CN104517040B - One kind is based on IC-card data public transit vehicle in-car degree of crowding computational methods - Google Patents
One kind is based on IC-card data public transit vehicle in-car degree of crowding computational methods Download PDFInfo
- Publication number
- CN104517040B CN104517040B CN201410853787.6A CN201410853787A CN104517040B CN 104517040 B CN104517040 B CN 104517040B CN 201410853787 A CN201410853787 A CN 201410853787A CN 104517040 B CN104517040 B CN 104517040B
- Authority
- CN
- China
- Prior art keywords
- mrow
- card
- bus
- station
- msub
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Landscapes
- Traffic Control Systems (AREA)
- Devices For Checking Fares Or Tickets At Control Points (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses one kind to be based on IC-card data public transit vehicle in-car degree of crowding computational methods, it is characterised in that 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, is sent by wireless network to server, and store into database；Crowding of the real-time estimation bus at jth station：Estimate total number of persons of the bus at jth station, calculate bus degree of crowding in jth station.The present invention based on IC-card data public transit vehicle in-car degree of crowding computational methods, the real-time calculating of crowding in bus is realized by using existing bus IC-card punched-card machine and vehicle-mounted machine, take full advantage of real-time data collection and the statistics based on historical data, accurate in-car congestion information is provided, selects the suitable travel time to provide foundation for traveler.
Description
Technical field
The invention belongs to technical field of intelligent traffic, is to be related to one kind to be based on IC-card data public transit vehicle car specifically
Interior degree of crowding computational methods.
Background technology
With the development of national economy, urban population is more and more, and the trip mode universal as citizen of public transport at present,
How to allow citizen to enjoy preferably service, turn into current public transport focus of attention.In order to facilitate citizens' activities, typically can only at present
The traveling-position information of inquiring bus, relatively it is difficult to obtain for in-car handling capacity of passengers, the information of in-car crowding can not be provided.
The content of the invention
The present invention in order to solve the current bus degree of crowding can not real-time estimation, or the problem of estimation precision difference, carry
One kind is gone out and has been based on IC-card data public transit vehicle in-car degree of crowding computational methods, can solve the above problems.
In order to solve the above-mentioned technical problem, the present invention is achieved using following technical scheme：
One kind is based on IC-card data public transit vehicle in-car degree of crowding computational methods, 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, passes through wireless network
Network is sent to server, and is stored into database, to the historical data of database in units of one day, is had to each and is beaten
All records of checking card are counted on the day of blocking the IC-card of record, estimate the IC-card when website of respectively checking card is got on the bus, it will get off
Get-off stop, the passenger flow data include public bus network, the ID of IC-card, IC-card site number of checking card, check card the time；
Crowding of the real-time estimation bus at jth station：Server real-time reception bus the i-th station passenger flow data,
According to the ID of IC-card, searched from database and estimate each IC-card get-off stop that it will get off when the i-th station is got on the bus, estimated
Calculate total number of persons R of the bus at jth stationj, calculate bus degree of crowding Z in jth stationj=Rj/ B, wherein, B is bus
Rated passenger capacity.
