CN104517040A - Method for calculating in-carriage congestion degree of public traffic vehicle based on IC card data - Google Patents

Abstract

The invention discloses a method for calculating the in-carriage congestion degree of a public traffic vehicle based on IC card data. The method is characterized by comprising the following steps: collecting and storing passenger data, to be specific, reading card swiping information in an IC card swiping machine by a vehicle-mounted machine, sending the card swiping information to a server through a wireless network, and storing the card swiping information in a database; estimating the congestion degree of the public traffic vehicle at a j-th station in real time, to be specific, estimating the total number Rj of passengers of the public traffic vehicle at the j-th station, and calculating the in-carriage congestion degree of the public traffic vehicle at the j-th station according to the formula that Zj=Rj/B. According to the method for calculating the in-carriage congestion degree of the public traffic vehicle based on IC card data disclosed by the invention, real-time calculation of the in-carriage congestion degree of the public traffic vehicle is realized by utilizing the existing IC card swiping machine and vehicle-mounted machine of the public traffic vehicle, real-time collected data are fully utilized, and accurate information of the in-carriage congestion degree is provided based on the statistics of historical data for providing a basis for travelers to select appropriate travel times.

Description

A kind of based on degree of crowding computing method in IC-card data public transit vehicle car

Technical field

The invention belongs to technical field of intelligent traffic, specifically, relate to a kind of based on degree of crowding computing method in IC-card data public transit vehicle car.

Background technology

Along with the development of national economy, urban population gets more and more, and at present public transport is as the general trip mode of citizen, how to allow citizen enjoy better service, becomes the focus that current public transport is paid close attention to.Conveniently citizens' activities, at present generally can only the traveling-position information of inquiring bus, compares and be difficult to obtain, cannot provide the information of crowding in car for handling capacity of passengers in car.

Summary of the invention

The present invention cannot real-time estimation in order to solve the current bus degree of crowding, or the problem of estimation precision difference, proposes a kind of based on degree of crowding computing method in IC-card data public transit vehicle car, can solve the problem.

In order to solve the problems of the technologies described above, the present invention is achieved by the following technical solutions:

A kind of based on degree of crowding computing method in IC-card data public transit vehicle car, comprise the following steps:

The collection of passenger flow data and storing step: vehicle-mounted machine reads the information of checking card in IC-card reading card device, server is sent to by wireless network, and be stored in database, to the historical data of database in units of one day, IC-card all records of checking card on the same day that each has record of checking card are added up, estimate that this IC-card is when website of respectively checking card is got on the bus, its get-off stop that will get off, described passenger flow data comprise public bus network, the ID of IC-card, IC-card site number of checking card, check card the time;

Real-time estimation bus is in the crowding at jth station: the passenger flow data of server real-time reception bus at the i-th station, according to the ID of IC-card, search from database and estimate each IC-card get-off stop that it will be got off when getting on the bus in the i-th station, the total number of persons R of estimation bus at jth station _j, calculate this bus degree of crowding Z in jth station _j=R _j/ B, wherein, B is bus rated passenger capacity.

Further, described estimation bus is in the step of the crowding at jth station, bus is in the passenger flow data at the i-th station, all IC-cards of checking card are divided into two classes, the first kind has historical data in a database, the historical data that Equations of The Second Kind is not got on the bus at the i-th station in a database, the ridership R of bus in the compartment at jth station _jevaluation method be: comprise and the IC-card of the first kind added up based on historical data, the total number of persons N that the IC-card that can obtain the first kind is got on the bus at the i-th station _i, and get on the bus at the i-th station and the number M got off at jth station _ij;

To the get-off stop evaluation method that the IC-card of Equations of The Second Kind attracts based on website, comprise the following steps:

Calculate the sucting strength W of website _i:

$W_i=\frac{S_i}{{\mathrm{\Σ}}_{k=1}^n{S}_k}$

Wherein, S _ifor the IC-card of Equations of The Second Kind to be got on the bus at the i-th station number;

