CN113159416B - Calculation method for bus single card swiping get-off station and intelligent terminal - Google Patents
Calculation method for bus single card swiping get-off station and intelligent terminal Download PDFInfo
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Abstract
The invention discloses a calculation method and an intelligent terminal for a bus stop of getting off by swiping a card once, wherein the method comprises the following steps: obtaining single card swiping data for processing and analyzing to obtain a travel rule of the single card swiping data, and classifying the single card swiping data according to the travel rule; the single card swiping data comprises regular single card swiping data and irregular single card swiping data; obtaining the type of the regular single card swiping data, and matching a corresponding getting-off station calculating method according to the type of the regular single card swiping data to obtain a getting-off station corresponding to the passenger in the trip; acquiring irregular single card swiping data, predicting passenger flow distribution among stations by combining the POI data and the IC card data of the bus stations, calculating a getting-off probability matrix, and calculating the getting-off stations. The invention accurately realizes the calculation of the bus departure stop of the bus single card swiping data.
Description
Technical Field
The invention relates to the technical field of urban public transport, in particular to a calculation method of a bus stop point of getting off by swiping a card once, an intelligent terminal and a computer readable storage medium.
Background
In recent years, the number of urban public transportation infrastructures is increased, but the proportion of the public transportation passenger volume in the total passenger volume is gradually reduced, the national public transportation passenger volume only accounts for 55.22% of the total passenger volume in 2018, and the first-line urban public transportation passenger volume is reduced year by year. In order to improve the urban bus passenger flow attraction capacity, a bus network plan needs to be formulated scientifically and reasonably, timely and effective bus operation scheduling management needs to be implemented, and the bus operation efficiency and the service level are further optimized. Bus optimization, bus scheduling, improvement of bus service level and the like all need bus passenger flow OD data as support, the bus passenger flow OD data refer to the number of passengers from a boarding station (origin) to a disembarking station (Destination) in a bus route network, the bus passenger flow OD data have wide application in the aspects of urban bus planning, bus route optimization, bus operation scheduling and the like, and the boarding station and the disembarking station of a passenger bus trip are the most effective basic data for obtaining the bus passenger flow OD.
The bus IC card swiping data mainly comprises information such as card number, card swiping time, account balance and the like, and information of getting-on and getting-off stations of passengers cannot be directly acquired. At present, the boarding station can be accurately acquired by combining passenger IC card swiping data, bus operation data and station GPS data. The traditional bus stop deduction method based on the bus IC card swiping data is divided into two types: a method for calculating a get-off station based on a trip chain by taking passengers as research objects; and the other method is to take the station as a research object and estimate the station for getting off the vehicle based on the attraction right of the station.
However, the calculation method based on the trip chain is not suitable for single card swiping data, while the calculation method based on the station attraction is suitable for single card swiping data, but in the determination process of the station attraction, a calculation model is constructed based on poisson distribution (which is a discrete probability distribution commonly seen in statistics and probability science), analysis on the single card swiping data is lacked in the whole analysis process, and the single card swiping data has higher randomness compared with multiple card swiping data.
Accordingly, the prior art is yet to be improved and developed.
Disclosure of Invention
The invention mainly aims to provide a calculation method, an intelligent terminal and a computer readable storage medium for a bus stop of getting off by swiping a card once, and aims to solve the problem that the prior art cannot effectively calculate the stop of getting off passengers in bus passenger transportation.
In order to achieve the purpose, the invention provides a method for calculating a bus single card-swiping get-off stop, which comprises the following steps:
obtaining single card swiping data for processing and analyzing to obtain a travel rule of the single card swiping data, and classifying the single card swiping data according to the travel rule; the single card swiping data comprises regular single card swiping data and irregular single card swiping data;
obtaining the type of the regular single card swiping data, and matching a corresponding getting-off station calculating method according to the type of the regular single card swiping data to obtain a getting-off station corresponding to the passenger in the trip;
acquiring irregular single card swiping data, predicting passenger flow distribution among stations by combining the POI data and the IC card data of the bus stations, calculating a getting-off probability matrix, and calculating the getting-off stations.
Optionally, the calculating method of the bus stop of getting off by swiping a card once includes the steps of obtaining data of swiping a card once for processing and analyzing, obtaining a travel rule of the data of swiping a card once, and classifying the data of swiping a card once according to the travel rule, and specifically includes:
acquiring single card swiping data, and analyzing the quantity distribution of the single card swiping data in space and time;
analyzing the travel behavior of the passenger who swipes the card once, and mining the travel rule, the travel characteristic and the travel purpose of the passenger to obtain the travel rule of the data which swipes the card once;
dividing the single card swiping data into regular single card swiping data and irregular single card swiping data according to the travel rule;
classifying the regularity of the obtained regular single card swiping data, and analyzing the irregular single card swiping data and the POI data.
Optionally, the bus stop calculation method by swiping a card once includes: occasionally occurring single swipe data, single swipe data for fixed-site rides, and single swipe data chained across days.
Optionally, the calculating method of the bus stop of getting off by swiping a card once includes the steps of obtaining the type of the regular data of swiping a card once, and matching the corresponding calculating method of the stop of getting off according to the type of the regular data of swiping a card once to obtain the stop of getting off corresponding to the passenger when going out, and specifically includes:
when the type of the regular single card swiping data is the single card swiping data which appears occasionally, if the getting-on station of the passenger for the first preset number of bus trips is a first station and the getting-off station is a second station, the getting-off station corresponding to the current trip of the passenger is the second station when the getting-on station of the passenger is the first station again;
when the type of the regular single card swiping data is single card swiping data of fixed station riding, if a passenger has single card swiping records for a second preset number of times, all the boarding stations are fixed stations, and the disembarking stations are the same, calculating the disembarking station of any card swiping record;
when the type of the regular single card swiping data is single card swiping data which is formed in a chain in a cross-day mode, if two card swiping records exist in a passenger, the bus taking stations are respectively a third station and a fourth station, the card swiping time interval is n days, the fourth station is the same-line station of the third station, and the passenger does not have other card swiping records between the two card swiping records, the single card swiping record of the passenger taking the bus at the third station in the same day is the single card swiping data which is formed in the chain in the cross-day mode, and the getting-off station recorded in the card swiping at this time is the fourth station.
