CN103279534B - Travel frequently OD distribution estimation method based on the mass transit card passenger of intelligent public transportation system data - Google Patents
Travel frequently OD distribution estimation method based on the mass transit card passenger of intelligent public transportation system data Download PDFInfo
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Abstract
Does the present invention provide a kind of based on intelligent public transportation system data (Advanced? Public? Transportation? Systems, APTS) mass transit card passenger travels frequently OD distribution estimation method, gather APTS data, by the association of bus IC card data and intelligent dispatching system data being obtained the site information of getting on the bus of bus passenger, according to early, the trip frequency of evening peak judges that public transport is travelled frequently passenger, the Time and place feature of commuter is utilized to determine inhabitation place and work point, the method that the present invention proposes has precision height, workable advantage, for fast, economy ground obtain public transport travel frequently OD distribution provide a kind of new approach, the ratio that commuter uses bus trip can be improved, can effectively alleviate Urban Traffic Jam Based.
Description
Technical field
The present invention relates to the mass transit card passenger based on intelligent public transportation system (AdvancedPublicTransportationSystems, APTS) data to travel frequently OD distribution estimation method field.
Background technology
Traffic of travelling frequently is the object that city bus focal points of service ensures, accurately grasps starting point (Origin) and the terminal (Destination) of bus passenger commuter, and namely the public transport OD that travels frequently is significant for line network planning and the operation management of public transit system. Traffic of travelling frequently refers to the process come and gone between family and work point (or school), and a usual working days has at least twice trip. Traffic of travelling frequently be city early, the main integral part of evening peak passenger flow, attract commuter to select transit trip, for alleviation urban traffic blocking, there is vital role. The public transport passenger that travels frequently is city inhabitant, general use mass transit card is by bus, obtain the OD distribution of travelling frequently of mass transit card passenger, the characteristic sum rule of public transport commuter demand can be analyzed, grasp the characteristic sum rule of commuter's bus trip demand, it is the basis optimized public transit system, meet to higher level commuter demand. Traditional bus trip information is generally obtained by the investigation of extensive resident trip, has costly, deficiency that data life period is short. The scarcity of decision-making information and delayed cause Public transport network planning and bus operation plan to be difficult to the dynamic change according to trip requirements to adjust in time. APTS system is except the control and management and traffic-information service of offer public transit system, also produce a large amount of system operation data, excavate the information that these data are implicit, provide new opportunity for setting up continuous print bus trip requirement analysis method with low cost, dynamic. Northeast USA university DavidS.Navick(2002) apply Los Angeles electronic bus ticketing data analysis passenger's wall scroll public bus network website between OD, this applies one of research that electronic bus ticketing data carry out analyzing in the world the earliest. The AlexCui(2006 of Massachusetts Institute of Technology) get-off stop of mass transit card passenger is calculated according to the relation of continuous twice transit riding. The JanineM.Farin(2008 of New York city bus) travelling OD of the mass transit card passenger between traffic zone, Sao Paulo City is calculated, and the data that the bus trip pattern obtained and resident trip investigation obtain are compared. But this research will be ridden as once going on a journey each time, it does not have consider to complete through the transfer of twice or repeatedly public transport with once trip. The reckoning of current bus passenger travelling OD, is calculated by OD between the website of initial wall scroll public bus network, to public traffic network layer viewpoint deeply.
Summary of the invention
It is an object of the invention to provide a kind of mass transit card passenger based on intelligent public transportation system data to travel frequently OD distribution estimation method.
For achieving the above object, present invention employs following technical scheme:
Gather intelligent public transportation system data, by the association of bus IC card data and intelligent dispatching system data being obtained the site information of getting on the bus of bus passenger, morning on working days, the evening peak period regular passenger taken pubic transport is judged as the passenger that travels frequently, by travelling frequently, passenger's morning peak period is judged as inhabitation place in the regular pick-up point in transit riding place first, and by travelling frequently, passenger's evening peak period is judged as work point or place of going to school in the regular pick-up point in transit riding place first.
