CN105574613A - Bus arrival time prediction method and apparatus - Google Patents

Bus arrival time prediction method and apparatus Download PDF

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CN105574613A
CN105574613A CN201510931538.9A CN201510931538A CN105574613A CN 105574613 A CN105574613 A CN 105574613A CN 201510931538 A CN201510931538 A CN 201510931538A CN 105574613 A CN105574613 A CN 105574613A
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website
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transport operation
time
operation time
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宋宪明
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Hisense Group Co Ltd
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Hisense Group Co Ltd
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

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Abstract

Embodiments of the invention provide a bus arrival time prediction method and apparatus, relate to the technical field of traffics, and aim to solve the problem of low accuracy of the existing bus arrival time prediction method. The bus arrival time prediction method comprises the steps of obtaining a real arrival time of a target bus arriving at a station a, and obtaining a bus running time fitting function for a station section between the station a and a station b, wherein the station a and the station b are adjacent stations; the bus running time fitting function is used for representing a functional relationship between the bus running time within the station section between the station a and the station b, and the arrival time of the bus arriving at the station a; and substituting the real arrival time into the bus running time fitting function to calculate bus running prediction time of the target bus for the station section between the station a and the station b. The prediction method and the prediction apparatus provided by the invention are applied to prediction of bus station time.

Description

A kind of public transport arrival time Forecasting Methodology and device
Technical field
The present invention relates to technical field of transportation, particularly relate to a kind of public transport arrival time Forecasting Methodology and device.
Background technology
Public transport arrival time forecasting techniques is the key of public transportation system intelligentize and informatization.And predict public transport arrival time in time and accurately, optimum riding route can not only be provided for traveler, make passenger can adjust the stroke of oneself in time according to the road conditions of public transport arrival time reaction, thus decrease the stand-by period of passenger, significantly can also promote the quantity of operation of urban bus passenger transport, thus alleviate urban traffic pressure to a certain extent.In the prior art, conventional public transport arrival time Forecasting Methodology is the history public transport operation time of all buses run on public transportation system directly collects between current site to targeted sites each adjacent sites section, then, the shortest history public transport operation time that each adjacent sites section between current site to targeted sites runs is superposed, thus dopes the public transport operation time of user from current site to targeted sites.
But, because the jam situation in different time sections every day section is different, such as, the time period of 8 o'clock to 9 o'clock every day and 6 o'clock to 7 o'clock owing to being the commuter time, thus causes most of section to get congestion, and the public transport operation time of bus is lengthened, therefore, if only just simply superposed the shortest history public transport operation time of each adjacent sites, and do not consider actual section congestion, then the public transport operation time accuracy of final prediction can be caused too low.
Summary of the invention
Embodiments of the invention provide a kind of public transport arrival time Forecasting Methodology and device, the problem that the accuracy in order to solve existing public transport arrival time Forecasting Methodology is too low.
For achieving the above object, embodiments of the invention adopt following technical scheme:
First aspect, provides a kind of public transport arrival time Forecasting Methodology, comprising:
Obtain the public transport operation time match function that target bus arrives the website section between the real-time arrival time of website a and described website a and website b, described website a and described website b is adjacent sites, and described public transport operation time match function is for representing that public transport operation time in the website section between described website a and described website b and bus arrive the funtcional relationship between the arrival time of described website a;
Described real-time arrival time is substituted in described public transport operation time match function, calculates the public transport operation predicted time of the website section of described target bus between described website a and described website b.
Second aspect, provides a kind of public transport arrival time prediction unit, comprising:
First acquisition module, the public transport operation time match function of the website section between the real-time arrival time of website a and described website a and website b is arrived for obtaining target bus, described website a and described website b is adjacent sites, and described public transport operation time match function is for representing that public transport operation time in the website section between described website a and described website b and bus arrive the funtcional relationship between the arrival time of described website a;
First computing module, described real-time arrival time for described logging modle being obtained substitutes in the described public transport operation time match function of described first acquisition module acquisition, calculates the public transport operation predicted time of the website section of described target bus between described website a and described website b.
