CN114331617B - Commuting private car pooling matching method based on artificial bee colony algorithm - Google Patents
Commuting private car pooling matching method based on artificial bee colony algorithm Download PDFInfo
- Publication number
- CN114331617B CN114331617B CN202111640253.1A CN202111640253A CN114331617B CN 114331617 B CN114331617 B CN 114331617B CN 202111640253 A CN202111640253 A CN 202111640253A CN 114331617 B CN114331617 B CN 114331617B
- Authority
- CN
- China
- Prior art keywords
- honey source
- vehicle
- honey
- commuter
- commute
- 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
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000011176 pooling Methods 0.000 title claims abstract description 19
- 235000012907 honey Nutrition 0.000 claims abstract description 151
- 241000257303 Hymenoptera Species 0.000 claims description 45
- 239000013598 vector Substances 0.000 claims description 33
- 235000013305 food Nutrition 0.000 claims description 24
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000006978 adaptation Effects 0.000 claims description 3
- 230000001174 ascending effect Effects 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 abstract description 4
- 238000011161 development Methods 0.000 abstract description 3
- 238000012545 processing Methods 0.000 abstract description 2
- 230000000694 effects Effects 0.000 abstract 1
- 238000007418 data mining Methods 0.000 description 2
- 238000003912 environmental pollution Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a commute private car pooling matching method based on a manual bee colony algorithm, belongs to the technical field of data algorithm processing, and particularly relates to the technical field of a commute private car pooling matching method. According to the method, an artificial bee colony optimization algorithm is improved, a honey source and fitness rules are redesigned, the departure time of each commuter vehicle is used as each dimension of the honey source, the interval of the departure time is used as the change interval of the honey source, and the path length saved after the vehicles are assembled is used as fitness, so that the vehicle has stronger searching capability and development capability. According to the method, the travel journey of the commute private car and the travel journey subsequence of the commute private car are obtained through statistics according to the RFID electronic license plate identification data of the vehicle, so that the experimental efficiency can be improved, the complexity is reduced, the parameters of the method are few, the efficiency is high, the effect is good, more journey can be saved compared with other methods, more private cars can be matched, and the solution is better.
Description
Technical Field
The invention belongs to the technical field of data algorithm processing, in particular to the technical field of a commute private car pooling matching method, and relates to a commute private car pooling matching method based on an artificial bee colony algorithm.
Background
In recent years, with the rapid development of economy, private cars are kept in an increasing amount. Especially in the peak early and late hours, a large number of commuting private cars cause urban traffic jam and also cause environmental pollution. Although ordinary private cars have a carrying capacity of at least 5 people, the empty rate of commuting private cars is high in the morning and evening peak, and a phenomenon of one person riding is common. The commute private cars with a large number and high empty space rate travel to increase urban road traffic flow, waste traffic resources, increase environmental pollution and travel cost.
The commuting private car carpooling trip in the morning and evening peak period is an effective way for solving the problems. Commute private car matching is a key problem for commute private car pooling. The current method for matching the carpool is mainly a traditional data mining method and a group intelligent optimization algorithm. Traditional data mining methods such as greedy algorithms find the vehicle for which the match is best for each vehicle based on the matching conditions. However, this method cannot obtain a globally optimal solution, and it takes a long time to search once, and has high time complexity and low efficiency. The intelligent group optimization algorithm is also a method commonly used for solving the problem of car matching, such as a genetic algorithm, a particle swarm algorithm, a hill climbing algorithm and the like, and the algorithms obtain an optimal solution by initializing a population and an evolutionary population, so that parameters are few, efficiency is high, searching is comprehensive, and therefore a global optimal solution is obtained.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for matching a commuter private car with a vehicle based on an artificial bee colony algorithm, which is characterized in that the artificial bee colony optimization algorithm is improved, a honey source and a fitness rule are redesigned, the departure time of each commuter car is used as each dimension of the honey source, the interval of the departure time is used as the variation interval of the honey source, and the path length saved after the vehicle is used as the fitness, so that the vehicle has stronger searching capability and development capability.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A commute private car carpooling matching method based on an artificial bee colony algorithm comprises the following steps:
S1, counting the commuter journey and departure time interval of the commuter vehicle, counting the subsequence paths of each journey, and storing all the subsequence paths into a database;
S2, determining the input of an artificial bee colony algorithm: the method comprises the steps of determining the dimension M of a honey source solution according to the number of commuter vehicles, wherein the number of honey sources is the population size s, and the number of scout bees is the number of honey sources s, the iteration number n and the maximum try number maxInvalidCount;
S3, initializing period: initializing the population, and initializing honey source vector by using the reconnaissance bees Wherein s is the size of the population; due to each honey source/>Are solution vectors of dimension M of the problem to be optimized, and therefore eachEach containing M variables x j (j=1, 2, … M), each x j is initialized; after the initialization of the honey source vector is completed, each solution vector/>, of the honey source is calculated according to the fitness calculation ruleThe fitness value of the optimal solution and the solution vector of the optimal solution are recorded, and the population initialization is completed;
S4, hiring a peak period: employing bees to search for neighbors based on the locations of the food sources in their memory, finding better food sources near the food sources; after the hiring bees find a food source, the adaptation value is evaluated, and the optimal solution, the optimal solution vector and the number of attempts are updated;
S5, observing the bee period: non-employment bees consist of two parts of a population: observing bees and spying bees; employing bees to share food source information they obtain with observing bees waiting in the cell, the observing bees making a random choice based on this information;
s6, a bee detection period: the unused bees randomly search for food sources, known as scouts; if the employment bee does not improve the quality of the solution after the maximum number of attempts maxInvalidCount is exceeded, the employment bee becomes a scout bee, the solution it owns is discarded, and the converted scout bee generates a solution by initializing the formula with a honey source;
and S7, iterating the steps S3 to S6 according to the input iteration times n.
Further, in step S1, the commuter track has the characteristics of high frequency, stability, space-time similarity, etc., and the resident having the commuter track is called a commuter, and the commuter private car of the commuter in the city can be found by using the RFID electronic license plate data, specifically comprising the following steps:
s11, extracting the commuting track of all RFID electronic license plate data of the commuting private car A;
S12, ordering the commuting tracks of the commuting private car A according to the time ascending sequence of the vehicles passing through the RFID acquisition points, representing the commuting tracks by a sequence, R＝<eid,r,t>，/>Wherein Tra A represents the track of the vehicle A, R represents an RFID electronic license plate data, eid represents the electronic license plate number of the vehicle, R represents the identification number of the RFID acquisition point, t represents the time when the vehicle is identified,/>Representing the time when vehicle A passes the ith RFID acquisition point, wherein the track of the commuter vehicle A passing the RFID is/>
S13, counting the commuting track of the commuting private car A according to the track of each working day, extracting the earliest time and the latest time of each acquisition point passing each day, and storing the earliest time and the latest time into a database, wherein the time interval data of the commuting track points of the commuting private car are expressed as:
Wherein Commuter A denotes commute track time interval data of the commute private car a, And/>Representing earliest time and latest time of the commute private car A passing through the nth commute track point;
and S14, finally, executing the steps on all the commute private cars, and storing the commute track point time interval data of all the commute private cars into a database after statistics.
Further, in step S2, a commuter sub-sequence table is created, including:
S21, based on the commute track point time interval data statistics of all the commute vehicles obtained in the step S1, selecting a commute private vehicle, recording eid, origin and destination thereof, putting the three pieces of information into original RFID electronic license plate data for searching, finding out RFID points passing between the origin and destination of the vehicle, recording the RFID points, wherein every two points are a subsequence, and storing the subsequence in a database, wherein the subsequence contained in the starting and destination of each vehicle can be expressed as:
si＝<eid,origin,destination,subsequence1…subsequencen>
Wherein s i represents the ith commuter sub-sequence data, eid represents the electronic license plate identification number of the vehicle, origin represents the commuter departure place of the commuter vehicle, destination represents the commuter destination of the vehicle, subsequence n represents the nth sub-sequence from the starting point to the end point of the vehicle;
s21, executing the steps on all commuter private cars in all commuter schedules, and storing the subsequences obtained by all the commuter private cars into the commuter subsequences.
