CN109584552A - A kind of public transport arrival time prediction technique based on network vector autoregression model - Google Patents
A kind of public transport arrival time prediction technique based on network vector autoregression model Download PDFInfo
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Abstract
The public transport arrival time prediction technique based on network vector autoregression model that the invention discloses a kind of, using bus station and crossing as node, urban traffic network is constructed based on urban road traffic information and public bus network planning situation, and it is extracted from intelligent transportation system database, infer communal facility quantity, travelling speed between website, the data such as traffic congestion degree, the low-dimensional of travelling speed matrix and urban traffic network implies the factor between website, it constructs regression relation between the implicit factor and predicts the travelling speed of respective stretch, historical data is learnt based on expansion network vector space autoregression model again, travelling speed between certain following period website of prediction, then according to hourage between distance between website and the travelling speed of prediction estimation website, this method considers the topological correlation of urban traffic network, take full advantage of public transport arrival time, it is public The data such as GPS positioning information are handed over, prediction effect is effectively improved.
Description
The technical field is as follows:
the invention relates to the technical field of urban intelligent public transport information processing, in particular to a public transport arrival time prediction method based on a network vector autoregressive model.
Background art:
in recent years, the rapid development of Chinese economy and the rapid progress of science and technology promote the great improvement of urban public transport level. Among them, buses are important components of urban public transportation and have become essential transportation means in people's modern life. With the continuous promotion of the urbanization process and the rapid expansion of the urban scale, the problems of the increase of the total passenger quantity, the large variation range of the bus passenger flow intensity, the large difference of the passenger transport effect in different time periods and the like become increasingly prominent. The accurate prediction of the bus arrival time is an important means for relieving the pressure of urban public transport. On one hand, the prediction of the arrival time of the bus can provide decision support for bus passenger flow guidance, bus safety management and operation coordination, and is beneficial to providing the operation efficiency of the urban bus network and reducing traffic jam. On the other hand, the bus arrival time inquiry service can be provided for passengers, so that the passengers can be helped to plan, and the anxiety of the passengers waiting for the bus can be relieved.
The bus arrival time prediction means that the time of the bus arriving at the station is predicted by modeling by using data acquired by an intelligent transportation system. The corresponding modeling method can be roughly divided into two strategies of time series analysis and machine learning. The time sequence analysis strategy extracts the travel time between the historical bus line stops as a time sequence, tests the stability, randomness and the like of the time sequence, and then selects a proper time sequence analysis model for prediction according to the test conditions. The machine learning strategy takes the travel situation between the sites as an object, takes the travel time between the sites as a prediction variable, extracts the length of the travel road section between the sites, the crowding degree, the nearby weather situation, the POI situation, the travel time of the upstream road section and the like as characteristics, and then selects a random forest, a support vector machine, a neural network and the like to construct a model. In summary, the influence of topological correlation between urban road traffic networks on bus travel time cannot be fully considered in the existing method. In addition, a large amount of missing usually exists in the acquired bus arrival time, and the missing data is usually discarded in the existing work without being properly processed.
Considering that the travel speed between stops can reflect the traffic condition and can be directly influenced by the travel speed of adjacent areas, the method converts the prediction of the bus arrival time into the prediction of the travel speed between stops. On the basis, a regression relationship is constructed by utilizing the urban traffic network and the station travel speed matrix, so that historical missing data is filled. Furthermore, a network vector autoregressive model is expanded on the basis of a partial linear single index model to predict the travel speed between sites. And finally, estimating the travel time between the stations according to the travel speed between the stations, thereby predicting the time of the bus reaching the target station.
The invention content is as follows:
in order to overcome the defects in the prior art, the invention considers the topological correlation of the urban traffic network, makes full use of the data such as the bus arrival time, the bus GPS positioning information and the like, provides the bus arrival time prediction method based on the network vector autoregressive model, and effectively improves the prediction effect.