Optionally, the method for calculating the get-off stop by swiping the card once in the bus includes the steps of obtaining irregular single card swiping data, predicting passenger flow distribution among stops by combining the POI data and the IC card data of the bus stop, calculating a get-off probability matrix, and calculating the get-off stop, and specifically includes:
calculating the area coefficient of POI data according to the known station incidence and the card swiping data volume of the bus IC card, and predicting the station attraction traffic volume by combining the station attraction rate;
carrying out traffic distribution prediction on the known station occurrence amount and station attraction amount by using an unconstrained weight model, obtaining passenger flow distribution among stations and forming an OD matrix;
and according to the calculated OD matrix among the stations, obtaining the getting-off probability of the passengers at each station to form a getting-off probability matrix, optimizing the attraction of the stations, and calculating the getting-off stations.
Optionally, the method for calculating the bus stop at which the bus is dropped off by swiping the card once is provided, wherein the stop occurrence amount is the number of people getting on the bus at the bus stop; the station attraction amount is the number of people getting off the station.
Optionally, the calculating method of the bus stop of getting-off by swiping a card once includes: the sum of the growth coefficients is a synthetic method;
the growth coefficient method is a method for predicting future OD distribution on the basis of assuming that the future OD distribution is the same as the current OD distribution;
the comprehensive method is a method for analyzing the distribution rule of the OD quantity from the actual analysis of the traffic quantity, expressing the distribution rule by using a model and carrying out traffic distribution quantity prediction by combining with the calibration parameters of the actually measured data.
Optionally, the method for calculating the getting-off stop of the bus by swiping the card once is further described, wherein the size of the getting-off probability represents the size of the attraction strength of each stop.
In addition, to achieve the above object, the present invention further provides an intelligent terminal, wherein the intelligent terminal includes: the calculation program of the bus single card swiping get-off stop is executed by the processor to realize the steps of the calculation method of the bus single card swiping get-off stop.
In addition, in order to achieve the above object, the present invention further provides a computer readable storage medium, wherein the computer readable storage medium stores an estimation program of a bus single card swiping get-off stop, and the estimation program of the bus single card swiping get-off stop is executed by a processor to implement the steps of the estimation method of the bus single card swiping get-off stop.
According to the method, the trip rule of the single card swiping data is obtained by acquiring the single card swiping data for processing and analyzing, and the single card swiping data is classified according to the trip rule; the single card swiping data comprises regular single card swiping data and irregular single card swiping data; obtaining the type of the regular single card swiping data, and matching a corresponding getting-off station calculating method according to the type of the regular single card swiping data to obtain a getting-off station corresponding to the passenger in the trip; acquiring irregular single card swiping data, predicting passenger flow distribution among stations by combining the POI data and the IC card data of the bus stations, calculating a getting-off probability matrix, and calculating the getting-off stations. The method processes and analyzes the single card swiping data, searches regularity of the single card swiping data, classifies the single card swiping data according to the regularity, correspondingly calculates the getting-off stations according to the regularity of the regular single card swiping data, predicts passenger flow distribution among stations for the irregular single card swiping data by combining the POI data and the IC card data of the bus stations, calculates the getting-off probability matrix to calculate the getting-off stations, and accurately realizes calculation of the getting-off stations of the single card swiping data of the bus.
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FIG. 1 is a flow chart of a preferred embodiment of a calculation method for a bus stop with a single card swiping operation according to the invention;
FIG. 2 is a flowchart of step S10 in the preferred embodiment of the method for calculating the bus stop of getting off the bus once swiping the card;
FIG. 3 is a data proportion diagram of various types of single card swiping in the preferred embodiment of the calculating method of the bus single card swiping getting-off stop of the invention;
FIG. 4 is a flowchart of step S20 in the preferred embodiment of the method for calculating the bus stop of getting off the bus with a single card swipe according to the present invention;
FIG. 5 is a flowchart of step S30 in the preferred embodiment of the method for calculating the bus stop of getting off the bus once a card is swiped;
FIG. 6 is a matrix diagram of bus probability intervals after superposition in a preferred embodiment of the calculation method for bus stop getting-off by single card swiping in the bus of the present invention;
fig. 7 is a schematic operating environment diagram of an intelligent terminal according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, the method for calculating a bus stop at which a bus is once taken by a card includes the following steps:
and step S10, obtaining the single card swiping data for processing and analyzing, obtaining the travel rule of the single card swiping data, and classifying the single card swiping data according to the travel rule.
The single card swiping data comprises regular single card swiping data and irregular single card swiping data.
Fig. 2 is a flowchart of step S10 in the method for calculating a bus stop with a single card swipe according to the present invention.
As shown in fig. 2, the step S10 includes:
s11, obtaining single card swiping data, and analyzing the quantity distribution of the single card swiping data in space and time;
s12, analyzing the travel behavior of the passenger who swipes the card once, and mining the travel rule, the travel characteristic and the travel purpose of the passenger to obtain the travel rule of the data which swipes the card once;
s13, dividing the single card swiping data into regular single card swiping data and irregular single card swiping data according to the travel rule;
and S14, classifying the regularity of the obtained regular single card swiping data, and analyzing the irregular single card swiping data and the POI data.
Specifically, the method for calculating the get-off station by the single card swiping data is deeply researched, the accuracy of the calculation result of the get-off station by the single card swiping data is improved, the method is very important for researching the travel rule of passengers and obtaining accurate bus passenger flow OD information, and meanwhile, more accurate, comprehensive and effective data support can be provided for management modes such as bus scheduling and network optimization. The method specifically comprises the steps of processing and analyzing single card swiping data, searching regularity of the single card swiping data, and classifying the single card swiping data according to the presented regularity, namely dividing the single card swiping data into regular single card swiping data and irregular single card swiping data according to a travel rule on the basis of data analysis.
