prediction The requirement of precision, realtime, versatility and practicability etc., most calculation amounts, most complicated calculating task of this case By completing offline.That is, this case obtains all kinds of prediction model parameters by offline training, only needed in line computation The prediction model generated offline is called to carry out simple computation.This have the effect that：It can preferably be improved by offline calculation The precision of prediction of prediction model, while online prediction speed can also be greatly improved, reduce online calculation so that prediction can be real Shi Shixian, to ensure that the realtime and practicability in engineer application.
In addition, the present invention is based on the journey time fusion forecasting method of traffic big data, uploaded using all online vehicles Data all kinds of prediction models and parameter are obtained by offline calculation or training, including, set daily from 0:To 24 when 00:When 00 For a complete cycle period, according to working day, Saturday, Sunday, festivals or holidays trip mode to travel time data into The division of row trip type and number, that is, go out row number；The travel time data in each period is arranged in a journey time sequence Row.The advantage of doing so is that：It is divided in conjunction with the periodic characteristic of road net traffic state, has preferably met the essence of road grid traffic Feature can greatly improve the precision of Forecasting of Travel Time, while can preferably hold the periodic law of road net traffic state.
The present invention is based on the journey time fusion forecasting method of traffic big data, all kinds of prediction models and parameter include Section or path forms time data periods rules model, section or statistical rules model, section or the road of path forms time Diameter journey time longterm prediction model, section or path forms time shorttime forecasting model, section or path forms time length When fusion forecasting model and intersection delay relation analysis model；Wherein,
Using periodic law series approximation PLSA（Periodic Law Series Approximation, PLSA）Algorithm obtains Section or path forms time data periods rules model are taken, and using least square method LSM（Least Square Method, LSM）Solve approximate model parameter.The cyclicallyvarying rule of section or path forms time can be preferably extracted as a result, together When, moreover it is possible to arithmetic speed is greatly improved, there is preferable versatility and engineering application value.
SRE is extracted using statistical law（Statistical Rule Extraction, SRE）Algorithm obtains section or road Diameter journey time statistical rules model, and using Density Estimator KDE（Kernel Density Estimation, KDE）It obtains The probability density changing rule of section or path forms time.For modeldriven, Link Travel Time has more been respected Selfvariation rule.SRE algorithms energy rapid extraction section or periods rules information the most stable in the path forms time, and And computational complexity is not also high, has preferable versatility and engineering application value.
Using it is long when roll correction prediction LRCF（Longtime Rolling Correction Forecast, LRCF） Algorithm obtains section or path forms time longterm prediction model, and obtains longterm prediction model parameter by offline calculation, leads to It crosses and fast implements section or path forms time prediction in line computation.It can be good at solving the realtime in engineer application with this Problem, meanwhile, preferable precision of prediction also ensures the engineering practicability of the algorithm.
SRFF is predicted using fitting is rolled in shortterm（Shorttime Rolling Fitting Forecast, SRFF）It calculates Method obtains section or path forms time shorttime forecasting model, and using time series autoregressive moving average ARMA (Auto Regressive and Moving Average, ARMA) algorithm construction shorttime forecasting model, and using least square method LSM （Least Square Method, LSM）Solving model parameter.The method has respected fully the tide of section or path forms time Nighttide changing rule.Since the algorithm obtains shorttime forecasting model parameter by offline training, the calculation amount of online prediction is very small, It can be good at solving the real time problems in engineer application, meanwhile, preferable precision of prediction also ensures the engineering of the algorithm Practicability.
Using sievelike fusion forecasting SFF（Sieve Fusion Forecast, SFF）Algorithm obtains section or path forms Time grows fusion forecasting model in shortterm, and by offline training, using GaussianNewton method GNIM（GuassianNewton Iterative Method, GNIM）Obtain fusion forecasting model parameter.The algorithm reduce to a certain extent LRCF algorithms and SRFF algorithms part weakness, while being the effective integration of LRCF algorithms and SRFF algorithms the advantages of strengthen them again.The algorithm By offline training, fusion forecasting model parameter is obtained using GNIM, precision of prediction can be effectively greatly improved, greatly improve simultaneously The speed of online prediction ensure that realtime and practicability in engineer application.