The invention relates to a bus arrival time prediction method based on a network vector autoregressive model, which comprises the following steps:
A. data preprocessing facing an intelligent traffic system: taking bus stops and intersections as nodes, constructing an urban traffic network based on urban road traffic information and bus route planning conditions, and extracting and deducing data such as public facility quantity, travel speed between stops, traffic jam degree and the like from an intelligent traffic system database;
B. and (3) filling the traveling speed loss between the sites based on singular value matrix decomposition: for a certain time period with missing travel speed, extracting a travel speed matrix between sites of the time period and a low-dimensional hidden factor of an urban traffic network, constructing a regression relation between the hidden factors and predicting the travel speed of a corresponding road section;
C. inter-site travel speed prediction based on a network vector partial linear autoregressive model: learning historical data based on an expanding network vector space autoregressive model, so as to predict the travel speed between sites in a certain period of time in the future;
D. predicting and correcting the bus arrival time: and estimating the travel time between the stations according to the distance between the stations and the predicted travel speed, further estimating and accumulating the travel time of each road section from the bus to the target station, and correcting by referring to historical data.
The step A related by the invention deduces a travel relation network between stations based on the urban road traffic network and the bus route planning condition, and calculates the distance between stations according to the included angle relation between stations.
The step A related by the invention utilizes the bus GPS data to deduce the congestion degree between stations.
Step B related by the invention constructs topological correlation between the inter-site travel speed matrix and the inter-site travel relation network, thereby filling up the missing travel speed between sites.
Step C related by the invention expands the network vector space autoregressive model based on a partial linear single index model, so that the direct nonlinear correlation of independent variables and dependent variables can be processed.
Compared with the prior art, the method has reliable principle, considers the topological correlation of the urban traffic network, fully utilizes the data of bus arrival time, bus GPS positioning information and the like, effectively improves the prediction effect, predicts accurate time and is environment-friendly in application.
Description of the drawings:
FIG. 1: the invention relates to a flow diagram of bus arrival time prediction based on a network vector autoregressive model.
FIG. 2: the invention relates to a flow chart diagram for filling missing values based on a singular value matrix decomposition method.
FIG. 3: example 1 three cases of inter-site included angle relationships
The specific implementation mode is as follows:
in order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Example 1:
the scheme related to the embodiment comprises the following steps:
A. data preprocessing oriented to intelligent traffic system
(1) Building an inter-site travel relationship network
Firstly, placing intersections as nodes in a geodetic coordinate system according to longitude and latitude, connecting the nodes according to urban road planning conditions, and specifically describing by using a network G (V, L); wherein, V represents intersection set, V ═ V1，v2，…vnN ═ V | is the total number of intersections, L represents a set of links existing between intersections, and L ═ a great face<vh，vl>|vh，vlThe element belongs to V, 1 is less than h, l is less than n, and the nodes in G are limited by the positions of longitude and latitude, so that the urban road condition can be reflected more truly;
then, adding bus stops to the urban road traffic network according to the bus route planning condition; on the basis, redefining the node set as V ═ V1∪V2，V1Representative intersection set, V2A representative site set; the longitude and latitude of the station, the distance between intersections and the distance between the station and the nearest intersection are obtained by a database of the intelligent traffic system; further, the travel distance between the stations is calculated, when both stations are adjacent to a crossing, the exact position relation between the stations and the crossing needs to be determined, and the azimuth angles Az of the stations i and j are givenij(0°＜Azij< 360 °) calculation from the following model
Wherein, WiRepresents the latitude, J, of node iiRepresents the longitude, W, of node ijRepresents the latitude, J, of node JjRepresents the longitude of node j; the distance between the stations can be calculated according to the angle relationship, and three cases are shown in FIG. 3