Mass transit card passenger is added up in the trip frequency of morning, evening peak period, K will be met=Kt, M >=MtAnd N >=NtThe mass transit card passenger of three conditions is judged to the passenger that travels frequently, K represents total number of times that morning on 5 working dayss of each mass transit card passenger, evening peak period ride first, the morning peak that M represents 5 working dayss of each mass transit card passenger total number of times by bus first, the evening peak that N represents 5 working dayss of each mass transit card passenger total number of times by bus first, KtSpan be 2��Kt�� 10, MtSpan be 1��Mt�� 5, NtSpan be 1��Nt��5��
Travel frequently passenger early, the evening peak period first the regular pick-up point in transit riding place adopt website by bus frequency statistics method determine, the concrete steps of website frequency statistics method by bus are:
First, with the passenger that travels frequently get on the bus site number count respectively travel frequently passenger 5 working dayss early, the website that uses the frequency the highest in the website ridden first of evening peak, website morning peak ridden first uses the use frequency corresponding to website that the frequency is the highest represent for Mmax, website evening peak ridden first uses the use frequency corresponding to website that the frequency is the highest represent for NmaxIf, MmaxThe morning peak being greater than these 5 working dayss of passenger of travelling frequently first by bus total number of times more than 1/2, be defined as travelling frequently the regular pick-up point of passenger in morning peak period first transit riding place by corresponding website, if NmaxThe evening peak being greater than these 5 working dayss of passenger of travelling frequently first by bus total number of times more than 1/2, be defined as travelling frequently the regular pick-up point of passenger in evening peak period first transit riding place by corresponding website.
For website by bus frequency statistics method do not determine the passenger that travels frequently of described regular pick-up point, application site coordinate space clustering method carries out getting on the bus the determination of website again, concrete steps are as follows: first, calculate the passenger that travels frequently respectively in morning peak and evening peak space line distance between website by bus first, the website that the space line distance that morning peak is ridden between website first is less than setting clustering distance is divided into a class, and the class comprising station number maximum is designated as M1, the website that the space line distance that evening peak is ridden between website first is less than setting clustering distance is divided into a class, and the class comprising station number maximum is designated as N1, if the morning peak that the website number of M1 exceedes 5 working dayss of passenger of travelling frequently first by bus total number of times more than 1/2, then calculate the coordinate central point A of website in M1, central point A is confirmed as the morning peak period regular pick-up point in transit riding place first, if the evening peak that the website number of N1 exceedes 5 working dayss of passenger of travelling frequently first by bus total number of times more than 1/2, then calculating the coordinate central point B of website in N1, central point B is confirmed as the evening peak period regular pick-up point in transit riding place first.
The useful effect of the present invention is embodied in:
Mass transit card passenger based on intelligent public transportation system data of the present invention travels frequently OD distribution estimation method, by differentiate mass transit card travel frequently passenger, calculate its residence and place of working positional information, obtain the every workday early, the OD distribution of travelling frequently of evening peak. The present invention, compared with traditional folk houses trip survey method, has the advantage that expense is low, sample size big, data can dynamically update. Compared with calculating mass transit card passenger's travelling OD method with based on " Trip chain ", it is by the restriction of bus passenger whole day by bus number of times, the precision height of algorithm, workable, it is possible to be applied to dynamic monitoring and the analysis of actual public transport commuter demand. The present invention fast, economy obtains public transport OD distribution of travelling frequently to provide a kind of new approach, it is possible to improve the ratio that commuter uses bus trip, it is possible to effectively alleviate Urban Traffic Jam Based.
Embodiment
The invention will be further described below.
1. data describe
The present invention acquires the system operation data of 57 the intelligent bus circuits in December, 2008 Nanning City, comprise bus IC card data, public transport gps data, bus station's point coordinate data and public transport operation record data, bus IC card data are from public transport automatic fare collection system, and other 3 kinds of data are from intelligent bus dispatching system. Import raw data at SQL-Server2005 database to go forward side by side row data pre-treatment. Intelligent dispatching system data and bus IC card data association are determined the website of getting on the bus of bus IC card passenger. Using the fundamental research data of the bus IC card data of known site information of getting on the bus as the present invention, its field describes in table 1.
