CN108053062A - A kind of customization public bus network generation method based on multi-source data - Google Patents
A kind of customization public bus network generation method based on multi-source data Download PDFInfo
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Abstract
The invention discloses a kind of customization public bus network generation methods based on multi-source data, belong to public traffic information processing technology field.The method includes matching between the customization public transport requirement extract based on multi-source data, website Cluster-Fusion, demand and website, the structure for customizing public bus network Optimized model, the heuristic design based on genetic manipulation and solving model and obtains customizing public bus network collection.This method combination bus IC card, GPS data and Novel Internet data, passenger's trip requirements are extracted, while consider that the coverage rate of circuit and passenger's average travel time cost structure customize public bus network Optimized model and outlet line scheme, the physical planning to customize public transport provides theoretical method.Compared with tradition is based on the method for investigation, this method can identify potential demand, to extract customization bus trip demand to a greater extent.Acquired results of the present invention can be actually to customize the formulation of public bus network and the frequency of subsequently dispatching a car, and the optimizing scheduling of timetable and crew member are provided decision support.
Description
Technical field
The present invention relates to public traffic information processing technology field, specifically a kind of customization based on multi-source data is public
Intersection road generation method.
Background technology
With the continuous quickening of Chinese Urbanization, motorization process, Urban traffic demand increases rapidly, road traffic peace
Entirely, the traffic problems getting worse such as congestion and environmental degradation.Therefore, " public traffic in priority " development strategy has become reply urban transportation
The common recognition of problem.However, due to current China's urban public traffic network layout and organizational scheduling and resident trip characteristic, road network
Lack of uniformity in terms of structure and means of transportation time-space distribution configuration, resident cannot be met increasingly by resulting in existing public transit system
The trip requirements of growth.Under the conspicuous contradiction of the diversification of citizens' activities demand and regular public traffic service mode unification, energetically
Develop diversification public transportation system, it is further Traffic Development to efficient, harmonious, ecological mode development to guide it
Inexorable trend.
In this context, to alleviate traffic above-ground congestion, one kind that passenger's diversification trip requirements are target is met
Demand response formula Public Transport Service pattern is come into being, that is, customizes public transport.It is this as the supplement of regular public traffic service mode
Emerging bus service pattern is by integrating individual trip requirements, for crowd's amount of offer that trip origin and destination, travel time are similar
The Public Transport Service of body customization, to provide efficiently through trip service.
Since customization public transport is a kind of emerging bus service pattern, developing history is shorter, therefore comes from present case
It sees and there is also some problems.Since current customization public transit system demand data acquisition method is relatively single, in most cases
Based on survey data, trip requirements can not be accurately captured, cause line network planning unreasonable, the circuit coverage rate started is low, can not
The trip requirements of resident are adapted to, so that attendance is not high, cause the waste of public resource.In recent years, " internet+traffic " is borrowed
The technologies such as mobile Internet, big data and theory are helped, internet is subjected to effectively infiltration with merging with conventional traffic transport service, and
Provide new collecting method.Therefore, in this context, traditional public transport data and Novel Internet data how are combined,
The trip requirements of abundant mining analysis passenger rationally design customization public bus network, promote public transport entirety efficiency of operation and clothes
Quality of being engaged in is the problem of needing to probe into.
The content of the invention
For deficiency existing for current technology, it is an object of the invention to provide a kind of customization public transport based on multi-source data
Circuit generation method, i.e., by analyzing data acquisitions passenger's trip requirements such as IC card, GPS, mobile terminal navigation programming；In this base
On plinth, the demand coverage rate of gauze and passenger's travel time cost, structure multiple target customization public bus network generation mould are taken into full account
Type, and design the corresponding corresponding customization public bus network of intelligent heuristics derivation algorithm generation.The realization of the present invention includes following
Step：
The first step, according to Based on Bus IC Card Data, GPS data, mobile terminal layout data extraction passenger's trip requirements.For
The situation of IC card data record passenger getting on/off website can directly extract the origin and destination of passenger；It is not recorded for IC card data
The situation of passenger getting on/off website need to combine GPS data and extract passenger getting on/off website.Mobile terminal layout data includes user
The starting point of inquiry, terminal, time and desired trip mode, reflect a kind of potential demand.By this step, obtain user's
Go out beginning-of-line, terminal and temporal information.Passenger's trip information based on extraction, according to the time and space frequency of its trip, and
With reference to corresponding professional standard, determine whether the origin and destination are the demand point for customizing public bus network.
Second step clusters website.Due to same demand region, there are multiple bus platforms, it is contemplated that following right
The planning of bus platform is customized, multiple bus platforms adjacent in demand region are carried out by Cluster-Fusion by K-means methods,
Determine candidate's website of customization public bus network, the scope of usual bus station service impact is 500 meters to 1000 meters of radius.
3rd step, the demand that the first step is extracted, which is spatially distributed, is assigned to corresponding public transport after second step Cluster-Fusion
Website.The demand distribution situation of each candidate's platform is thus obtained.
4th step, on the basis of trip requirements extraction, consider gauze demand coverage rate and passenger's travel time into
This structure customizes public bus network Optimized model.
Finally, design a kind of heuritic approach based on genetic manipulation and solve the Optimized model, according to the target optimized,
Different types of customization public bus network set is generated, reference is provided for actual railroad embankment.
The advantage of the invention is that：
1. the present invention is different from most of research at present based on survey data design customization public transport network, but using multi-source
Data structure customization public bus network Optimized model, identifies potential demand, can extract customization bus trip demand to a greater extent.
2. considering demand coverage rate and time cost structure multiple target line optimization model, meet multiply to greatest extent
Visitor uses the trip requirements for customizing public transport；And heuritic approach generation customization public bus network collection is designed, can be actual customization public transport
Offer support is provided.
3. acquired results of the present invention can be the formulation of actual customization public bus network and the frequency of subsequently dispatching a car, timetable and
The optimizing scheduling of crew member is provided decision support.
Description of the drawings
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is bus station cluster result schematic diagram in embodiment；
Fig. 3 be embodiment in resident trip spatial and temporal distributions schematic diagram, (a) departure place；(b) destination；
Fig. 4 is the conspectus of model generation.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples, to make those skilled in the art's reference
Specification word can be implemented according to this.
The first step, the customization public transport requirement extract based on multi-source data.Data source according to the present invention includes public transport IC
Card, public transport GPS and mobile facility planning data.Based on Bus IC Card Data critical field includes card number, charge time, circuit volume
Number, car number, website of getting on the bus (urban), get-off stop (urban)；Public transport GPS data critical field includes vehicle
Number, timestamp, longitude, latitude, bus platform number.In the case of IC card data recorded passenger getting on/off website,
The origin and destination (OD) of passenger can directly be extracted；In the case of IC card data do not record passenger getting on/off website, GPS need to be combined
Data extract passenger getting on/off website.Mobile facility planning data critical field includes User ID, planning time, trip mode
(such as walking is ridden, automobile), starting point longitude, starting point latitude, terminal longitude, terminal latitude；The data type reflects a kind of latent
In demand.By this step, the beginning-of-line that goes out of user, terminal and temporal information can be extracted.Passenger based on extraction, which goes on a journey, to be believed
Breath according to the time and space frequency of its trip, and combines corresponding professional standard, determines whether the origin and destination are customization public transport
The demand point of circuit.In the present invention, monthly to get on or off the bus 5 times in same time period and identical platform and above for standard, system
It counts in passenger's whole day period (using a hour as section) in the frequency of getting on or off the bus of all bus platforms, determines customization public transport demand
OD。
Second step carries out Cluster-Fusion to website.It is since same demand region is there are multiple bus platforms, i.e., identical
Beginning or end, but nearby can there are multiple bus platforms, these platforms can serve the beginning or end, Cheng Kehui
One of platform is selected to go on a journey, this has resulted in the overlapping of bus platform.Therefore in view of following to customizing bus platform
Planning is, it is necessary to cluster these platforms.The present invention is by K-means methods by multiple bus stations adjacent in demand region
Platform carries out Cluster-Fusion, determines candidate's website of customization public bus network, the scope of a usual bus station service impact is half
500 meters to 1000 meters of footpath.I.e. centered on a platform, all platforms in 500 meters to 1000 meters radius overlay areas all belong to
In same class.K-means algorithm steps are as follows：
(1) n website coordinates are inputted, from K initial website is wherein chosen as K initial cluster center.
(2) n website is calculated to the Euclidean distance of this K cluster centre, with 500 meters of cluster centre for radius, owning
Website be all referred to distance and each nearest center go.If there is the situation of coincidence, select in the cluster nearest from the website
The heart.
(3) after whole websites is all classified, K cluster is generated, recalculates the center of this K cluster at this time,
It has been changed the K new cluster of cluster centre.
(4) after step (2) and the update of step (3) iteration, relatively it is front and rear respectively cluster twice in website to cluster centre
Distance if remaining unchanged, is gone to step (5)；Otherwise (2) are gone to step and continue iteration.
(5) result of website cluster is exported.
Passenger's trip terminus that the first step extracts is corresponded to the candidate stations in second step after Cluster-Fusion by the 3rd step
Point has thus obtained the volume of the flow of passengers of each candidate's website.
4th step, on the basis of trip requirements extraction, consider gauze demand coverage rate and passenger's travel time into
This structure customizes public bus network Optimized model.Make P={ P1,P2,…,PNIt is the customization public transport candidate stations extracted in second step
Point, any two website PiTo PjBetween passenger flow demand can be represented respectively with matrix TD and TC with hourage, wherein travelling
Time obtains after being extracted by GPS data.Define R={ R1,R2,..,RnIt is to meet one group of sets of lines of model constraints, R
Middle kth line definitions are Rk=R1kR2k…Rik…Rnk, wherein RikFor website sequence number described in P.The model is excellent comprising two
Change target, first aim function is：
Wherein, TDijIt is vacancies of the website i to website j, the denominator in formula (1) represents whole trips of survey region
Demand. TD(Ri) represent circuit RiAggregate demand；The molecule of formula (1) represents whole demands of candidate line collection R.For actual fortune
The public bus network of battalion considers the factors such as cost, possibly can not cover whole trip requirements.Therefore, the first aim of model
It is the trip requirements for looking after more passengers as far as possible, i.e. optimization customizes the coverage rate of public bus network.
Second target function is：
Wherein, rij(R) be in sets of lines R website i to the aggregate demand of website j, TCijIt is the trip between website i to website j
The Trip Costs of row time, i.e. passenger can be extracted by public transport GPS data and obtained.The object function is to optimize sets of lines R
In all passengers average hourage, promoted customization public transport traffic efficiency.
In addition, the model includes following constraints.(1) in order to improve the efficiency of service and comfort of customization public transport, with
It provides to go directly to passenger and service, the quantity of website cannot be excessive in every circuit, controls within five stations.(2) according to actual need
It asks and Planning Standard, the length of circuit should be controlled in rational scope, within 15KM；(3) non-linear coefficient of circuit is about
The ratio between the distance between Euclidean distance and circuit physical length between beam, i.e. circuit first and last station should within the scope of rational,
It should be less than 1.4 under normal circumstances；(4) there should not be the circuit of repetition in each sets of lines.
5th step designs the heuritic approach based on genetic manipulation and solves the line optimization model, and this method includes following
Step：(1) chromosome coding.Every chromosome represents a sets of lines.It is defined and known by the 4th step, every circuit is by website
Represented by digital number.Therefore, algorithm uses integer coding, if for example, there is 8 candidate's websites, is encoded to 1 to 8.
(2) initiating line collection.The sets of lines that random device generation meets model constraints can be used in this step, also may be used
By voluntarily selection generation sets of lines.Define NpopsizeFor the quantity of the population scale of genetic algorithm, i.e. sets of lines.Each sets of lines
The quantity of circuit can be defined as Nroutenum。
(3) fitness calculates.Fitness is used for evaluating the quality of each individual, the high chromosome of fitness or individual inheritance
It is larger to follow-on probability, to ensure the quality of genetic algorithm solution.The computational methods of fitness f are to each object function
Value is weighted：
Wherein, fk(x) and wkThe functional value and weight of respectively k-th target.
(4) selection operation.According to the fitness result of calculation of (3) step, using a method choice part for roulette
Body enters follow-on genetic manipulation.The selected probability P of i-th of chromosome in populationiIt is calculated by fitness：
Wherein, fiAnd fminThe fitness of all chromosome minimums in the fitness value of respectively i-th chromosome and population
Value.
(5) crossover operation.The operation follows the steps below.First, the chromosome of selection or individual are carried out two-by-two
Pairing.For each pair chromosome, according to given crossover probability, judge whether two individuals carry out crossover operation.Second, at random
Generate a crossover frequency.3rd step, for each pair chromosome intersected, each chromosome selects a gene position at random
Carry out crossover operation；4th step repeats second step, until reaching the crossover frequency specified.
(6) mutation operation.The operation follows the steps below.A line is randomly choosed firstly, for every chromosome
Road.Second step for each gene position for the circuit chosen, randomly generates the random number between one [0,1].3rd step,
For each gene position, if the random number that second step generates is less than given crossover probability, mutation operation is carried out；Otherwise,
Keep gene invariant position.
(7) offspring is detected.This step is whether new individual of the detection after the operations such as select, intersect, make a variation meets mould
Type constrains.If satisfied, then regarding it as offspring, and enter follow-on genetic manipulation；If not satisfied, it removes it.
(8) elitism strategy.The high individual of fitness in population or chromosome are directly introduced to next-generation progress by the step
Genetic manipulation.
(9) population scale is detected.Detect whether quantity individual in new population reaches preset value NpopsizeIf
Meet, go in next step；Otherwise, (4) step is returned.
(10) end condition.If algorithm reaches preset iterations, the algorithm is terminated, exports result；Otherwise, return
Return (3) step.
Embodiment
A kind of customization public bus network generation method based on multi-source data, it is specific as follows：
This example is navigated in May, 2017 using Hangzhou public transport on May 1st, 2017 to IC card on May 31 and GPS data, high moral
Hangzhou layout data planning customization public bus network.
Due to the supreme get-off stop of Hangzhou IC card data, it is therefore desirable to extract passenger getting on/off website with reference to GPS data.It
Cluster analysis, with 500 meters for radius, cluster result are carried out to website afterwards as shown in Fig. 2, obtaining customization public transport candidate's website.It connects
Get off, judge whether each trip origin and destination are OD pairs of customization public transport demand；With monthly in same time period and identical in this example
Platform gets on or off the bus 5 times and is above standard, counts in passenger's whole day period (using a hour as section) in all bus platforms
The frequency of getting on or off the bus, determine customization public transport demand OD.By the convergence analysis of several data, families in Hangzhou City trip origin and destination
Spatial distribution is as shown in Figure 3.
Model is solved followed by the heuritic approach based on genetic manipulation.In this example, since website is excessive,
70 demand OD of passenger flow maximum are filtered out to carrying out example.The parameter of genetic manipulation is arranged to population scale 60, each circuit
Line concentration way is 10, crossover probability 0.6, mutation probability 0.1.In 500 generation of iterations, the results are shown in Table 1 for each objective optimization.
Table 1. customizes public bus network Optimized model test result
As shown in table 1, option A is the highest scheme of demand coverage rate, and option b is the minimum side of passenger's average time cost
Case.As can be seen that according to different optimization aims, algorithm can export different circuit types, and option A looks after more passengers'
Trip requirements.Option b is in order to optimize the trip of passenger and customize the efficiency of operation of public transport, the shorter Decision Making of Line Schemes of output distance.
In actually runing, planning personnel can carry out according to different situations on the circuit of generation being adapted to needs.Fig. 4 is output
One of public bus network scheme example, which includes 4 websites, 965 people of demand, 13 kilometers of line length, run time
45 minutes.
The present invention, with reference to traditional public transport data and Novel Internet data, passes through multi-source under " internet+traffic " background
Passenger's trip requirements are extracted in data analysis.Structure customization public bus network Model for Multi-Objective Optimization on this basis, while consider line
The demand coverage rate on road and the average travel time cost of passenger；And it designs a kind of heuritic approach based on genetic manipulation and solves
Optimized model, generation customization public bus network collection.The method for being planned customization public transport based on survey data with tradition, this method can recognize that
Potential trip requirements, can be the planning of customization public transport, and operation and management provides reference.
The case study on implementation of the present invention described in detail above, but it is specific the invention is not limited in above-mentioned case study on implementation
Details in the range of the overall structure of the present invention, can carry out the part steps of the present invention a variety of conversion and reconfigure, this
Various combinations of possible ways are not listed in invention, these conversion combinations all belong to the scope of protection of the present invention.
Claims (4)
1. a kind of customization public bus network generation method based on multi-source data, which is characterized in that comprise the following steps：
The first step, the customization public transport requirement extract based on multi-source data
According to Based on Bus IC Card Data, GPS data, mobile terminal layout data extraction passenger's trip requirements；Remember for IC card data
The situation of passenger getting on/off website is recorded, directly extracts the origin and destination of passenger；Passenger getting on/off station is not recorded for IC card data
The situation of point extracts passenger getting on/off website with reference to GPS data.Mobile facility planning data critical field includes User ID, rule
Draw time, trip mode, starting point longitude, starting point latitude, terminal longitude, terminal latitude；By this step, the trip of user is obtained
Starting point, terminal and temporal information；Passenger's trip information based on extraction according to the time and space frequency of its trip, and combines
Corresponding professional standard determines whether the origin and destination are the demand point for customizing public bus network.
Second step clusters website
Multiple bus platforms adjacent in demand region are carried out by Cluster-Fusion by K-means clustering methods, determine that customization is public
Candidate's website on intersection road, the scope of bus station service impact is 500 meters to 1000 meters of radius.
3rd step, the demand that the first step is extracted, which is spatially distributed, is assigned to corresponding bus station after second step Cluster-Fusion
Point.The demand distribution situation of each candidate's platform is thus obtained；
4th step on the basis of trip requirements extraction, considers gauze demand coverage rate and passenger's travel time cost structure
Build customization public bus network Optimized model；
5th step designs a kind of heuritic approach based on genetic manipulation and solves the Optimized model, raw according to the target optimized
Into different types of customization public bus network set, reference is provided for actual railroad embankment.
2. a kind of customization public bus network generation method based on multi-source data according to claim 1, which is characterized in that the
In one step, multi-source data includes IC card data, GPS data and mobile facility planning data, can also include other related datas.
3. a kind of customization public bus network generation method based on multi-source data according to claim 1, which is characterized in that the
In four steps, the customization public bus network Optimized model of structure is as described below.
First aim function is：
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<mrow>
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<mo>&Sigma;</mo>
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<mi>R</mi>
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<mn>1</mn>
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</munderover>
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<mi>TD</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
</mrow>
</msub>
</mrow>
</mfrac>
</mrow>
Wherein, TDijIt is vacancies of the website i to website j, the denominator in formula represents whole trip requirements of survey region；TD
(Ri) represent circuit RiAggregate demand；The molecule of formula represents whole demands of candidate line collection R；
Second target function is：
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<mi>min</mi>
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</mrow>
Wherein, rij(R) be in sets of lines R website i to the aggregate demand of website j, TCijWhen being the travelling between website i to website j
Between, i.e. the Trip Costs of passenger can be extracted by public transport GPS data and obtained；
In addition, the model includes following constraints：(1) quantity of website cannot be excessive in every circuit, control five stations with
It is interior；(2) according to actual demand and Planning Standard, the length of circuit should be controlled in rational scope, within 15KM；(3) line
The non-linear coefficient constraint on road, i.e. the ratio between the distance between Euclidean distance between circuit first and last station and circuit physical length should be
Within the scope of rational, 1.4 are should be less than under normal circumstances；(4) there should not be the circuit of repetition in each sets of lines.
4. a kind of customization public bus network generation method based on multi-source data according to claim 1, which is characterized in that the
In five steps, the heuritic approach based on genetic manipulation, step is as follows：
(1) chromosome coding, every chromosome represent a sets of lines, and every circuit is represented by the digital number of website；Cause
This, algorithm uses integer coding, when there is 8 candidate's websites, is encoded to 1 to 8；
(2) initiating line collection meets the sets of lines of model constraints using random device generation or is generated by voluntarily selection
Sets of lines；Define NpopsizeFor the quantity of the population scale of genetic algorithm, i.e. sets of lines；The quantity of each sets of lines circuit can be with
It is defined as Nroutenum；
(3) fitness calculates, and the computational methods of fitness f are that the value of each object function is weighted：
<mrow>
<mi>f</mi>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mn>2</mn>
</munderover>
<msub>
<mi>&omega;</mi>
<mi>k</mi>
</msub>
<msub>
<mi>f</mi>
<mi>k</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>)</mo>
</mrow>
</mrow>
fk(x) and wkThe functional value and weight of respectively k-th target；
(4) selection operation, according to the fitness result of calculation of (3) step, using roulette method choice part individual into
Enter follow-on genetic manipulation.The selected probability P of i-th of chromosome in populationiIt is calculated by fitness：
<mrow>
<msub>
<mi>P</mi>
<mi>i</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<msub>
<mi>f</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>f</mi>
<mi>min</mi>
</msub>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mrow>
<mi>N</mi>
<mi>p</mi>
<mi>o</mi>
<mi>p</mi>
<mi>s</mi>
<mi>i</mi>
<mi>z</mi>
<mi>e</mi>
</mrow>
</munderover>
<mrow>
<mo>(</mo>
<msub>
<mi>f</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<msub>
<mi>f</mi>
<mrow>
<mi>m</mi>
<mi>i</mi>
<mi>n</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
</mfrac>
</mrow>
fiAnd fminThe fitness value of all chromosome minimums in the fitness value of respectively i-th chromosome and population；J is expression
The variable of the population scale of genetic algorithm；
(5) crossover operation；The operation follows the steps below：First, the chromosome of selection or individual are matched two-by-two；
For each pair chromosome, according to given crossover probability, judge whether two individuals carry out crossover operation；Second, it randomly generates
One crossover frequency；3rd step, for each pair chromosome intersected, each chromosome is selected a gene position and is carried out at random
Crossover operation；4th step repeats second step, until reaching the crossover frequency specified；
(6) mutation operation；The operation follows the steps below：A circuit is randomly choosed firstly, for every chromosome；The
Two steps for each gene position for the circuit chosen, randomly generate the random number between one [0,1]；3rd step, for every
One gene position if the random number that second step generates is less than given crossover probability, carries out mutation operation；Otherwise, base is kept
Because of invariant position；
(7) offspring is detected；This step is whether new individual of the detection after the operations such as select, intersect, make a variation meets model about
Beam；
(8) elitism strategy；The high individual of fitness in population or chromosome are directly introduced to next-generation progress heredity by the step
Operation；
(9) population scale is detected；Detect whether quantity individual in new population reaches preset value, if satisfied, going to
In next step；Otherwise, (4) step is returned.
(10) end condition；If algorithm reaches preset iterations, the algorithm is terminated, exports result；Otherwise, the is returned
(3) step.
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