Further, the estimation bus is in the step of crowding at jth station, passenger flow number of the bus at the i-th station
In, all IC-cards checked card are divided into two classes, and the first kind has historical data in database, and the second class does not have in database
In the historical data that the i-th station is got on the bus, ridership R of the bus in the compartment at jth stationjEvaluation method be：Including to first
The IC-card of class is counted based on historical data, can obtain the total number of persons N that the IC-card of the first kind is got on the bus at the i-th stationi, and on the i-th station
Car and the number M to be got off at jth stationij；
The get-off stop evaluation method attracted based on website the IC-card of the second class, is comprised the following steps：
Calculate the sucting strength W of websitei：
Wherein, SiGot on the bus for the IC-card of the second class at the i-th station number；
Calculate the probability P that the IC-card of the second class is got on the bus and got off at jth station at the i-th stationij：
Wherein,The probability got on the bus and got off at jth station at the i-th station for normalized any passenger；
Calculate the total number of persons D that the IC-card of the second class is got on the bus at the i-th stationij：
Dij=SI×Pij
Calculate the probability X that all passengers for beating IC-card get on the bus from the i-th station, got off at jth station in busij：
Calculate total number of persons R of the bus at jth stationj：
Wherein, S 'iFor all numbers checked 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, the probability that normalized any passenger gets on the bus and got off at jth station at the i-th stationCalculating side
Method is：
Under set travel direction, passenger's trip stops are approximate to obey Poisson distribution, calculate any passenger got on the bus at the i-th station,
And the probability F to be got off at jth stationij：
Wherein, λ is the average riding station number of bus, when later website number is less than average riding station number when i stations, takes λ
=n-1, n are bus unidirectional bus stop points on its circuit；
To FijNormalized, obtain the probability that normalized any passenger gets on the bus and got off at jth station at the i-th station
Further, in the collection and storing step of passenger flow data, to the historical data of database in units of one day,
There are the IC-card same day all records of checking card for record of checking card to carry out statistics each to comprise the following steps：
(31), to each IC-card checked card, according to its ID, the whole that the IC-card same day is found out from database is swiped the card number
According to sorting sequentially in time；
(32) adjacent check card data and data of checking card from beginning to end, are taken, it is previous to check card in judging per adjacent data of checking card twice
Time check card the most short website A of website that gets on the bus on ridden in a bus route in all websites after distance, and calculate between two station away from
, will distance Q and threshold value Q from Q0Compare, if Q≤Q0, then it is when previous website of checking card is got on the bus to judge website A, will be got off
Get-off stop.
Preferably, in the step (32), if Q ＞ Q0, then more days data of checking card in the recent period are extracted, whether therefrom find out has row
Sail similar in circuit public bus network to go on a journey, if so, then finding out on previous ridden in a bus route of checking card distance institute in all websites
The most short website B of data of checking card in public bus network similar in vehicle line is stated, then it is in previous website of checking card to judge website B
When getting on the bus, the get-off stop that will get off.
Compared with prior art, the advantages and positive effects of the present invention are：The present invention based on IC-card data public transit vehicle
In-car degree of crowding computational methods, gathered around by using existing bus IC-card punched-card machine and vehicle-mounted machine to realize in bus
The real-time calculating of degree is squeezed, takes full advantage of real-time data collection and the statistics based on historical data, there is provided accurate in-car is gathered around
Degree information is squeezed, selects the suitable travel time to provide foundation for traveler.
After the detailed description of embodiment of the present invention is read in conjunction with the figure, the other features and advantages of the invention will become more
Add clear.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is a kind of implementation proposed by the invention based on IC-card data public transit vehicle in-car degree of crowding computational methods
Example flow chart.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
Embodiment one, the present invention propose one kind and are based on IC-card data public transit vehicle in-car degree of crowding computational methods, wrap
Include 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, passes through wireless network
Network is sent to server, and is stored into database, to the historical data of database in units of one day, is had to each and is beaten
All records of checking card are counted on the day of blocking the IC-card of record, estimate the IC-card when website of respectively checking card is got on the bus, it will get off
Get-off stop, the passenger flow data include public bus network, the ID of IC-card, IC-card site number of checking card, check card the time；
Crowding of the real-time estimation bus at jth station：Server real-time reception bus the i-th station passenger flow data,
According to the ID of IC-card, searched from database and estimate each IC-card get-off stop that it will get off when the i-th station is got on the bus, estimated
Calculate total number of persons R of the bus at jth stationj, calculate bus degree of crowding Z in jth stationj=Rj/ B, wherein, B is bus
Rated passenger capacity.