Table 1 data field describes
2. basic ideas
The traffic of travelling frequently of bus trip mode is adopted to there is such rule: for major part bus passenger, the website and what get-off stop was always changed mutually of getting on the bus of its commuter, namely get-off stop when website of getting on the bus during backhaul is exactly set out, and the get-off stop when website of getting on the bus when setting out is backhaul. The Liu Jing (2008) of Tongji University utilizes the trip characteristicses such as the website frequency of OD, website first-hitting time, use between the website of Shenzhen IC-card data research subway passenger, also demonstrate that the traffic behavior of bus passenger has very strong time and space idea, namely major part people is in and launches trip between 2, work unit, and travel time and place present 24 hours periods. Therefore, by differentiate bus passenger early, the regular website ridden of evening peak, it is possible to the inhabitation place of the passenger that judges to travel frequently and work point. Based on public transport commuter rule, it is proposed to 3 fundamental assumptions of the present invention:
Assuming that 1: working days early, the evening peak period the regular passenger taken pubic transport travel frequently passenger for public transport.
Assuming that 2: public transport passenger's morning peak period regular website by bus of travelling frequently is inhabitation place.
Assuming that 3: public transport passenger's evening peak period regular website by bus of travelling frequently is work point.
Analyzing according to above and suppose, the basic ideas of put forward the methods of the present invention are as follows:
1st step: determine morning, evening peak time range and possible residence and place, place of working
Using the elementary cell that the bus IC card data of the known website of getting on the bus on each working days on Friday are analyzed as commuter OD. By the analysis that the bus passenger travel time is distributed, it is determined that early, the evening peak period. Using the website of riding first of passenger's morning peak of travelling frequently as place of may living, evening peak rides website first as possibility work point (comprising place of going to school). Total frequency table that the peak period on 5 working dayss of each mass transit card number rides first is 10 times to the maximum illustrated as K, K, morning peak and evening peak first by bus total number of times be expressed as M and N, M and N is 5 times to the maximum.
2nd step: the passenger that travels frequently judges
The trip frequency of 5 peak periods on working days of bus passenger being added up, the passenger being greater than certain trip number of times is judged to the passenger that travels frequently. Public transport is travelled frequently the minimum value that passenger K, M and N should meet, it is expressed as Kt��Mt��Nt, the value of these 3 parameters is more big, and the accuracy that the OD that travels frequently judges is more high. K will be met >=Kt, M >=Mt, N >=NtThe mass transit card passenger of 3 conditions is judged to the passenger that travels frequently.
3rd step: the passenger that travels frequently estimates live place and work point
Travel frequently and there are many public bus networks between passenger place of working and residence when can take, the public bus network that each commuter is taken may be different, even if take same bar public bus network every time, the different websites contiguous in place of working or residence also can be selected by bus. Consequently, it is desirable to determine the website of getting on the bus of commuter from the website of riding first of morning, evening peak. Adopt by bus frequency statistics and space clustering 2 methods herein, the residence of the passenger that determines to travel frequently and place of working.
(1) by bus frequency statistics method
With passenger get on the bus site number count 5 working dayss early, in the website ridden first of evening peak, it may also be useful to the website that the frequency is the highest, used the use frequency corresponding to website that the frequency is the highest to represent for M in website morning peak ridden firstmax, website evening peak ridden first uses the use frequency corresponding to website that the frequency is the highest represent for Nmax, work as MmaxIt is greater than more than 1/2 (the i.e. M of morning peak by bus total number of times M firstmax>=Int (M/2)+1), then by MmaxCorresponding website is defined as residence website (such as M=5, MmaxDuring >=3). With reason, work as Nmax>=Int (N/2)+1, by NmaxCorresponding website is defined as place of working website.
(2) space clustering method
When traffic of travelling frequently has different public transport website can select to take, adopt website by bus frequency statistics method calculate that failing to judge may occur in the OD that travels frequently. When having bus station's point coordinate data, for website by bus frequency statistics method do not determine the card number of travelling frequently of the OD that travels frequently, then the application site coordinate space clustering method OD that it travelled frequently determines. First, calculate card morning peak (or evening peak) by bus space line distance between website first of travelling frequently, site distance is divided into 1 class from the website being less than setting clustering distance (getting 500m). Then, obtain 1 class that website number is maximum, if such website number MmaxWhen exceeding the 1/2 of the website sum M (or N) that morning peak (or evening peak) is ridden first, calculate the coordinate central point of such website. This center point coordinate is confirmed as residence (or place of working) location coordinates.
4th step: the every workday is early, evening peak public transport travels frequently OD statistics
By travel frequently passenger card number and residence thereof and the place of working information on obtain above 5 working dayss, with travel frequently passenger early, the site information of getting on the bus of evening peak compare, the public transport extracting the every workday is travelled frequently OD.
3. algorithm proposes
According to above basic ideas, it is proposed to distribute algorithm for estimating based on the mass transit card passenger of the APTS data OD that travels frequently:
Step0: given Kt��Mt��Nt3 parameter values.
Step1: by 1 working days on Friday known website of getting on the bus mass transit card data importing to the new table CommuterOD in database, retain the record the earliest of charge time in each mass transit card morning, evening peak period, other records deletion.
Step2: delete K < K in table CommuterODt��M<MtAnd N < NtAll records of mass transit card number.
Step3: the whole records extracting a mass transit card number from table CommuterOD, adopts frequency statistics method by bus to judge. Work as Mmax>=Int(M/2)+1andNmaxDuring >=Int (N/2)+1, obtain the OD that travels frequently, get next card number and judge, otherwise to Step4.
Step4: adopt space clustering method to carry out judging (being judged as example with residence website):
Step4.1: take out 5, this card number all websites that working days, morning peak was ridden first, sum is M.
Step4.2: from, M website, taking out a website Sj, calculate the space line distance L of itself and all websitesj, the website that distance is less than 500m is divided into and SjIn same class.
Step4.3: computed range LjIt is less than the website number m of 500mj(comprise Sj). If mj=M, then jump to Step5, otherwise to Step4.4. (mj=M represents that the spacing of M website is all less than 500m, i.e. mj=M=Mmax)
Step4.4: the next website S taking out this card numberj+1, repeat Step4.2��Step4.3, until M website all calculates complete.
Step4.5: seek mjIn maximum value MmaxIf, Mmax>=Int (M/2)+1, then calculate mjCoordinate center between website in this maximum class. Coordinate center obtains by calculating the average of website coordinate, and this coordinate central point is inhabitation place.
Step4.6: if there is two or more mjIdentical website, and be maximum value M simultaneouslymax, then m is calculated againjThe average of identical grouping center point coordinate, as residence point coordinate.
Step5: repeat Step3��Step4, until whole card number calculates complete in CommuterOD table.
Step6: the OD that travels frequently extracting the every workday.
Algorithm Step1��Step2, Step6 adopt T-SQL order to complete in a database, and Step3��Step5 adopts VB.NET program to realize.
4. algorithm test
With 182,007 mass transit cards number on 57, Nanning City intelligent scheduling circuit, 5 working dayss of 5 (Friday) of (Monday)��December on December 1st, 2008, amount to 899,174 bus IC card records as example data. The computer hardware environment that algorithm routine runs is: double-core 2.8GHzCUP; 1GB internal memory; 300GB hard disk. Software environment is: WindowsXP operating system; VisualStudio2008 development environment; SQLServer2005 database.
Due to Kt��Mt��NtThe value of 3 parameters is more big, and the accuracy that the OD that travels frequently judges is more high, to Kt��MtAnd NtThe minimum condition of 3 parameter values is tested, and sets K in testt=2��Mt=1��Nt=1��
Within algorithm routine Step3��Step5,3 hour, complete computing, it is determined that go out 5 working dayss totally 36,882 commuters travel frequently OD pair, wherein adopt frequency statistics method by bus to judge 23,624 OD that travel frequently, operation result field describes in table 2; Adopting space clustering method to judge that 13,258 are travelled frequently OD pair, operation result field describes in table 3.
Table 2 operation result field describes
Table 3 operation result field describes
According to 5 working days commuter residence and place of working information, extract the card number of travelling frequently of every workday morning peak (06:30��09:30) and evening peak (16:30��19:30) respectively, and obtaining the commuter OD of morning, evening peak, operation result is added up, in table 4.
Table 4 operation result is added up
5 card ratios of travelling frequently that working days, morning peak was extracted, all about 50%, the card ratio of travelling frequently that evening peak is extracted is totally lower than morning peak, and wherein December 5 (Friday) is minimum is 40.5%, and other 4 work are average daily about 45%. Evening peak has the next rear also non-immediate of part citizen to go home, so evening peak is travelled frequently, the ratio shared by traffic is totally lower than morning peak, and weekend (December 5) is the most remarkable.
Through statistics, in this example, " square, Chaoyang " is the maximum website of number of work, and this and square, Chaoyang are the down towns of Nanning City, and the regional feature the most intensive for employed population matches. " square, Chaoyang " and " elegant market, railway carriage or compartment " stands as maximum website OD pair of the amount of travelling frequently, and two site distance 2.9 kilometers, approach 7 websites, have 6 public bus networks to go directly.
5. algorithm checking
5.1 checking thinkings
The bus trip OD projectional technique based on " Trip chain " that put forward the methods of the present invention and prior art propose is compared. Based on the projectional technique of " Trip chain " mainly based on following 3 hypothesis:
(1) " go on a journey " is assumed next time: the website of getting on the bus once ridden on the get-off stop of same passenger transit riding on the same day is mostly close.
(2) " for the last time go on a journey " is assumed: the get-off stop that same passenger rides for the last time on the same day is close to the website of getting on the bus ridden for the first time on the same day.
(3) " return trip " assume: if the same passenger circuit that continuous twice is ridden on the same day is identical, direction (uplink and downlink) is contrary, the get-off stop that then first time rides is the website of getting on the bus that second time is ridden, and it is the website of getting on the bus ridden first time that second time rides on and off website.
Passenger's whole day can only be applied to based on the method for " Trip chain " and have more than 2 times transit ridings, for whole day only 1 time by bus time, its OD can not be calculated. The public transport that the present invention proposes travels frequently OD distribution estimation method not by the restriction of whole day trip number of times. 2 kinds of methods can be calculated out that the mass transit card number of OD extracts jointly, calculate the consistent ratio of results by calculating 2 kinds of bus trip OD, it is determined that proposed public transport is travelled frequently the precision of OD projectional technique.
5.2 checking results
By the operation result of Section 4, compare with adopting the operation result based on " Trip chain " method. By computing, extracting the OD for comparing to 11072,2 kinds of methods calculate that the consistent rate of result is 75.27%. According to existing result of study, the accuracy rate of the bus passenger travelling OD can extrapolated based on " Trip chain " method is more than 90%[5,6]. Assume that the precision based on " Trip chain " method is 90%, then the public transport commuter OD of this example calculates that the accuracy rate of result is 83.63%(75.27/90%). Due to Kt��Mt��NtThe value of 3 parameters is more big, and the accuracy that the OD that travels frequently judges is more high, to Kt��Mt��Nt3 parameters have been tested in the precision of different value condition, the results are shown in Table 5.
Table 5 different parameters condition checking result
In table 5, work as Kt=6��Mt=2��NtWhen=2,2 kinds of methods calculate that the consistent rate of results is 88.51%, and the public transport OD that travels frequently calculates that the accuracy rate of result has reached 98.34%(82.0/90%). Checking result shows, its precision of method that the present invention proposes can meet the data requirement of Urban Traffic Planning.
6. conclusion
The method that application APTS data estimation mass transit card passenger commuter OD distributes is studied by the present invention, the method proposed has following feature: (1) can differentiate that mass transit card is travelled frequently passenger, calculate its residence and place of working positional information, obtain the OD distribution of travelling frequently of morning every workday, evening peak. (2) compared with traditional folk houses trip survey method, there is the advantage that expense is low, sample size big, data can dynamically update. (3) compared with calculating mass transit card passenger's travelling OD method with based on " Trip chain ", the restriction of its number of times of not riding by bus passenger whole day. (4) the precision height of algorithm, workable, it is possible to be applied to dynamic monitoring and the analysis of actual public transport commuter demand. The present invention is limited to data condition, use only the data of part public bus network to have tested, along with the widespread use of intelligent public transportation system, when using whole public bus network data to calculate, the precision of OD is travelled frequently in the public transport obtained and sample size can also be further enhanced.
Claims (2)
1. the mass transit card passenger based on intelligent public transportation system data travels frequently OD distribution estimation method, it is characterized in that: gather intelligent public transportation system data, by the association of bus IC card data and intelligent dispatching system data being obtained the site information of getting on the bus of bus passenger, by morning on working days, the evening peak period, the regular passenger taken pubic transport was judged as the passenger that travels frequently, by travelling frequently, passenger's morning peak period is judged as inhabitation place in the regular pick-up point in transit riding place first, by travelling frequently, passenger's evening peak period is judged as work point or place of going to school in the regular pick-up point in transit riding place first,
The determination methods of the described passenger of travelling frequently is:
Mass transit card passenger is added up in the trip frequency of morning, evening peak period, K will be met=Kt, M >=MtAnd N >=NtThe mass transit card passenger of three conditions is judged to the passenger that travels frequently, K represents total number of times that morning on 5 working dayss of each mass transit card passenger, evening peak period ride first, the morning peak that M represents 5 working dayss of each mass transit card passenger total number of times by bus first, the evening peak that N represents 5 working dayss of each mass transit card passenger total number of times by bus first, KtSpan be 2��Kt�� 10, MtSpan be 1��Mt�� 5, NtSpan be 1��Nt�� 5;
Travel frequently passenger early, the evening peak period first the regular pick-up point in transit riding place adopt website by bus frequency statistics method determine, the concrete steps of website frequency statistics method by bus are:
First, with the passenger that travels frequently get on the bus site number count respectively travel frequently passenger 5 working dayss early, the website that uses the frequency the highest in the website ridden first of evening peak, website morning peak ridden first uses the use frequency corresponding to website that the frequency is the highest represent for Mmax, website evening peak ridden first uses the use frequency corresponding to website that the frequency is the highest represent for NmaxIf, MmaxThe morning peak being greater than these 5 working dayss of passenger of travelling frequently first by bus total number of times more than 1/2, be defined as travelling frequently the regular pick-up point of passenger in morning peak period first transit riding place by corresponding website, if NmaxThe evening peak being greater than these 5 working dayss of passenger of travelling frequently first by bus total number of times more than 1/2, be defined as travelling frequently the regular pick-up point of passenger in evening peak period first transit riding place by corresponding website.
2. a kind of mass transit card passenger based on intelligent public transportation system data travels frequently OD distribution estimation method according to claim 1, it is characterized in that: for website by bus frequency statistics method do not determine the passenger that travels frequently of described regular pick-up point, application site coordinate space clustering method carries out getting on the bus the determination of website again, concrete steps are as follows: first, calculate the passenger that travels frequently respectively in morning peak and evening peak space line distance between website by bus first, the website that the space line distance that morning peak is ridden between website first is less than setting clustering distance is divided into a class, and the class comprising station number maximum is designated as M1, the website that the space line distance that evening peak is ridden between website first is less than setting clustering distance is divided into a class, and the class comprising station number maximum is designated as N1, if the morning peak that the website number of M1 exceedes 5 working dayss of passenger of travelling frequently first by bus total number of times more than 1/2, then calculate the coordinate central point A of website in M1, central point A is confirmed as the morning peak period regular pick-up point in transit riding place first, if the evening peak that the website number of N1 exceedes 5 working dayss of passenger of travelling frequently first by bus total number of times more than 1/2, then calculating the coordinate central point B of website in N1, central point B is confirmed as the evening peak period regular pick-up point in transit riding place first.
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