Using intersection delay association analysis IDCA（Intersection Delay Correlation Analysis, IDCA）Algorithm construction intersection delay relation analysis model, and pass through least square method LSM（Least Square Method, LSM）Solving model parameter obtains model parameter, by the quick of line computation realizing route journey time by offline training Compensation.This method can effectively make up intersection delay deviation caused by the journey time of path.Due to IDCA algorithms pass through it is offline Training obtains model parameter and both ensure that computational accuracy, simultaneously by the quick compensation in line computation realizing route journey time It in turn ensures the speed in line computation, there is preferable engineering practicability.
Wherein, the longterm prediction refers to obtaining section or the longterm prediction value of path forms time by LRCF algorithms, The value range of longterm prediction duration is set as 0 minute to 3 months, and specific longterm prediction duration can be in the prediction that user selectes It is obtained on the basis of duration by linearly converting；The shortterm prediction refers to when obtaining section or path forms by SRFF algorithms Between shortterm prediction value, the value range of shortterm prediction duration is set as 0 minute to 3 hour, specific shortterm prediction duration It can be obtained on the basis of the prediction duration that user selectes by linearly converting.
Attached drawing 1 is that data dictionary data in section of the present invention store and update flow diagram, as seen from the figure, the present invention is based on The journey time fusion forecasting method of traffic big data, the section data dictionary be used for store journey time periodic sequence, Probability sequence and various models and parameter, data storage and update include the following steps：
S101, historical data is read, historical data is read from the data dictionary of section, including section number, even time interval are adopted Link Travel Time data, the date and hour data of sample；
S102, section is chosen, a untreated section is chosen according to the sequencing of section number；
S103, the classification of Link Travel Time data, it is theoretical based on road network tidal current, it sets daily from 0:To 24 when 00:00 The Shi Weiyi complete cycle period, according to the trip mode on working day, Saturday, Sunday, festivals or holidays, to travel time data Trip Type division and number are carried out, that is, goes out row number, the travel time data in each period is arranged in a journey time sequence Row；
S104, Link Travel Time data are chosen, a kind of untreated section is chosen according to the sequencing for going out row number Travel time data；
S105, travel time data periods rules model and parameter are obtained, section or path is obtained using PLSA algorithms Travel time data periods rules model, and approximate model parameter is solved using LSM；Including：
S1051, degree of correlation clustering is carried out to the journey time sequence of any two different cycles, the extraction degree of correlation is big Travel time data form collection and be merged into row and ask and calculate, obtain average travel time sequence；
S1052, take " 4 π/hour " as basis pi, one Fourier space model of construction is come when approaching average stroke Between sequence, equation is approached by LSM solutions and obtains model parameter；
S1053, the sequence according to energy from high to low, the interception model parameter of gross energy >=98%, remaining parameter zero setting, from And obtain PLSA model parameters；
S1054, a cycle sequence that Link Travel Time is generated by PLSA algorithms, by the periodic sequence and PLSA models In the information storages such as parameter to section data dictionary；
S106, Link Travel Time statistical law model and parameter are obtained, section or path forms is obtained using SRE algorithms The statistical rules model of time, and section or the probability density changing rule of path forms time are obtained using KDE；Including：
Sometime, the travel time data for selecting all periods at the moment forms data set, uses KDE for S1061, calibration Probability density function is obtained, the corresponding journey time of probability density maximum value, i.e. maximum probability journey time are found；
S1062, corresponding maximum probability journey time of all moment is solved, is chronologically arranged in the general of Link Travel Time Rate sequence is simultaneously stored into section data dictionary；