In this public transit vehicle in-car degree of crowding computational methods, collection and storing step by passenger flow data, based on going through
History passenger flow data, for each there is the IC-card for record of checking card to estimate the debarkation stop that will be got off when its website of checking card is got on the bus
Point, to calculate bus in real time in the degree of crowding step at jth station, there is provided reliable historical data supports that real-time estimation is public
Car is handed over to take full advantage of real-time data collection and the statistics based on historical data in the crowding step at jth station, there is provided essence
True in-car congestion information, the suitable travel time is selected to provide foundation for traveler.
Because in the IC-card checked card, some IC-cards do not have the historical data got on the bus at the station, therefore, in order to count
Entirely, the accuracy of result of calculation is improved, the estimation bus is in the step of crowding at jth station, and bus is at the i-th station
In passenger flow data, all IC-cards checked card are divided into two classes, and the first kind has historical data in database, and the second class is in database
In the historical data do not got on the bus at the i-th station, ridership R of the bus in the compartment at jth stationjEvaluation method be：Including
The IC-card of the first kind is counted based on historical data, the total number of persons N that the IC-card of the first kind is got on the bus at the i-th station can be obtainedi, and
The number M that i gets on the bus at station and got off at jth stationij；
The get-off stop evaluation method attracted based on website the IC-card of the second class, is comprised the following steps：
The sucting strength gone on a journey to passenger of attraction rate reflection website, because up-downlink direction passenger flow has symmetry, i.e., on
The number of getting on the bus at line direction station accounts for the ratio for passenger traffic total number of persons of getting on the bus and the number of getting off for the website of getting off accounts for the descending passenger traffic
The ratio of total number of persons is suitable.Therefore there is the passenger flow of website to occur to attract total amount to keep in balance substantially for some, that is to say, that website
Generating capacity can reflect the traffic attraction of website simultaneously.Judged according to the website of getting on the bus of public transport IC data, can count to obtain each station
The number total amount of getting on the bus of point, thus calculate the sucting strength W of websitei：
Wherein, SiGot on the bus for the IC-card of the second class at the i-th station number；
Probability of getting off is relevant with go-outside for civilian by bus approach station number and website sucting strength, a certain traveling side of construction bus
To calculating the IC-card probability P getting on the bus and got off at jth station at the i-th station of the second classij：
Wherein,The probability got on the bus and got off at jth station at the i-th station for normalized any passenger；
Calculate the total number of persons D that the IC-card of the second class is got on the bus at the i-th stationij：
Dij=SI×Pij
Calculate the probability X that all passengers for beating IC-card get on the bus from the i-th station, got off at jth station in busij：
Calculate total number of persons R of the bus at jth stationj：
Wherein, S 'iFor all numbers checked card of vehicle-mounted machine real-time Transmission, t is that passenger flow of checking card accounts for the ratio of total passenger flow.By
Calculated in this method based on the number that IC-card is checked card, some people does not beat IC-card in actual conditions, and selects coin
Mode, therefore, in order to improve accuracy in computation, it is necessary to which the crowd for selecting coin is taken into account, based on statistical property, coin with
The ratio of the people to check card is substantially stable, therefore, by checking card passenger flow divided by passenger flow of checking card accounts for the ratio t of total passenger flow, is
Total passenger flow.
It is preferred that in the present embodiment, the probability that normalized any passenger gets on the bus and got off at jth station at the i-th station's
Computational methods are：
Under set travel direction, passenger's trip stops are approximate to obey Poisson distribution, calculate any passenger got on the bus at the i-th station,
And the probability F to be got off at jth stationij：
Wherein, λ is the average riding station number of bus, when later website number is less than average riding station number when i stations, takes λ
=n-1, n are bus unidirectional bus stop points on its circuit；
When being gone on a journey due to passenger using bus, the station number of seating is at least 1, is at most that (n-1) stands, according to probability distribution
Property, to above-mentioned probability FijIt is normalized：Normalized any passenger is obtained to get on the bus at the i-th station and under jth station
The probability of car
Further, in the collection and storing step of passenger flow data, to the historical data of database in units of one day,
Comprise the following steps as shown in figure 1, there are the IC-card same day all records of checking card for record of checking card to carry out statistics each：
S31, to each IC-card checked card, according to its ID, the whole that the IC-card same day is found out from database is swiped the card number
According to sorting sequentially in time；
S32, adjacent check card data and data of checking card from beginning to end are taken, it is previous to check card in judging per adjacent data of checking card twice
Time check card the most short website A of website that gets on the bus on ridden in a bus route in all websites after distance, and calculate between two station away from
, will distance Q and threshold value Q from Q0Compare, if Q≤Q0, then it is when previous website of checking card is got on the bus to judge website A, will be got off
Get-off stop.
