CN108334996A - A kind of Passenger Flow analysis system based on big data - Google Patents
A kind of Passenger Flow analysis system based on big data Download PDFInfo
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- CN108334996A CN108334996A CN201810299866.5A CN201810299866A CN108334996A CN 108334996 A CN108334996 A CN 108334996A CN 201810299866 A CN201810299866 A CN 201810299866A CN 108334996 A CN108334996 A CN 108334996A
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- 238000005206 flow analysis Methods 0.000 title claims abstract description 13
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- 238000007405 data analysis Methods 0.000 claims abstract description 12
- 238000004140 cleaning Methods 0.000 claims abstract description 9
- 238000007781 pre-processing Methods 0.000 claims abstract description 9
- 238000009826 distribution Methods 0.000 claims description 19
- 238000011156 evaluation Methods 0.000 claims description 18
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Abstract
The Passenger Flow analysis system based on big data that the present invention provides a kind of, the system include passenger ticket data acquisition module, passenger ticket data preprocessing module, passenger ticket data analysis module and decision-making module；Passenger ticket data acquisition module is for obtaining passenger ticket sale source data；Passenger ticket data preprocessing module is used to clean passenger ticket sale source data, and passenger ticket data analysis module is used to analyze the passenger ticket sale source data after cleaning, and analysis result is sent to decision-making module；Decision-making module predicts the volume of the flow of passengers according to analysis result, and then adjusts the starting scheme that train was sold with presell information and optimized to passenger ticket.Using above system, Passenger Flow feature and rule are carried out going deep into excavation using passenger ticket sale source data, it obtains Passenger Flow data and hides information, and then it adjusts passenger ticket and sells and presell information and optimization train running scheme, the generation of passenger flow congestion phenomenon is reduced, and then reaches and fully improves train operation efficiency.
Description
Technical field
The present invention relates to railway transport of passengers technologies, and in particular to a kind of Passenger Flow analysis system based on big data.
Background technology
With the fast development of modernization construction, rail traffic is just gradually entering big data and is runing the epoch comprehensively.Substantially
While promoting traffic transport power and convenient trip, running scheduling brings new problem and challenge.In big data operation and " nothing
Under conditions of seam transfer ", diversity is more presented in travelling path and trip mode, and passenger flow is in spatio-temporal distribution
Lack of uniformity also highlights further.On the one hand, this can form periodic passenger flow congestion phenomenon, increase operation security risk；Separately
On the one hand, it will also result in and be unable to fully utilize rail vehicle transportation ability, reduce rail traffic operational efficiency.
Invention content
In view of the above-mentioned problems, the present invention provides a kind of Passenger Flow analysis system based on big data.
The purpose of the present invention is realized using following technical scheme：
A kind of Passenger Flow analysis system based on big data, the system include passenger ticket data acquisition module, passenger ticket data
Preprocessing module, passenger ticket data analysis module and decision-making module；
Passenger ticket data acquisition module sells source data for extracting passenger ticket from railway ticket selling and checking system passenger flow data library；
Passenger ticket data preprocessing module, for being cleaned to passenger ticket sale source data, in removal passenger ticket sale source data
Redundant data, passenger ticket after being cleaned sells source data；
Passenger ticket data analysis module, for being analyzed the volume of the flow of passengers according to the passenger ticket sale source data after cleaning, and will
Analysis result is sent to decision-making module；
Decision-making module, for excavating hiding passenger flow data from analysis result, and according to hiding passenger flow data pair
The volume of the flow of passengers is predicted that railway relevant departments take corresponding measure according to prediction result, alleviates communications and transportation pressure.
Advantageous effect：Using above-mentioned Passenger Flow analysis system, source data is sold to Passenger Flow feature using passenger ticket
It carries out going deep into excavation with rule, and then obtains the hiding information of Passenger Flow data, passenger flow forecast amount is sold to adjust passenger ticket
With presell information and optimization train running scheme, the generation of passenger flow congestion phenomenon is reduced, and reach and fully improve train operation
Efficiency.