Further, the regular single card swiping data comprises: the data processing method comprises the following steps of (1) occasionally occurring single card swiping data (occasionally occurring single card swiping data, namely occasionally occurring once), fixed station riding single card swiping data (fixed station riding single card swiping data, frequently riding at one station), and across-the-day chaining single card swiping data (across-the-day chaining single card swiping data, namely data with intervals in the middle, but capable of forming a complete trip chain).
As shown in fig. 3, the regular single card swiping data accounts for about 55%, and is mainly divided into occasional single card swiping data, fixed-station-taking single card swiping data, and cross-day chained single card swiping data; irregular single card swiping data accounts for 45%.
And step S20, obtaining the type of the regular single card swiping data, and obtaining the getting-off station corresponding to the passenger traveling according to the matching of the type of the regular single card swiping data and the corresponding getting-off station calculating method.
According to the above description, the regular single card swiping data comprises: the system comprises occasionally occurring single card swiping data, single card swiping data of fixed station riding and single card swiping data of a cross-day chaining; the corresponding calculation of the get-off station is performed according to the three types of data.
Fig. 4 is a flowchart of step S20 in the method for calculating a bus stop with a single card swipe according to the present invention.
As shown in fig. 4, the step S20 includes:
s21, when the type of the regular single card swiping data is the single card swiping data which appears occasionally, if the getting-on station of the passenger who has the bus trip for the first preset number of times is the first station and the getting-off station is the second station, when the getting-on station of the passenger appears as the first station again, the getting-off station corresponding to the passenger' S trip is the second station.
Assuming that a certain passenger has 3 bus trips (i.e. when the first preset number of times is 3), the getting-on station is station a (i.e. the first station), the getting-off station is station B (i.e. the second station), and the card swiping time is relatively fixed, because the passenger has a certain time difference during the bus trips, the bus time error is generally about 20-30 minutes. If the passenger appears a card swiping record with the boarding station as station A (first station) and the card swiping time within the time error range of the passenger taking the bus on a certain day, the card swiping record of the passenger at this time can be classified into single card swiping data which appears occasionally, and then the getting-off station corresponding to the passenger going out at this time is station B (second station) according to the regularity of the type of the single card swiping data.
And S22, when the type of the regular single card swiping data is single card swiping data of fixed station riding, if the passenger has single card swiping records of a second preset number of times, the getting-on stations are all fixed stations, and the getting-off stations are the same, calculating the getting-off stations of any card swiping record.
Assuming that a passenger has four times (i.e. when the second preset number of times is 4) of single card swiping records, all stations above the passenger are fixed stations (e.g. station a), and the card swiping time is relatively concentrated, the four times of card swiping records of the passenger are single card swiping data of a fixed station, the travel regularity of the single card swiping data is relatively strong, and the stations below the passenger are basically the same, and then the station leaving calculation of the passenger can be performed on any card swiping record.
S23, when the type of the regular single card swiping data is single card swiping data which is formed in a chain mode in a cross-day mode, if two card swiping records exist in a passenger, the riding stations are respectively a third station and a fourth station, the card swiping time interval is n days, the fourth station is the same-line station of the third station, and no other card swiping records exist between the two card swiping records, the single card swiping record of the passenger riding at the third station in the same day is the single card swiping data which is formed in the chain mode in the cross-day mode, and the getting-off station recorded in the card swiping at this time is the fourth station.
Assuming that the passenger has two card swiping records, the bus taking stations are respectively station C (i.e. the third station) and station D (i.e. the fourth station), and the card swiping time is separated by n days (n is a positive integer, for example, n is 3). And the station D (fourth station) is the same-line station as the station C (third station), and no other card swiping records exist between two card swiping records, so that the single card swiping record of the passenger riding at the station C (third station) on the same day is the single card swiping data which is formed by spanning days and chaining, and the get-off station recorded by the card swiping at this time is the station D (fourth station).
And step S30, acquiring irregular single card swiping data, predicting passenger flow distribution among stations by combining the POI data and the IC card data of the bus stations, calculating a getting-off probability matrix, and calculating the getting-off stations.
Fig. 5 is a flowchart of step S30 in the method for calculating a bus stop with a single card swipe according to the present invention.
As shown in fig. 5, the step S30 includes:
s31, calculating the area coefficient of the POI data according to the known station incidence rate and the card swiping data volume of the bus IC card, and predicting the station attraction traffic volume by combining the station attraction rate;
s32, carrying out traffic distribution prediction on the known station occurrence amount and station attraction amount by using an unconstrained weight model, obtaining passenger flow distribution among stations and forming an OD matrix;
wherein the station occurrence amount is the number of people getting on the bus at the station; the station attraction amount is the number of people getting off the station;
wherein the traffic distribution prediction comprises: the sum of the growth coefficients is a synthetic method; the growth coefficient method is a method for predicting future OD distribution on the basis of assuming that the future OD distribution is the same as the current OD distribution; the comprehensive method is a method for analyzing the distribution rule of the OD quantity from the actual analysis of the traffic quantity, expressing the distribution rule by using a model and predicting the traffic distribution quantity by combining with the calibration parameters of the actually measured data;
and S33, obtaining the getting-off probability of the passenger at each station (the size of the getting-off probability represents the size of the attraction strength of each station) to form a getting-off probability matrix according to the calculated OD matrix among the stations, optimizing the attraction right of the stations and estimating the getting-off stations.