Preferably, in the step S32, if Q ＞ Q0, then more days data of checking card in the recent period are extracted, whether therefrom find out has row
Sail similar in circuit public bus network to go on a journey, if so, then finding out on previous ridden in a bus route of checking card distance institute in all websites
The most short website B of data of checking card in public bus network similar in vehicle line is stated, then it is in previous website of checking card to judge website B
When getting on the bus, the get-off stop that will get off.
The present embodiment based on IC-card data public transit vehicle in-car degree of crowding computational methods, make use of on current bus
The Wireless Telecom Equipment and IC-card punched-card machine filled, from hardware aspect without infusion of financial resources, checked card data by gathering IC-card
Carry out statistical analysis, can the website that will get off of the IC-card currently checked card of Accurate Prediction, therefore, it is possible to count bus
Crowding at each station, greatly facilitates citizen, selects the suitable travel time to provide foundation to go out driving, gathers in real time
IC-card data of checking card are combined with based on probability calculation, by it is various may be in the crowd of riding be considered in, including check card with going through
One kind of history data, IC-card check card one kind without historical data and do not check card selection coin mode one kind, crowded journey
It is high to spend estimation accuracy.
Certainly, described above is not limitation of the present invention, and the present invention is also not limited to the example above, this technology neck
The variations, modifications, additions or substitutions that the those of ordinary skill in domain is made in the essential scope of the present invention, should also belong to this hair
Bright protection domain.
Claims (4)
1. one kind is based on IC-card data public transit vehicle in-car degree of crowding computational methods, it is characterised in that 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, is sent out by wireless network
Server is delivered to, and is stored into database, to the historical data of database in units of one day, there is note of checking card to each
All records of checking card are counted on the day of the IC-card of record, estimate the IC-card when website of respectively checking card is got on the bus, it will get off down
Station point, the passenger flow data include public bus network, the ID of IC-card, IC-card site number of checking card, check card the time；
Crowding of the real-time estimation bus at jth station：Server real-time reception bus the i-th station passenger flow data, according to
The ID of IC-card, searched from database and estimate each IC-card get-off stop that it will get off when the i-th station is got on the bus, estimation is public
Car is handed in the total number of persons R at jth stationj, calculate bus degree of crowding Z in jth stationj=Rj/ B, wherein, B is that bus is specified
Seating capacity；
For the estimation bus in the step of crowding at jth station, bus is all to check card in the passenger flow data at the i-th station
IC-card be divided into two classes, the first kind has historical data in database, what the second class was not got on the bus in database at the i-th station
Historical data, ridership R of the bus in the compartment at jth stationjEvaluation method be：Including being based on going through to the IC-card of the first kind