Description of the drawings
Using attached drawing, the invention will be further described, but the embodiment in attached drawing does not constitute any limit to the present invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is Passenger Flow analysis system structural schematic diagram of the present invention；
Fig. 2 is the frame construction drawing of passenger ticket data analysis module of the present invention.
Reference numeral：Passenger ticket data acquisition module 1；Passenger ticket data preprocessing module 2；Passenger ticket data analysis module 3；Decision
Module 4；Travelling behavioural analysis unit 31；Passenger flow space-time characterisation analytic unit 32.
Specific implementation mode
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of Passenger Flow analysis system based on big data, the system include passenger ticket data acquisition module 1,
Passenger ticket data preprocessing module 2, passenger ticket data analysis module 3 and decision-making module 4.
Passenger ticket data acquisition module 1 sells source data for extracting passenger ticket from railway ticket selling and checking system passenger flow data library.
Passenger ticket data preprocessing module 2, for being cleaned to passenger ticket sale source data, in removal passenger ticket sale source data
Redundant data, passenger ticket after being cleaned sells source data.
Passenger ticket data analysis module 3, for being analyzed the volume of the flow of passengers according to the passenger ticket sale source data after cleaning, and will
Analysis result is sent to decision-making module 4.
Decision-making module 4, for excavating hiding passenger flow data from analysis result, and according to hiding passenger flow data pair
The volume of the flow of passengers is predicted that railway relevant departments take corresponding measure according to prediction result, alleviates communications and transportation pressure.
Preferably, passenger ticket sale source data includes passenger ticket buying mode, passenger ticket buying period, travelling time
Section, passenger fare information, passage ticket site information.
Preferably, passenger ticket data analysis module 3 includes travelling behavioural analysis unit 31 and passenger flow space-time characterisation point
Analyse unit 32；
Travelling behavioural analysis unit 31 is used to sell source data according to the passenger ticket after cleaning, by establishing travelling
Comprehensive evaluation index analyzes travelling behavior.
Passenger flow space-time characterisation analytic unit 32 is used to sell source data according to the passenger ticket after cleaning, to the Passenger Flow time point
Cloth situation and Passenger Flow space distribution situation are analyzed.
Preferably, by establishing travelling comprehensive evaluation index, travelling behavior is analyzed, it is specifically sharp
Travelling comprehensive evaluation index value is calculated with following formula：
In formula,It is in this trip section station i to station j, passenger selects kth kind mode of transportation trip overall merit
Index value, αkThe penalty factor of kth kind mode of transportation, the penalty factor is selected to select kth kind to hand over for describing passenger for passenger
Comfortable, the comfort level of logical mode, β1、β2、β3For the parameter being accordingly arranged,It is that passenger selects kth kind mode of transportation from station
Running times of the i to station j；It is that passenger selects kth kind mode of transportation from station i to the admission fee of station j,It is passenger's choosing
That selects booking mode of the kth kind mode of transportation from station i to station j is subordinate to angle value, αlIt is that passenger selects l kind modes of transportation
Penalty factor, the penalty factor are used to describe comfortable, comfort level that passenger selects l kind modes of transportation,It is passenger's selection
L kinds mode of transportation is from station i to the running time of station j；It is that passenger selects l kinds mode of transportation from station i to station j
Admission fee,The angle value M that is subordinate to for being booking mode of passenger's selection l kinds mode of transportation from station i to station j is for trip
The mode of transportation of visitor's selection, wherein l={ 1,2 ... k ..., M }.
Advantageous effect：By establishing travelling behavior integration evaluation index, travelling behavior is evaluated, the calculation
Method will influence whether the influence factor (running time of the mode of transportation, the mode of transportation of such as passenger's selection of Railway Passenger Demand
And admission fee) all take into account, be conducive to the accuracy for improving later stage passenger flow estimation result, and can also effectively assist railway
Relevant departments timely and effectively carry out Passenger Transportation Marketing decision and transport project according to obtained travelling comprehensive evaluation index value
Establishment adjusts passenger traffic information so that railway relevant departments most scientific, rational can utilize all resources to greatest extent.