The public transport arrival time Forecasting Methodology that embodiments of the invention provide and device, the public transport operation time match function of the website section between the website b be adjacent by the real-time arrival time and website a that obtain target bus arrival website a, because this public transport operation time match function is for representing that public transport operation time in the website section between website a and website b and bus arrive the funtcional relationship between the arrival time of website a, therefore, after getting this public transport operation time match function, the real-time arrival time that just the target bus of record can be arrived website a substitutes into wherein, calculate the public transport operation predicted time of the website section of target bus between website a and website b.Due to the public transport operation time match function that obtains in the present invention be based on bus the running time matching of all history out, realistic public transport road conditions, therefore, the bus station time accuracy gone out based on this public transport operation time match function prediction is high, and this fitting function model is simple, computation process is succinct.
Accompanying drawing explanation
In order to be illustrated more clearly in the technical scheme of the embodiment of the present invention, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
The schematic flow sheet of a kind of public transport arrival time Forecasting Methodology that Fig. 1 provides for the embodiment of the present invention;
The schematic flow sheet of the another kind of public transport arrival time Forecasting Methodology that Fig. 2 provides for the embodiment of the present invention;
The structural representation of a kind of public transport arrival time prediction unit that Fig. 3 provides for the embodiment of the present invention;
The structural representation of the another kind of public transport arrival time prediction unit that Fig. 4 provides for the embodiment of the present invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Embodiments of the invention provide a kind of public transport arrival time Forecasting Methodology, and for adjacent website a and website b, as shown in Figure 1, the method comprises the steps:
101, public transport arrival time prediction unit obtains the public transport operation time match function that target bus arrives the real-time arrival time of website a and the website section between website a and website b.
Exemplary, the website a in the present embodiment and website b is adjacent sites.Public transport operation time match function in the present embodiment is for representing that public transport operation time in the website section between website a and website b and bus arrive the funtcional relationship between the arrival time of website a.Exemplary, the public transport operation time match function in the present embodiment is that the public transport information of calling out the stops that the bus of website a and the website b stored in the database according to public transport reporting station system is driven a vehicle in historical record obtains.
102, real-time arrival time substitutes in public transport operation time match function by public transport arrival time prediction unit, calculates the public transport operation predicted time of the website section of target bus between website a and website b.
Exemplary, the acquisition methods of the public transport operation time match function of the website a in step 101 and the website section between website b comprises the steps:
The public transport operation time of the website section that 102a, public transport arrival time prediction unit obtain between website a and website b gathers.
Exemplary, funtcional relationship set between the public transport operation time that above-mentioned public transport operation time set is the bus on all website sections operated between website a with website b with corresponding public transport arrival time, all public transport arrival times in the set of this public transport operation time belong to the time period belonging to real-time arrival time.
102b, public transport arrival time prediction unit, according to the set of public transport operation time, calculate the public transport operation time match function of the website section between website a and website b.
Concrete, the acquisition process that the public transport operation time of the website section between the website a that each time period in the embodiment of the present invention is corresponding and website b gathers is as described below:
A1, public transport arrival time prediction unit obtain the history public transport of website a and website b and to call out the stops information.
The history public transport information of calling out the stops of the website in the present embodiment comprises the information such as car plate mark ID, arrival time and the berthing time of each bus stopping this website.
A2, the public transport arrival time prediction unit history public transport information of calling out the stops to website a and website b is added up, and the public transport operation time obtaining the website section between website a and website b gathers.