S107, Link Travel Time longterm prediction model and parameter are obtained, Link Travel Time is obtained using LRCF algorithms Longterm prediction model and parameter, and longterm prediction model parameter is obtained by offline calculation；Including：
S1071, by the periodic sequence of journey time and probability sequence sumaverage arithmetic, obtain journey time it is long when initial value sequence Row
S1072, calibration sometime, calculate all periods the moment corresponding journey time with it is long when initial value compared Compared with, acquisition difference, and chronologically it is arranged in a sequence of differences；
S1073, in view of the number of days spacing between adjacent difference, construction one multiple multinomial model of binary approaches this Sequence of differences adaptively adjusts polynomial item number, finds the corresponding multinomial model of minimum fitness bias；
S1074, the process for solving multinomial model are LRCF algorithms, obtain corresponding LRCF model parameters of all moment And it is stored in the data dictionary of section；
S108, the shorttime forecasting model and parameter for obtaining Link Travel Time, when obtaining link travel using SRFF algorithms Between shorttime forecasting model, and use ARMA algorithm construction shorttime forecasting models, and use LSM solving model parameters；Including：
S1081, chronologically by the rhythmic journey time series arrangement of institute at a long sequence；
S1082, assume that the time interval of the long sequence is impartial, with ARMA algorithm constructions oneNMultinomial model is intended After conjunctionNA journey time asks its parameter and error of fitting with LSM；
S1083, by adjustingNThe size of error of fitting is adjusted, chooses corresponding multinomial model when error minimum；
S1084, the process for solving the multinomial model are SRFF algorithms, and SRFF model parameters are stored in section data word In allusion quotation；
S109, Link Travel Time length fusion forecasting model and parameter in shortterm are obtained, link travel is obtained using SFF algorithms Time grows fusion forecasting model in shortterm, and by offline training, and fusion forecasting model parameter is obtained using GNIM；Including：
S1091, calibration initial time, obtain the item number of SRFF modelsN, construction one 2 ×NSievelike coefficient matrix, wherein It is 1 that each element, which is the sum of the element of nonnegative and each row perseverance, and element value is unknown；
S1092, journey time is predicted since initial time, by LRCF algorithms come initial value sequence when compensating long Row obtain futureNA longterm prediction value obtains future by SRFF algorithmsNA shortterm prediction value, by both prediction value sequences Composition one 2 ×NPrediction matrix；
S1093, coefficient matrix is added to processing with row after prediction matrix dot product, obtain one 1 ×NFusion vector, with melting Resultant vector approaches corresponding journey time sequence, obtains corresponding related coefficient equation；
S1094, initial time is gradually adjusted backward, corresponding related coefficient equation is obtained with same method, by these Equation constitutes a related coefficient equation group；
S1095, equation group is solved with GNIM, obtains the element value namely SFF model parameters of sievelike coefficient matrix, it will SFF model parameters are stored in the data dictionary of section；
S110, judge whether that all Link Travel Times are disposed？It is that then, sequence executes step S111, otherwise, returns Execute step S104；
S111, judge whether all sections are disposed？It is that then, sequence executes step S112 and otherwise returns to step S102；
S112, the data storage and update for terminating this section data dictionary；
Wherein, the longterm prediction refers to obtaining section or the longterm prediction value of path forms time by LRCF algorithms, The value range of longterm prediction duration is set as 0 minute to 3 months, and specific longterm prediction duration can be in the prediction that user selectes It is obtained on the basis of duration by linearly converting；The shortterm prediction refers to when obtaining section or path forms by SRFF algorithms Between shortterm prediction value, the value range of shortterm prediction duration is set as 0 minute to 3 hour, specific shortterm prediction duration It can be obtained on the basis of the prediction duration that user selectes by linearly converting.