Preferably, selling source data according to the passenger ticket after cleaning, Passenger Flow Annual distribution situation and railway passenger are analyzed
Fluid space distribution situation specifically analyzes Passenger Flow Annual distribution and Passenger Flow space point using spatial and temporal distributions function
Cloth obtains the evaluation index about flow space-time distribution, wherein the expression formula of spatial and temporal distributions function is：
In formula, Y (d) is the flow space-time distribution evaluation index value of d-th of website, κ1It is for describing the Passenger Flow time
The weight coefficient of distribution, κ2It is the weight coefficient for describing Passenger Flow spatial distribution, and meets κ1+κ2=1,For passenger flow
Saturation degree within e-th of period, E are the time hop counts of setting, and e ∈ { 1,2 ..., E }, θdIt is passenger flow in d-th of website
Saturation degree, wedFor the ticketing number of d-th of website within e-th of period, BdnTo there is n passenger to stand herein in d-th of website
It gets on the bus, get off or transfers to, D is website sum.
Advantageous effect：By establishing spatial and temporal distributions function, the flow space-time distribution situation of each website is evaluated, side
Just the evaluation index that railway relevant departments evaluate according to the spatial and temporal distributions of obtained each website is in real time to train running scheme
It optimizes, while adjusting passenger transport market marketing strategy, realize the maximization to transporting interests.
Preferably, for excavating hiding passenger flow data from analysis result, and according to hiding passenger flow data pair
The volume of the flow of passengers is predicted that railway relevant departments take corresponding measure according to prediction result, alleviates communications and transportation pressure, specifically root
According to the evaluation index of obtained travelling behavior integration evaluation index and flow space-time distribution, passenger ticket data mining function is utilized
Hiding passenger flow data is obtained, and further the volume of the flow of passengers is predicted, railway relevant departments adjust passenger ticket according to prediction result
Sell with presell information and optimize the starting scheme of train, wherein passenger flow data excavates function and is：
In formula, UijFunctional value, η are excavated for the passenger flow data from station i to station j in this trip section1Go out for passenger
Row comprehensive evaluation index excavates passenger flow data the impact factor of accuracy,For this trip section from station i to station j
It is interior, the mean value of travelling comprehensive evaluation index value, andη2It is passenger flow spatial distribution evaluation index to passenger flow number
According to excavate accuracy impact factor,For the average value of flow space-time distribution evaluation index, whereinD is total
Website number, inventor analyze the historical data of railway department's passenger ticket, give the empirical value of the two impact factors, point
It is not：0.35,0.65.
Advantageous effect：The hiding information that function obtains passenger flow data is excavated using above-mentioned passenger flow data, and for predicting visitor
Flow, the algorithm are excavated based on existing passenger flow information, and the influence of subjective human factor has been abandoned, so as to get result
It is objective and accurate, can accurately passenger flow forecast information, facilitate railway department's adjustment train running scheme and passenger ticket to sell and in advance
The plan of selling.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although being explained in detail to the present invention with reference to preferred embodiment, those skilled in the art answer
Work as understanding, technical scheme of the present invention can be modified or replaced equivalently, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (4)
1. a kind of Passenger Flow analysis system based on big data, which is characterized in that the system include passenger ticket data acquisition module,
Passenger ticket data preprocessing module, passenger ticket data analysis module and decision-making module；
The passenger ticket data acquisition module sells source data for extracting passenger ticket from railway ticket selling and checking system passenger flow data library；
The passenger ticket data preprocessing module removes the passenger ticket sale for being cleaned to passenger ticket sale source data
Redundant data in source data, the passenger ticket after being cleaned sell source data；
The passenger ticket data analysis module, for being analyzed the volume of the flow of passengers according to the passenger ticket sale source data after cleaning, and will
Analysis result is sent to the decision-making module；
The decision-making module, for excavating hiding passenger flow data from the analysis result, and according to hiding passenger flow number
It is predicted according to the volume of the flow of passengers, railway relevant departments take corresponding measure according to prediction result, alleviate communications and transportation pressure.