Exemplary, history public transport the call out the stops information of public transport arrival time prediction unit to website a and website b is added up, count the public transport operation time between the website section of each bus running between website a and website b, then according to the bus arrival time that each public transport operation time is corresponding, this public transport operation time and bus arrival time one_to_one corresponding are stored to during the public transport operation time gathers.In addition, in order to obtain the set of more accurate public transport operation time, the whole bus running time cycle can be divided into by the present invention: working day low peak period, the weekday rush phase, nonworkdays peak period and these four time periods of nonworkdays low peak period, then, the corresponding public transport operation chronon set of each time period, this public transport arrival time prediction unit just can based on the public transport operation chronon set of different time sections like this, calculate public transport operation time match function corresponding to each time period such as, using a week as the bus running time cycle, can by Mon-Fri morning 8:00-9:00 and late 6:00-7:00 time period be set to the weekday rush phase, time period corresponding for the other times of Mon-Fri is set to low peak period on working day, by Saturday and Sunday morning 8:00-9:00 and late 6:00-7:00 time period be set to the peak period of nonworkdays, the other times section on Sunday Saturday is set to the low peak period of nonworkdays.It should be noted that, the division of above-mentioned time period is only a kind of example, in actual applications, and can according to the habits and customs of the actual conditions of different sections of highway and locals according to dividing.
Such as, represent that the public transport operation time of the website section of low peak period bus running on working day between website a and website b gathers with S set 1;
S set 2: represent that the public transport operation time of the website section of weekday rush phase bus running between website a and website b gathers;
S set 3: represent that the public transport operation time of the website section of nonworkdays low peak period bus running between website a and website b gathers;
S set 4: represent that the public transport operation time of the website section of nonworkdays peak period bus running between website a and website b gathers.
Exemplary, this public transport arrival time prediction unit can adopt least square method polynomial curve fitting algorithm, and public transport operation time of bus on the website section operating between website a and website b in above-mentioned public transport operation time set and the funtcional relationship of public transport arrival time, calculate the matrix of coefficients of public transport operation time match function, then, according to the matrix of coefficients of this public transport operation time match function, determine public transport operation time match function.
Concrete, determine public transport operation time match function according to least square method polynomial curve fitting algorithm, then the acquisition process of this public transport operation time match function is as follows:
Step1: suppose that polynomial fitting is:
Y=h θ(x)=θ 0+ θ 1x+ θ 2x 2(formula one)
Wherein, a kind of x of above-mentioned formula represents the arrival time of bus, and y represents the public transport operation time of bus running between certain adjacent two website on website section, and wherein i=1,2....m, asks curve of approximation y=h (x).And make the deviation of curve of approximation and y=f (x) minimum, the θ that above-mentioned formula is a kind of 0, θ 1, θ 2for the fitting function coefficient of this polynomial fitting.
Step2: error of calculation quadratic sum:
Eorror 2 = Σ i = 1 m [ y i - ( θ 0 + θ 1 x i + θ 2 x i 2 ) ] 2 (formula two)
Step3: the error minimize of the above-mentioned formula two made, namely asks local derviation to formula two:
∂ Eorror 2 ∂ θ j = ∂ ( Σ i = 1 m [ y i - ( θ 0 + θ 1 x i + θ 2 x i 2 ) ] 2 ) ∂ θ j = 0 (formula three)
Step4: formula three is carried out being simplified shown as matrix form, this matrix function formula is as follows:
m Σ i = 1 m x i Σ i = 1 m x i 2 Σ i = 1 m x i Σ i = 1 m x i 2 Σ i = 1 m x i 3 Σ i = 1 m x i 2 Σ i = 1 m x i 3 Σ i = 1 m x i 4 θ 0 θ 1 θ 2 = Σ i = 1 m y i Σ i = 1 m x i y i Σ i = 1 m x i 2 y i (formula four)
Finally, the public transport operation chronon set that the public transport operation time gathers or different time sections is corresponding is substituted in formula four, obtains different matrix of coefficients θ 0 θ 1 θ 2 , Finally determine matched curve y=h according to this matrix of coefficients θ(x).
Exemplary, when user needs to take target bus slave site c arrival website d, based on the Forecasting Methodology of the public transport operation time of the bus that the arbitrary adjacent sites section described by above-mentioned step 101 to step 103 runs, the forecasting process that user takes the public transport operation predicted time of target bus slave site c arrival website d is as described below:
B1, public transport arrival time prediction unit obtain number n between the station between website c and website d, and target bus arrives the public transport operation predicted time t3 on berthing time t2 and each associated station section of this target bus running between website c and website d that the arrival time t1 of website c and each website of this target bus between website c and website d stop.