FIG. 3-a shows that the azimuth angles of station A and station B are not equal to each other, and the distance D between station A and station BABIs the sum of the distances between the two stations and the intersection; FIG. 3-B represents station A and station B having unequal azimuths relative to the intersection, but 360 of the sums, and a distance D between station A and station BABIs the sum of the distances between the two stations and the intersection; 3-c represent station A and station B having equal azimuth relative to the intersection, and distance between station A and station BFrom DABIs the absolute value of the distance difference between the two stations and the intersection. Aiming at the three conditions, calculating to obtain the distance between the stations; then, generating a travel distance matrix D between stations according to the bus route planning condition;
finally, the travel relation network G between sites is extracted from GBus＝(VBus，LBus) Wherein V isBusRepresenting a set of bus stops, VBus＝{v1，…，vN}，N＝|VBusL is the number of bus stops in the travel relationship network between stops, LBusRepresenting sets of adjacent road sections between stations, LBus＝{<vh，vl>|vh，vl∈VBusH is more than 1, l is less than N, and meanwhile, a travel relation network G between sites is generated according to a travel distance matrix between sitesBusIs A ═ aij)∈RN×NWherein when (v)h，vl)∈LBus，aij1, otherwiseij＝0；
(2) Extracting travel speeds between stops
The traffic jam condition of a certain road section is influenced by the adjacent road sections, and the travel time cannot directly reflect the traffic jam condition; for this purpose, the embodiment extracts the travel time from the intelligent transportation system database, then converts the travel time into the travel speed, and models and predicts the travel speed;
(2-1) taking the initial time of the extracted data as the starting time, and dividing T time periods at intervals of a fixed time period;
(2-2)Yt∈RN×Na travel time matrix of t time period, whose elements areRepresents the average travel time from site i to site j for time period t, and therefore,form a high dimension of T dimensionA degree vector;
(2-3) acquiring travel speed data
Given travel time between certain sitesThe travel speed between stations can be calculated according to the following model
Sequentially converting the travel time matrix among the sites into a travel speed matrix among the sites to generate a high-dimensional travel speed vector
(3) Extracting related covariates
The prediction of the travel speed between the stations not only needs to consider the topological correlation of the urban road traffic network, but also has other factors which can influence the speed; for this purpose, the present embodiment selects a public infrastructure condition (POI, Point Of Interest) and a traffic congestion degree as covariates;
(3-1) public infrastructure situation
POI (Point Of interest) represents the amount Of public infrastructure (e.g., school, hospital, mall, movie theater) in the area between transit stations; in this embodiment, use is made ofRecording the number of public facilities near the travel from the station i to the station j; (by usingRecording number of public facilities near travel from station i to station j)
(3-2) degree of traffic congestion
The method adopts the bus GPS data to evaluate the congestion degree of the travel road section; giving two adjacent stations i and j, and counting the number of buses between the adjacent stations i and j in a t period according to GPS dataAnd based on the historical number sequence of the buses in the road sectionMinimum value of (2)First quartileMedian numberFourth quartileMaximum valueThe traffic congestion degree is divided into four levels:
wherein 1 represents unobstructed, 2 represents comparatively unobstructed, 3 represents comparatively congested, 4 represents congested,
in summary, the covariate matrix model Z can be expressed as
Z＝(ZPOI，ZTPI)T(4)。
B. Singular value matrix decomposition-based inter-site travel speed deficiency filling
Travel time velocity matrix S for t time periodt∈RN×NIn this embodiment, low-dimensional implicit factors of the travel speed matrix and the inter-site travel relationship network adjacency matrix are extracted, and regression relationships between the implicit factors are constructed to fill up StThe missing data in (1) specifically comprises the following three steps of operation;
(1) extracting low dimensional implicit factors
The hidden space network model related to the embodiment extracts the low-dimensional hidden factor, and the hidden space network model is
Wherein,Etis an n × n white noise matrix, μtIs the overall mean value, at、btRepresenting the output and receiving effects of the node, Ut、VtRepresenting interaction effects, the above parameters constituting low-dimensional implicit factorsIt can be estimated by SVD model