Attached drawing 2 is that path data dictionary data of the present invention stores and updates flow diagram, as seen from the figure, the present invention is based on The journey time fusion forecasting method of traffic big data, the path data dictionary be used for store journey time periodic sequence, Probability sequence and various models and parameter, data storage and update include the following steps：
S201, historical data is read, historical data, including roadnet node, road is read from path and section data dictionary Segment number, path number, the section of even time interval sampling and path forms time data, date and hour data；
S202, combination of nodes is chosen, according to the permutation and combination relationship that roadnet node is numbered, chooses one group of untreated two section Point combination；
S203, path selection choose a untreated path according to two combination of nodes；
S204, the classification of path forms time data, it is theoretical based on road network tidal current, it sets daily from 0:To 24 when 00:00 The Shi Weiyi complete cycle period, according to the trip mode on working day, Saturday, Sunday, festivals or holidays, to travel time data Trip Type division and number are carried out, that is, goes out row number, the travel time data in each period is arranged in a journey time sequence Row；
S205, path selection travel time data choose a kind of untreated path according to the sequencing for going out row number Travel time data；
S206, travel time data periods rules model and parameter are obtained, section or path is obtained using PLSA algorithms Travel time data periods rules model, and approximate model parameter is solved using LSM；Including：
S2061, degree of correlation clustering is carried out to the journey time sequence of any two different cycles, the extraction degree of correlation is big Travel time data form collection and be merged into row and ask and calculate, obtain average travel time sequence；
S2062, take " 4 π/hour " as basis pi, one Fourier space model of construction is come when approaching average stroke Between sequence, equation is approached by LSM solutions and obtains model parameter；
S2063, the sequence according to energy from high to low, the interception model parameter of gross energy >=98%, remaining parameter zero setting, from And obtain PLSA model parameters；
S2064, a cycle sequence that the path forms time is generated by PLSA algorithms, by the periodic sequence and PLSA models In the information storages such as parameter to path data dictionary；
S207, Link Travel Time statistical law model and parameter are obtained, section or path forms is obtained using SRE algorithms The statistical rules model of time, and section or the probability density changing rule of path forms time are obtained using KDE；Including：
Sometime, the travel time data for choosing all periods at the moment forms data set, uses KDE for S2071, calibration Probability density function is obtained, the corresponding journey time of probability density maximum value, i.e. maximum probability journey time are found；
S2072, corresponding maximum probability journey time of all moment is solved, is chronologically arranged in the general of path forms time Rate sequence is simultaneously stored into path data dictionary；
S208, intersection delay relation analysis model and parameter are obtained, is associated with using IDCA algorithm construction intersection delays Analysis model, and by LSM solving model parameters, and model parameter is obtained by offline training；Including：
S2081, it demarcates some period sometime, calculates the sum of all Link Travel Times in the path, Yi Jilu Diameter journey time and Link Travel Time and its difference, the difference are intersection total delay；
S2082, all periods and all moment are traversed, calculates all intersection total delays, path forms is established by LSM Incidence relation between time and corresponding intersection total delay and solution obtain LSM model parameters；
S2083, all intersection total delays are chronologically arranged, it is contemplated that the number of days spacing between consecutive value, construction one A multiple multinomial of binary approaches the sequence, adaptively adjusts polynomial item number, it is corresponding more to find minimum fitness bias Item formula model；
S2084, LSM model of fit and multinomial model are subjected to equal weight merging, pooled model is IDCA models, will In the storage to path data dictionary of IDCA model parameters；
S209, acquisition approach journey time longterm prediction model and parameter obtain section or path row using LRCF algorithms Journey time longterm prediction model, and longterm prediction model parameter is obtained by offline calculation；Including：
S2091, by the periodic sequence of journey time and probability sequence sumaverage arithmetic, obtain journey time it is long when initial value sequence Row
S2092, calibration sometime, calculate all periods the moment corresponding journey time with it is long when initial value compared Compared with, acquisition difference, and chronologically it is arranged in a sequence of differences；