History data statistics, the total number of persons N that the IC-card of the first kind is got on the bus at the i-th station can be obtainedi, and get on the bus and under jth station at the i-th station
The number M of carij；
The get-off stop evaluation method attracted based on website the IC-card of the second class, is comprised the following steps：
Calculate the sucting strength W of websitei：
<mrow>
<msub>
<mi>W</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>S</mi>
<mi>i</mi>
</msub>
<mrow>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
<msub>
<mi>S</mi>
<mi>k</mi>
</msub>
</mrow>
</mfrac>
</mrow>
Wherein, SiGot on the bus for the IC-card of the second class at the i-th station number；
Calculate the probability P that the IC-card of the second class is got on the bus and got off at jth station at the i-th stationij：
<mrow>
<msub>
<mi>P</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mfrac>
<msubsup>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mo>*</mo>
</msubsup>
<mrow>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mi>i</mi>
<mo>+</mo>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
<msubsup>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
<mo>*</mo>
</msubsup>
<mo>&times;</mo>
<msub>
<mi>W</mi>
<mi>k</mi>
</msub>
</mrow>
</mfrac>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mo><</mo>
<mi>j</mi>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mo>&GreaterEqual;</mo>
<mi>j</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Wherein,The probability got on the bus and got off at jth station at the i-th station for normalized any passenger；
Calculate the total number of persons D that the IC-card of the second class is got on the bus at the i-th stationij：
Dij=Si×Pij
Calculate the probability X that all passengers for beating IC-card get on the bus from the i-th station, got off at jth station in busij：
<mrow>
<msub>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mfrac>
<mrow>
<msub>
<mi>D</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>M</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
<mrow>
<msub>
<mi>S</mi>
<mi>i</mi>
</msub>
<mo>+</mo>
<msub>
<mi>N</mi>
<mi>i</mi>
</msub>
</mrow>
</mfrac>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mo><</mo>
<mi>j</mi>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mn>0</mn>
</mtd>
<mtd>
<mrow>
<mi>i</mi>
<mo>&GreaterEqual;</mo>
<mi>j</mi>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
</mrow>
Calculate total number of persons R of the bus at jth stationj：
<mrow>
<msub>
<mi>R</mi>
<mi>j</mi>
</msub>
<mo>=</mo>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>j</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mo>(</mo>
<mrow>
<msup>
<msub>
<mi>S</mi>
<mi>i</mi>
</msub>
<mo>&prime;</mo>
</msup>
<mo>/</mo>
<mi>t</mi>
</mrow>
<mo>)</mo>
<mo>-</mo>
<msubsup>
<mi>&Sigma;</mi>
<mrow>
<mi>m</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>j</mi>
</msubsup>
<mo>(</mo>
<mrow>
<mrow>
<mo>(</mo>
<mrow>
<msup>
<msub>
<mi>S</mi>
<mi>i</mi>
</msub>
<mo>&prime;</mo>
</msup>
<mo>/</mo>
<mi>t</mi>
</mrow>
<mo>)</mo>
</mrow>
<mo>&times;</mo>
<msub>
<mi>X</mi>
<mrow>
<mi>i</mi>
<mi>m</mi>
</mrow>
</msub>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
Wherein, Si' it is vehicle-mounted machine real-time Transmission in all numbers checked card in the i-th station, t is that passenger flow of checking card accounts for the ratio of total passenger flow,
XimFor the probability that all passengers for beating IC-card get on the bus, got off at m stations from the i-th station in bus, m span for 1~j it
Between integer.