Wherein,andis an N x k non-singular matrix,is a diagonal matrix with (k x k) diagonal elements being non-zero elements,n-dimensional vectorAre respectivelyAndcolumn mean of (1); further, travel time velocity matrix StIs covered by a low-dimensional implicit factorExtracting; similarly, a low-dimensional implication factor, N, of the inter-site travel relationship network adjacency matrix A may be extractedA＝[aA，bA,UA,VA]；
(2) Construction of a model of regression relationships between low-dimensional hidden factors
First, S is obtainedtThe row number and column number in which the missing value exists, and then S is deletedtThe rows and columns corresponding to the adjacency matrix A and denoted as St'and A'; further, extracting their low-dimensional implicit factorsAndand constructing a regression model
The model f (-) can be a linear model, a nonlinear model or a nonparametric model, a random forest algorithm is adopted in the embodiment, and the number of decision trees is set to be 200;
(3) predicting and filling missing values
First, S is obtainedtThe row number and column number in which the missing value exists are then extracted StThe rows and columns corresponding to the adjacency matrix A and denoted as St"and A", and further, extracting the low-dimensional hidden factor N of AA″＝[aA″，bA″，UA″，VA″]. Will NA″Substituting into the model (7) to obtain corresponding low-dimensional hidden factorsFinally, obtainColumn mean ofAndcolumn mean ofSubstitution intoDeriving an ensemble meanThen substituted into the following model
To obtainAccording to the row number and the column numberData substitution of corresponding position StTo obtain a travel speed matrix between the sites
C. Inter-site travel speed prediction based on network vector partial linear autoregressive model
The present embodiment adopts a network vector partial linear autoregressive model of
Wherein,representing the influence of the nonlinear variable (characteristic variables such as the number of public facilities and the degree of congestion which are independent of time) on the dependent variable,in (1)Representing the associated covariate vector, g (z), between site i and site jijγ) of γ ═ y (γ)1，γ2)TAre covariate coefficients or nodal effect coefficients,representing the total number of nodes i connected to other nodes, in the modelRepresenting the average effect of other sites k on site i at time t-1, in the modelThe influence of the traveling speed at the moment before the road section from the station i to the station j on the current traveling speed is shown, namely the dependent variable at the time t-1 has influence on the value of the dependent variable at the time t,is an error term which is related to the covariate zijAre independent of each other and follow a normal distribution; its expectation and variance are respectively
Let β be (β)1，β2)T，
The model (9) is rewritten as:
let mu leti＝zijγ，The following can be obtained:
estimate unknown parameter ξ ═ (γ)T，βT)TThe steps are as follows:
(1) estimate g (·)
For a givenThe objective function model is minimized using a local linear regression method as follows:
wherein,k (-) is a kernel function, h is bandwidth, K (-) is a bounded, non-negative, tightly-supported with 0 symmetry and Lipschitz's continuous density function
Obtaining an estimator:
wherein,
(2) estimate ζ
In obtaining (1)Then, it is obtained by minimizing the following profile least square function
Get a pairRepeating the step (1) to obtainThen repeating the step (2) again to obtainContinuously repeat until
D. Bus arrival time prediction and correction
In order to improve the prediction accuracy and correct the interference of the extension of the prediction period on the prediction result, the embodiment adds a correction coefficient α (0 ≦ α ≦ 1) to adjust the prediction result to improve the prediction accuracy.
The total time interval from the station i to the station j is l, the travel time data is extracted from the intelligent transportation system, and a vector with l dimension is formedFurther, willSplit into two h-dimensional vectorsAndwherein,then according to the model (2) willConverting the inter-site travel velocity vector into an inter-site travel velocity vector, and substituting the inter-site travel velocity vector into an inter-site travel velocity prediction model to obtain an inter-site travel velocity estimation vectorAnd calculating the travel time between the stations according to the following formula
Deriving travel time prediction vectors
Finally, let
And finding the optimum correction coefficient α according to equation (15)0
The travel time correction model output from the station i to the station j in the time period t is
And obtaining data from the intelligent transportation system, calculating according to the steps to obtain the travel time of all road sections between the station m and the station n, adding and summing, and finally adding and outputting the sum and the departure time of the station m, namely finishing the bus arrival time prediction.