Further, in step S3, the method specifically includes the following steps:
s31, inputting parameters required by a manual bee colony algorithm, determining the dimension M of a honey source solution according to the number of commuter vehicles, wherein the number of honey sources is the population size S, the number of scout bees is the number S of honey sources, the iteration number n and the maximum try number maxInvalidCount;
S32, initializing honey source vectors by using the number of the spy bees being the same as that of the honey sources Wherein s is the size of the population; due to each honey source/>Are solution vectors of dimension M of the problem to be optimized, and therefore eachAll containing M variables x j (j=1, 2, … M), each x j is initialized according to the following formula:
xj＝lj+rand(0,1)*(uj-lj)
Wherein l j and u j are minimum and maximum values of the j-th vehicle commute departure time interval, rand (0, 1) is a random number from 0 to 1;
s33, after the initialization of the honey source vector is completed, calculating each solution vector of the honey source according to the following fitness calculation rule And recording the optimal solution and the solution vector of the optimal solution, thus finishing population initialization:
fitness＝∑m,n∈Mfitnessm,n
fitnessm,n＝carpoolm,n-tripm-tripn
Wherein:
Xm≤Xn
|Xm-Xn|≤1800
fitnessm,n≤0
in the formula, fitness m,n is the pooling loss of the mth vehicle and the nth vehicle when the mth vehicle is pooling and m is the driver, carpool m,n is the pooling mileage of the mth vehicle and the nth vehicle when the mth vehicle is pooling and the mth vehicle is the driver, trip m is the commuter mileage of the mth commuter vehicle, O m,Dm is the starting point and the end point of the commuter travel of the mth commuter vehicle, For mileage from point O m to point D n, x m is departure time of the mth vehicle, track of the mth vehicle at T m bits,/>For the earliest time of the time interval when vehicle m passes point O n, the same applies for/>The latest time of the time interval for vehicle m to pass point O n.
Further, in step S4, specifically including:
s41, employing bees to search neighbors according to the positions of the food sources in the memory of the bees, finding better honey sources nearby the food sources, and determining the neighbor honey sources by adopting the following formula:
Wherein the method comprises the steps of Is a newly generated neighbor honey source, g and k are random values, phi t is a random value of interval [0,1 ];
S42, after the newly generated honey source is found, calculating the fitness value of the new honey source according to a fitness formula, if the fitness value of the new honey source is better than that of the original honey source, replacing the original honey source with the new honey source, adding 1 to the try times of the honey source, otherwise, keeping the try times of the honey source unchanged, comparing the fitness value of the new honey source with that of the optimal honey source, and if the fitness value is better than that of the optimal honey source, updating the optimal fitness value and the optimal honey source, otherwise, keeping the optimal fitness value and the optimal honey source unchanged.
Further, in step S5, specifically including:
s51, substituting the optimal solution into the following formula to calculate a correction solution for each honey source:
fitness′t＝(0.9*fitnesst/bestfitness+0.1)；
S52, generating a random number rand E [0,1], if the fitness' t is more than or equal to rand, repeating the step S4 on the honey source to generate a new neighbor honey source, then calculating the fitness value of the neighbor honey source, updating the honey source and the try times, and updating the optimal fitness value and the optimal honey source.
Further, in step S6, specifically including:
S61, traversing the trial times of all honey sources;
s62, selecting honey sources with the try times smaller than the maximum try times maxInvalidCount, and initializing a corresponding new honey source for each selected honey source;
s63, calculating the fitness value of the new honey source corresponding to each selected honey source;
s64, if the fitness value of the new honey source is larger than that of the old honey source, replacing the old honey source with the new honey source, otherwise, keeping unchanged.
The invention has the beneficial effects that:
1) According to the RFID electronic license plate identification data of the vehicles, the travel journey of the commute private car and the travel journey subsequence of the commute private car are obtained through statistics, so that the experimental efficiency can be improved, and the complexity is reduced.
2) The traditional artificial bee colony algorithm is improved, a new fitness calculation method is designed, and a vehicle carpooling matching method based on the artificial bee colony algorithm is provided.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objects and other advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the specification.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a general flow chart of the method of the present invention;
FIG. 2 is a schematic illustration of a ride share;
Fig. 3 is an algorithm flow chart.
Detailed Description
The technical scheme of the invention is described in detail below with reference to the accompanying drawings.
FIG. 1 is a general flow chart of the method of the present invention, as shown, the method provided by the present invention comprises the following steps:
S1, counting the commuter journey and departure time interval of the commuter vehicle, counting the subsequence paths of each journey, and storing all the subsequence paths into a database;
S2, determining the input of an artificial bee colony algorithm: the method comprises the steps of determining the dimension M of a honey source solution according to the number of commuter vehicles, wherein the number of honey sources is the population size s, and the number of scout bees is the number of honey sources s, the iteration number n and the maximum try number maxInvalidCount;
S3, initializing period: initializing the population, and initializing honey source vector by using the reconnaissance bees Wherein s is the size of the population; due to each honey source/>Are solution vectors of dimension M of the problem to be optimized, and therefore eachEach containing M variables x j (j=1, 2, … M), each x j is initialized; after the initialization of the honey source vector is completed, each solution vector/>, of the honey source is calculated according to the fitness calculation ruleThe fitness value of the optimal solution and the solution vector of the optimal solution are recorded, and the population initialization is completed;
S4, hiring a peak period: employing bees to search for neighbors based on the locations of the food sources in their memory, finding better food sources near the food sources; after the hiring bees find a food source, the adaptation value is evaluated, and the optimal solution, the optimal solution vector and the number of attempts are updated;
S5, observing the bee period: non-employment bees consist of two parts of a population: observing bees and spying bees; employing bees to share food source information they obtain with observing bees waiting in the cell, the observing bees making a random choice based on this information;
s6, a bee detection period: the unused bees randomly search for food sources, known as scouts; if the employment bee does not improve the quality of the solution after the maximum number of attempts maxInvalidCount is exceeded, the employment bee becomes a scout bee, the solution it owns is discarded, and the converted scout bee generates a solution by initializing the formula with a honey source;
and S7, iterating the steps S3 to S6 according to the input iteration times n.
Fig. 2 is a schematic diagram of a carpool, and fig. 3 is a flowchart of an algorithm. The method specifically comprises the following steps:
in step S1, the commuter track has the characteristics of high frequency, stability, space-time similarity, etc., the resident with the commuter track is called a commuter, and the commuter private car of the commuter in the city can be found by using the RFID electronic license plate data, specifically comprising the following steps:
s11, extracting the commuting track of all RFID electronic license plate data of the commuting private car A;
S12, ordering the commuting tracks of the commuting private car A according to the time ascending sequence of the vehicles passing through the RFID acquisition points, representing the commuting tracks by a sequence, R＝<eid,r,t>，/>Wherein Tra A represents the track of the vehicle A, R represents an RFID electronic license plate data, eid represents the electronic license plate number of the vehicle, R represents the identification number of the RFID acquisition point, t represents the time when the vehicle is identified,/>Representing the time when vehicle A passes the ith RFID acquisition point, wherein the track of the commuter vehicle A passing the RFID is/>
S13, counting the commuting track of the commuting private car A according to the track of each working day, extracting the earliest time and the latest time of each acquisition point passing each day, and storing the earliest time and the latest time into a database, wherein the time interval data of the commuting track points of the commuting private car are expressed as:
Wherein Commuter A denotes commute track time interval data of the commute private car a, And/>Representing earliest time and latest time of the commute private car A passing through the nth commute track point;
and S14, finally, executing the steps on all the commute private cars, and storing the commute track point time interval data of all the commute private cars into a database after statistics.
In step S2, a commuter sub-sequence list is created, including:
S21, based on the commute track point time interval data statistics of all the commute vehicles obtained in the step S1, selecting a commute private vehicle, recording eid, origin and destination thereof, putting the three pieces of information into original RFID electronic license plate data for searching, finding out RFID points passing between the origin and destination of the vehicle, recording the RFID points, wherein every two points are a subsequence, and storing the subsequence in a database, wherein the subsequence contained in the starting and destination of each vehicle can be expressed as:
si＝<eid,origin,destination,subsequence1…subsequencen>
Wherein s i represents the ith commuter sub-sequence data, eid represents the electronic license plate identification number of the vehicle, origin represents the commuter departure place of the commuter vehicle, destination represents the commuter destination of the vehicle, subsequence n represents the nth sub-sequence from the starting point to the end point of the vehicle;
s21, executing the steps on all commuter private cars in all commuter schedules, and storing the subsequences obtained by all the commuter private cars into the commuter subsequences.
In step S3, the method specifically includes the following steps:
s31, inputting parameters required by a manual bee colony algorithm, determining the dimension M of a honey source solution according to the number of commuter vehicles, wherein the number of honey sources is the population size S, the number of scout bees is the number S of honey sources, the iteration number n and the maximum try number maxInvalidCount;
S32, initializing honey source vectors by using the number of the spy bees being the same as that of the honey sources Wherein s is the size of the population; due to each honey source/>Are solution vectors of dimension M of the problem to be optimized, and therefore eachAll containing M variables x j (j=1, 2, … M), each x j is initialized according to the following formula:
xj＝lj+rand(0,1)*(uj-lj)
Wherein l j and u j are minimum and maximum values of the j-th vehicle commute departure time interval, rand (0, 1) is a random number from 0 to 1;
s33, after the initialization of the honey source vector is completed, calculating each solution vector of the honey source according to the following fitness calculation rule And recording the optimal solution and the solution vector of the optimal solution, thus finishing population initialization:
fitness＝∑m,n∈Mfitnessm,n
fitnessm,n＝carpoolm,n-tripm-tripn
Wherein:
Xm≤Xn
|Xm-Xn|≤1800
fitnessm,n≤0
in the formula, fitness m,n is the pooling loss of the mth vehicle and the nth vehicle when the mth vehicle is pooling and m is the driver, carpool m,n is the pooling mileage of the mth vehicle and the nth vehicle when the mth vehicle is pooling and the mth vehicle is the driver, trip m is the commuter mileage of the mth commuter vehicle, O m,Dm is the starting point and the end point of the commuter travel of the mth commuter vehicle, For mileage from point O m to point D n, x m is departure time of the mth vehicle, track of the mth vehicle at T m bits,/>For the earliest time of the time interval when vehicle m passes point O n, the same applies for/>The latest time of the time interval for vehicle m to pass point O n.
In step S4, specifically, the method includes:
s41, employing bees to search neighbors according to the positions of the food sources in the memory of the bees, finding better honey sources nearby the food sources, and determining the neighbor honey sources by adopting the following formula:
Wherein the method comprises the steps of Is a newly generated neighbor honey source, g and k are random values, phi t is a random value of interval [0,1 ];
S42, after the newly generated honey source is found, calculating the fitness value of the new honey source according to a fitness formula, if the fitness value of the new honey source is better than that of the original honey source, replacing the original honey source with the new honey source, adding 1 to the try times of the honey source, otherwise, keeping the try times of the honey source unchanged, comparing the fitness value of the new honey source with that of the optimal honey source, and if the fitness value is better than that of the optimal honey source, updating the optimal fitness value and the optimal honey source, otherwise, keeping the optimal fitness value and the optimal honey source unchanged.
In step S5, specifically, the method includes:
s51, substituting the optimal solution into the following formula to calculate a correction solution for each honey source:
fitness′t＝(0.9*fitnesst/bestfitness+0.1)；
S52, generating a random number rand E [0,1], if the fitness' t is more than or equal to rand, repeating the step S4 on the honey source to generate a new neighbor honey source, then calculating the fitness value of the neighbor honey source, updating the honey source and the try times, and updating the optimal fitness value and the optimal honey source.
In step S6, specifically, the method includes:
S61, traversing the trial times of all honey sources;
s62, selecting honey sources with the try times smaller than the maximum try times maxInvalidCount, and initializing a corresponding new honey source for each selected honey source;
s63, calculating the fitness value of the new honey source corresponding to each selected honey source;
s64, if the fitness value of the new honey source is larger than that of the old honey source, replacing the old honey source with the new honey source, otherwise, keeping unchanged.
In summary, the commute private car matching method based on the artificial bee colony algorithm provided by the invention can select reasonable population size s, maximum try times maxInvalidCount and iteration times n as input parts of the algorithm, determine constraint conditions of car matching and a calculation method of fitness, and finally obtain an optimal car matching scheme.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified without departing from the spirit and scope of the technical solution, and all such modifications are included in the scope of the claims of the present invention.
Claims (2)
1. A commute private car carpooling matching method based on an artificial bee colony algorithm is characterized by comprising the following steps of: the method comprises the following steps:
S1, counting the commuter journey and departure time interval of the commuter vehicle, counting the subsequence paths of each journey, and storing all the subsequence paths into a database;
S2, determining the input of an artificial bee colony algorithm: the method comprises the steps of determining the dimension M of a honey source solution according to the number of commuter vehicles, wherein the number of honey sources is the population size s, and the number of scout bees is the number of honey sources s, the iteration number n and the maximum try number maxInvalidCount;
S3, initializing period: initializing the population, and initializing honey source vector by using the reconnaissance bees Wherein s is the size of the population; due to each honey source/>Are solution vectors with dimension M of the problem to be optimized, and therefore each/>Each containing M variables x j (j=1, 2, … M), each x j is initialized; after the initialization of the honey source vector is completed, each solution vector/>, of the honey source is calculated according to the fitness calculation ruleThe fitness value of the optimal solution and the solution vector of the optimal solution are recorded, and the population initialization is completed;
S4, hiring a peak period: employing bees to search for neighbors based on the locations of the food sources in their memory, finding better food sources near the food sources; after the hiring bees find a food source, the adaptation value is evaluated, and the optimal solution, the optimal solution vector and the number of attempts are updated;
S5, observing the bee period: non-employment bees consist of two parts of a population: observing bees and spying bees; employing bees to share food source information they obtain with observing bees waiting in the cell, the observing bees making a random choice based on this information;
s6, a bee detection period: the unused bees randomly search for food sources, known as scouts; if the employment bee does not improve the quality of the solution after the maximum number of attempts maxInvalidCount is exceeded, the employment bee becomes a scout bee, the solution it owns is discarded, and the converted scout bee generates a solution by initializing the formula with a honey source;
s7, iterating the steps S3 to S6 according to the input iteration times n;
In step S1, the method specifically includes the following steps:
s11, extracting the commuting track of all RFID electronic license plate data of the commuting private car A;
S12, ordering the commuting tracks of the commuting private car A according to the time ascending sequence of the vehicles passing through the RFID acquisition points, representing the commuting tracks by a sequence, Wherein Tra A represents the track of the vehicle A, R represents an RFID electronic license plate data, eid represents the electronic license plate number of the vehicle, R represents the identification number of the RFID acquisition point, t represents the time when the vehicle is identified,/>Representing the time when vehicle A passes the ith RFID acquisition point, wherein the track of the commuter vehicle A passing the RFID is/>
S13, counting the commuting track of the commuting private car A according to the track of each working day, extracting the earliest time and the latest time of each acquisition point passing each day, and storing the earliest time and the latest time into a database, wherein the time interval data of the commuting track points of the commuting private car are expressed as:
Wherein Commuter A denotes commute track time interval data of the commute private car a, And/>Representing earliest time and latest time of the commute private car A passing through the nth commute track point;
S14, finally, executing the steps on all the commute private cars, and storing the commute track point time interval data of all the commute private cars into a database after statistics;
in step S2, a commuter sub-sequence list is created, including:
S21, based on the commute track point time interval data statistics of all the commute vehicles obtained in the step S1, selecting a commute private vehicle, recording eid, origin and destination thereof, putting the three pieces of information into original RFID electronic license plate data for searching, finding out RFID points passing between the origin and destination of the vehicle, recording the RFID points, wherein every two points are a subsequence, storing the subsequence contained in the starting and destination of each vehicle in a database, and expressing the subsequence as:
si＝<eid,origin,destination,subsequence1…subsequencen>
Wherein s i represents the ith commuter sub-sequence data, eid represents the electronic license plate identification number of the vehicle, origin represents the commuter departure place of the commuter vehicle, destination represents the commuter destination of the vehicle, subsequence n represents the nth sub-sequence from the starting point to the end point of the vehicle;
S22, executing the steps on all commuter private cars in all commuter schedules, and storing subsequences obtained by all the commuter private cars into the commuter subsequences;
in step S3, the method specifically includes the following steps:
s31, inputting parameters required by a manual bee colony algorithm, determining the dimension M of a honey source solution according to the number of commuter vehicles, wherein the number of honey sources is the population size S, the number of scout bees is the number S of honey sources, the iteration number n and the maximum try number maxInvalidCount;
S32, initializing honey source vectors by using the number of the spy bees being the same as that of the honey sources Wherein s is the size of the population; due to each honey source/>Are solution vectors with dimension M of the problem to be optimized, and therefore each/>All containing M variables x j (j=1, 2, … M), each x j is initialized according to the following formula:
xj＝lj+rand(0,1)*(uj-lj)
Wherein l j and u j are minimum and maximum values of the j-th vehicle commute departure time interval, rand (0, 1) is a random number from 0 to 1;
s33, after the initialization of the honey source vector is completed, calculating each solution vector of the honey source according to the following fitness calculation rule And recording the optimal solution and the solution vector of the optimal solution, thus finishing population initialization:
fitness＝∑m,n∈Mfitnessm,n
fitnessm,n＝carpoolm,n-tripm-tripn
Wherein:
Xm≤Xn
|Xm-Xn|≤1800
fitnessm,n≤0
in the formula, fitness m,n is the pooling loss of the mth vehicle and the nth vehicle when the mth vehicle is pooling and m is the driver, carpool m,n is the pooling mileage of the mth vehicle and the nth vehicle when the mth vehicle is pooling and the mth vehicle is the driver, trip m is the commuter mileage of the mth commuter vehicle, O m,Dm is the starting point and the end point of the commuter travel of the mth commuter vehicle, For mileage from point O m to point D n, x m is departure time of the mth vehicle, track of the mth vehicle at T m bits,/>For the earliest time of the time interval in which vehicle m passes point O n, and similarly,The latest time of the time interval for vehicle m to pass point O n;
In step S4, specifically, the method includes:
s41, employing bees to search neighbors according to the positions of the food sources in the memory of the bees, finding better honey sources nearby the food sources, and determining the neighbor honey sources by adopting the following formula:
Wherein the method comprises the steps of Is a newly generated neighbor honey source, g and k are random values, phi t is a random value of interval [0,1 ];
S42, after finding a newly generated honey source, calculating an adaptability value of the new honey source according to an adaptability formula, if the adaptability value of the new honey source is better than that of the original honey source, replacing the original honey source with the new honey source, adding 1 to the try times of the honey source, otherwise, keeping the try times of the honey source unchanged, comparing the adaptability value of the new honey source with that of the optimal honey source, and if the adaptability value of the new honey source is better than that of the optimal honey source, updating the optimal adaptability value and the optimal honey source, otherwise, keeping the optimal adaptability value and the optimal honey source unchanged;
In step S5, specifically, the method includes:
s51, substituting the optimal solution into the following formula to calculate a correction solution for each honey source:
fitness′t＝(0.9*fitnesst/bestfitness+0.1)；
S52, generating a random number rand E [0,1], if the fitness' t is more than or equal to rand, repeating the step S4 on the honey source to generate a new neighbor honey source, then calculating the fitness value of the neighbor honey source, updating the honey source and the try times, and updating the optimal fitness value and the optimal honey source.
2. The method for matching commuting private cars based on artificial bee colony algorithm as defined in claim 1, wherein the method comprises the following steps: in step S6, specifically, the method includes:
S61, traversing the trial times of all honey sources;
s62, selecting honey sources with the try times smaller than the maximum try times maxInvalidCount, and initializing a corresponding new honey source for each selected honey source;
s63, calculating the fitness value of the new honey source corresponding to each selected honey source;
s64, if the fitness value of the new honey source is larger than that of the old honey source, replacing the old honey source with the new honey source, otherwise, keeping unchanged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111640253.1A CN114331617B (en) | 2021-12-29 | 2021-12-29 | Commuting private car pooling matching method based on artificial bee colony algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111640253.1A CN114331617B (en) | 2021-12-29 | 2021-12-29 | Commuting private car pooling matching method based on artificial bee colony algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114331617A CN114331617A (en) | 2022-04-12 |