Specifically, corresponding analysis is performed on irregular single card swiping data and five types of POI data (such data are mainly data of some building types, names and the like around a bus stop) of residence, office, business, medical treatment and education; according to the method, for irregular single card swiping data, the POI data and the IC card data of the bus stop are combined, a four-stage method (the four-stage method is a method for predicting traffic demands in traditional traffic planning and is called as the four-stage method because of traffic generation, traffic distribution, mode division and traffic distribution) is used for predicting passenger flow distribution among the stops, the stop getting-off station is calculated on the basis of further calculating a probability matrix of getting-off, namely after the probability matrix of getting-off is possessed, the determination of the attraction right of the stop can be optimized, then the stop getting-off station calculation is carried out by a random simulation method, and the stop getting-off station calculation is carried out on the irregular single card swiping data according to the probability of getting-off in the whole process.
In the invention, POI (Point of Interest) data is one of available data sources for bus planning and can be used as basic data for measuring the bus service range and bus network optimization; POI interest point is a term in the geographic information system, and generally refers to all geographic objects that can be abstracted as points, especially some geographic entities closely related to people's lives, such as schools, banks, restaurants, gas stations, hospitals, supermarkets, etc.; the main purpose of the interest points is to describe the addresses of the things or events, so that the description capability and the query capability of the positions of the things or events can be greatly enhanced, and the accuracy and the speed of geographic positioning are improved.
The main travel destinations of citizens include residential districts, working places, shopping malls, scenic spots and other places closely related to daily work and life, the places can be regarded as POI data, the current situation of bus service can be analyzed based on the POI data and by combining other data, for example, the coverage condition of bus stops within the range of 500 meters of the POI positions, the accessibility of the POI positions or the traffic districts where the POI positions are located to the bus stops, the position setting or the platform capacity setting analysis of the bus stops, the passenger flow travel characteristics among different POI positions and the like, and a basis is provided for optimizing and adjusting the bus network.
In addition, for the condition that data is lost but bus network planning is needed, the analysis of POI data can be used for guiding bus network layout and station setting. Currently, research and application of POI data mainly focuses on the aspects of bus reachability analysis, bus stop setting, support for bus network planning and the like by using the POI data.
Firstly, predicting the bus stop attraction amount by an original unit method (the original unit method is a common method in traffic generation prediction, taking an area original unit method as an example, namely, giving the generation rate of a unit area, and then calculating the traffic volume according to the building area), calculating the area coefficient of POI data on the premise of knowing the stop occurrence rate and the bus IC card swiping data volume, and predicting the stop attraction traffic volume by combining the stop attraction rate, wherein the original unit method is selected to predict the stop passenger flow attraction amount, and the expression form is as follows:
wherein the parameter T is expressed as predicted traffic volume, and the parameter anTrip rate expressed as nth land (where n represents serial numbers of different land types, and nth land represents a certain land); the parameter is expressed as the area of the nth-type plot. According to the traffic travel characteristic survey of various land types, the daily average travel incidence rate and the attraction rate of different land properties are different, as shown in table 1:
table 1: reference table for travel rate of different land properties
In the original unit method, the predicted traffic volume of each bus stop is predicted by multiplying the area of each type of land by the trip rate of the type of land. However, the data owned at present does not contain the used area, so the method is adjusted and optimized according to the basic model of the original unit method, and the basic model is more suitable.
Since the POI data can only reflect the quantity of each type of land and the area of each type of land needs to be used in the traffic generation and prediction process, the POI data and each type of land need to be analyzed by combining the bus card swiping dataThe relationship between the land occupation areas; let QnExpressing the number of n-th POI data, introducing variable MnExpressed as POI data unit area of the nth category. The area S of the nth landnExpressed as: sn＝QnMnAnd the original formula is: t ═ ΣnanQnMn。
At present, known data comprise real occurrence amount of each bus stop, quantity of each type of POI data and travel rate of each land, and a group of area coefficients related to the unit area of the POI data can be calculated through multiple linear regression according to the unit area of each type of POI data. Let xnThe unit area travel generation rate is expressed as the unit area travel generation rate of each type of POI data, namely, the number of each type of POI data is multiplied by the corresponding travel rate (x)n＝anQn) The area coefficient of the unit area M of each type of POI data can be fitted by a regression analysis method, and the multiple linear regression equation is as follows:
T＝M0+M1x1+M2x2+M3x3+M4x4+M5x5wherein x is1Denoted office, x2Denoted as home, x3Denoted education, x4Denoted as medical, x5Denoted as business; m0-M5Area coefficients of POI data of different properties are represented.
After the attraction of each bus stop for the passengers who take a card once is successfully predicted, traffic distribution prediction is needed, and the occurrence and attraction of each bus stop for the data which take a card once are distributed among the bus stops to form an OD matrix (the number in the OD matrix represents the passenger flow, and the getting-off probability matrix represents a probability value).
Traffic distribution prediction methods are mainly classified into two categories: one is a growth coefficient method and the other is a synthesis method. The former predicts future OD distribution on the basis of assuming that the future OD distribution is the same as the current OD distribution, and common methods include a growth coefficient method, a detroit method and the like. The latter is to analyze the distribution rule of OD quantity and to use the model to express from the actual analysis of traffic quantity, then to combine the measured data to calibrate the parameter, finally to predict the traffic distribution quantity, the method includes gravity model method, maximum entropy model method, etc. Because the existing OD table of the bus stop aiming at the single card swiping data cannot be obtained, the traffic distribution prediction method preferentially selects the comprehensive method to predict the occurrence amount and the attraction amount of the single card swiping data. The urban resident trip prediction generally selects a gravity model as a traffic distribution prediction model.
The gravity model considers the strength of mutual attraction between two bus stops and the resistance between the two bus stops, and considers that the travel distribution between the two bus stops is in direct proportion to the traffic volume of the two bus stops and in inverse proportion to the traffic impedance between the bus stops. The traffic impedance is an index reflecting the passing convenience degree between two bus stops and can be represented by the number of the travel stops.