The arrival time t1 that berthing time (t2*n) total to public transport operation predicted time t3 on each associated station section of this target bus running between website c and website d, this target bus and target bus arrive website c adds up by b2, public transport arrival time prediction unit, obtains user and takes the public transport operation predicted time that target bus slave site c arrives website d.
The public transport arrival time Forecasting Methodology that embodiments of the invention provide, the public transport operation time match function of the website section between the website b that the website a that belonging to the real-time arrival time arriving website a by obtaining target bus, the time period is corresponding is adjacent, because this public transport operation time match function is for representing that public transport operation time in the website section between website a and website b and bus arrive the funtcional relationship between the arrival time of website a, therefore, after getting this public transport operation time match function, the real-time arrival time that just the target bus of record can be arrived website a substitutes into wherein, calculate the public transport operation predicted time of the website section of target bus between website a and website b.Due to the public transport operation time match function that obtains in the present invention be based on bus the matching of all history driving recordings out, realistic public transport road conditions, therefore, the bus station time accuracy gone out based on this public transport operation time match function prediction is high, and this fitting function model is simple, computation process is succinct.
Embodiments of the invention provide another kind of public transport arrival time Forecasting Methodology, and for adjacent website a and website b, as shown in Figure 2, the method comprises the steps:
201, the public transport operation time that public transport arrival time prediction unit obtains the website section between website a and website b that belonging to real-time arrival time that target bus arrives website a, the time period is corresponding gathers.
Exemplary, above-mentioned public transport operation time set is for operating in the funtcional relationship set between the public transport operation time of the bus on the website section between website a and website b and public transport arrival time, and all public transport arrival times in the set of this public transport operation time belong to the time period belonging to real-time arrival time.
202, public transport arrival time prediction unit was averaged to all public transport operation times in the set of public transport operation time, obtained the history public transport operation averaging time of the website section of target bus between website a and website b.
203, public transport arrival time prediction unit is according to the ratio between public transport operation predicted time and history public transport operation averaging time, dopes the real-time road of the website section between website a and website b.
Exemplary, real-time road can be divided into by the present embodiment: heavy congestion, moderate are blocked up, slightly block up, unimpeded and extremely unimpeded five grades, the corresponding grade threshold of each grade, when ratio between the history public transport operation averaging time of the website section when between above-mentioned website a and website b and the public transport operation predicted time doped meets that grade threshold, then judge that current real-time road is as road condition grade corresponding to this grade threshold.
The public transport arrival time Forecasting Methodology that embodiments of the invention provide, the public transport operation time match function of the website section between the website b that the website a that belonging to the real-time arrival time arriving website a by obtaining target bus, the time period is corresponding is adjacent, because this public transport operation time match function is for representing that public transport operation time in the website section between website a and website b and bus arrive the funtcional relationship between the arrival time of website a, therefore, after getting this public transport operation time match function, the real-time arrival time that just the target bus of record can be arrived website a substitutes into wherein, calculate the public transport operation predicted time of the website section of target bus between website a and website b.Due to the public transport operation time match function that obtains in the present invention be based on bus all periods the matching of history driving recording out, realistic public transport road conditions, therefore, the bus station time accuracy gone out based on this public transport operation time match function prediction is high, and this fitting function model is simple, computation process is succinct.
Embodiments of the invention provide a kind of public transport arrival time prediction unit, and this device is for realizing above-mentioned public transport arrival time Forecasting Methodology, and as shown in Figure 3, this device comprises: the first acquisition module 31 and the first computing module 32, wherein:
First acquisition module 31, the public transport operation time match function of the real-time arrival time of website a and the website section between website a and website b is arrived for obtaining target bus, website a and website b is adjacent sites, and this public transport operation time match function is for representing that public transport operation time in the website section between website a and website b and bus arrive the funtcional relationship between the arrival time of website a.