2. according to claim 1 be based on IC-card data public transit vehicle in-car degree of crowding computational methods, it is characterised in that
The probability that normalized any passenger gets on the bus and got off at jth station at the i-th stationComputational methods be：
Under set travel direction, passenger's trip stops are approximate to obey Poisson distribution, calculate any passenger got on the bus at the i-th station and
The probability F that jth station is got offij：
<mrow>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mi>&lambda;</mi>
</mrow>
</msup>
<msup>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msup>
</mrow>
<mrow>
<mo>(</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
<mo>!</mo>
</mrow>
</mfrac>
</mrow>
Wherein, λ is the average riding station number of bus, when later website number is less than average riding station number when i stations, takes λ=n-
1, n is bus unidirectional bus stop points on its circuit；
To FijNormalized, obtain the probability that normalized any passenger gets on the bus and got off at jth station at the i-th station
<mrow>
<msup>
<msub>
<mi>F</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
<mo>*</mo>
</msup>
<mo>=</mo>
<mfrac>
<mrow>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mi>&lambda;</mi>
</mrow>
</msup>
<msup>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msup>
</mrow>
<mrow>
<mo>(</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
<mo>!</mo>
</mrow>
</mfrac>
<mo>/</mo>
<munderover>
<mi>&Sigma;</mi>
<mrow>
<mi>i</mi>
<mo>=</mo>
<mn>0</mn>
</mrow>
<mrow>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</munderover>
<mfrac>
<mrow>
<msup>
<mi>e</mi>
<mrow>
<mo>-</mo>
<mi>&lambda;</mi>
</mrow>
</msup>
<msup>
<mi>&lambda;</mi>
<mrow>
<mo>(</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</msup>
</mrow>
<mrow>
<mo>(</mo>
<mi>j</mi>
<mo>-</mo>
<mn>1</mn>
<mo>)</mo>
<mo>!</mo>
</mrow>
</mfrac>
<mo>.</mo>
</mrow>
3. according to claim 2 be based on IC-card data public transit vehicle in-car degree of crowding computational methods, it is characterised in that
In the collection and storing step of passenger flow data, to the historical data of database in units of one day, there is note of checking card to each
The IC-card same day all records of checking card of record carry out statistics and comprised the following steps：
(31), to each IC-card checked card, according to its ID, whole brushing card datas on the IC-card same day are found out from database,
Sort sequentially in time；
(32) adjacent check card data and data of checking card from beginning to end, are taken, in judging per adjacent data of checking card twice, previous check card is multiplied
Time check card the most short website A of website that gets on the bus on bus route in all websites after distance, and calculates the distance Q between two station,
Will distance Q and threshold value Q0Compare, if Q≤Q0, then it is that what will be got off gets off when previous website of checking card is got on the bus to judge website A
Website.
4. according to claim 3 be based on IC-card data public transit vehicle in-car degree of crowding computational methods, it is characterised in that
In the step (32), if Q ＞ Q0, then more days data of checking card in the recent period are extracted, whether therefrom find out has public affairs similar in vehicle line
Intersection road is gone on a journey, if so, then finding out on previous ridden in a bus route of checking card, vehicle line described in distance is close in all websites
Public bus network in the most short website B of data of checking card, then judge website B be when previous website of checking card is got on the bus, will under
The get-off stop of car.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410853787.6A CN104517040B (en) | 2014-12-31 | 2014-12-31 | One kind is based on IC-card data public transit vehicle in-car degree of crowding computational methods |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410853787.6A CN104517040B (en) | 2014-12-31 | 2014-12-31 | One kind is based on IC-card data public transit vehicle in-car degree of crowding computational methods |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104517040A CN104517040A (en) | 2015-04-15 |
| CN104517040B true CN104517040B (en) | 2017-12-05 |
Family
ID=52792330
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410853787.6A Active CN104517040B (en) | 2014-12-31 | 2014-12-31 | One kind is based on IC-card data public transit vehicle in-car degree of crowding computational methods |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104517040B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106935058A (en) * | 2015-12-29 | 2017-07-07 | 中国移动通信集团公司 | A kind of Bus information method for pushing, equipment and system |
| CN106448135B (en) * | 2016-09-30 | 2018-07-10 | 百度在线网络技术（北京）有限公司 | Bus routes recommend method and device |