S2093, in view of the number of days spacing between adjacent difference, construction one multiple multinomial model of binary approaches this Sequence of differences adaptively adjusts polynomial item number, finds the corresponding multinomial model of minimum fitness bias；
S2094, the process for solving multinomial model are LRCF algorithms, obtain corresponding LRCF model parameters of all moment And it is stored in path data dictionary；
S210, acquisition approach journey time shorttime forecasting model and parameter, using SRFF algorithm acquisition approach journey times Shorttime forecasting model, and ARMA algorithm construction shorttime forecasting models are used, and use LSM solving model parameters；Including：
S2101, chronologically by the rhythmic journey time series arrangement of institute at a long sequence；
S2102, assume that the time interval of the long sequence is impartial, with ARMA algorithm constructions oneNMultinomial model is intended After conjunctionNA journey time asks its parameter and error of fitting with LSM；
S2103, by adjustingNThe size of error of fitting is adjusted, chooses corresponding multinomial model when error minimum；
S2104, the process for solving the multinomial model are SRFF algorithms, and SRFF model parameters are stored in path data word In allusion quotation；
S211, acquisition approach journey time grow fusion forecasting model and parameter in shortterm, using SFF algorithm acquisition approach strokes Time grows fusion forecasting model in shortterm, and by offline training, and fusion forecasting model parameter is obtained using GNIM；Including：
S2111, calibration initial time, obtain the item number of SRFF modelsN, construction one 2 ×NSievelike coefficient matrix, wherein It is 1 that each element, which is the sum of the element of nonnegative and each row perseverance, and element value is unknown；
S2112, journey time is predicted since initial time, by LRCF algorithms come initial value sequence when compensating long Row obtain futureNA longterm prediction value obtains future by SRFF algorithmsNA shortterm prediction value, by both prediction value sequences Composition one 2 ×NPrediction matrix；
S2112, coefficient matrix is added to processing with row after prediction matrix dot product, obtain one 1 ×NFusion vector, with melting Resultant vector approaches corresponding journey time sequence, obtains corresponding related coefficient equation；
S2114, initial time is gradually adjusted backward, corresponding related coefficient equation is obtained with same method, by these Equation constitutes a related coefficient equation group；
S2115, equation group is solved with GNIM, obtains the element value namely SFF model parameters of sievelike coefficient matrix, it will SFF model parameters are stored in path data dictionary；
S212, judge whether all path forms time datas are disposed？It is that then, sequence executes step S213, otherwise, Return to step S205；
S213, judge whether all paths are disposed, be that then, sequence executes step S214 and otherwise returns to step S203；
S214, judge whether all combination of nodes are disposed, be that then, sequence executes step S215, otherwise, return and execute Step S202；
S215, the data storage and update for terminating this path data dictionary；
Wherein, the longterm prediction refers to obtaining section or the longterm prediction value of path forms time by LRCF algorithms, The value range of longterm prediction duration is set as 0 minute to 3 months, and specific longterm prediction duration can be in the prediction that user selectes It is obtained on the basis of duration by linearly converting；The shortterm prediction refers to when obtaining section or path forms by SRFF algorithms Between shortterm prediction value, the value range of shortterm prediction duration is set as 0 minute to 3 hour, specific shortterm prediction duration It can be obtained on the basis of the prediction duration that user selectes by linearly converting.
Attached drawing 3 is path forms time change querying flow schematic diagram of the present invention, and as seen from the figure, it is big that the present invention is based on traffic The journey time fusion query method of data is set using the journey time fusion forecasting method pair the present invention is based on traffic big data Fixed query path and the journey time variation for going out row number predict and prediction result are supplied to user, including, it uses The data that all online vehicles upload are by offline calculation or all kinds of prediction models of training acquisition and parameter and according to all kinds of predictions Model and parameter are established and dynamically update the data dictionary；According to query path set by user and go out row number, calls all kinds of pre When survey model and parameter combine stroke of the realtime travel time data of the path and respective stretch to the path and respective stretch Between carry out predict and prediction result is supplied to user；The data dictionary include vehicle data dictionary, section data dictionary and Path data dictionary；The online vehicle refers to login network access and uploads the vehicle of positioning and speed data automatically.