The general gravity model is of the form:
wherein q isijThe predicted value of the travel distribution quantity between the partitions i and j (the meaning between the two sites) is expressed; cijExpressed as an impedance parameter (formula for impedance function selection) between two sub-regions; o isi、DjExpressed as the amount of generation and the amount of attraction; alpha and beta are parameters; f (C)ij) Expressed as an impedance function; k is only a coefficient in the model and has no practical significance. The invention takes the tenth root of reciprocal of Poisson distribution as traffic impedance, and the expression form is as follows:
where θ represents the average number of passengers' trip stations.
Calculating the passenger flow qijThen, if qijIf the constraint condition is not met, iterative computation is required to enable the constraint condition to be met. If iterative calculations are required, the following formula can be used:
the results are shown in table 2:
| site | 1 | 2 | … | i | … | j | … | n-1 | n | Getting on bus |
| 1 | 0 | q12 | … | q1i | … | qij | … | q1(n-1) | q1n | B1 |
| 2 | 0 | 0 | … | q2i | … | q2j | … | q2(n-1) | q2n | B2 |
| … | … | … | … | … | … | … | … | … | … | … |
| i | 0 | 0 | 0 | 0 | … | qij | … | qi(n-1) | qin | Bi |
| … | … | … | … | … | … | … | … | … | … | … |
| j | 0 | 0 | 0 | 0 | 0 | 0 | qj(n-1) | qjn | Bj | |
| … | … | … | … | … | … | … | … | … | … | … |
| n-1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | q(n-1)n | Bn-1 |
| n | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | Bn＝0 |
| Get-off vehicle | A1＝0 | A2 | ... | Ai | … | Aj | … | An-1 | An |
Table 2: bus passenger flow OD meter
And finally, obtaining the getting-off probability of the passengers at each station on the basis of calculating the OD passenger flow among the stations, wherein the size of the getting-off probability represents the size of the attraction strength of each station to form a getting-off probability matrix, optimizing the attraction right of the stations and calculating the getting-off stations.
In order to further explain the technical scheme of the invention, the following description is given by taking the bus card swiping data of a city as an example:
for example, taking 243 routes, 4 months and 25 days of Guangzhou buses as an example, T takes the occurrence amount (i.e. the card swiping amount) of each bus stop and xnAnd the unit area travel occurrence rate of each type of POI data of each bus stop of the line is obtained. The regression coefficient table is shown in table 3:
| Coefficients | standard error of | tStat | P-value | The lower limit is 95.0% | The upper limit is 95.0% | |
| Intercept | -380.417 | 48.73376 | -7.80602 | 1.04E-08 | -479.944527 | -280.8892935 |
| XVariable1 | 0.281833 | 0.029463 | 9.5658 | 1.27E-10 | 0.221662076 | 0.342003061 |
| XVariable2 | 0.012525 | 0.007786 | 1.608623 | 0.118173 | -0.003376429 | 0.028426172 |
| XVariable5 | 0.013146 | 0.051841 | 0.253588 | 0.801544 | -0.092726748 | 0.119019085 |
Table 3: regression parameter table
The prediction model is:
T＝0.281833a1Q1+0.012525a2Q2+0.013146a5Q5-380.417
where a represents the traffic generation rate of different types of POIs, and Q represents different types of POI data.
Let bnFor the incidence of each type of POI data, according to the formula
THair-like device0281833b1Q1+O.O12525b2Q2+O.O13146b5Q5-
380.417 predicting the occurrence of 243 buses, wherein THair-like deviceIs the predicted amount of occurrence, and b represents the incidence of different types of POI.
The predicted result obtained by the traffic flow prediction model needs corresponding performance evaluation indexes to evaluate the predicted result. In order to effectively judge the performance of the algorithm model on traffic flow prediction, the average absolute error is taken as an evaluation index of an occurrence prediction result to measure the prediction effect of the prediction model. Generally, when MAPE (mean absolute error of prediction model prediction) is less than 15%, the accuracy of the prediction result is better. Through calculation, the average absolute error MAPE of the prediction result of the prediction model is 11% < 15%, and the prediction result of the prediction model is relatively good and can be used for predicting the traffic volume of the bus stop.
The result of the error analysis in the previous step shows that the prediction effect of the prediction model is relatively good, and therefore the incidence of each type of POI data is converted into the attraction rate, and the c is enablednFor the incidence of each type of POI data, according to the formula
TSuction device＝0.281833c1Q1+0.012525c2Q2+0.013146c5Q5-380.417
Predicting the attraction of each bus stop, wherein TSuction deviceRepresenting the predicted attraction traffic and c representing the attraction rate for different POIs.
Partial attraction of each bus stop can be calculated through data of card swiping for many times. And comparing and analyzing the predicted attraction amount with the data of the card swiping for many times, wherein the predicted attraction amount is larger than the data of the card swiping for many times, the passenger flow distribution of the predicted attraction amount and the passenger flow distribution of the data of the card swiping for many times are similar, the predicted result is reasonable, and the predicted result can be used as the data basis of follow-up research. And subtracting part of the attraction amount from the predicted total attraction amount to obtain the attraction amount of each bus stop point to the single card swiping data, and combining the boarding information of the single card swiping data to obtain the generation amount and the attraction amount of the single card swiping data at each bus stop point.
On the basis of the data of the occurrence amount and the attraction amount of the single card swiping data of each bus stop, traffic distribution prediction can be carried out, the occurrence amount and the attraction amount of the stop are distributed among the stops, and a basis is provided for calculating the attraction strength of each bus stop. The invention adopts an exponential impedance function, and parameters alpha and beta in an unconstrained gravity model form need to be calibrated. And (3) carrying out coefficient calibration by using current OD data (derived from a data set of multiple card swiping) of 243 continuous roads of Guangzhou buses for 7 days. After calibration, the model of the unconstrained weight is represented by a-0.000124, β -0.0563:
generating quantity O of each bus stop for single card swiping dataiSuction volume DjAnd an impedance parameter CijSubstituting into it to calculate the OD between the stations. After the traffic distribution prediction is completed and the passenger flow OD matrix between the stations is obtained, the station attraction can be calculated based on this and the completion probability intervals are superimposed, as shown in fig. 6.