The IC-card calculating Equations of The Second Kind is got on the bus at the i-th station and the probability P of getting off at jth station _ij:

$P_{\mathrm{ij}}=\left\{\begin{array}{cc}\frac{F_{\mathrm{ij}}^{*}}{{\mathrm{\&Sigma;}}_{k=i+1}^n{F}_{\mathrm{ij}}^{*}\&times;W_k}& i<j\\ 0& i\&GreaterEqual;j\end{array}\right.$

Wherein, for normalized arbitrary passenger gets on the bus and the probability of getting off at jth station at the i-th station;

The total number of persons D that the IC-card calculating Equations of The Second Kind is got on the bus at the i-th station _ij:

D _ij＝S _I×P _ij

Calculate the probability X that in bus, all passengers beating IC-card get on the bus from the i-th station, get off at jth station _ij:

$X_{\mathrm{ij}}=\left\{\begin{array}{cc}\frac{D_{\mathrm{IJ}}+M_{\mathrm{IJ}}}{S_I+N_I}& i<j\\ 0& i\&GreaterEqual;j\end{array}\right.$

Calculate the total number of persons R of bus at jth station _j:

$R_j={\mathrm{\&Sigma;}}_{i=1}^j((S_i^{\&prime;}/t)-{\mathrm{\&Sigma;}}_{m=1}^j((S_i^{\&prime;}/t)\&times;X_{\mathrm{im}}))$

Wherein, S ' _ifor all numbers of checking card of vehicle-mounted machine real-time Transmission, t is that passenger flow of checking card accounts for the ratio of total passenger flow.

Further again, normalized arbitrary passenger gets on the bus at the i-th station and the probability of getting off at jth station computing method be:

Under set travel direction, passenger's trip stops is approximate obeys Poisson distribution, calculates arbitrary passenger and gets on the bus at the i-th station and the probability F got off at jth station _ij:

$F_{\mathrm{ij}}=\frac{e^{-\mathrm{\&lambda;}}{\mathrm{\&lambda;}}^{(j-1)}}{(j-1)!}$

Wherein, λ is the average riding station number of bus, when the website number that i stands later is less than average riding station number, gets λ=n-1, and n is that bus unidirectional bus stop on its circuit is counted;

To F _ijnormalized, obtains normalized arbitrary passenger and gets on the bus at the i-th station and the probability of getting off at jth station

$F_{\mathrm{ij}}^{*}=\frac{e^{-\mathrm{\&lambda;}}{\mathrm{\&lambda;}}^{(j-1)}}{(j-1)!}/\underset{i=0}{\overset{j-1}{\mathrm{\&Sigma;}}}\frac{e^{-\mathrm{\&lambda;}}{\mathrm{\&lambda;}}^{(j-1)}}{(j-1)!}.$

Further, in the collection of passenger flow data and storing step, to the historical data of database in units of one day, statistics is carried out to each IC-card all record of checking card on the same day with record of checking card and comprises the following steps:

(31), to each IC-card of checking card, according to its ID, from database, find out whole brushing card datas on this IC-card same day, sort according to time sequencing;

(32), adjacent data and the data of checking card from beginning to end of checking card are got, judge often adjacent checking card for twice in data, previous ridden in a bus route of checking card time to be checked card after all website middle distances the shortest website A of website that gets on the bus, and calculates the distance Q between this two station, will apart from Q and threshold value Q ₀relatively, if Q≤Q ₀, then judge that website A is when previous website of checking card is got on the bus, the get-off stop that will get off.

Preferably, in described step (32), if Q > is Q ₀then extract recent many days data of checking card, therefrom find out the public bus network trip whether having vehicle line close, if, then find out the website B that data of checking card in the public bus network that on previous ridden in a bus route of checking card, described in all website middle distances, vehicle line is close are the shortest, then judge that website B is when previous website of checking card is got on the bus, the get-off stop that will get off.

Compared with prior art, advantage of the present invention and good effect are: of the present invention based on degree of crowding computing method in IC-card data public transit vehicle car, by the real-time calculating utilizing existing bus IC-card punched-card machine and vehicle-mounted machine to realize crowding in bus, take full advantage of real-time data collection and the statistics based on historical data, there is provided congestion information in accurate car, for traveler selects the suitable travel time to provide foundation.

After reading the detailed description of embodiment of the present invention by reference to the accompanying drawings, the other features and advantages of the invention will become clearly.

Accompanying drawing explanation

In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, 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.

Fig. 1 is a kind of embodiment process flow diagram based on degree of crowding computing method in IC-card data public transit vehicle car proposed by the 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.

Embodiment one, the present invention proposes a kind of based on degree of crowding computing method in IC-card data public transit vehicle car, comprises the following steps:

The collection of passenger flow data and storing step: vehicle-mounted machine reads the information of checking card in IC-card reading card device, server is sent to by wireless network, and be stored in database, to the historical data of database in units of one day, IC-card all records of checking card on the same day that each has record of checking card are added up, estimate that this IC-card is when website of respectively checking card is got on the bus, its get-off stop that will get off, described passenger flow data comprise public bus network, the ID of IC-card, IC-card site number of checking card, check card the time;

Real-time estimation bus is in the crowding at jth station: the passenger flow data of server real-time reception bus at the i-th station, according to the ID of IC-card, search from database and estimate each IC-card get-off stop that it will be got off when getting on the bus in the i-th station, the total number of persons R of estimation bus at jth station _j, calculate this bus degree of crowding Z in jth station _j=R _j/ B, wherein, B is bus rated passenger capacity.

In this public transit vehicle car in degree of crowding computing method, by collection and the storing step of passenger flow data, based on history passenger flow data, the IC-card having record of checking card for each estimates the get-off stop that will get off when its website of checking card is got on the bus, for calculating bus in real time in the degree of crowding step at jth station, reliable historical data support is provided, real-time estimation bus is in the crowding step at jth station, take full advantage of real-time data collection and the statistics based on historical data, congestion information in accurate car is provided, for traveler selects the suitable travel time to provide foundation.

Due in the IC-card of checking card, some IC-cards do not have the historical data of getting on the bus at this station, therefore, in order to add up complete, improve the degree of accuracy of result of calculation, described estimation bus is in the step of the crowding at jth station, bus is in the passenger flow data at the i-th station, and all IC-cards of checking card are divided into two classes, and the first kind has historical data in a database, the historical data that Equations of The Second Kind is not got on the bus at the i-th station in a database, the ridership R of bus in the compartment at jth station _jevaluation method be: comprise and the IC-card of the first kind added up based on historical data, the total number of persons N that the IC-card that can obtain the first kind is got on the bus at the i-th station _i, and get on the bus at the i-th station and the number M got off at jth station _ij;

To the get-off stop evaluation method that the IC-card of Equations of The Second Kind attracts based on website, comprise the following steps:

The sucting strength that attraction rate reflection website is gone on a journey to passenger, because up-downlink direction passenger flow has symmetry, namely the number of getting on the bus at the up direction station ratio that accounts for passenger traffic total number of persons of getting on the bus and the number of getting off of this website of the getting off ratio that accounts for this descending passenger traffic total number of persons is suitable.Therefore certain passenger flow with website occurs to attract total amount substantially to keep in balance, and that is website generating capacity can reflect the traffic attraction of website simultaneously.Website of getting on the bus according to public transport IC data judges, can add up number of the getting on the bus total amount obtaining each website, calculate the sucting strength W of website thus _i:

$W_i=\frac{S_i}{{\mathrm{\&Sigma;}}_{k=1}^n{S}_k}$

Wherein, S _ifor the IC-card of Equations of The Second Kind to be got on the bus at the i-th station number;

Probability of getting off is relevant with website sucting strength with go-outside for civilian by bus approach station number, and a certain travel direction of structure bus, the IC-card calculating Equations of The Second Kind is got on the bus at the i-th station and the probability P of getting off at jth station _ij:

$P_{\mathrm{ij}}=\left\{\begin{array}{cc}\frac{F_{\mathrm{ij}}^{*}}{{\mathrm{\&Sigma;}}_{k=i+1}^n{F}_{\mathrm{ij}}^{*}\&times;W_k}& i<j\\ 0& i\&GreaterEqual;j\end{array}\right.$

Wherein, for normalized arbitrary passenger gets on the bus and the probability of getting off at jth station at the i-th station;

The total number of persons D that the IC-card calculating Equations of The Second Kind is got on the bus at the i-th station _ij:

D _ij＝S _I×P _ij

Calculate the probability X that in bus, all passengers beating IC-card get on the bus from the i-th station, get off at jth station _ij:

$X_{\mathrm{ij}}=\left\{\begin{array}{cc}\frac{D_{\mathrm{IJ}}+M_{\mathrm{IJ}}}{S_I+N_I}& i<j\\ 0& i\&GreaterEqual;j\end{array}\right.$

Calculate the total number of persons R of bus at jth station _j:

$R_j={\mathrm{\&Sigma;}}_{i=1}^j((S_i^{\&prime;}/t)-{\mathrm{\&Sigma;}}_{m=1}^j((S_i^{\&prime;}/t)\&times;X_{\mathrm{im}}))$

Wherein, S ' _ifor all numbers of checking card of vehicle-mounted machine real-time Transmission, t is that passenger flow of checking card accounts for the ratio of total passenger flow.The number of checking card based on IC-card due to this method calculates, in actual conditions, some people does not beat IC-card, and select coin mode, therefore, in order to improve accuracy in computation, need to select the crowd of coin to take into account, Corpus--based Method characteristic, inserting coins with the ratio of the people checked card is stable substantially, therefore, accounted for the ratio t of total passenger flow by passenger flow of checking card divided by passenger flow of checking card, be total passenger flow.

Preferably in the present embodiment, normalized arbitrary passenger gets on the bus at the i-th station and the probability of getting off at jth station computing method be:

Under set travel direction, passenger's trip stops is approximate obeys Poisson distribution, calculates arbitrary passenger and gets on the bus at the i-th station and the probability F got off at jth station _ij:

$F_{\mathrm{ij}}=\frac{e^{-\mathrm{\&lambda;}}{\mathrm{\&lambda;}}^{(j-1)}}{(j-1)!}$

Wherein, λ is the average riding station number of bus, when the website number that i stands later is less than average riding station number, gets λ=n-1, and n is that bus unidirectional bus stop on its circuit is counted;

Due to passenger utilize bus to go on a journey time, the station number taken is at least 1, is that (n-1) stands at the most, according to the character of probability distribution, to above-mentioned probability F _ijbe normalized: obtain normalized arbitrary passenger and get on the bus at the i-th station and the probability of getting off at jth station

$F_{\mathrm{ij}}^{*}=\frac{e^{-\mathrm{\&lambda;}}{\mathrm{\&lambda;}}^{(j-1)}}{(j-1)!}/\underset{i=0}{\overset{j-1}{\mathrm{\&Sigma;}}}\frac{e^{-\mathrm{\&lambda;}}{\mathrm{\&lambda;}}^{(j-1)}}{(j-1)!}.$

Further, in the collection of passenger flow data and storing step, to the historical data of database in units of one day, as shown in Figure 1, the IC-card all records of checking card on the same day having to each record of checking card carry out statistics and comprise the following steps:

S31, to each IC-card of checking card, according to its ID, from database, find out whole brushing card datas on this IC-card same day, sort according to time sequencing;

S32, get adjacent data and the head and the tail of checking card and to check card data, judge often adjacent checking card for twice in data, previous ridden in a bus route of checking card time to be checked card after all website middle distances the shortest website A of website that gets on the bus, and calculates the distance Q between this two station, will apart from Q and threshold value Q ₀relatively, if Q≤Q ₀, then judge that website A is when previous website of checking card is got on the bus, the get-off stop that will get off.

Preferably, in described step S32, if Q > is Q ₀then extract recent many days data of checking card, therefrom find out the public bus network trip whether having vehicle line close, if, then find out the website B that data of checking card in the public bus network that on previous ridden in a bus route of checking card, described in all website middle distances, vehicle line is close are the shortest, then judge that website B is when previous website of checking card is got on the bus, the get-off stop that will get off.

The present embodiment based on degree of crowding computing method in IC-card data public transit vehicle car, make use of Wireless Telecom Equipment and IC-card punched-card machine that current bus fills, from hardware aspect without the need to infusion of financial resources, statistical study is carried out by gathering IC-card data of checking card, can the website that will get off of current the checked card IC-card of Accurate Prediction, therefore, the crowding of bus at each station can be counted, greatly facilitate citizen, the suitable travel time is selected to provide foundation for going out driving, the IC-card of Real-time Collection data of checking card combine with based on probability calculation, the various crowd that may ride all is taken into account, comprising checks card has a class of historical data, IC-card is checked card and is not had a class of historical data, and a class of selection coin mode of not checking card, degree of crowding estimation accuracy is high.

Certainly; above-mentioned explanation is not limitation of the present invention; the present invention is also not limited in above-mentioned citing, the change that those skilled in the art make in essential scope of the present invention, remodeling, interpolation or replacement, also should belong to protection scope of the present invention.

Claims (5)

1., based on a degree of crowding computing method in IC-card data public transit vehicle car, it is characterized in that, comprise the following steps:

The collection of passenger flow data and storing step: vehicle-mounted machine reads the information of checking card in IC-card reading card device, server is sent to by wireless network, and be stored in database, to the historical data of database in units of one day, IC-card all records of checking card on the same day that each has record of checking card are added up, estimate that this IC-card is when website of respectively checking card is got on the bus, its get-off stop that will get off, described passenger flow data comprise public bus network, the ID of IC-card, IC-card site number of checking card, check card the time;

Real-time estimation bus is in the crowding at jth station: the passenger flow data of server real-time reception bus at the i-th station, according to the ID of IC-card, search from database and estimate each IC-card get-off stop that it will be got off when getting on the bus in the i-th station, the total number of persons R of estimation bus at jth station _j, calculate this bus degree of crowding Z in jth station _j=R _j/ B, wherein, B is bus rated passenger capacity.

2. according to claim 1 based on degree of crowding computing method in IC-card data public transit vehicle car, it is characterized in that, described estimation bus is in the step of the crowding at jth station, bus is in the passenger flow data at the i-th station, all IC-cards of checking card are divided into two classes, the first kind has historical data in a database, the historical data that Equations of The Second Kind is not got on the bus at the i-th station in a database, the ridership R of bus in the compartment at jth station _jevaluation method be: comprise and the IC-card of the first kind added up based on historical data, the total number of persons N that the IC-card that can obtain the first kind is got on the bus at the i-th station _i, and get on the bus at the i-th station and the number M got off at jth station _ij;

To the get-off stop evaluation method that the IC-card of Equations of The Second Kind attracts based on website, comprise the following steps:

Calculate the sucting strength W of website _i:

$W_i=\frac{S_i}{{\mathrm{\&Sigma;}}_{k=1}^n{S}_k}$

Wherein, S _ifor the IC-card of Equations of The Second Kind to be got on the bus at the i-th station number;

The IC-card calculating Equations of The Second Kind is got on the bus at the i-th station and the probability P of getting off at jth station _ij:

$P_{\mathrm{ij}}=\left\{\begin{array}{cc}\frac{F_{\mathrm{ij}}^{*}}{{\mathrm{\&Sigma;}}_{k=i+1}^n{F}_{\mathrm{ij}}^{*}\&times;W_k}& i<j\\ 0& i\&GreaterEqual;j\end{array}\right.$

Wherein, for normalized arbitrary passenger gets on the bus and the probability of getting off at jth station at the i-th station;

The total number of persons D that the IC-card calculating Equations of The Second Kind is got on the bus at the i-th station _ij:

D _ij＝S _I×P _ij

Calculate the probability X that in bus, all passengers beating IC-card get on the bus from the i-th station, get off at jth station _ij:

$X_{\mathrm{ij}}=\left\{\begin{array}{cc}\frac{D_{\mathrm{IJ}}+M_{\mathrm{IJ}}}{S_I+N_I}& I<j\\ 0& i\&GreaterEqual;j\end{array}\right.$

Calculate the total number of persons R of bus at jth station _j:

$R_j={\mathrm{\&Sigma;}}_{i=1}^j(({S_i}^{\&prime;}/t)-{\mathrm{\&Sigma;}}_{m=1}^j(({S_i}^{\&prime;}/t)\&times;X_{\mathrm{im}}))$

Wherein, S ' _ifor all numbers of checking card of vehicle-mounted machine real-time Transmission, t is that passenger flow of checking card accounts for the ratio of total passenger flow.

3. according to claim 2ly it is characterized in that based on degree of crowding computing method in IC-card data public transit vehicle car, normalized arbitrary passenger gets on the bus at the i-th station and the probability of getting off at jth station computing method be:

Under set travel direction, passenger's trip stops is approximate obeys Poisson distribution, calculates arbitrary passenger and gets on the bus at the i-th station and the probability F got off at jth station _ij:

$F_{\mathrm{ij}}=\frac{e^{-\mathrm{\&lambda;}}{\mathrm{\&lambda;}}^{(j-1)}}{(j-1)!}$

Wherein, λ is the average riding station number of bus, when the website number that i stands later is less than average riding station number, gets λ=n-1, and n is that bus unidirectional bus stop on its circuit is counted;

To F _ijnormalized, obtains normalized arbitrary passenger and gets on the bus at the i-th station and the probability of getting off at jth station

${F_{\mathrm{ij}}}^{*}=\frac{e^{-\mathrm{\&lambda;}}{\mathrm{\&lambda;}}^{(j-1)}}{(j-1)!}/\underset{i=0}{\overset{j-1}{\mathrm{\&Sigma;}}}\frac{e^{-\mathrm{\&lambda;}}{\mathrm{\&lambda;}}^{(j-1)}}{(j-1)!}.$

4. according to claim 3 based on degree of crowding computing method in IC-card data public transit vehicle car, it is characterized in that, in the collection of passenger flow data and storing step, to the historical data of database in units of one day, statistics is carried out to each IC-card all record of checking card on the same day with record of checking card and comprises the following steps:

(31), to each IC-card of checking card, according to its ID, from database, find out whole brushing card datas on this IC-card same day, sort according to time sequencing;

(32), adjacent data and the data of checking card from beginning to end of checking card are got, judge often adjacent checking card for twice in data, previous ridden in a bus route of checking card time to be checked card after all website middle distances the shortest website A of website that gets on the bus, and calculates the distance Q between this two station, will apart from Q and threshold value Q ₀relatively, if Q≤Q ₀, then judge that website A is when previous website of checking card is got on the bus, the get-off stop that will get off.

5. according to claim 4ly to it is characterized in that based on degree of crowding computing method in IC-card data public transit vehicle car, in described step (32), if Q > is Q ₀then extract recent many days data of checking card, therefrom find out the public bus network trip whether having vehicle line close, if, then find out the website B that data of checking card in the public bus network that on previous ridden in a bus route of checking card, described in all website middle distances, vehicle line is close are the shortest, then judge that website B is when previous website of checking card is got on the bus, the get-off stop that will get off.