CN110689174B - Personnel route planning method and device based on public transportation - Google Patents
Personnel route planning method and device based on public transportation Download PDFInfo
- Publication number
- CN110689174B CN110689174B CN201910870544.6A CN201910870544A CN110689174B CN 110689174 B CN110689174 B CN 110689174B CN 201910870544 A CN201910870544 A CN 201910870544A CN 110689174 B CN110689174 B CN 110689174B
- Authority
- CN
- China
- Prior art keywords
- route
- planning
- public transportation
- initial
- information
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/12—Computing arrangements based on biological models using genetic models
- G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Abstract
The invention discloses a personnel route planning method and device based on public transportation. The method comprises the steps of obtaining travel information of personnel, obtaining a plurality of public transportation route information meeting the travel information based on the route information of public transportation, obtaining an initial planning route from the plurality of public transportation route information according to a saving algorithm, generating a large number of initial populations from all the obtained initial planning routes, and carrying out iteration by combining a genetic algorithm to obtain an optimal planning route. The method overcomes the defects of low route planning efficiency, high calculation cost, poor planning result effect or lack of flexibility based on manual experience in the prior art, can intelligently realize route planning, has low algorithm complexity and high operation efficiency, can simultaneously expand calculation in parallel, and can process a large amount of data. The method can be widely applied to the field of route planning.
Description
Technical Field
The invention relates to the field of route planning, in particular to a personnel route planning method and device based on public transportation.
Background
The public transportation travel route is planned according to the business requirement, which belongs to the NP-hard problem, and along with the increasing complexity, the traditional methods, such as a manual experience method, a linear programming method, a dynamic programming method and the like, gradually meet the requirements of the problem, the traditional methods have high calculation precision, can list all feasible solutions of the problem and calculate and determine the optimal solution, but have very large calculation amount, and only solve the problems of a few very simple and small-scale route wire arrangement planning paths. Therefore, it is necessary to find other methods to solve the existing route planning problem, and with the rapid development of computers, heuristic algorithms show great advantages in solving such problems, and especially, group intelligent algorithms are widely applied in solving the route planning problem and the like. Most of the existing intelligent algorithms can rapidly solve the problem of route planning, and compared with manual scheduling, the intelligent algorithm has obvious advantages. However, each intelligent algorithm has the defects, only a single intelligent algorithm is used for solving the problem of planning the wire arrangement of the route, so that the problem of local optimum is easily solved, an approximate optimum solution cannot be found, the time spent for iterative solution is very long, and real-time calculation of daily requirements in reality is not met. Therefore, a route planning method based on public transportation personnel, which can intelligently schedule routes and has low algorithm complexity and high operation efficiency, needs to be provided.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, the invention aims to provide the public transportation personnel-based route planning method which can intelligently schedule routes and has low algorithm complexity and high calculation efficiency.
The technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a method for planning a route for persons based on public transportation, comprising:
acquiring travel information of a person, wherein the travel information comprises: a start address, a target address, a departure time, and a service time window;
acquiring a plurality of pieces of public transportation route information satisfying the travel information based on route information of public transportation, the public transportation route information including: route time, route distance and specific traffic mode;
acquiring an initial planning route from a plurality of public transportation route information according to a saving algorithm;
and generating a large number of initial populations from all the acquired initial planning routes, and carrying out iteration by combining a genetic algorithm to obtain the optimal planning route.
Further, the method also comprises the step of inquiring the longitude and latitude of the map, and judging whether the initial address and the target address accord with real coordinate information or not.
Further, the obtaining an initial planned route from the public transportation route information according to the saving algorithm specifically includes:
and selecting a site closest to the initial address, evaluating other sites by using a saving algorithm to judge whether the selected site is used as the selected site or not until an iteration condition is reached, and forming the initial planning route by all the selected sites according to a time sequence.
Further, all the initial planned routes are arranged in a random order, each arrangement mode is used as one chromosome, and a plurality of chromosomes form the initial population.
Further, the fitness evaluation function of the genetic algorithm is expressed as:
wherein f i (T) is the fitness value of chromosome i in the T generation population, z i And (T) is the objective function value of the chromosome i in the T generation population, namely the total transportation cost corresponding to the chromosome i.
Further, the saving algorithm and the genetic algorithm realize an operation process under a spark architecture based on a Hadoop distributed cluster.
In a second aspect, the present invention also provides a public transportation-based personnel route planning apparatus, including:
the travel information acquisition module is used for acquiring travel information: the method is used for acquiring travel information of each person, and the travel information comprises the following steps: a start address, a target address, a departure time, and a service time window;
the original route module is obtained: a method for acquiring a plurality of pieces of public transportation route information satisfying the travel information based on route information of public transportation, the public transportation route information including: route time, route distance and specific traffic mode;
obtaining an initial planning route module: the method comprises the steps of obtaining an initial planning route from a plurality of public transportation route information corresponding to each person according to a saving algorithm;
the optimal planning route module is used for obtaining: and the method is used for generating a large number of initial populations from all the acquired initial planning routes, and carrying out iteration by combining a genetic algorithm to obtain the optimal planning route.
Further, the system also comprises a map longitude and latitude query module which is used for judging whether the initial address and the target address accord with real coordinate information.
In a third aspect, the present invention provides a mass transit based personal route planning apparatus comprising:
at least one processor, and a memory communicatively coupled to the at least one processor;
wherein the processor is adapted to perform the method according to any of the first aspects by invoking a computer program stored in the memory.
In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method of any one of the first aspects.
The beneficial effects of the invention are as follows:
according to the invention, travel information of personnel is acquired, a plurality of public transportation route information meeting the travel information is acquired based on the route information of public transportation, an initial planning route is acquired from the plurality of public transportation route information according to a saving algorithm, a large number of initial populations are generated from all the acquired initial planning routes, and an optimal planning route is obtained by iteration in combination with a genetic algorithm. The method overcomes the defects of low route planning efficiency, high calculation cost, poor planning result effect or lack of flexibility based on manual experience in the prior art, can intelligently realize route planning, has low algorithm complexity and high operation efficiency, can simultaneously expand calculation in parallel, and can process a large amount of data. The method can be widely applied to the field of route planning.
Drawings
FIG. 1 is a flow chart of an implementation of one embodiment of a mass transit based personnel route planning method of the present invention;
FIG. 2 is a schematic diagram of an exemplary embodiment of a method for mass transit based personnel route planning in accordance with the present invention;
FIG. 3 is a schematic overall flow chart of a genetic algorithm of an embodiment of a mass transit based personal route planning method of the present invention;
fig. 4 is a block diagram of an embodiment of a mass transit based personal route planning device of the present invention.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
Embodiment one:
one scenario of the present embodiment is represented as follows: the method comprises the steps that a certain number of salesmen need to go to a plurality of destination addresses with different positions from a starting address to do business, the destination addresses are dynamically changed every day, the selected traffic mode is public traffic, each destination address has a specified business time window, namely a service time range, so that the salesmen need to go to the destination address to do business within the specified business time window, in addition, the maximum number of the service destination addresses of each salesmen every day can be assumed, an optimal planning route is provided by carrying out route planning on the scene, and the time spent on the road by all the salesmen is as small as possible.
An embodiment of the present invention provides a method for planning a personnel route based on public transportation, fig. 1 is a flowchart of implementation of the method for planning a personnel route based on public transportation, as shown in fig. 1, and the method includes the following steps:
s1: and acquiring travel information of the personnel. In this embodiment, the travel information includes: start address, target address, departure time, traffic time window.
The starting address and the target address are both represented as latitude and longitude information, so the embodiment further includes: and inquiring the longitude and latitude of the map, judging whether the initial address and the target address accord with the real coordinate information, eliminating or correcting the information which does not accord with the real coordinate information, and numbering all longitude and latitude of the coordinate positions at the same time, so that the subsequent calculation is convenient.
S2: acquiring a plurality of pieces of public transportation route information satisfying travel information based on route information of public transportation, the public transportation route information including: route time, route distance, specific traffic pattern.
The public transportation route information is obtained, a plurality of public transportation route information meeting the travel information in the step S1 is generated, the travel requirement is only needed to be met, whether the public transportation route information is an optimal route is not needed to be considered, and meanwhile, peak routes which are operated in the peak-going period are eliminated.
S3: acquiring an initial planning route from a plurality of public transportation route information according to a saving algorithm;
s4: and generating a large number of initial populations from all the acquired initial planning routes, and carrying out iteration by combining a genetic algorithm to obtain the optimal planning route.
In a specific step S3, the core idea of the saving algorithm is to sequentially merge two route loops into one loop, that is, select the site closest to the starting address, evaluate the other sites with the saving algorithm to determine whether to be the selected site, until the iteration condition is reached, and form the initial planning route by all the selected sites according to the time sequence.
As shown in fig. 2, which is a schematic diagram of the conservation algorithm of the present embodiment, assuming that there are 3 stations, denoted as O, i and j, and in conjunction with fig. 2, the route distance constraint is denoted as:
S(Oi+Oj+ij)<S(Oi)+S(iO)+S(Oj)+S(jO) (1)
wherein S represents mileage cost, and the time/distance cost before the adoption of the saving algorithm is checked to be lower than the time/distance cost after the adoption of the saving algorithm.
In some scenarios, the traffic time window limit represents a back-push from the point in time of arrival at the current site, if the arrival destination point time is not within the traffic time window, the current site is reached before its rest time while ensuring that the departure time is not within the traffic time window of the last site.
In this embodiment, the bus route information between all the obtained coordinates of the stations is obtained, the station with the shortest time corresponding to the starting address is obtained as the starting station according to the information and in combination with the travel information, and all the stations which are next to the starting station are optimized by using the saving algorithm, that is, the predicted time from the station to the next station is obtained and the predicted time from the starting station to the next station is compared, the stations with larger time difference meet the saving principle, and a plurality of initial planning routes are obtained by circulating for many times until the limiting condition is reached.
Because a single algorithm is easy to trap into local optimum, an optimum solution cannot be found, and the time spent for iterative solution is quite long, the method combines a saving algorithm with a genetic algorithm, and provides a better initial population for the genetic algorithm by using the saving algorithm, so that the number of times of calculation iteration can be reduced when the genetic algorithm is used, and the calculation time can be shortened.
In step S4, the genetic algorithm (Genetic Algorithm) is a calculation model of the biological evolution process simulating the natural selection and genetic mechanism of the darwinian biological evolution theory, and is a method for searching the optimal solution by simulating the natural evolution process.
In this embodiment, all the initial planned routes obtained in step S3 are arranged in a random order, each arrangement mode is used as a chromosome, a plurality of chromosomes form an initial population, and iteration is performed by combining a genetic algorithm to obtain an optimal planned route, wherein the chromosome composition is that coordinates of each site in the included initial planned route are encoded in sequence, special symbols are used for encoding among routes, a complete gene individual is generated, meanwhile, the chromosomes are replicated and expanded to form a population, and each chromosome can acquire an adaptive value of the chromosome through an adaptive evaluation function, namely the shortest time value. The method comprises the steps of crossing any two chromosomes in an initial population to obtain a next generation chromosome, reserving if the adaptive value of the next generation chromosome is smaller than that of a parent, copying the relatively low value of the parent, and inheriting the next generation in sequence until an individual with the most suitable adaptive value is obtained as an optimal planning route.
The steps for generating the initial population are as follows:
the initial population is generally formed in a random manner, so that the initial population is distributed uniformly in the whole solution space to the greatest extent, the local optimum can be jumped out to a certain extent, and the solution with the highest adaptability is selected from feasible solutions generated in ten times continuously to serve as the initial optimum solution.
As shown in fig. 3, which is an overall flowchart of the genetic algorithm of the present embodiment, it can be seen from fig. 3 that the genetic algorithm includes obtaining an initialized population, performing fitness detection on the initialized population, performing selection, crossover and mutation operations on the population subjected to fitness monitoring, judging whether the population meets a termination condition, if not, re-performing fitness detection, and repeating the selection, crossover and mutation operations, if the termination condition is met, performing termination operation, wherein the termination condition is a preset iteration number, the fitness detection refers to that a solution with a larger fitness evaluation function is reserved, and the fitness evaluation function is expressed as:
wherein f i (T) is the fitness value of chromosome i in the T generation population, z i (T) is the objective function value of chromosome i in the T generation population, namely the total transportation cost corresponding to chromosome i, f i The larger the value of (T), the closer the route corresponding to chromosome i is to the optimal planned route.
When selecting, the current group size is set as N, wherein the adaptive value of the chromosome j is f i Then j is selected with probability P sj Expressed as:
wherein the probability P sj Reflecting the proportion of chromosome fitness in the sum of chromosome fitness values of the whole population, the greater the chromosome fitness, the higher the probability of being selected, and vice versa.
The crossover process is described as follows:
1) Acting a crossover operator on a designated group, selecting any one parent chromosome (parent 1) from chromosomes of each pair of parents, and arbitrarily selecting 2 crossover points;
2) The genes between these 2 junctions were removed and designated T;
3) Taking out the gene at the crossing point of the other parent chromosome (parent 2) and the parent 1, and inserting the gene into the vacancy of the parent 1 according to the sequence position of the gene in the parent 2 to obtain a child 1;
4) And supplementing the chromosome extracted from the parent 1 into a vacancy in the parent 2 according to the position of the chromosome in the parent 1, so as to obtain a child 2.
At present, a PMX-like method is generally adopted, and it is assumed that there are two parents A, B, and the intersection position is "|":
A＝1 2||4 5 7 6||3 8 9
B＝2 1||5 4 3 7||8 6 9
wherein A and B respectively represent chromosomes, numerals 1 to 9 represent a daughter, 4 5 6 7 in A is sequentially given to the first 4 positions of the daughter A1, then elements in B are compared with 4 5 6 7 one by one, if the elements are the same, the elements are not used, and if the elements are different, the elements are sequentially placed at the subsequent positions of the daughter A1, so that the daughter A1 is 4 5 7 6 2 1 3 8 9, and the daughter B1 is 5 4 3 7 1 2 6 8 9 in the same way. The two chromosomes crossed in this step are from the two chromosomes produced in the previous step selection, and two new daughter are produced as parents of the next step mutation operation according to the PMX-like method.
The mutation is less likely, so that the mutation operation only plays an auxiliary role in the genetic algorithm. Chromosome mutation is carried out on each generation of population with mutation probability Pm. Here, a mutation strategy of exchanging two-point gene values is adopted for the chromosome, that is, a random multiple-time exchange mode is adopted, and whether two new chromosomes generated in the previous step are subjected to mutation operation is determined according to a certain mutation probability Pm. For example, with one chromosome C1 2 5 4 7 3 6 9 8, two designated crossover locations 3, 7 are randomly generated, and element 3, 5 and element 7, 6 are swapped to obtain a new chromosome 1 2 6 4 7 3 5 9 8.
Further, if the route distance constraint and the business time window constraint are considered, a route cutting, i.e., a new route, needs to be opened up when the two cannot be satisfied at the same time.
In addition, because the data volume processed in the embodiment is larger, the spark architecture based on the Hadoop distributed cluster is selected to realize the operation process, so that the operation efficiency is improved.
The Hadoop is a distributed computing platform and can run an application program for processing mass data, and the Hadoop comprises two core modules, a distributed storage module HDFS and a distributed computing module MapReduce. The HDFS, hadoop Distributed File System, is an open source system, and is an extensible file storage and delivery system capable of being used for large-scale data, allowing files to be shared on multiple hosts through a network, enabling multiple users on multiple machines to share files and storage space, enabling access to files through the network, and enabling the users to access local disks in a general manner from the perspective of the users, and enabling the system to operate continuously without data loss as a whole even if some nodes in the system are offline.
It is divided into two parts: the Name Node and the Date Node manage the Data nodes in the cluster, and when the client sends a request, the Name Node can specify which Data nodes to store according to the situation, but does not store real Data. An HDFS cluster is composed of a Name Node and a number of Data nodes, where the Name Node is a central server responsible for managing namespaces of file systems and access to files by clients. The Date Node of the cluster is generally a Data Node process run by a Node and is responsible for managing the storage on the Node where it is located.
Internally, a file is actually divided into one or more Data blocks, these blocks are stored on a set of Data nodes, the Name Node performs namespace operations of the file system, such as opening, closing, renaming a file or directory, and is responsible for determining the mapping of the Data blocks to specific Data Node nodes, the Data Node is responsible for processing the read/write requests of the file system client, and the creation, deletion and duplication of the Data blocks are performed under the unified scheduling of the Name Node, which is an efficient distributed cluster system.
Furthermore, the present embodiment selects Spark architecture to implement the operation process, where Spark is a big data processing framework constructed around speed, usability and complex analysis, and Spark is used to manage the requirements of big data processing of various data sets with different properties (text data, chart data, etc.) and data sources (batch data or real-time stream data). The Spark architecture adopts a Master-Slave model in distributed computing, wherein the Master is a node containing a Master process in a corresponding cluster, the Slave is a node containing a Worker process in the cluster, the Master is used as a controller of the whole cluster and is responsible for the normal operation of the whole cluster, the Worker is equivalent to a computing node, a Master node command is received and a state report is carried out, and an Executor is responsible for the execution of tasks; the Client is used as a Client of a user and is responsible for submitting an application, and the Driver is responsible for controlling the execution of one application.
After Spark cluster deployment, master processes and workbench processes are required to be started at a Master node and a slave node respectively, the whole cluster is controlled, and in the execution process of one Spark application, drivers and a workbench are two important roles. The Driver program is the starting point of application logic execution and is responsible for scheduling jobs, i.e., the distribution of Task tasks, while a plurality of workers are used to manage computing nodes and create Executor parallel processing tasks. In the execution stage, the Driver sequences the Task and the file and jar on which the Task depends and then transmits the sequence to the corresponding workbench machine, and meanwhile, the Executor processes the Task of the corresponding data partition.
The Spark operation mode is various, flexible and changeable, when deployed on a single machine, the Spark operation mode can be operated in a local mode or in a pseudo-distributed mode, when deployed in a distributed Cluster mode, a plurality of operation modes can be selected, the bottom resource scheduling depends on the actual requirement condition of the Cluster, an external resource scheduling frame can be relied on, a Spark built-in standby mode can be used, and the mode adopted by the embodiment is a YARN-Cluster mode.
In YARN-Cluster mode, when a user submits an application to YARN, YARN runs the application in two phases: the first stage is to start the Driver of Spark as an application Master in the YARN cluster; the second phase is to create an application by the application Master, then apply resources for it to the resource manager, and start the Executor to run the Task while monitoring its entire run until the run is complete.
In this embodiment, spark itself does not provide a distributed file system, so most of Spark analysis depends on the distributed file system HDFS of Hadoop, and both MapReduce and Spark of Hadoop can perform data calculation, but compared with MapReduce, spark has a faster speed and provides richer functions. Therefore, the present embodiment adopts the Hadoop storage module HDFS and the Spark calculation module.
The method and the device overcome the defects of low route planning efficiency, high calculation cost, poor planning result effect or lack of flexibility based on manual experience in the prior art, can intelligently realize route planning, have low algorithm complexity and high operation efficiency, can simultaneously expand calculation in parallel, and can process a large amount of data.
Embodiment two:
the present embodiment provides a public transportation-based personnel route planning device, configured to perform the method according to the first embodiment, as shown in fig. 4, which is a structural block diagram of the public transportation-based personnel route planning device, including:
the travel information acquisition module 100: the method is used for acquiring travel information of each person, and the travel information comprises the following steps: a start address, a target address, a departure time, and a service time window;
the get original route module 200: a plurality of pieces of public transportation route information for acquiring travel information based on route information of public transportation, the public transportation route information including: route time, route distance and specific traffic mode;
the get initial planned route module 300: the method comprises the steps of obtaining an initial planning route from a plurality of public transportation route information corresponding to each person according to a saving algorithm;
the get optimal planned route module 400: and the method is used for generating a large number of initial populations from all the acquired initial planning routes, and carrying out iteration by combining a genetic algorithm to obtain the optimal planning route.
Further, the map latitude and longitude query module 500 is further included, and is configured to determine whether the start address and the target address conform to real coordinate information.
In addition, the invention also provides a personnel route planning device based on public transportation, which comprises the following components:
at least one processor, and a memory communicatively coupled to the at least one processor;
wherein the processor is configured to perform the method according to embodiment one by invoking a computer program stored in the memory.
In addition, the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores computer executable instructions for causing a computer to execute the method according to the first embodiment.
According to the invention, travel information of personnel is acquired, a plurality of public transportation route information meeting the travel information is acquired based on the route information of public transportation, an initial planning route is acquired from the plurality of public transportation route information according to a saving algorithm, a large number of initial populations are generated from all the acquired initial planning routes, and an optimal planning route is obtained by iteration in combination with a genetic algorithm, so that the method can be widely applied to the field of route planning.
The above embodiments are only for illustrating the technical solution of the present invention, not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention, and are intended to be included within the scope of the appended claims and description.
Claims (9)
1. A method for planning a route for persons based on public transportation, comprising:
acquiring travel information of a person, wherein the travel information comprises: a start address, a target address, a departure time, and a service time window; the service time window is a time range of the personnel to perform service at the target address;
acquiring a plurality of pieces of public transportation route information satisfying the travel information based on route information of public transportation, the public transportation route information including: route time, route distance and specific traffic mode;
obtaining an initial planned route from a plurality of public transportation route information according to a saving algorithm, wherein the method comprises the following steps: selecting a site closest to the starting address as a starting site, calculating the estimated time from other sites to the next site and the estimated time from the starting site to the next site, evaluating the time difference according to a saving algorithm to judge whether the other sites are used as selected sites or not until an iteration condition is reached, and forming the initial planning route by all the selected sites according to the time sequence;
and generating a large number of initial populations from all the acquired initial planning routes, and carrying out iteration by combining a genetic algorithm to obtain the optimal planning route.
2. The method of claim 1, further comprising performing a map latitude and longitude query to determine whether the start address and the destination address match real coordinate information.
3. A mass transit based personal routing method as claimed in claim 1, wherein all initially planned routes are arranged in a random order, each arrangement being defined as a chromosome, a plurality of said chromosomes constituting said initial population.
4. A mass transit based personal route planning method according to claim 3, characterized in that the fitness evaluation function of the genetic algorithm is expressed as:
wherein f i (T) is the fitness value of chromosome i in the T generation population, z i And (T) is the objective function value of the chromosome i in the T generation population, namely the total transportation cost corresponding to the chromosome i.
5. A method of mass transit based personnel route planning according to any of claims 1 to 4, wherein the conservation algorithm and the genetic algorithm implement an algorithm under a spark architecture based on Hadoop distributed clusters.
6. A mass transit based personnel route planning apparatus applying the mass transit based personnel route planning method according to claim 1, comprising:
the travel information acquisition module is used for acquiring travel information: the method is used for acquiring travel information of each person, and the travel information comprises the following steps: a start address, a target address, a departure time, and a service time window;
the original route module is obtained: a method for acquiring a plurality of pieces of public transportation route information satisfying the travel information based on route information of public transportation, the public transportation route information including: route time, route distance and specific traffic mode;
obtaining an initial planning route module: the method for acquiring an initial planning route from a plurality of public transportation route information corresponding to each person according to the saving algorithm comprises the following steps: selecting a site closest to the starting address as a starting site, calculating the estimated time from other sites to the next site and the estimated time from the starting site to the next site, evaluating the time difference according to a saving algorithm to judge whether the other sites are used as selected sites or not until an iteration condition is reached, and forming the initial planning route by all the selected sites according to the time sequence;
the optimal planning route module is used for obtaining: and the method is used for generating a large number of initial populations from all the acquired initial planning routes, and carrying out iteration by combining a genetic algorithm to obtain the optimal planning route.
7. The mass transit-based personnel route planning device of claim 6, further comprising a map latitude and longitude query module for determining whether the start address and the destination address match real coordinate information.
8. A mass transit-based personnel route planning apparatus, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the processor is adapted to perform the method of any of claims 1 to 5 by invoking a computer program stored in the memory.
9. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910870544.6A CN110689174B (en) | 2019-09-16 | 2019-09-16 | Personnel route planning method and device based on public transportation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910870544.6A CN110689174B (en) | 2019-09-16 | 2019-09-16 | Personnel route planning method and device based on public transportation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110689174A CN110689174A (en) | 2020-01-14 |
| CN110689174B true CN110689174B (en) | 2023-09-26 |
Family
ID=69109313
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910870544.6A Active CN110689174B (en) | 2019-09-16 | 2019-09-16 | Personnel route planning method and device based on public transportation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110689174B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112163754A (en) * | 2020-09-22 | 2021-01-01 | 深圳市北斗智能科技有限公司 | Personnel scheduling planning method, device and computer storage medium |
| CN112183838B (en) * | 2020-09-22 | 2024-02-02 | 湘潭大学 | Method for optimally solving intelligent unmanned vehicle path planning problem based on multi-constraint correction C-W algorithm |
| CN113393024A (en) * | 2021-06-03 | 2021-09-14 | 北京沃东天骏信息技术有限公司 | Indoor space planning method and device, electronic equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105427001A (en) * | 2015-12-03 | 2016-03-23 | 北京理工大学 | Optimal route of school bus of regional middle and primary school |
| CN107330559A (en) * | 2017-07-03 | 2017-11-07 | 华南理工大学 | A kind of hybrid customization public bus network planing method of many terminus multi-vehicle-types |
| CN108921483A (en) * | 2018-07-16 | 2018-11-30 | 深圳北斗应用技术研究院有限公司 | A kind of logistics route planing method, device and driver arrange an order according to class and grade dispatching method, device |
| CN109934401A (en) * | 2019-03-09 | 2019-06-25 | 吉林大学 | A kind of customization public transport method based on mobile interchange |
| CN110084402A (en) * | 2019-03-25 | 2019-08-02 | 广东工业大学 | A kind of public transport self-adapting dispatching method preferably to be tracked with ant based on website |
-
2019
- 2019-09-16 CN CN201910870544.6A patent/CN110689174B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105427001A (en) * | 2015-12-03 | 2016-03-23 | 北京理工大学 | Optimal route of school bus of regional middle and primary school |
| CN107330559A (en) * | 2017-07-03 | 2017-11-07 | 华南理工大学 | A kind of hybrid customization public bus network planing method of many terminus multi-vehicle-types |
| CN108921483A (en) * | 2018-07-16 | 2018-11-30 | 深圳北斗应用技术研究院有限公司 | A kind of logistics route planing method, device and driver arrange an order according to class and grade dispatching method, device |
| CN109934401A (en) * | 2019-03-09 | 2019-06-25 | 吉林大学 | A kind of customization public transport method based on mobile interchange |
| CN110084402A (en) * | 2019-03-25 | 2019-08-02 | 广东工业大学 | A kind of public transport self-adapting dispatching method preferably to be tracked with ant based on website |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110689174A (en) | 2020-01-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110689174B (en) | Personnel route planning method and device based on public transportation | |
| CN103413209B (en) | Many client many warehouses logistics distribution routing resources | |
| Jia et al. | An intelligent cloud workflow scheduling system with time estimation and adaptive ant colony optimization | |
| Huang et al. | Large scale real-time ridesharing with service guarantee on road networks | |
| Bukata et al. | Solving the resource constrained project scheduling problem using the parallel tabu search designed for the CUDA platform | |
| De Coninck et al. | Dynamic auto-scaling and scheduling of deadline constrained service workloads on IaaS clouds | |
| Mannion et al. | Parallel reinforcement learning for traffic signal control | |
| Grzybowska et al. | Decision support system for real-time urban freight management | |
| CN103914556A (en) | Large-scale graph data processing method | |
| Lei et al. | Hybrid heuristic search approach for deadlock-free scheduling of flexible manufacturing systems using Petri nets | |
| Kaleeswaran et al. | Dynamic scheduling of data using genetic algorithm in cloud computing | |
| CN105824686A (en) | Selecting method and selecting system of host machine of virtual machine | |
| CN103116525A (en) | Map reduce computing method under internet environment | |
| Zheng et al. | Solving two-stage stochastic route-planning problem in milliseconds via end-to-end deep learning | |
| CN100531070C (en) | Network resource scheduling simulation system | |
| CN114611755A (en) | Hybrid meta-heuristic algorithm-based optimal path determination method for traveling salesman | |
| CN112836974A (en) | DQN and MCTS based box-to-box inter-zone multi-field bridge dynamic scheduling method | |
| Er et al. | Parallel genetic algorithm to solve traveling salesman problem on mapreduce framework using hadoop cluster | |
| Golmakani et al. | An artificial immune algorithm for multiple-route job shop scheduling problem with preventive maintenance constraints | |
| CN115688495A (en) | Distributed LVC simulation system collaborative planning method, server and storage medium | |
| CN107679126A (en) | Laser three-D cloud data stores and management method and its system | |
| US8554704B2 (en) | Cruncher—an MIP solver accelerator | |
| Bosmans et al. | Adaptivity in distributed agent-based simulation: A generic load-balancing approach | |
| Potuzak | Distributed/parallel genetic algorithm for road traffic network division using step parallelization | |
| Sędziwy | On acceleration of multi-agent system performance in large scale photometric computations |
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 |