First computing module 32, the real-time arrival time for being obtained by described first acquisition module 31 substitutes in public transport operation time match function, calculates the public transport operation predicted time of the website section of target bus between website a and website b.
Optionally, as shown in Figure 4, this device also comprises: the second acquisition module 33, second computing module 34 and prediction module 35, wherein:
Second acquisition module 33, the public transport operation time for obtaining the website section between website a and website b gathers, the funtcional relationship set between the public transport operation time that the set of this public transport operation time is the bus on all website sections operated between website a with website b with the corresponding arrival time arriving website a.
Second computing module 34, averages for all public transport operation times in the public transport operation time set to the second acquisition module 33 acquisition, obtains the history public transport operation averaging time of the website section of target bus between website a and website b.
Prediction module 35, the ratio between the history public transport operation averaging time calculated for the public transport operation predicted time that calculates according to the first computing module 32 and the second computing module 34, dopes the real-time road of the website section between website a and website b.
Optionally, as shown in Figure 4, this device also comprises: the 3rd computing module 36, wherein:
Second acquisition module 33, the public transport operation time for obtaining the website section between website a and website b gathers, the funtcional relationship set between the public transport operation time that the set of public transport operation time is the bus on all website sections operated between website a with website b with the corresponding arrival time arriving website a.
3rd computing module 36, for the public transport operation time set obtained according to the second acquisition module 33, calculates the public transport operation time match function of the website section between website a and website b.
Further alternative, the 3rd computing module 36 specifically for:
Adopt least square method polynomial curve fitting algorithm, and second bus on the website section operating between website a with website b in the public transport operation time set that obtains of acquisition module 33 the public transport operation time and the corresponding funtcional relationship arriving the arrival time of website a, calculate the matrix of coefficients of public transport operation time match function.
According to the matrix of coefficients of public transport operation time match function, determine public transport operation time match function.
Optionally, this second acquisition module 33 specifically for:
Obtain the history public transport of website a and website b to call out the stops information, the car plate that this history public transport information of calling out the stops comprises each bus of bus stop point identifies ID, arrival time and berthing time; Add up the history public transport information of calling out the stops of website a and website b, the public transport operation time obtaining the website section between website a corresponding to each time period of the bus running time cycle of presetting and website b gathers.
The public transport arrival time prediction unit that embodiments of the invention provide, the public transport operation time match function of the website section between the website b be adjacent by the real-time arrival time and website a that obtain target bus arrival website a, because this public transport operation time match function is for representing that public transport operation time in the website section between website a and website b and bus arrive the funtcional relationship between the arrival time of website a, therefore, after getting this public transport operation time match function, the real-time arrival time that just the target bus of record can be arrived website a substitutes into wherein, calculate the public transport operation predicted time of the website section of target bus between website a and website b.Due to the public transport operation time match function that obtains in the present invention be based on bus the matching of all history driving recordings out, realistic public transport road conditions, therefore, the bus station time accuracy gone out based on this public transport operation time match function prediction is high, and this fitting function model is simple, computation process is succinct.
In several embodiments that the application provides, should be understood that disclosed terminal and method can realize by another way.Such as, device embodiment described above is only schematic, such as, the division of described unit, be only a kind of logic function to divide, actual can have other dividing mode when realizing, such as multiple unit or assembly can in conjunction with or another system can be integrated into, or some features can be ignored, or do not perform.Another point, shown or discussed coupling each other or direct-coupling or communication connection can be by some interfaces, and the indirect coupling of device or unit or communication connection can be electrical, machinery or other form.
The described unit illustrated as separating component or can may not be and physically separates, and the parts as unit display can be or may not be physical location, namely can be positioned at a place, or also can be distributed in multiple network element.Some or all of unit wherein can be selected according to the actual needs to realize the object of the present embodiment scheme.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, also can be that the independent physics of unit comprises, also can two or more unit in a unit integrated.Above-mentioned integrated unit both can adopt the form of hardware to realize, and the form that hardware also can be adopted to add SFU software functional unit realizes.