Equally, the present invention is based on the journey time fusion query method of traffic big data, it is described go out row number refer to being based on Road network tidal current is theoretical, sets daily from 0:To 24 when 00:When 00 be a complete cycle period, according to working day, Saturday, The trip mode on Sunday, festivals or holidays carries out trip Type division and number to travel time data, that is, goes out row number, each The travel time data in period is arranged in a journey time sequence.
Equally, the present invention is based on the journey time fusion query method of traffic big data, all kinds of prediction models and ginsengs Number includes section or path forms time data periods rules model, section or the statistical rules model of path forms time, road When section or path forms time longterm prediction model, section or path forms time shorttime forecasting model, section or path forms Between the long model of fusion forecasting in shortterm and intersection delay relation analysis model；Wherein,
Using periodic law series approximation PLSA（Periodic Law Series Approximation, PLSA）Algorithm obtains Section or path forms time data periods rules model are taken, and using least square method LSM（Least Square Method, LSM）Solve approximate model parameter；
SRE is extracted using statistical law（Statistical Rule Extraction, SRE）Algorithm obtains section or road Diameter journey time statistical rules model, and using Density Estimator KDE（Kernel Density Estimation, KDE）It obtains The probability density changing rule of section or path forms time；
Using it is long when roll correction prediction LRCF（Longtime Rolling Correction Forecast, LRCF） Algorithm obtains section or path forms time longterm prediction model, and obtains longterm prediction model parameter by offline calculation, leads to It crosses and fast implements section or path forms time prediction in line computation；
SRFF is predicted using fitting is rolled in shortterm（Shorttime Rolling Fitting Forecast, SRFF）It calculates Method obtains section or path forms time shorttime forecasting model, and using time series autoregressive moving average ARMA (Auto Regressive and Moving Average, ARMA) algorithm construction shorttime forecasting model, and using least square method LSM （Least Square Method, LSM）Solving model parameter；
Using sievelike fusion forecasting SFF（Sieve Fusion Forecast, SFF）Algorithm obtains section or path forms Time grows fusion forecasting model in shortterm, and by offline training, using GaussianNewton method GNIM（GuassianNewton Iterative Method, GNIM）Obtain fusion forecasting model parameter；
Using intersection delay association analysis IDCA（Intersection Delay Correlation Analysis, IDCA）Algorithm construction intersection delay relation analysis model, and pass through least square method LSM（Least Square Method, LSM）Solving model parameter obtains model parameter, by the quick of line computation realizing route journey time by offline training Compensation；
Wherein, the longterm prediction refers to obtaining section or the longterm prediction value of path forms time by LRCF algorithms, The value range of longterm prediction duration is set as 0 minute to 3 months, and specific longterm prediction duration can be in the prediction that user selectes It is obtained on the basis of duration by linearly converting；The shortterm prediction refers to when obtaining section or path forms by SRFF algorithms Between shortterm prediction value, the value range of shortterm prediction duration is set as 0 minute to 3 hour, specific shortterm prediction duration It can be obtained on the basis of the prediction duration that user selectes by linearly converting.
The present invention is based on the journey time fusion query methods of traffic big data, include the following steps：
S301, user's inquiry；
S302, acquisition history and real time traffic data, including roadnet node number, section number, path number, even time interval The section of sampling and path forms time, all kinds of Forecasting of Travel Time data, all kinds of model parameters, date and hour, data point Class is numbered；
S303, setting path forms time change whether is inquired？It is then, to continue to execute step S304, otherwise, executes step S314；
S304, querying condition setting, user according to the demand of oneself set inquiry content, including the duration of shortterm prediction and The period of longterm prediction, according to even time interval principle, the corresponding starting point and ending point with longterm prediction steplength in shortterm of calibration, and Go out row number accordingly according to calendar setting；Meanwhile user sets the starting point and ending point of roadnet node, passes through searching route Data dictionary demarcates the path between the two roadnet nodes and path number；
S305, path selection travel time data choose a kind of untreated path according to the sequencing for going out row number Travel time data；
S306, path selection choose a untreated path, passage path data according to the sequencing of path number It numbers in the section that dictionary search is included to the path；
S307, section is chosen, a untreated section is chosen according to the sequencing of section number；
S308, Link Travel Time fusion forecasting are read accordingly according to user's search request from the data dictionary of section Model and parameter are as follows by step process in conjunction with realtime travel time data：
S3081, by LRCF algorithms obtain journey time it is long when at the beginning of value sequence and sequence of differences, the two, which is added, is gone The longterm prediction sequence of journey time；
S3082, by user choose shortterm prediction steplength starting point and ending point on the basis of, obtained by SRFF algorithms The shortterm prediction sequence of journey time, then fusion forecasting sequence in shortterm is obtained by SFF algorithms；
The starting point and ending point for the longterm prediction steplength that S3083, user of being subject to choose, it is short with longterm prediction sequence pair When fusion forecasting sequence expanded, obtain the final fusion forecasting sequence of Link Travel Time；
S309, judge whether all sections are disposed？It is that then, sequence executes step S310 and otherwise returns to step S307；
S310, the prediction of path forms Fusion in Time, set according to user in predicting, as follows by step process：
S3101, the forecasting sequence that intersection total delay is obtained by IDCA algorithms, it is according to temporal order, intersection is total The forecasting sequence of delay is added with the final fusion forecasting sequence in the included section in the path, obtains the compensation pre of journey time Sequencing row；
S3102, by LRCF algorithms obtain journey time it is long when at the beginning of value sequence and sequence of differences, the two be added i.e. obtains The longterm prediction sequence of journey time；
S3103, by user choose shortterm prediction steplength starting point and ending point on the basis of, obtained by SRFF algorithms The shortterm prediction sequence of journey time, then fusion forecasting sequence in shortterm is obtained by SFF algorithms；
The starting point and ending point for the longterm prediction steplength that S3104, user of being subject to choose, it is short with longterm prediction sequence pair When fusion forecasting sequence expanded, obtain the expansion type forecasting sequence of journey time；
S3105, expansion type forecasting sequence is added averaging with compensation forecasting sequence, obtains the path forms time most Whole fusion forecasting sequence；
S311, judge whether all paths are disposed？It is that then, sequence executes step S312 and otherwise returns to step S306；
S312, judge whether all trips are disposed？It is that then, sequence executes step S313 and otherwise returns to step S305；
S313, path forms time prediction recombination and Dynamic Announce, by all path forms time final fusion forecastings Sequence, by path, the logical order of classifying and numbering and sequential carry out recombination arrangement, obtain the final forecasting sequence of each path, And the chronologically Dynamic Announce on road network；
S314, terminate this path query；
Wherein, the longterm prediction refers to obtaining section or the longterm prediction value of path forms time by LRCF algorithms, The value range of longterm prediction duration is set as 0 minute to 3 months, and specific longterm prediction duration can be in the prediction that user selectes It is obtained on the basis of duration by linearly converting；The shortterm prediction refers to when obtaining section or path forms by SRFF algorithms Between shortterm prediction value, the value range of shortterm prediction duration is set as 0 minute to 3 hour, specific shortterm prediction duration It can be obtained on the basis of the prediction duration that user selectes by linearly converting.
Obviously, the present invention is based on the advantageous effects of the journey time fusion forecasting of traffic big data and querying method is Overcome existing for the traffic status prediction method of prior art road network or bicycle that realtime is poor, versatility is poor and practicability Survey precision greatly improved in the problems such as not strong, while the speed of online prediction greatly improved, and ensure that the reality in engineer application Shi Xing, practicability and versatility.
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