Claims (5)
1. A bus arrival time prediction method based on a network vector autoregressive model is characterized by mainly comprising the following steps:
A. data preprocessing facing an intelligent traffic system: taking bus stops and intersections as nodes, constructing an urban traffic network based on urban road traffic information and bus route planning conditions, and extracting and deducing data such as public facility quantity, travel speed between stops, traffic jam degree and the like from an intelligent traffic system database;
B. and (3) filling the traveling speed loss between the sites based on singular value matrix decomposition: for a certain time period with missing travel speed, extracting a travel speed matrix between sites of the time period and a low-dimensional hidden factor of an urban traffic network, constructing a regression relation between the hidden factors and predicting the travel speed of a corresponding road section;
C. inter-site travel speed prediction based on a network vector partial linear autoregressive model: learning historical data based on an expanding network vector space autoregressive model, so as to predict the travel speed between sites in a certain period of time in the future;
D. predicting and correcting the bus arrival time: and estimating the travel time between the stations according to the distance between the stations and the predicted travel speed, further accumulating the travel time of each road section from the bus to the target station, and correcting by referring to historical data.
2. The method for predicting the arrival time of the bus based on the network vector autoregressive model according to claim 1, wherein the step A mainly comprises the following steps:
(1) building an inter-site travel relationship network
Firstly, placing intersections as nodes in a geodetic coordinate system according to longitude and latitude, connecting the nodes according to urban road planning conditions, and specifically describing by using a network G (V, L); wherein, V represents intersection set, V ═ V1，v2，…vnN ═ V | is the total number of intersections, L represents a set of links existing between intersections, and L ═ a great face<vh，vl)|vh，vlE is V, 1 is more than h, l is more than n, and the nodes in G are limited by the positions of longitude and latitude to reflect the urban road condition; then adding bus stops to the urban road traffic network according to the planning condition of the bus route; redefining a set of nodes as V ═ V1∪V2，V1Representative intersection set, V2A representative site set; the longitude and latitude of the station, the distance between intersections and the distance between the station and the nearest intersection are obtained by a database of the intelligent traffic system; further, the travel distance between the stations is calculated, the two stations are both close to a crossing, the exact position relation between the two stations and the crossing is determined, and the azimuth angles Az of the two stations i and j are givenij(0°＜Azij< 360 °) was obtained from the following model:
cos(c)＝cos(90-Wi)×cos(90-Wj)+sin(90-Wi)×sin(90-Wj)×cos(Ji-Jj)
wherein, WiRepresents the latitude, J, of node iiRepresents the longitude, W, of node ijRepresents the latitude, J, of node JjRepresents the longitude of node j; the distance between the stations can be calculated according to the included angle relationship, wherein the three conditions are that the azimuth angles of the station A and the station B relative to the intersection are unequal, and the distance D between the station A and the station BABIs the sum of the distances between the two stations and the intersection; the azimuth angles of the station A and the station B relative to the intersection are not equal, but the sum is 360 DEG, and the distance D between the station A and the station BABIs the sum of the distances between the two stations and the intersection; the azimuth angles of the station A and the station B relative to the intersection are equal, and the distance D between the station A and the station BABIs the absolute value of the distance difference between the two stations and the intersection; aiming at the three conditions, calculating to obtain the distance between the stations; then, generating a travel distance matrix D between stations according to the bus route planning condition;