The above-mentioned integrated unit realized with the form of SFU software functional unit, can be stored in a computer read/write memory medium.Above-mentioned SFU software functional unit is stored in a storage medium, comprising some instructions in order to make a computer equipment (can be personal computer, server, or the network equipment etc.) perform the part steps of method described in each embodiment of the present invention.And aforesaid storage medium comprises: USB flash disk, portable hard drive, ROM (read-only memory) (Read-OnlyMemory, be called for short ROM), random access memory (RandomAccessMemory, be called for short RAM), magnetic disc or CD etc. various can be program code stored medium.
Last it is noted that above embodiment is only in order to illustrate technical scheme of the present invention, be not intended to limit; Although with reference to previous embodiment to invention has been detailed description, those of ordinary skill in the art is to be understood that: it still can be modified to the technical scheme described in foregoing embodiments, or carries out equivalent replacement to wherein portion of techniques feature; And these amendments or replacement, do not make the essence of appropriate technical solution depart from the spirit and scope of various embodiments of the present invention technical scheme.

Claims (10)

1. a public transport arrival time Forecasting Methodology, is characterized in that, comprising:
Obtain the public transport operation time match function that target bus arrives the website section between the real-time arrival time of website a and described website a and website b, described website a and described website b is adjacent sites, and described public transport operation time match function is for representing that public transport operation time in the website section between described website a and described website b and bus arrive the funtcional relationship between the arrival time of described website a;
Described real-time arrival time is substituted in described public transport operation time match function, calculates the public transport operation predicted time of the website section of described target bus between described website a and described website b.
2. method according to claim 1, is characterized in that, described method also comprises:
The public transport operation time obtaining the website section between described website a and described website b gathers, the funtcional relationship set between the public transport operation time that the set of described public transport operation time is the bus on all website sections operated between described website a with described website b with the corresponding arrival time arriving described website a;
All public transport operation times in described public transport operation time set are averaged, obtains the history public transport operation averaging time of the website section of described target bus between described website a and described website b;
According to the ratio between described public transport operation predicted time and described history public transport operation averaging time, dope the real-time road of the website section between described website a and described website b.
3. method according to claim 1, is characterized in that, before the public transport operation time match function of the website section between described acquisition described website a and website b, described method also comprises:
The public transport operation time obtaining the website section between described website a and described website b gathers, the funtcional relationship set between the public transport operation time that the set of described public transport operation time is the bus on all website sections operated between described website a with described website b with the corresponding arrival time arriving described website a;
According to described public transport operation time set, calculate the public transport operation time match function of the website section between described website a and website b.
4. method according to claim 3, is characterized in that, described according to described public transport operation time set, the public transport operation time match function calculating the website section between described website a and website b specifically comprises:
Adopt least square method polynomial curve fitting algorithm, and the public transport operation time of bus on the website section operating between described website a with described website b in the set of described public transport operation time and the corresponding funtcional relationship arriving the arrival time of described website a, calculate the matrix of coefficients of described public transport operation time match function;
According to the described matrix of coefficients stating public transport operation time match function, determine described public transport operation time match function.
5. the method according to claim 3 or 4, is characterized in that, the public transport operation time of the website section between described acquisition described website a and described website b gathers and specifically comprises:
The history public transport obtaining described website a and described website b is called out the stops information, and the described history public transport information of calling out the stops comprises car plate mark ID, arrival time and the berthing time of each bus stopping described website;
Add up the history public transport information of calling out the stops of described website a and described website b, the public transport operation time obtaining the website section between described website a and described website b gathers.
6. a public transport arrival time prediction unit, is characterized in that, comprising:
First acquisition module, the public transport operation time match function of the website section between the real-time arrival time of website a and described website a and website b is arrived for obtaining target bus, described website a and described website b is adjacent sites, and described public transport operation time match function is for representing that public transport operation time in the website section between described website a and described website b and bus arrive the funtcional relationship between the arrival time of described website a;
First computing module, described real-time arrival time for being obtained by described first acquisition module substitutes in described public transport operation time match function, calculates the public transport operation predicted time of the website section of described target bus between described website a and described website b.