Finally, the calculation of the get-off station of the irregular single card swiping data is completed by using a random simulation method, and the calculation result is shown in table 4:
table 4: calculation result of get-off station
The bus single card swiping OD calculation method based on the station POI provided by the invention takes the bus card swiping data in Guangzhou city as an example, processes and analyzes the single card swiping data, searches the regularity of the single card swiping data, and classifies the single card swiping data according to the presented regularity; providing a corresponding getting-off station calculation method according to the regularity of regular single card swiping data; according to irregular single card swiping data, the bus station POI data and the bus IC card data are combined, passenger flow distribution among stations is predicted by using a four-stage method, the calculation of the bus station leaving station can be realized on the basis of further calculating the leaving probability matrix, and the calculation of the bus station leaving station based on the bus station leaving data can be realized.
Further, as shown in fig. 7, based on the above calculation method for the bus stop at which the bus is single-time swiped and alight, the invention also provides an intelligent terminal, which includes a processor 10, a memory 20 and a display 30. Fig. 7 shows only some of the components of the smart terminal, but it should be understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead.
The memory 20 may be an internal storage unit of the intelligent terminal in some embodiments, such as a hard disk or a memory of the intelligent terminal. The memory 20 may also be an external storage device of the Smart terminal in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the Smart terminal. Further, the memory 20 may also include both an internal storage unit and an external storage device of the smart terminal. The memory 20 is used for storing application software installed in the intelligent terminal and various data, such as program codes of the installed intelligent terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In an embodiment, the memory 20 stores an estimation program 40 of a bus single card-swiping get-off stop, and the estimation program 40 of the bus single card-swiping get-off stop can be executed by the processor 10, so as to implement the estimation method of the bus single card-swiping get-off stop in the present application.
The processor 10 may be, in some embodiments, a Central Processing Unit (CPU), a microprocessor or other data Processing chip, and is configured to run program codes stored in the memory 20 or process data, such as executing a calculation method of the bus stop of getting off by swiping a card once.
The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch panel, or the like in some embodiments. The display 30 is used for displaying information at the intelligent terminal and for displaying a visual user interface. The components 10-30 of the intelligent terminal communicate with each other via a system bus.
In one embodiment, when the processor 10 executes the calculation program 40 for the bus single-time card-swiping get-off stop in the memory 20, the following steps are implemented:
obtaining single card swiping data for processing and analyzing to obtain a travel rule of the single card swiping data, and classifying the single card swiping data according to the travel rule; the single card swiping data comprises regular single card swiping data and irregular single card swiping data;
obtaining the type of the regular single card swiping data, and matching a corresponding getting-off station calculating method according to the type of the regular single card swiping data to obtain a getting-off station corresponding to the passenger in the trip;
acquiring irregular single card swiping data, predicting passenger flow distribution among stations by combining the POI data and the IC card data of the bus stations, calculating a getting-off probability matrix, and calculating the getting-off stations.
The method comprises the steps of obtaining single card swiping data, processing and analyzing the single card swiping data, obtaining a travel rule of the single card swiping data, classifying the single card swiping data according to the travel rule, and specifically comprising the following steps:
acquiring single card swiping data, and analyzing the quantity distribution of the single card swiping data in space and time;
analyzing the travel behavior of the passenger who swipes the card once, and mining the travel rule, the travel characteristic and the travel purpose of the passenger to obtain the travel rule of the data which swipes the card once;
dividing the single card swiping data into regular single card swiping data and irregular single card swiping data according to the travel rule;
classifying the regularity of the obtained regular single card swiping data, and analyzing the irregular single card swiping data and the POI data.
Wherein the regular single card swiping data comprises: occasionally occurring single swipe data, single swipe data for fixed-site rides, and single swipe data chained across days.
The obtaining of the type of the regular single card swiping data and the obtaining of the get-off station corresponding to the passenger trip according to the get-off station calculation method corresponding to the type matching of the regular single card swiping data specifically include:
when the type of the regular single card swiping data is the single card swiping data which appears occasionally, if the getting-on station of the passenger for the first preset number of bus trips is a first station and the getting-off station is a second station, the getting-off station corresponding to the current trip of the passenger is the second station when the getting-on station of the passenger is the first station again;
when the type of the regular single card swiping data is single card swiping data of fixed station riding, if a passenger has single card swiping records for a second preset number of times, all the boarding stations are fixed stations, and the disembarking stations are the same, calculating the disembarking station of any card swiping record;
when the type of the regular single card swiping data is single card swiping data which is formed in a chain in a cross-day mode, if two card swiping records exist in a passenger, the bus taking stations are respectively a third station and a fourth station, the card swiping time interval is n days, the fourth station is the same-line station of the third station, and the passenger does not have other card swiping records between the two card swiping records, the single card swiping record of the passenger taking the bus at the third station in the same day is the single card swiping data which is formed in the chain in the cross-day mode, and the getting-off station recorded in the card swiping at this time is the fourth station.
The method comprises the following steps of acquiring irregular single card swiping data, predicting passenger flow distribution among stations by combining bus station POI data and bus IC card data, calculating a getting-off probability matrix, and calculating a getting-off station, and specifically comprises the following steps:
calculating the area coefficient of POI data according to the known station incidence and the card swiping data volume of the bus IC card, and predicting the station attraction traffic volume by combining the station attraction rate;
carrying out traffic distribution prediction on the known station occurrence amount and station attraction amount by using an unconstrained weight model, obtaining passenger flow distribution among stations and forming an OD matrix;
and according to the calculated OD matrix among the stations, obtaining the getting-off probability of the passengers at each station to form a getting-off probability matrix, optimizing the attraction of the stations, and calculating the getting-off stations.