finally, the travel relation network G between sites is extracted from GBus＝(VBus，LBus) Wherein V isBusRepresenting a set of bus stops, VBus＝{v1，…，vN}，N＝|VBusL is the number of bus stops in the travel relationship network between stops, LBusRepresenting sets of adjacent road sections between stations, LBus＝{<vh，vl>|vh，vl∈VBusH is more than 1, l is less than N, and the travel relation network G between sites is generated according to the travel distance matrix between sitesBusIs A ═ aij)∈RN×NWherein when (v)h，vl)∈LBus，aij1, otherwiseij＝0；
(2) Extracting travel speeds between stops
Extracting travel time from an intelligent traffic system database, converting the travel time into travel speed, and modeling and predicting the travel speed; taking the initial time of the extracted data as the starting time, and dividing T time periods at intervals of a fixed time period; y ist∈RN×NA travel time matrix of t time period, whose elements areRepresents the average travel time from site i to site j for time period t, and therefore,a high-dimensional vector of a T dimension is formed; given travel time between certain sitesThe travel speed between stations can be obtained according to the following model
Sequentially converting the travel time matrix among the sites into a travel speed matrix among the sites to generate a high-dimensional travel speed vector
(3) Extracting related covariates
By usingRecording the number of public facilities near the travel from the station i to the station j;
evaluating the congestion degree of the travel road section by adopting bus GPS data; giving two adjacent stations i and j, and counting the number of buses between the adjacent stations i and j in a t period according to GPS dataAnd based on the historical number sequence of the buses in the road sectionMinimum value of (count)ij min) First quartile (count)ij 0.25) Median (count)ij median) The third quartile (count)ij 0.75) Maximum value (count)ij max) The traffic congestion degree is divided into four levels:
where 1 represents unobstructed, 2 represents comparatively unobstructed, 3 represents comparatively congested, and 4 represents congested, the covariate matrix model Z can be represented as
Z＝(ZPOI，ZTPI)T(4)。
3. The bus arrival time prediction method based on the network vector autoregressive model according to claim 1, wherein the step B specifically comprises the following steps:
travel time velocity matrix S for t time periodt∈RN×NExtracting low-dimensional hidden factors of the travel speed matrix and the travel relation network adjacency matrix between sites, and constructing a regression relation between the hidden factors to fill StThe missing data in (1) specifically comprises the following three steps;
(1) extracting low dimensional implicit factors
Extracting low-dimensional hidden factors by adopting a hidden space network model which is
Wherein,Etis an n × n white noise matrix, μtIs the overall mean value, at、btRepresenting the output and receiving effects of the node, Ut、VtRepresenting interaction effects, the above parameters constituting low-dimensional implicit factorsIt can be estimated by SVD model
Wherein,andis an N x k non-singular matrix,is a diagonal matrix with (k x k) diagonal elements being non-zero elements,n-dimensional vectorAre respectivelyAndcolumn mean of (1); travel time velocity matrix StIs covered by a low-dimensional implicit factorExtracting and then extracting a low-dimensional implicit factor, N, of an inter-site travel relationship network adjacency matrix AA＝[aA，bA，UA，VA]；
(2) Construction of a model of regression relationships between low-dimensional hidden factors
First obtaining StThe row number and column number in which the missing value exists, and then S is deletedtThe rows and columns corresponding to the adjacency matrix A and denoted as St'and A'; re-extracting their low-dimensional implicit factorsAndand constructing a regression model
The model f (-) is one or more of a linear model, a nonlinear model or a non-parameter model, a random forest algorithm is adopted, and the number of decision trees is set to be 200;
(3) predicting and filling missing values
First obtaining StThe row number and column number in which the missing value exists are then extracted StThe rows and columns corresponding to the adjacency matrix A and denoted as St"and A", and extracting the low-dimensional hidden factor N of AA″＝[aA″，bA″，UA″，VA″]Is a reaction of NA″Substituting into the model (7) to obtain corresponding low-dimensional hidden factorsFinally, obtainColumn mean ofAndcolumn mean ofSubstitution intoDeriving an ensemble meanThen substituted into the following model
To obtainAccording to the row number and the column numberData substitution of corresponding position StTo obtain a travel speed matrix between the sites
4. The bus arrival time prediction method based on the network vector autoregressive model according to claim 1, wherein the step C specifically comprises the following steps:
the network vector part linear autoregressive model is adopted as
Wherein g (z)ijγ) represents the time-independent influence of the number of utilities, the degree of congestion, and the non-linear variable on the dependent variable, g (z)ijγ) inRepresenting the associated covariate vector, g (z), between site i and site jijγ) of γ ═ y (γ)1，γ2)TAre covariate coefficients or nodal effect coefficients,representing the total number of nodes i connected to other nodes, in the modelRepresenting the average effect of other sites k on site i at time t-1, in the modelThe influence of the traveling speed at the moment before the road section from the station i to the station j on the current traveling speed is shown, namely the dependent variable at the time t-1 has influence on the value of the dependent variable at the time t,is the error term and the covariate zijAre independent of each other and follow a normal distribution;respectively of the expectation and variance of
Let β be (β)1，β2)T，
The model (9) is rewritten as:
let mu leti＝zijγ，The following can be obtained:
estimate unknown parameter ξ ═ (γ)T，βT)TThe method comprises the following steps:
(1) estimate g (·)
For a givenThe following objective function model is minimized using local linear regression:
wherein,k (-) is a kernel function, h is bandwidth, K (-) is a bounded, non-negative, tightly-supported with 0 symmetry and Lipschitz's continuous density function
Obtaining an estimator:
wherein:
(2) estimate ξ
In obtaining (1)Then, it is obtained by minimizing the following profile least square function
Get a pairRepeating the step (1) to obtainThen repeating the step (2) again to obtainContinuously repeat until
5. The bus arrival time prediction method based on the network vector autoregressive model according to claim 1, wherein the step D specifically comprises:
the total time interval from the station i to the station j is l, the travel time data is extracted from the intelligent transportation system, and a vector with l dimension is formedThen will beSplit into two h-dimensional vectorsAndwherein,then according to the model (2) willConverting the inter-site travel velocity vector into an inter-site travel velocity vector, and substituting the inter-site travel velocity vector into an inter-site travel velocity prediction model to obtain an inter-site travel velocity estimation vectorAnd calculating the travel time between the stations according to the following formula
Deriving travel time prediction vectors
Finally, let
And finding the optimal correction factor α according to the model (15)0
The travel time correction model output from the station i to the station j in the time period t is
I.e. the bus arrival time.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111464937A (en) * | 2020-03-23 | 2020-07-28 | 北京邮电大学 | Positioning method and device based on multipath error compensation |
| CN111667689A (en) * | 2020-05-06 | 2020-09-15 | 浙江师范大学 | Method, device and computer device for predicting vehicle travel time |
| WO2021051329A1 (en) * | 2019-09-19 | 2021-03-25 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for determining estimated time of arrival in online to offline services |
| CN112632462A (en) * | 2020-12-22 | 2021-04-09 | 天津大学 | Synchronous measurement missing data restoration method and device based on time sequence matrix decomposition |
| CN113239198A (en) * | 2021-05-17 | 2021-08-10 | 中南大学 | Subway passenger flow prediction method and device and computer storage medium |
| CN113470365A (en) * | 2021-09-01 | 2021-10-01 | 北京航空航天大学杭州创新研究院 | Bus arrival time prediction method oriented to missing data |
| CN113487872A (en) * | 2021-09-07 | 2021-10-08 | 南通飞旋智能科技有限公司 | Bus transit time prediction method based on big data and artificial intelligence |
| CN114446039A (en) * | 2021-12-31 | 2022-05-06 | 深圳云天励飞技术股份有限公司 | Passenger flow analysis method and related equipment |
| CN115018454A (en) * | 2022-05-24 | 2022-09-06 | 北京交通大学 | Travel time value calculation method for passengers based on travel mode identification |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102708701A (en) * | 2012-05-18 | 2012-10-03 | 中国科学院信息工程研究所 | System and method for predicting arrival time of buses in real time |
| CN102074124B (en) * | 2011-01-27 | 2013-05-08 | 山东大学 | Dynamic bus arrival time prediction method based on support vector machine (SVM) and H-infinity filtering |
| CN104064028A (en) * | 2014-06-23 | 2014-09-24 | 银江股份有限公司 | Bus arrival time predicting method and system based on multivariate information data |
| CN105243868A (en) * | 2015-10-30 | 2016-01-13 | 青岛海信网络科技股份有限公司 | Bus arrival time forecasting method and device |
| US20180123929A1 (en) * | 2016-10-31 | 2018-05-03 | Accedian Networks Inc. | Precise statistics computation for communication networks |
| CN108831181A (en) * | 2018-05-04 | 2018-11-16 | 东南大学 | A kind of method for establishing model and system for Forecasting of Travel Time for Public Transport Vehicles |
-
2018
- 2018-11-28 CN CN201811430278.7A patent/CN109584552B/en not_active Expired - Fee Related
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102074124B (en) * | 2011-01-27 | 2013-05-08 | 山东大学 | Dynamic bus arrival time prediction method based on support vector machine (SVM) and H-infinity filtering |
| CN102708701A (en) * | 2012-05-18 | 2012-10-03 | 中国科学院信息工程研究所 | System and method for predicting arrival time of buses in real time |
| CN104064028A (en) * | 2014-06-23 | 2014-09-24 | 银江股份有限公司 | Bus arrival time predicting method and system based on multivariate information data |
| CN105243868A (en) * | 2015-10-30 | 2016-01-13 | 青岛海信网络科技股份有限公司 | Bus arrival time forecasting method and device |
| US20180123929A1 (en) * | 2016-10-31 | 2018-05-03 | Accedian Networks Inc. | Precise statistics computation for communication networks |
| CN108831181A (en) * | 2018-05-04 | 2018-11-16 | 东南大学 | A kind of method for establishing model and system for Forecasting of Travel Time for Public Transport Vehicles |
Non-Patent Citations (5)
| Title |
|---|
| HANSHENG WANG等: "Network vector autoregression", 《SOCIAL SCIENCE ELECTRONIC PUBLISHING》 * |
| 李文正: "使用ARIMA模型预测公交到站时间", 《中国优秀硕士学位论文全文数据库（电子期刊）》 * |
| 谢玲等: "一种动态和自适应公交到站时间预测方法", 《计算机科学》 * |
| 赵衍青: "公交车辆到站时间预测方法研究", 《中国优秀硕士学位论文全文数据库》 * |
| 隋毅等: "急于向量空间的多子网复合复杂网络模型动态组网运算的形式描述", 《软件学报》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114365205A (en) * | 2019-09-19 | 2022-04-15 | 北京嘀嘀无限科技发展有限公司 | System and method for determining estimated time of arrival in online-to-offline service |
| WO2021051329A1 (en) * | 2019-09-19 | 2021-03-25 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for determining estimated time of arrival in online to offline services |
| CN111464937A (en) * | 2020-03-23 | 2020-07-28 | 北京邮电大学 | Positioning method and device based on multipath error compensation |
| CN111667689A (en) * | 2020-05-06 | 2020-09-15 | 浙江师范大学 | Method, device and computer device for predicting vehicle travel time |
| CN112632462A (en) * | 2020-12-22 | 2021-04-09 | 天津大学 | Synchronous measurement missing data restoration method and device based on time sequence matrix decomposition |
| CN112632462B (en) * | 2020-12-22 | 2022-03-18 | 天津大学 | Synchronous measurement missing data restoration method and device based on time sequence matrix decomposition |
| CN113239198A (en) * | 2021-05-17 | 2021-08-10 | 中南大学 | Subway passenger flow prediction method and device and computer storage medium |
| CN113239198B (en) * | 2021-05-17 | 2023-10-31 | 中南大学 | Subway passenger flow prediction method and device and computer storage medium |
| CN113470365A (en) * | 2021-09-01 | 2021-10-01 | 北京航空航天大学杭州创新研究院 | Bus arrival time prediction method oriented to missing data |
| CN113487872A (en) * | 2021-09-07 | 2021-10-08 | 南通飞旋智能科技有限公司 | Bus transit time prediction method based on big data and artificial intelligence |
| CN113487872B (en) * | 2021-09-07 | 2021-11-16 | 南通飞旋智能科技有限公司 | Bus transit time prediction method based on big data and artificial intelligence |
| CN114446039A (en) * | 2021-12-31 | 2022-05-06 | 深圳云天励飞技术股份有限公司 | Passenger flow analysis method and related equipment |
| CN114446039B (en) * | 2021-12-31 | 2023-05-19 | 深圳云天励飞技术股份有限公司 | Passenger flow analysis method and related equipment |
| CN115018454A (en) * | 2022-05-24 | 2022-09-06 | 北京交通大学 | Travel time value calculation method for passengers based on travel mode identification |
| CN115018454B (en) * | 2022-05-24 | 2024-04-05 | 北京交通大学 | Passenger travel time value calculation method based on travel mode identification |
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