2. Passenger Flow analysis system according to claim 1, which is characterized in that the passenger ticket sale source data includes trip
Objective booking mode, passenger ticket buying period, travelling period, passenger fare information, passage ticket site information.
3. Passenger Flow analysis system according to claim 2, which is characterized in that the passenger ticket data analysis module includes
Travelling behavioural analysis unit and passenger flow space-time characterisation analytic unit；
The travelling behavioural analysis unit is used to sell source data according to the passenger ticket after cleaning, comprehensive by establishing travelling
Evaluation index is closed, travelling behavior is analyzed；
The passenger flow space-time characterisation analytic unit is used to sell source data according to the passenger ticket after cleaning, to Passenger Flow Annual distribution
Situation and Passenger Flow space distribution situation are analyzed.
4. Passenger Flow analysis system according to claim 3, which is characterized in that described by establishing travelling synthesis
Evaluation index analyzes travelling behavior, specifically following formula is utilized to calculate the travelling comprehensive evaluation index value：
In formula,It is in this trip section station i to station j, passenger selects kth kind mode of transportation trip comprehensive evaluation index
Value, αkThe penalty factor of kth kind mode of transportation, β are selected for passenger1、β2、β3For the parameter being accordingly arranged,It is that passenger selects kth
Kind mode of transportation is from station i to the running time of station j；It is that passenger selects kth kind mode of transportation from station i to station j
Admission fee,Be passenger select booking mode of the kth kind mode of transportation from station i to station j be subordinate to angle value, αlIt is passenger's selection
The penalty factor of l kind modes of transportation,It is that passenger selects l kinds mode of transportation from station i to the running time of station j；
It is that passenger selects l kinds mode of transportation from station i to the admission fee of station j,It is that passenger selects l kinds mode of transportation from station i
Booking mode to station j is subordinate to angle value, and M is the mode of transportation selected for passenger, wherein l={ 1,2 ..., k ..., M }.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201810299866.5A CN108334996A (en) | 2018-04-04 | 2018-04-04 | A kind of Passenger Flow analysis system based on big data |
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| CN201810299866.5A CN108334996A (en) | 2018-04-04 | 2018-04-04 | A kind of Passenger Flow analysis system based on big data |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110807651A (en) * | 2019-09-26 | 2020-02-18 | 北京交通大学 | Intercity railway passenger ticket time-sharing pricing method based on generalized cost function |
| CN111985716A (en) * | 2020-08-21 | 2020-11-24 | 北京交通大学 | Passenger traffic prediction system with visualized passenger traffic information |
| CN113516500A (en) * | 2021-04-23 | 2021-10-19 | 深圳市威斯登信息科技有限公司 | Implementation method and system based on big data business and travel operation platform |
| CN111985716B (en) * | 2020-08-21 | 2024-05-14 | 北京交通大学 | Passenger traffic volume prediction system with passenger traffic information visualization function |
-
2018
- 2018-04-04 CN CN201810299866.5A patent/CN108334996A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110807651A (en) * | 2019-09-26 | 2020-02-18 | 北京交通大学 | Intercity railway passenger ticket time-sharing pricing method based on generalized cost function |
| CN111985716A (en) * | 2020-08-21 | 2020-11-24 | 北京交通大学 | Passenger traffic prediction system with visualized passenger traffic information |
| CN111985716B (en) * | 2020-08-21 | 2024-05-14 | 北京交通大学 | Passenger traffic volume prediction system with passenger traffic information visualization function |
| CN113516500A (en) * | 2021-04-23 | 2021-10-19 | 深圳市威斯登信息科技有限公司 | Implementation method and system based on big data business and travel operation platform |
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Application publication date: 20180727 |
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