Wherein the station occurrence amount is the number of people getting on the bus at the station; the station attraction amount is the number of people getting off the station.
Wherein the traffic distribution prediction comprises: the sum of the growth coefficients is a synthetic method;
the growth coefficient method is a method for predicting future OD distribution on the basis of assuming that the future OD distribution is the same as the current OD distribution;
the comprehensive method is a method for analyzing the distribution rule of the OD quantity from the actual analysis of the traffic quantity, expressing the distribution rule by using a model and carrying out traffic distribution quantity prediction by combining with the calibration parameters of the actually measured data.
Wherein the size of the get-off probability represents the size of the attraction strength of each station.
The invention also provides a computer readable storage medium, wherein the computer readable storage medium stores an estimation program of the bus single card swiping get-off stop, and the estimation program of the bus single card swiping get-off stop is executed by a processor to realize the steps of the estimation method of the bus single card swiping get-off stop.
In summary, the present invention provides a method for calculating a bus stop point of getting off by swiping a card once, an intelligent terminal and a computer readable storage medium, wherein the method comprises: obtaining single card swiping data for processing and analyzing to obtain a travel rule of the single card swiping data, and classifying the single card swiping data according to the travel rule; the single card swiping data comprises regular single card swiping data and irregular single card swiping data; obtaining the type of the regular single card swiping data, and matching a corresponding getting-off station calculating method according to the type of the regular single card swiping data to obtain a getting-off station corresponding to the passenger in the trip; acquiring irregular single card swiping data, predicting passenger flow distribution among stations by combining the POI data and the IC card data of the bus stations, calculating a getting-off probability matrix, and calculating the getting-off stations. The method processes and analyzes the single card swiping data, searches regularity of the single card swiping data, classifies the single card swiping data according to the regularity, correspondingly calculates the getting-off stations according to the regularity of the regular single card swiping data, predicts passenger flow distribution among stations for the irregular single card swiping data by combining the POI data and the IC card data of the bus stations, calculates the getting-off probability matrix to calculate the getting-off stations, and accurately realizes calculation of the getting-off stations of the single card swiping data of the bus.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Of course, it will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer readable storage medium, and when executed, the program can include the processes of the embodiments of the methods described above. The computer readable storage medium may be a memory, a magnetic disk, an optical disk, etc.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (8)
1. A calculation method for a bus single card-swiping get-off stop is characterized by comprising the following steps:
obtaining single card swiping data for processing and analyzing to obtain a travel rule of the single card swiping data, and classifying the single card swiping data according to the travel rule; the single card swiping data comprises regular single card swiping data and irregular single card swiping data; the regular single card swiping data comprises: the system comprises occasionally occurring single card swiping data, single card swiping data of fixed station riding and single card swiping data of a cross-day chaining;
obtaining the type of the regular single card swiping data, and matching a corresponding getting-off station calculating method according to the type of the regular single card swiping data to obtain a getting-off station corresponding to the passenger in the trip;
acquiring irregular single card swiping data, predicting passenger flow distribution among stations by combining the POI data and the IC card data of the bus stations, calculating a getting-off probability matrix, and calculating the getting-off stations;
the method comprises the following steps of acquiring irregular single card swiping data, predicting passenger flow distribution among stations by combining bus station POI data and bus IC card data, calculating a getting-off probability matrix, and calculating a getting-off station, and specifically comprises the following steps:
calculating the area coefficient of POI data according to the known station incidence and the card swiping data volume of the bus IC card, and predicting the station attraction traffic volume by combining the station attraction rate;
carrying out traffic distribution prediction on the known station occurrence amount and station attraction amount by using an unconstrained weight model, obtaining passenger flow distribution among stations and forming an OD matrix;
and according to the calculated OD matrix among the stations, obtaining the getting-off probability of the passengers at each station to form a getting-off probability matrix, optimizing the attraction of the stations, and calculating the getting-off stations.
2. The method for calculating the bus stop of getting off the bus once by swiping the card according to claim 1, wherein the obtaining of the data of the once by swiping the card is processed and analyzed to obtain the travel rule of the data of the once by swiping the card, and the data of the once by swiping the card is classified according to the travel rule, and the method specifically comprises the following steps:
acquiring single card swiping data, and analyzing the quantity distribution of the single card swiping data in space and time;
analyzing the travel behavior of the passenger who swipes the card once, and mining the travel rule, the travel characteristic and the travel purpose of the passenger to obtain the travel rule of the data which swipes the card once;
dividing the single card swiping data into regular single card swiping data and irregular single card swiping data according to the travel rule;
classifying the regularity of the obtained regular single card swiping data, and analyzing the irregular single card swiping data and the POI data.
3. The method for calculating the bus stop of getting off with a single card swiping according to claim 1, wherein the obtaining of the type of the regular single card swiping data matches the corresponding method for calculating the bus stop of getting off according to the type of the regular single card swiping data to obtain the bus stop corresponding to the passenger traveling at this time specifically comprises:
when the type of the regular single card swiping data is the single card swiping data which appears occasionally, if the getting-on station of the passenger for the first preset number of bus trips is a first station and the getting-off station is a second station, the getting-off station corresponding to the current trip of the passenger is the second station when the getting-on station of the passenger is the first station again;
when the type of the regular single card swiping data is single card swiping data of fixed station riding, if a passenger has single card swiping records for a second preset number of times, all the boarding stations are fixed stations, and the disembarking stations are the same, calculating the disembarking station of any card swiping record;
when the type of the regular single card swiping data is single card swiping data which is formed in a chain in a cross-day mode, if two card swiping records exist in a passenger, the bus taking stations are respectively a third station and a fourth station, the card swiping time interval is n days, the fourth station is the same-line station of the third station, and the passenger does not have other card swiping records between the two card swiping records, the single card swiping record of the passenger taking the bus at the third station in the same day is the single card swiping data which is formed in the chain in the cross-day mode, and the getting-off station recorded in the card swiping at this time is the fourth station.
4. The method for calculating the bus stop of getting off the bus by swiping card once according to claim 1, wherein the station occurrence is the number of people getting on the bus at the station; the station attraction amount is the number of people getting off the station.
5. The method for calculating the bus stop of getting-off by swiping card once according to claim 1, wherein the traffic distribution prediction comprises: a growth coefficient method and a synthesis method;
the growth coefficient method is a method for predicting future OD distribution on the basis of assuming that the future OD distribution is the same as the current OD distribution;
the comprehensive method is a method for analyzing the distribution rule of the OD quantity from the actual analysis of the traffic quantity, expressing the distribution rule by using a model and carrying out traffic distribution quantity prediction by combining with the calibration parameters of the actually measured data.
6. The method for calculating the bus stop getting-off station with the single card swiping function as claimed in claim 1, wherein the size of the getting-off probability represents the size of the attraction strength of each station.
7. An intelligent terminal, characterized in that, intelligent terminal includes: the calculation program of the bus single card swiping get-off stop is stored on the memory and can run on the processor, and when being executed by the processor, the calculation program of the bus single card swiping get-off stop realizes the steps of the calculation method of the bus single card swiping get-off stop as claimed in any one of claims 1-6.
8. A computer-readable storage medium, wherein the computer-readable storage medium stores an estimation program of a bus single-time card-swiping get-off stop, and the estimation program of the bus single-time card-swiping get-off stop is executed by a processor to realize the steps of the estimation method of the bus single-time card-swiping get-off stop according to any one of claims 1 to 6.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104809344A (en) * | 2015-04-23 | 2015-07-29 | 中山大学 | IC (Integrated Circuit) card data-based estimation method for passenger flow in bus station interval |
| CN105788260A (en) * | 2016-04-13 | 2016-07-20 | 西南交通大学 | Public transportation passenger OD calculation method based on intelligent public transportation system data |
| CN105809292A (en) * | 2016-03-21 | 2016-07-27 | 广州地理研究所 | Passenger getting-off station reckoning method of bus IC (Integrated Circuit) card |
| CN105868861A (en) * | 2016-04-08 | 2016-08-17 | 青岛海信网络科技股份有限公司 | Bus passenger flow evolution analysis method based on time-space data fusion |
| CN107818412A (en) * | 2017-10-18 | 2018-03-20 | 福州大学 | A kind of extensive bus passenger OD parallel calculating methods based on MapReduce |
| CN107886723A (en) * | 2017-11-13 | 2018-04-06 | 深圳大学 | A kind of traffic trip survey data processing method |
| CN110084442A (en) * | 2019-05-16 | 2019-08-02 | 重庆大学 | A kind of method of joint public transport and the progress passenger flow OD calculating of rail traffic brushing card data |
| CN110197335A (en) * | 2019-06-04 | 2019-09-03 | 湖南智慧畅行交通科技有限公司 | A kind of get-off stop number calculation method based on probability OD distributed model |
| CN110390349A (en) * | 2019-06-20 | 2019-10-29 | 浙江大学 | Bus passenger flow volume based on XGBoost model predicts modeling method |
| CN110400177A (en) * | 2019-07-26 | 2019-11-01 | 厦门卫星定位应用股份有限公司 | Bus passenger trip information matching process, medium, equipment and device |
| CN111932867A (en) * | 2020-06-18 | 2020-11-13 | 东南大学 | Multisource data-based bus IC card passenger getting-off station derivation method |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2014061111A1 (en) * | 2012-10-17 | 2014-04-24 | 株式会社日立製作所 | Transportation analysis system |
-
2021
- 2021-04-19 CN CN202110419504.7A patent/CN113159416B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104809344A (en) * | 2015-04-23 | 2015-07-29 | 中山大学 | IC (Integrated Circuit) card data-based estimation method for passenger flow in bus station interval |
| CN105809292A (en) * | 2016-03-21 | 2016-07-27 | 广州地理研究所 | Passenger getting-off station reckoning method of bus IC (Integrated Circuit) card |
| CN105868861A (en) * | 2016-04-08 | 2016-08-17 | 青岛海信网络科技股份有限公司 | Bus passenger flow evolution analysis method based on time-space data fusion |
| CN105788260A (en) * | 2016-04-13 | 2016-07-20 | 西南交通大学 | Public transportation passenger OD calculation method based on intelligent public transportation system data |
| CN107818412A (en) * | 2017-10-18 | 2018-03-20 | 福州大学 | A kind of extensive bus passenger OD parallel calculating methods based on MapReduce |
| CN107886723A (en) * | 2017-11-13 | 2018-04-06 | 深圳大学 | A kind of traffic trip survey data processing method |
| CN110084442A (en) * | 2019-05-16 | 2019-08-02 | 重庆大学 | A kind of method of joint public transport and the progress passenger flow OD calculating of rail traffic brushing card data |
| CN110197335A (en) * | 2019-06-04 | 2019-09-03 | 湖南智慧畅行交通科技有限公司 | A kind of get-off stop number calculation method based on probability OD distributed model |
| CN110390349A (en) * | 2019-06-20 | 2019-10-29 | 浙江大学 | Bus passenger flow volume based on XGBoost model predicts modeling method |
| CN110400177A (en) * | 2019-07-26 | 2019-11-01 | 厦门卫星定位应用股份有限公司 | Bus passenger trip information matching process, medium, equipment and device |
| CN111932867A (en) * | 2020-06-18 | 2020-11-13 | 东南大学 | Multisource data-based bus IC card passenger getting-off station derivation method |
Non-Patent Citations (1)
| Title |
|---|
| 基于IC和GPS数据的公交客流分析及预测算法研究;涂一霜;《中国优秀硕士毕业论文全文数据库工程科技II辑》;20190215;第C034-478页 * |
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