7. device according to claim 6, is characterized in that, described device also comprises:
Second acquisition module, the public transport operation time for obtaining the website section between described website a and described website b gathers, the funtcional relationship set between the public transport operation time that the set of described public transport operation time is the bus on all website sections operated between described website a with described website b with the corresponding arrival time arriving described website a;
Second computing module, average for all public transport operation times in the described public transport operation time set to described second acquisition module acquisition, obtain the history public transport operation averaging time of the website section of described target bus between described website a and described website b;
Prediction module, ratio between the described history public transport operation averaging time calculated for the described public transport operation predicted time that calculates according to described first computing module and described second computing module, dopes the real-time road of the website section between described website a and described website b.
8. device according to claim 6, is characterized in that, described device also comprises:
Second acquisition module, the public transport operation time for obtaining the website section between described website a and described website b gathers, the funtcional relationship set between the public transport operation time that the set of described public transport operation time is the bus on all website sections operated between described website a with described website b with the corresponding arrival time arriving described website a;
3rd computing module, for the described public transport operation time set obtained according to described second acquisition module, calculates the public transport operation time match function of the website section between described website a and website b.
9. device according to claim 8, is characterized in that, described 3rd computing module specifically for:
Adopt least square method polynomial curve fitting algorithm, and the public transport operation time of bus on the website section operating between described website a with described website b in the described public transport operation time set that obtains of described second acquisition module and the corresponding funtcional relationship arriving the arrival time of described website a, calculate the matrix of coefficients of described public transport operation time match function;
According to the described matrix of coefficients stating public transport operation time match function, determine described public transport operation time match function.
10. device according to claim 8 or claim 9, is characterized in that, described second acquisition module specifically for:
The history public transport obtaining described website a and described website b is called out the stops information, and the described history public transport information of calling out the stops comprises car plate mark ID, arrival time and the berthing time of each bus stopping described website; Add up the history public transport information of calling out the stops of described website a and described website b, the public transport operation time obtaining the website section between described website a and described website b gathers.
CN201510931538.9A 2015-12-14 2015-12-14 Bus arrival time prediction method and apparatus Pending CN105574613A (en)

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106228830A (en) * 2016-07-27 2016-12-14 安徽聚润互联信息技术有限公司 A kind of bus arrival time real-time estimate system and method
CN109059952A (en) * 2018-10-11 2018-12-21 国家卫星海洋应用中心 A kind of stroke duration prediction method and device
CN109214542A (en) * 2017-06-30 2019-01-15 北京嘀嘀无限科技发展有限公司 Frequency predictor method and device based on big data
CN110189518A (en) * 2019-05-20 2019-08-30 深圳市众行网科技有限公司 Predict method, apparatus, computer equipment and the storage medium of public transport arrival time
CN113299064A (en) * 2021-05-15 2021-08-24 苏州智能交通信息科技股份有限公司 Bus accurate station forecasting method, system, device and storage medium
CN117910660A (en) * 2024-03-18 2024-04-19 华中科技大学 Bus arrival time prediction method and system based on GPS data and space-time correlation

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106228830A (en) * 2016-07-27 2016-12-14 安徽聚润互联信息技术有限公司 A kind of bus arrival time real-time estimate system and method
CN109214542A (en) * 2017-06-30 2019-01-15 北京嘀嘀无限科技发展有限公司 Frequency predictor method and device based on big data
CN109059952A (en) * 2018-10-11 2018-12-21 国家卫星海洋应用中心 A kind of stroke duration prediction method and device
CN110189518A (en) * 2019-05-20 2019-08-30 深圳市众行网科技有限公司 Predict method, apparatus, computer equipment and the storage medium of public transport arrival time
CN113299064A (en) * 2021-05-15 2021-08-24 苏州智能交通信息科技股份有限公司 Bus accurate station forecasting method, system, device and storage medium
CN117910660A (en) * 2024-03-18 2024-04-19 华中科技大学 Bus arrival time prediction method and system based on GPS data and space-time correlation

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Application publication date: 20160511