| CN106778521A (en) * | 2016-11-23 | 2017-05-31 | 宇龙计算机通信科技(深圳)有限公司 | A kind of transit riding reminding method, apparatus and system |
| CN108536794A (en) * | 2018-04-02 | 2018-09-14 | 山东省计算中心（国家超级计算济南中心） | Meet the normalized method of orderly more classified variables of Poisson distribution |
| CN108831182A (en) * | 2018-05-07 | 2018-11-16 | 佛山科学技术学院 | A kind of Urban Transit Network OD matrix construction methods |
| CN109409563B (en) * | 2018-09-07 | 2021-11-09 | 北明软件有限公司 | Method, system and storage medium for analyzing real-time number of people in public transport operation vehicle |
| CN109524116A (en) * | 2018-11-30 | 2019-03-26 | 深圳大学 | The inhalable fine particle exposure appraisal procedure of bus trip individual |
| CN109615036B (en) * | 2018-11-30 | 2019-12-24 | 深圳大学 | Fine particle exposure risk assessment method based on bus IC card swiping system |
| CN110376585B (en) * | 2019-07-23 | 2022-02-15 | 交控科技股份有限公司 | Carriage congestion degree detection method, device and system based on 3D radar scanning |
| CN110853156B (en) * | 2019-11-18 | 2020-11-17 | 西南交通大学 | Passenger OD identification method integrating bus GPS track and IC card data |
| CN117592788B (en) * | 2024-01-17 | 2024-04-16 | 北京工业大学 | Bus running risk identification method and device |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101615340A (en) * | 2009-07-24 | 2009-12-30 | 北京工业大学 | Real-time information processing method in the bus dynamic dispatching |
| CN103730008A (en) * | 2014-01-15 | 2014-04-16 | 汪涛 | Bus congestion degree analysis method based on real-time data of bus GPS (Global Position System) and IC (Integrated Circuit) cards |
-
2014
- 2014-12-31 CN CN201410853787.6A patent/CN104517040B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101615340A (en) * | 2009-07-24 | 2009-12-30 | 北京工业大学 | Real-time information processing method in the bus dynamic dispatching |
| CN103730008A (en) * | 2014-01-15 | 2014-04-16 | 汪涛 | Bus congestion degree analysis method based on real-time data of bus GPS (Global Position System) and IC (Integrated Circuit) cards |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104517040A (en) | 2015-04-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104517040B (en) | One kind is based on IC-card data public transit vehicle in-car degree of crowding computational methods | |
| CN104239726B (en) | Passenger flow estimating system under the conditions of a kind of urban rail road network emergency event | |
| CN104157139B (en) | A kind of traffic congestion Forecasting Methodology and method for visualizing | |
| CN104778834B (en) | Urban road traffic jam judging method based on vehicle GPS data | |
| CN105427594B (en) | A kind of public transport section volume of the flow of passengers acquisition methods and system based on two-way passenger flow of getting on the bus | |
| CN105303854B (en) | A kind for the treatment of method and apparatus of traffic path data | |
| CN107330547A (en) | A kind of city bus dynamic dispatching optimization method and system | |
| CN106781490A (en) | Urban highway traffic analysis ＆ appraisement on operation system | |
| CN104217129A (en) | Passenger flow estimation method for urban rail road network | |
| CN106448135A (en) | Bus route recommendation method and device | |
| CN101964085A (en) | Method for distributing passenger flows based on Logit model and Bayesian decision | |
| CN106601005B (en) | A kind of municipal intelligent traffic abductive approach based on RFID and wechat platform | |
| CN106203662A (en) | A kind of based on ETC system realize highway pass through reservation method | |
| CN104021671A (en) | Real-time road condition determination method through combined svm and fuzzy determination mode | |
| CN101226687A (en) | Method for analysis of prototype run route in urban traffic | |
| CN106056242A (en) | High-speed train operating scheme evaluation method based on passenger flow dynamic allocation | |
| CN106898142B (en) | A kind of path forms time reliability degree calculation method considering section correlation | |
| CN106991727B (en) | Highway intercommunication charging method and system | |
| CN108961804A (en) | Method is determined based on the alternative set of public bus network adjustment of multi objective classification intersection | |
| CN108648453A (en) | A method of traffic trip data portrait is carried out based on mobile phone location fresh information | |
| CN107545757A (en) | Urban road flow rate measuring device and method based on Car license recognition | |
| CN110197335A (en) | A kind of get-off stop number calculation method based on probability OD distributed model | |
| CN105184875B (en) | Rate under pahtfinder hard is calculated and split system and method | |
| CN109389243A (en) | In conjunction with the freeway traffic condition survey website distribution method of networked fee collection data | |
| CN101604477B (en) | Intelligent dynamic route inducible system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |