CN115186049A - Intelligent bus alternative station site selection method, electronic equipment and storage medium - Google Patents
Intelligent bus alternative station site selection method, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN115186049A CN115186049A CN202211082338.7A CN202211082338A CN115186049A CN 115186049 A CN115186049 A CN 115186049A CN 202211082338 A CN202211082338 A CN 202211082338A CN 115186049 A CN115186049 A CN 115186049A
- Authority
- CN
- China
- Prior art keywords
- station
- bus
- site
- affected
- passenger
- 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.)
- Granted
Links
- 238000010187 selection method Methods 0.000 title claims abstract description 21
- 238000003860 storage Methods 0.000 title claims abstract description 15
- 238000000034 method Methods 0.000 claims abstract description 35
- 238000013178 mathematical model Methods 0.000 claims abstract description 14
- 238000012216 screening Methods 0.000 claims abstract description 7
- 238000004590 computer program Methods 0.000 claims description 17
- 238000011144 upstream manufacturing Methods 0.000 claims description 15
- 230000006870 function Effects 0.000 claims description 9
- 230000006399 behavior Effects 0.000 claims description 7
- 230000011664 signaling Effects 0.000 claims description 7
- 238000004220 aggregation Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 6
- 102000002423 Octamer Transcription Factor-6 Human genes 0.000 claims description 3
- 108010068113 Octamer Transcription Factor-6 Proteins 0.000 claims description 3
- 230000002776 aggregation Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 7
- 230000009191 jumping Effects 0.000 abstract description 4
- 238000010276 construction Methods 0.000 abstract description 3
- 238000012423 maintenance Methods 0.000 abstract description 3
- 230000009471 action Effects 0.000 description 3
- 238000001035 drying Methods 0.000 description 2
- 239000004744 fabric Substances 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000003550 marker Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Tourism & Hospitality (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- General Business, Economics & Management (AREA)
- General Health & Medical Sciences (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Traffic Control Systems (AREA)
Abstract
An intelligent bus alternative station site selection method, electronic equipment and a storage medium belong to the technical field of bus data processing and analysis. The bus stop can not provide getting-on and getting-off services due to bus stop construction, pavement maintenance and the like. The method comprises the steps of collecting data of affected stations of a bus route, data of other bus stations in a certain range of the bus route and passenger travel chain data of the affected stations, screening a feasible station set based on the data of the affected stations of the bus route and the data of the other bus stations in the bus route region, and then clustering to obtain an alternative station set; clustering the collected passenger trip chain data to obtain a passenger position clustering set of the affected station; and establishing a mathematical model, setting constraint conditions, and solving to obtain the intelligent bus alternative stop. When the station is influenced, the bus company selects a proper alternative station instead of directly jumping the station, so that the influence on the travel of passengers on the original line can be effectively reduced.
Description
Technical Field
The invention belongs to the technical field of bus data processing and analysis, and particularly relates to an intelligent bus alternative station site selection method, electronic equipment and a storage medium.
Background
The original station of the bus line can not provide the service of getting on or off the bus due to the reasons of bus station construction, pavement maintenance and the like, and the bus company usually adopts the mode of station jumping treatment for the above situations, directly neglects the affected station and drives to the downstream station. If the station can not be served, if the station can be directly jumped to process, the passengers in the original line can be lost, the service rate of the line is reduced, and the profit situation is correspondingly reduced.
The invention discloses an invention patent with publication number CN113053156A and invention name as an intelligent bus radius method station addressing method, and discloses the following technical scheme: a circular area is defined according to a first radius by taking the passenger starting point position as the center of a circle to serve as a station addressing range, and the bus station falling into the range is taken as a starting station; the passenger ending point position is taken as the center of a circle, a circular area is defined according to a second radius to serve as a station addressing range, and stations falling into the range are taken as ending stations; inquiring all bus routes communicating the starting station with the final station to obtain a plurality of to-be-determined bus routes; acquiring the riding heat of each bus route to be determined; determining the undetermined bus route with the highest riding heat as an alternative bus route, and grading the alternative bus route; and acquiring congestion information of the road where the alternative bus route passes. However, the following technical problems exist: firstly, the passenger trip data acquisition mode mainly includes questionnaire survey and the like, and the data obtained is less accurate than mobile phone signaling data and trip chain data due to the influence of the sampling mode. In addition, automatic and intelligent processing of scheduling cannot be realized. The patent does not take into account the distribution of the positions of the passengers and the result of the addressing is too coarse. Secondly, the patent only aims at the problem of address selection of transfer, and is not suitable for the address selection of bus stop alternative schemes.
Disclosure of Invention
The invention aims to provide an intelligent bus alternative station address selection method, electronic equipment and a storage medium, aiming at the situation that bus stations cannot provide boarding and disembarking services due to bus station construction, pavement maintenance and the like.
In order to achieve the purpose, the invention is realized by the following technical scheme:
an intelligent bus alternative station site selection method comprises the following steps:
s1, acquiring data of affected stations of a bus line, and extracting data of other bus stations and data of passenger travel chains of the affected stations in a certain range of the bus line according to mobile phone signaling data;
s2, screening a feasible station set based on the bus route affected station data acquired in the step S1 and other bus station data in the bus route area;
s3, clustering the feasible station set screened in the step S2 to obtain an alternative station set;
s4, clustering the passenger trip chain data of the affected station collected in the step S1 to obtain a passenger position clustering set of the affected station;
s5, establishing a mathematical model and setting constraint conditions;
and S6, substituting the candidate station set obtained in the step S3 and the passenger position cluster set obtained in the step S4 into a mathematical model to solve to obtain the intelligent bus candidate station.
Further, the specific implementation method of step S2 includes the following steps:
s2.2, based on the data of the bus route affected stations and the data of other bus stations in the bus route area acquired in the step S1, range is defined by using the linear distance, and the geographic position of the affected stations is taken as the center of a circle and the data of other bus stations in the bus route area are taken as the center of a circleA round range is defined by taking the radius of the meter as the radius, and a feasible site set is screened;
wherein: r represents the radius of the earth, and takes the value of 6378.137,representing sitesAnd siteThe difference in the latitude of the vehicle,representing sitesAnd siteThe difference in the longitude of (a) to (b),representing affected sitesThe latitude of the user is determined by the latitude of the user,representing other sitesThe latitude of (c).
Further, the specific implementation method of step S3 includes the following steps:
s3.1, clustering the feasible site set screened in the step S2 by adopting a DBSCAN algorithm, firstly initializing, and setting cluster labelsSet of feasible sites,Is an empty set;
s3.2, randomly selecting sites in a feasible site setExtracting the sum in the site setThe distance is less than or equal toIf the site is aggregatedThe number of intermediate sites is less thanIs marked byIs a noise point; otherwise, markThe core sample point is marked with the cluster labelUpdating setsWhereinis the inter-site distance threshold within a cluster,minimum number of cluster samples;
s3.3, forRepeating the step S3.2 until the number of the sites is less than or equal toIn-range site aggregationIs empty;
s3.4, updateSelecting a station which is not marked yet, and repeating the step S3.2-3.3 to obtain a final station cluster label ofThe DBSCAN algorithm cluster set;
s3.5, performing secondary clustering on the longitude and latitude coordinates of the bus stops of the set obtained in the step S3.4 by using a KMeans clustering algorithm, and outputting a KMeans clustering result;
s3.6, based on the KMeans clustering result of the step S3.5, for the secondSite aggregation for clustersAnd sequentially calculating the line straight line coefficient and the line length of the sub-path formed by the upstream station and the downstream station, and selecting the representative station of each cluster according to the straight degree and the line length to obtain a candidate station set.
Further, the specific implementation method of step S4 includes the following steps:
s4.1, identifying passengers getting off from the passenger travel chain data: if the initial position is matched with the affected station and the travel mode is walking, the travel chain path is the behavior of the get-off passenger;
s4.2, identifying passengers getting on the bus according to the passenger trip chain data: the matching of the terminal position is the affected station and the travel mode is walking, and the travel chain path is the behavior of the passengers getting on the bus;
s4.3, screening the trip chain data of passengers getting on and off the bus at the affected station;
s4.4, clustering the boarding starting position and the alighting end position set of the passengers at the affected station by using a KMeans clustering algorithm to form clustersAnd clustering to obtain a passenger position cluster set of the affected station, wherein,the value is 5-8.
Further, the specific implementation method of step S5 includes the following steps:
s5.1, the objective function that the walking distance from the passenger to the replacement station is recommended to be shortest is as follows:
Sas a set of alternative stations, the station may,,in order to select the number of stations,sis any one of a set of candidate stations;Bcluster sets are clustered for passenger locations of affected sites,,to be composed ofThe number of passenger position clusters in a circular range is defined by the radius of the meter,bany one of the set of passenger position clusters for the affected site,u b clustering passenger locationsbThe number of people getting on the vehicle,d b clustering passenger locationsbThe number of people getting off the vehicle,x s a variable that is either 0 or 1, and,x s representing sitesWhether the selected site is the final site or not is judged, 0 is not selected, 1 is selected, and min is a minimum function;
s5.2, setting a constraint condition 1 to select at most one station as a station replacement constraint:
s5.3, setting a constraint condition 2 as an inter-station distance constraint:
wherein,representing nodesThe actual navigation distance to the upstream station,representing nodesThe actual navigation distance to the downstream station,the maximum value of the upstream station spacing is,is the minimum value of the distance between the downstream stations;
s5.4, setting a constraint condition 3 as a linear coefficient constraint:
wherein,in order to allow the minimum straight-line coefficient,in order to allow the maximum linear coefficient,representing the spherical linear distance from the upstream station to the downstream station;
s5.5, setting a constraint condition 4 as a detour coefficient constraint:
wherein,is a nodeAnd nodeThe time of the navigation travel in between,is a nodeAnd nodeThe time of the navigation travel in between,is a nodeAnd nodeThe time of the navigation travel in between,in order to allow for the minimum bypass factor,is the maximum allowed bypass factor.
Further, in the step S6, a linear programming solver of the GUROBI, CPLEX and SCIP is used for solving, if the mathematical model has an optimal solution, the model is output to select the site as the candidate point of the current affected site for selection by the user, otherwise, the jump is output.
The electronic equipment comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the intelligent bus alternative station address selection method when executing the computer program.
The computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the intelligent bus alternative site selection method.
The invention has the beneficial effects that:
the invention relates to an intelligent bus alternative station site selection method, which is designed to select a certain station as a line alternative station within a certain range of an original station, and carry out station jumping processing if no suitable station exists.
The intelligent bus alternative station site selection method can sense the travel behavior of passengers based on travel chain data obtained by processing passenger travel big data and mobile phone signaling data, so that the departure place and the destination of getting-off of the passengers are obtained. According to the departure position of a passenger, the destination position data of the passenger, the feasible station data in the range and the travel network data, a mathematical model is established to select the address of the alternative station of the bus station, so that the total travel distance of the passenger after the route is changed is the minimum, and the following constraint conditions are met:
(1) Selecting at most one station from all selectable stations as an alternative station;
(2) The distance between the alternative station and the upstream and downstream stations can not exceed a certain distance;
(3) The nonlinear coefficient with the upstream and downstream stations cannot be too large;
(4) The detour coefficient of the passenger's upstream and downstream outgoing lines OD cannot exceed a certain range.
In order to improve the solving speed of the model, the following two effective measures are adopted:
(1) Clustering feasible sites in the area by adopting a DBSCAN algorithm, and selecting a representative site as an alternative site for each site cluster, thereby reducing the number of the feasible sites;
(2) The positions of passengers are clustered, the service range of one bus station usually covers 5-8 cells, and the number of variables can be reduced after clustering. The parameters are freely configurable by the user.
According to the intelligent bus alternative station site selection method, when a station is influenced, a bus company selects a proper alternative station instead of directly jumping the station, and the influence on the travel of passengers on the original line can be effectively reduced.
According to the intelligent bus alternative station site selection method, the travel position of the passenger can be more accurately obtained through the passenger travel chain data acquisition compared with other methods such as a manual survey method and the like, and data guarantee is provided for a site selection model.
According to the intelligent bus alternative station site selection method, the feasible stations are clustered, and the passenger positions are clustered, so that the solution space can be reduced and the mathematical model can be rapidly solved on the premise of ensuring the solution quality.
Drawings
Fig. 1 is a flowchart of an intelligent bus alternative station site selection method according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings and the detailed description. It is to be understood that the embodiments described herein are illustrative only and are not limiting, i.e., that the embodiments described are only a few embodiments, rather than all, of the present invention. While the components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations, the present invention is capable of other embodiments.
Thus, the following detailed description of specific embodiments of the present invention presented in the accompanying drawings is not intended to limit the scope of the invention as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the detailed description of the invention without inventive step, are within the scope of protection of the invention.
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to fig. 1:
the first embodiment is as follows:
an intelligent bus alternative station site selection method comprises the following steps:
s1, acquiring data of affected stations of a bus line, and extracting data of other bus stations and data of passenger travel chains of the affected stations in a certain range of the bus line according to mobile phone signaling data;
s2, screening a feasible station set based on the bus route affected station data acquired in the step S1 and other bus station data in the bus route area;
further, the specific implementation method of step S2 includes the following steps:
s2.2, based on the data of the bus route affected stations and the data of other bus stations in the bus route area acquired in the step S1, range is defined by using the linear distance, and the geographic position of the affected stations is taken as the center of a circle and the data of other bus stations in the bus route area are taken as the center of a circleA round range is defined by taking the radius of the meter as the radius, and a feasible site set is screened;
wherein: r represents the radius of the earth, and takes the value of 6378.137,representing sitesAnd siteThe difference in the latitude of (a) is,representing sitesAnd siteThe difference in the longitude of (a) and (b),representing affected sitesThe latitude of (a) is determined,representing other sitesThe latitude of (d);
sample data for the screened feasible sites are shown in table 1:
TABLE 1 sample data for feasible sites
Site ID | Station longitude | Station latitude | Site name | Distance from affected site |
… | … | … | … | … |
a7677c6dc | 114.117562 | 22.551927 | Drying cloth | 161.32 |
… | … | … | … | … |
S3, clustering the feasible station set screened in the step S2 to obtain an alternative station set;
and based on the screened feasible site set, clustering the feasible site set in order to reduce the number of alternative schemes and improve the solving efficiency of the mathematical model. Because the number of the site clusters is unknown in advance, the DBSCAN algorithm is adopted for clustering to obtain the appropriate number of the site clusters. And then, performing secondary clustering on the sites by using a KMeans clustering algorithm to serve as a final result. Finally, selecting a site from each cluster as an alternative site;
further, the specific implementation method of step S3 includes the following steps:
s3.1, clustering the feasible site set screened in the step S2 by adopting a DBSCAN algorithm, firstly initializing, and setting cluster labelsSet of feasible sites,Is an empty set;
s3.2, randomly selecting sites in a feasible site setExtracting site-specific sumsDistance is less than or equal toIf the site is aggregatedThe number of the intermediate stations is less than that of the intermediate stationsThen, markIs a noise point; otherwise, markThe core sample point is marked with the cluster labelUpdating a setWherein, in the process,is the inter-site distance threshold within a cluster,minimum number of cluster samples;
s3.3, forRepeating the step S3.2 until the number of the stations is less than or equal toIn-range site aggregationIs empty;
s3.4, updateSelecting a station which is not marked yet, and repeating the step S3.2-3.3 to obtain a final station cluster label ofThe DBSCAN algorithm cluster set;
s3.5, performing secondary clustering on the longitude and latitude coordinates of the bus stops of the set obtained in the step S3.4 by using a KMeans clustering algorithm, and outputting a KMeans clustering result;
s3.6, based on the KMeans clustering result of the step S3.5, for the secondSite aggregation for clustersSequentially calculating the line straight line coefficient and the line length of a sub-path formed by the upstream station and the downstream station, and selecting a representative station of each cluster according to the straight degree and the line length to obtain a candidate station set;
sample data of the clustered alternative sites are shown in table 2:
table 2 example data for alternative sites
Site ID | Station longitude | Station latitude | Site name | Site cluster marking |
… | … | … | … | … |
a7677c6dc | 114.117562 | 22.551927 | Drying cloth | 2 |
… | … | … | … | … |
S4, clustering the passenger trip chain data of the affected station acquired in the step S1 to obtain a passenger position clustering set of the affected station;
the trip chain data is processed based on mobile phone signaling data, and characteristic data of passenger trip can be obtained, wherein the characteristic data comprises passenger trip ID, shift matching, departure time, departure starting position, trip end point position and the like. Based on travel data of passengers, the positions of the passengers served by the affected stations can be obtained;
sample data for the trip chain is shown in table 3:
table 3 sample data for the trip chain
Trip chain ID | Line ID | Travel chain index | Starting position | End position | Travel mode |
… | … | … | … | … | … |
1 | a4d11 | 0 | a7677c6ef | a7677c6dc | Walking device |
… | … | … | … | … | … |
The travel chain ID represents the unique ID of the whole complete travel record of a certain passenger mobile phone signaling, the line ID is the ID bound with a bus line and a shift identification, the travel chain index refers to the sequence of the whole complete travel record of the section of the path, the starting point position is the starting longitude and latitude coordinate of the passenger on the section of the path, the end point position is the end point longitude and latitude coordinate of the passenger on the section of the path, and the travel mode comprises walking, buses and subways;
further, the specific implementation method of step S4 includes the following steps:
s4.1, identifying passengers getting off from the passenger travel chain data: if the initial position is matched with the affected station and the travel mode is walking, the travel chain path is the behavior of the get-off passenger;
s4.2, identifying passengers getting on the bus according to the passenger trip chain data: the terminal point position is matched with the affected station, the travel mode is walking, and the travel chain path is the behavior of the boarding passenger;
s4.3, screening the data of the passenger getting on and off the bus at the affected station;
s4.4, clustering the boarding starting position and the alighting end position set of the passengers at the affected station by using a KMeans clustering algorithm to form clustersAnd clustering to obtain a passenger position cluster set of the affected station, wherein,the value is 5-8;
sample data for passenger location clustering results for affected sites is shown in table 4:
TABLE 4 example data for passenger location clustering results for affected sites
Passenger location ID | Location longitude | Location latitude | Passenger position cluster marker |
… | … | … | … |
a7677c6dc | 114.117562 | 22.551927 | 2 |
… | … | … | … |
S5, establishing a mathematical model and setting constraint conditions;
further, the specific implementation method of step S5 includes the following steps:
s5.1, the objective function that the walking distance from the passenger to the replacement station is recommended to be shortest is as follows:
Sas a set of alternative stations, the station may,,in order to select the number of stations,sis any one of a set of candidate stations;Bcluster sets are clustered for passenger locations of affected stations,,to be composed ofThe number of passenger position clusters in a circular range is defined by taking the radius of meter as the center,bany one of a set of passenger position cluster clusters for the affected site,u b clustering passenger locationsbThe number of the passengers getting on the vehicle,d b clustering passenger locationsbThe number of people getting off the vehicle,x s a variable that is either 0 or 1, and,x s representing sitesWhether the selected station is the final station or not is judged, 0 is not selected, 1 is selected, and min is a minimum function;
s5.2, setting a constraint condition 1 to select at most one station as a constraint of replacing the station:
s5.3, setting a constraint condition 2 as an interstation distance constraint:
wherein,representing nodesThe actual navigation distance to the upstream station,representing nodesThe actual navigation distance to the downstream station,the maximum value of the upstream station spacing is,is the minimum value of the distance between the downstream stations;
s5.4, setting a constraint condition 3 as a linear coefficient constraint:
wherein,in order to allow the minimum straight-line coefficient,in order to allow the maximum straight-line coefficient,representing the spherical linear distance from the upstream station to the downstream station;
s5.5, setting a constraint condition 4 as a bypass coefficient constraint:
wherein,is a nodeAnd nodeThe time of the navigation travel in between,is a nodeAnd nodeIn betweenThe time of the navigation travel is calculated,is a nodeAnd nodeThe time of the navigation travel in between,in order to allow the minimum bypass factor,the maximum allowable detour coefficient;
s6, substituting the candidate station set obtained in the step S3 and the passenger position cluster set obtained in the step S4 into a mathematical model to solve to obtain an intelligent bus candidate station;
further, in the step S6, a linear programming solver of the GUROBI, CPLEX and SCIP is used for solving, if the mathematical model has an optimal solution, the model is output to select the site as the candidate point of the current affected site for selection by the user, otherwise, the jump is output.
The second embodiment is as follows:
the electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to realize the steps of the intelligent bus alternative station addressing method in the specific implementation mode.
The computer device of the present invention may be a device including a processor, a memory, and the like, for example, a single chip microcomputer including a central processing unit and the like. And the processor is used for implementing the steps of the recommendation method capable of modifying the relationship-driven recommendation data based on the CREO software when executing the computer program stored in the memory. The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The third concrete implementation mode:
the computer-readable storage medium, on which a computer program is stored, is characterized in that, when being executed by a processor, the computer program implements an intelligent bus alternative station addressing method according to a specific implementation manner.
The computer readable storage medium of the present invention may be any form of storage medium that can be read by a processor of a computer device, including but not limited to non-volatile memory, ferroelectric memory, etc., and the computer readable storage medium has stored thereon a computer program that, when the computer program stored in the memory is read and executed by the processor of the computer device, can implement the above-mentioned steps of the CREO-based software that can modify the modeling method of the relationship-driven modeling data. The computer program comprises computer program code which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
The key points and points to be protected of the technology of the invention are as follows:
(1) The patent provides a technical route for selecting the address of the alternative station of the affected station;
(2) The mathematical optimization model proposed by the patent;
(3) The mathematical model provided by the patent can be solved by other precise numerical solutions or heuristics, and the method belongs to the patent protection scope no matter how the solving algorithm is.
It is noted that relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
While the application has been described above with reference to specific embodiments, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the application. In particular, the various features of the embodiments disclosed herein may be used in any combination that is not inconsistent with the structure, and the failure to exhaustively describe such combinations in this specification is merely for brevity and resource conservation. Therefore, it is intended that the application not be limited to the particular embodiments disclosed, but that the application will include all embodiments falling within the scope of the appended claims.
Claims (8)
1. An intelligent bus alternative station site selection method is characterized by comprising the following steps: the method comprises the following steps:
s1, acquiring data of affected stations of a bus line, and extracting data of other bus stations and data of passenger travel chains of the affected stations in a certain range of the bus line according to mobile phone signaling data;
s2, screening a feasible station set based on the data of the bus line affected stations acquired in the step S1 and the data of other bus stations in the bus line area;
s3, clustering the feasible station set screened in the step S2 to obtain an alternative station set;
s4, clustering the passenger trip chain data of the affected station acquired in the step S1 to obtain a passenger position clustering set of the affected station;
s5, establishing a mathematical model and setting constraint conditions;
and S6, substituting the candidate station set obtained in the step S3 and the passenger position cluster set obtained in the step S4 into a mathematical model to solve to obtain the intelligent bus candidate station.
2. The intelligent bus alternative station site selection method according to claim 1, characterized in that: the specific implementation method of the step S2 comprises the following steps:
s2.2, collecting based on the step S1The bus route affected station data and other bus station data in the bus route area are subjected to range division by utilizing the linear distance, and the geographic position of the affected station is taken as the center of a circle and the data of the affected station is taken as the center of a circleA round range is defined by taking the radius of the meter as the radius, and a feasible site set is screened;
wherein: r represents the radius of the earth, and takes the value of 6378.137,representing sitesAnd siteThe difference in the latitude of the vehicle,representing sitesAnd siteThe difference in the longitude of (a) to (b),representing affected sitesThe latitude of (a) is determined,representing other sitesThe latitude of (c).
3. The intelligent bus alternative station site selection method according to claim 1 or 2, characterized in that: the specific implementation method of the step S3 comprises the following steps:
s3.1, clustering the feasible site set screened in the step S2 by adopting a DBSCAN algorithm, firstly initializing, and setting cluster labelsSet of feasible sites,Is an empty set;
s3.2, randomly selecting sites in a feasible site setExtracting site-specific sumsThe distance is less than or equal toIf the site is aggregatedThe number of intermediate sites is less thanThen, markIs a noise point; otherwise, markIs a core sample point and is marked with a cluster labelUpdating setsWherein, in the process,is the inter-site distance threshold within a cluster,minimum number of cluster samples;
s3.3, forRepeating the step S3.2 until the number of the sites is less than or equal toIn-range site aggregationIs empty;
s3.4, updateSelecting a station which is not marked yet, and repeating the step S3.2-3.3 to obtain a final station cluster label ofThe DBSCAN algorithm cluster set;
s3.5, performing secondary clustering on the longitude and latitude coordinates of the bus stops of the set obtained in the step S3.4 by using a KMeans clustering algorithm, and outputting a KMeans clustering result;
s3.6 KMeans clustering result based on step S3.5Site aggregation for clustersAnd sequentially calculating the line straight line coefficient and the line length of the sub-path formed by the upstream station and the downstream station, and selecting the representative station of each cluster according to the straight degree and the line length to obtain a candidate station set.
4. The intelligent bus alternative station site selection method according to claim 3, characterized in that: the specific implementation method of the step S4 comprises the following steps:
s4.1, identifying passengers getting off from the passenger travel chain data: if the initial position is matched with the affected station and the travel mode is walking, the travel chain path is the behavior of the get-off passenger;
s4.2, identifying passengers getting on the bus according to the passenger trip chain data: the terminal point position is matched with the affected station, the travel mode is walking, and the travel chain path is the behavior of the boarding passenger;
s4.3, screening the data of the passenger getting on and off the bus at the affected station;
5. The intelligent bus alternative station site selection method according to claim 4, characterized in that: the specific implementation method of the step S5 comprises the following steps:
s5.1, the objective function for suggesting that the walking distance from the passenger to the replacement station is shortest is as follows:
Sfor the set of alternative stations to be used,,in order to select the number of stations,sis any one of a set of candidate stations;Bcluster sets are clustered for passenger locations of affected sites,,to be composed ofThe radius of the meter defines the position of the passenger in the circular rangeThe number of clusters to be clustered,bany one of a set of passenger position cluster clusters for the affected site,u b clustering passenger locationsbThe number of the passengers getting on the vehicle,d b clustering passenger locationsbThe number of people getting off the vehicle,x s a variable that is either 0 or 1, and,x s representing sitesWhether the selected site is the final site or not is judged, 0 is not selected, 1 is selected, and min is a minimum function;
s5.2, setting a constraint condition 1 to select at most one station as a station replacement constraint:
s5.3, setting a constraint condition 2 as an inter-station distance constraint:
wherein,representing nodesThe actual navigation distance to the upstream station,representing nodesThe actual navigation distance to the downstream station,the maximum value of the upstream station spacing is,is the minimum value of the distance between the downstream stations;
s5.4, setting a constraint condition 3 as a linear coefficient constraint:
wherein,in order to allow the minimum straight-line coefficient,in order to allow the maximum straight-line coefficient,representing the spherical linear distance from the upstream station to the downstream station;
s5.5, setting a constraint condition 4 as a bypass coefficient constraint:
6. The intelligent bus alternative stop site selection method according to claim 5, wherein the method comprises the following steps: and S6, solving by adopting a linear programming solver in GUROBI, CPLEX and SCIP, if the mathematical model has an optimal solution, outputting a model selection site as an alternative point of the current affected site for selection by a user, and otherwise, outputting a station jump.
7. Electronic equipment, characterized in that it comprises a memory and a processor, the memory stores a computer program, the processor executes the computer program to realize the steps of the intelligent bus alternative station addressing method as claimed in any one of claims 1-6.
8. Computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for intelligent bus alternative site selection according to any one of claims 1 to 6.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202211082338.7A CN115186049B (en) | 2022-09-06 | 2022-09-06 | Intelligent bus alternative station site selection method, electronic equipment and storage medium |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202211082338.7A CN115186049B (en) | 2022-09-06 | 2022-09-06 | Intelligent bus alternative station site selection method, electronic equipment and storage medium |
Publications (2)
Publication Number | Publication Date |
---|---|
CN115186049A true CN115186049A (en) | 2022-10-14 |
CN115186049B CN115186049B (en) | 2023-02-03 |
Family
ID=83522315
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202211082338.7A Active CN115186049B (en) | 2022-09-06 | 2022-09-06 | Intelligent bus alternative station site selection method, electronic equipment and storage medium |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN115186049B (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115587657A (en) * | 2022-10-19 | 2023-01-10 | 华中科技大学 | Station determining and route optimizing method for night customized bus |
Citations (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060045909A1 (en) * | 2004-08-30 | 2006-03-02 | Colgate-Palmolive Company | Genome-based diet design |
CN107609677A (en) * | 2017-08-17 | 2018-01-19 | 华侨大学 | A kind of customization public bus network planing method based on taxi GPS big datas |
CN108734337A (en) * | 2018-04-18 | 2018-11-02 | 北京交通大学 | Based on the modified customization public transport rideshare website generation method of cluster centre |
CN109035770A (en) * | 2018-07-31 | 2018-12-18 | 上海世脉信息科技有限公司 | The real-time analyzing and predicting method of public transport passenger capacity under a kind of big data environment |
CN109359682A (en) * | 2018-10-11 | 2019-02-19 | 北京市交通信息中心 | A kind of Shuttle Bus candidate's website screening technique based on F-DBSCAN iteration cluster |
CN109657843A (en) * | 2018-11-28 | 2019-04-19 | 深圳市综合交通设计研究院有限公司 | A kind of integrated programmed decision-making support system of city feeder bus sytem system |
CN110598942A (en) * | 2019-09-18 | 2019-12-20 | 北京工业大学 | Community public transport network and departure frequency synchronous optimization method considering area full coverage for connecting subways |
CN111581325A (en) * | 2020-07-13 | 2020-08-25 | 重庆大学 | K-means station area division method based on space-time influence distance |
CN112132236A (en) * | 2020-11-20 | 2020-12-25 | 深圳市城市交通规划设计研究中心股份有限公司 | Demand subarea dividing and line planning method and device based on clustering algorithm |
WO2022041262A1 (en) * | 2020-08-31 | 2022-03-03 | 苏州大成电子科技有限公司 | Big data-based method for calculating anchor point of urban rail transit user |
CN114358386A (en) * | 2021-12-07 | 2022-04-15 | 江苏大学 | Double-trip-mode ride-sharing site generation method based on reserved trip demand |
CN114897445A (en) * | 2022-07-12 | 2022-08-12 | 深圳市城市交通规划设计研究中心股份有限公司 | Method and device for adjusting and optimizing stop points of public transport network and readable storage medium |
-
2022
- 2022-09-06 CN CN202211082338.7A patent/CN115186049B/en active Active
Patent Citations (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060045909A1 (en) * | 2004-08-30 | 2006-03-02 | Colgate-Palmolive Company | Genome-based diet design |
CN107609677A (en) * | 2017-08-17 | 2018-01-19 | 华侨大学 | A kind of customization public bus network planing method based on taxi GPS big datas |
CN108734337A (en) * | 2018-04-18 | 2018-11-02 | 北京交通大学 | Based on the modified customization public transport rideshare website generation method of cluster centre |
CN109035770A (en) * | 2018-07-31 | 2018-12-18 | 上海世脉信息科技有限公司 | The real-time analyzing and predicting method of public transport passenger capacity under a kind of big data environment |
CN109359682A (en) * | 2018-10-11 | 2019-02-19 | 北京市交通信息中心 | A kind of Shuttle Bus candidate's website screening technique based on F-DBSCAN iteration cluster |
CN109657843A (en) * | 2018-11-28 | 2019-04-19 | 深圳市综合交通设计研究院有限公司 | A kind of integrated programmed decision-making support system of city feeder bus sytem system |
CN110598942A (en) * | 2019-09-18 | 2019-12-20 | 北京工业大学 | Community public transport network and departure frequency synchronous optimization method considering area full coverage for connecting subways |
CN111581325A (en) * | 2020-07-13 | 2020-08-25 | 重庆大学 | K-means station area division method based on space-time influence distance |
WO2022041262A1 (en) * | 2020-08-31 | 2022-03-03 | 苏州大成电子科技有限公司 | Big data-based method for calculating anchor point of urban rail transit user |
CN112132236A (en) * | 2020-11-20 | 2020-12-25 | 深圳市城市交通规划设计研究中心股份有限公司 | Demand subarea dividing and line planning method and device based on clustering algorithm |
CN114358386A (en) * | 2021-12-07 | 2022-04-15 | 江苏大学 | Double-trip-mode ride-sharing site generation method based on reserved trip demand |
CN114897445A (en) * | 2022-07-12 | 2022-08-12 | 深圳市城市交通规划设计研究中心股份有限公司 | Method and device for adjusting and optimizing stop points of public transport network and readable storage medium |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN115587657A (en) * | 2022-10-19 | 2023-01-10 | 华中科技大学 | Station determining and route optimizing method for night customized bus |
Also Published As
Publication number | Publication date |
---|---|
CN115186049B (en) | 2023-02-03 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Jenelius | Data-driven metro train crowding prediction based on real-time load data | |
Zhao et al. | Using truck probe GPS data to identify and rank roadway bottlenecks | |
Chen et al. | Long-term travel time prediction using gradient boosting | |
CN110175691B (en) | Method and device for planning regional traffic operation line and electronic equipment | |
CN108444486B (en) | Navigation route sorting method and device | |
CN110008413A (en) | A kind of traffic trip problem querying method and device | |
CN111881243B (en) | Taxi track hot spot area analysis method and system | |
CN114363842B (en) | Bus passenger departure station prediction method and device based on mobile phone signaling data | |
CN115186049B (en) | Intelligent bus alternative station site selection method, electronic equipment and storage medium | |
Cici et al. | Quantifying the potential of ride-sharing using call description records | |
CN112651546A (en) | Bus route optimization method and system | |
CN104636457B (en) | A kind of method and device of location finding cognition | |
CN111461489A (en) | Route generation method and device, electronic equipment and readable storage medium | |
CN116233823B (en) | Identification method of cross-city commute ring, electronic equipment and storage medium | |
CN110969861A (en) | Vehicle identification method, device, equipment and computer storage medium | |
CN116913073A (en) | Road congestion prediction method, device, equipment and computer storage medium | |
CN115862331A (en) | Vehicle travel track reconstruction method considering bayonet network topological structure | |
CN114841428A (en) | Bus route planning method and system | |
Zou et al. | Measuring retiming responses of passengers to a prepeak discount fare by tracing smart card data: a practical experiment in the Beijing subway | |
Mathew et al. | Comparative assessment of geospatial and statistical methods to estimate local road annual average daily traffic | |
Kattan et al. | LRT passengers’ responses to advanced passenger information system (APIS) in case of information inconsistency and train crowding | |
Wang et al. | Road network design in a developing country using mobile phone data: An application to Senegal | |
Cai et al. | Holiday destination choice behavior analysis based on AFC data of urban rail transit | |
CN115294773A (en) | Bus lane optimal configuration method, electronic device and storage medium | |
CN114428915A (en) | Information pushing method and device |
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 |