| CN114331617B true CN114331617B (en) | 2024-05-31 |
Family
ID=81016695
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111640253.1A Active CN114331617B (en) | 2021-12-29 | 2021-12-29 | Commuting private car pooling matching method based on artificial bee colony algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114331617B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117910782B (en) * | 2024-03-19 | 2024-05-14 | 华侨大学 | Inter-city carpooling static scheduling method based on complete subgraph and insert genetic algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102016204957A1 (en) * | 2015-03-25 | 2016-09-29 | Magna Steyr Fahrzeugtechnik Ag & Co Kg | Automated lane change in dynamic traffic based on driving dynamics restrictions |
| CN107038858A (en) * | 2017-05-25 | 2017-08-11 | 厦门大学 | Method is recommended in the dynamic share-car of the private car that commutes |
| CN108491670A (en) * | 2018-04-17 | 2018-09-04 | 常熟理工学院 | A kind of parameter matching of electronic logistic car driving motor and optimization method |
| CN112185120A (en) * | 2020-10-09 | 2021-01-05 | 重庆大学 | Commuting private car identification method based on electronic license plate data |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107450498B (en) * | 2017-09-11 | 2018-08-28 | 合肥工业大学 | Based on the production scheduling method and system for improving artificial bee colony algorithm |
-
2021
- 2021-12-29 CN CN202111640253.1A patent/CN114331617B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102016204957A1 (en) * | 2015-03-25 | 2016-09-29 | Magna Steyr Fahrzeugtechnik Ag & Co Kg | Automated lane change in dynamic traffic based on driving dynamics restrictions |
| CN107038858A (en) * | 2017-05-25 | 2017-08-11 | 厦门大学 | Method is recommended in the dynamic share-car of the private car that commutes |
| CN108491670A (en) * | 2018-04-17 | 2018-09-04 | 常熟理工学院 | A kind of parameter matching of electronic logistic car driving motor and optimization method |
| CN112185120A (en) * | 2020-10-09 | 2021-01-05 | 重庆大学 | Commuting private car identification method based on electronic license plate data |
Non-Patent Citations (2)
| Title |
|---|
| A Particle Swarm-Based Commuter Matching Approach for Stable;Chenglin Ye;《2022 IEEE International Conference on Systems, Man, and Cybernetics》;20221118;650-655 * |
| An artificial bee colony algorithm with variable neighborhood search and tabu list for long-term carpooling problem with time window;Sheng Su;《Applied Soft Computing》;20190930;第85卷;1-14 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114331617A (en) | 2022-04-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8229666B2 (en) | Generating and using pattern keys in navigation systems to predict user destinations | |
| CN110516870B (en) | Multi-recovery site garbage collection and transportation method based on co-evolution | |
| CN107977734B (en) | Prediction method based on mobile Markov model under space-time big data | |
| Pan et al. | DHPA: Dynamic human preference analytics framework: A case study on taxi drivers’ learning curve analysis | |
| CN111462489B (en) | Traffic congestion area prediction method and device | |
| CN114331617B (en) | Commuting private car pooling matching method based on artificial bee colony algorithm | |
| Jiau et al. | Services-oriented computing using the compact genetic algorithm for solving the carpool services problem | |
| CN116050672B (en) | Urban management method and system based on artificial intelligence | |
| Shim et al. | Evolutionary algorithms for solving multi-objective travelling salesman problem | |
| Xing et al. | A short-term traffic flow prediction method based on kernel extreme learning machine | |
| Reich et al. | Survey of ETA prediction methods in public transport networks | |
| CN112579921B (en) | Track indexing and query method and system based on inverted sorting index and prefix tree | |
| CN116957174A (en) | Freight line integrated planning method and system based on data fusion | |
| Li et al. | Feature selection and model fusion approach for predicting urban macro travel time | |
| CN117251722A (en) | Intelligent traffic management system based on big data | |
| CN114253975B (en) | Load-aware road network shortest path distance calculation method and device | |
| Karczmarek et al. | Choquet integral-based aggregation for the analysis of anomalies occurrence in sustainable transportation systems | |
| CN109859467B (en) | Mining analysis method for environmental impact factors in traffic model | |
| Mu et al. | Recommend taxi pick-up hotspots based on density-based clustering | |
| CN113344239B (en) | Traffic congestion condition prediction method and system based on two-stage spectral clustering | |
| CN109492116B (en) | Method and device for building character relationship network | |
| Neto et al. | Predicting the next location for trajectories from stolen vehicles | |
| Xiao et al. | Level-wise driving knowledge induction for embedded automatic train driving system | |
| CN118378060B (en) | Private car carbon emission key feature and space-time pattern recognition method, equipment and medium | |
| Forke et al. | Feature engineering techniques and spatio-temporal data processing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |