CN115186049B - 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 PDF

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CN115186049B
CN115186049B CN202211082338.7A CN202211082338A CN115186049B CN 115186049 B CN115186049 B CN 115186049B CN 202211082338 A CN202211082338 A CN 202211082338A CN 115186049 B CN115186049 B CN 115186049B
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张晓春
徐巍
祝佳祥
陈振武
周勇
刘星
李鋆元
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Shenzhen Urban Transport Planning Center Co Ltd
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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 route can be effectively reduced.

Description

Intelligent bus alternative station site selection method, electronic equipment and storage medium
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
Due to the reasons of bus stop construction, road surface maintenance and the like, the original bus stop of the bus line cannot provide boarding and disembarking services, and the bus company usually adopts a bus jumping process for the situations, directly ignores the affected bus stop and drives to a downstream bus stop. 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 has the publication number of CN113053156A and the invention name of the invention is an intelligent bus station addressing method by a bus radius 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 terminal arrival point position is taken as a circle center, 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 terminal arrival 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 to-be-determined bus route; 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 siting 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 site selection method, electronic equipment and a storage medium, aiming at the condition that bus stations cannot provide boarding and disembarking services due to bus station construction, road surface maintenance and the like.
In order to realize the purpose, the invention is realized by the following technical scheme:
an intelligent bus alternative station site selection method comprises the following steps:
s1, collecting data of affected stations of a bus line, and extracting data of other bus stations and data of passenger trip 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 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.
Further, the specific implementation method of step S2 includes the following steps:
s2.1, setting the walking limit distance of the passenger for selecting to sit on the bus as
Figure 198548DEST_PATH_IMAGE001
Rice;
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 circle
Figure 601847DEST_PATH_IMAGE001
A round range is defined by taking the radius of the meter as the radius, and a feasible site set is screened;
affected site
Figure 860790DEST_PATH_IMAGE002
With other sites
Figure 677437DEST_PATH_IMAGE003
Of (2) is
Figure 414448DEST_PATH_IMAGE004
Calculated according to the following formula:
Figure 937834DEST_PATH_IMAGE005
wherein: r represents the earth radius and takes the value of 6378.137,
Figure 429995DEST_PATH_IMAGE006
representing sites
Figure 406041DEST_PATH_IMAGE002
And site
Figure 946744DEST_PATH_IMAGE003
The difference in the latitude of the vehicle,
Figure 153997DEST_PATH_IMAGE007
representing sites
Figure 489163DEST_PATH_IMAGE002
And site
Figure 218085DEST_PATH_IMAGE003
The difference in the longitude of (a) to (b),
Figure 296899DEST_PATH_IMAGE008
representing affected sites
Figure 857193DEST_PATH_IMAGE002
The latitude of (a) is determined,
Figure 363261DEST_PATH_IMAGE009
representing other sites
Figure 579479DEST_PATH_IMAGE003
The 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, initialization is carried out, and cluster labels are set
Figure 789880DEST_PATH_IMAGE010
Set of feasible sites
Figure 142364DEST_PATH_IMAGE011
，
Figure 84912DEST_PATH_IMAGE012
Is an empty set;
s3.2, randomly selecting sites in a feasible site set
Figure 585164DEST_PATH_IMAGE013
Extracting the sum in the site set
Figure 5781DEST_PATH_IMAGE013
Distance is less than or equal to
Figure 478351DEST_PATH_IMAGE014
If the site is aggregated
Figure 890003DEST_PATH_IMAGE015
The number of the intermediate stations is less than that of the intermediate stations
Figure 815233DEST_PATH_IMAGE016
Then, mark
Figure 39541DEST_PATH_IMAGE013
Is a noise point; otherwise, mark
Figure 428934DEST_PATH_IMAGE013
The core sample point is marked with the cluster label
Figure 713285DEST_PATH_IMAGE017
Updating a set
Figure 125812DEST_PATH_IMAGE018
Wherein, in the step (A),
Figure 950548DEST_PATH_IMAGE019
is the inter-site distance threshold within a cluster,
Figure 132131DEST_PATH_IMAGE016
minimum number of cluster samples;
s3.3, for
Figure 587383DEST_PATH_IMAGE020
Repeating the step S3.2 until the number of the stations is less than or equal to
Figure 487206DEST_PATH_IMAGE019
In-range site aggregation
Figure 115633DEST_PATH_IMAGE015
Is empty;
s3.4, update
Figure 151723DEST_PATH_IMAGE021
Selecting a station which is not marked yet, and repeating the step S3.2-3.3 to obtain a final station cluster label of
Figure 777876DEST_PATH_IMAGE017
The 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 S3.5
Figure 430574DEST_PATH_IMAGE017
Site aggregation for clusters
Figure 534796DEST_PATH_IMAGE013
And sequentially 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.
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 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 clusters
Figure 690971DEST_PATH_IMAGE022
And clustering to obtain a passenger position cluster set of the affected station, wherein,
Figure 51808DEST_PATH_IMAGE022
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:
Figure 660644DEST_PATH_IMAGE023
Sas a set of alternative stations, the station may,
Figure 834136DEST_PATH_IMAGE024
，
Figure 907134DEST_PATH_IMAGE025
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,
Figure 875090DEST_PATH_IMAGE026
，
Figure 971222DEST_PATH_IMAGE027
to be composed of
Figure 479564DEST_PATH_IMAGE001
The number of passenger position clusters in a circular range is defined by the radius of the meter,bany one of a set of passenger position cluster 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 sites
Figure 610331DEST_PATH_IMAGE028
Whether 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:
Figure 749188DEST_PATH_IMAGE029
s5.3, setting a constraint condition 2 as an inter-station distance constraint:
Figure 394933DEST_PATH_IMAGE030
Figure 644649DEST_PATH_IMAGE031
wherein the content of the first and second substances,
Figure 629923DEST_PATH_IMAGE032
representing nodes
Figure 503463DEST_PATH_IMAGE028
The actual navigation distance to the upstream station,
Figure 574187DEST_PATH_IMAGE033
representing nodes
Figure 627594DEST_PATH_IMAGE028
The actual navigation distance to the downstream station,
Figure 467374DEST_PATH_IMAGE034
the maximum value of the upstream station spacing is,
Figure 10351DEST_PATH_IMAGE035
is the minimum value of the distance between the downstream stations;
s5.4, setting a constraint condition 3 as a linear coefficient constraint:
Figure 302792DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure 159889DEST_PATH_IMAGE037
in order to allow the minimum straight-line coefficient,
Figure 182072DEST_PATH_IMAGE038
in order to allow the maximum linear coefficient,
Figure 833633DEST_PATH_IMAGE039
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:
Figure 878949DEST_PATH_IMAGE040
wherein the content of the first and second substances,
Figure 336476DEST_PATH_IMAGE041
is a node
Figure 885269DEST_PATH_IMAGE028
And node
Figure 973310DEST_PATH_IMAGE042
The time of the navigation travel in between,
Figure 804125DEST_PATH_IMAGE043
is a node
Figure 737446DEST_PATH_IMAGE028
And node
Figure 406325DEST_PATH_IMAGE044
The time of the navigation travel in between,
Figure 727585DEST_PATH_IMAGE045
is a node
Figure 216335DEST_PATH_IMAGE042
And node
Figure 218926DEST_PATH_IMAGE044
The time of the navigation travel in between,
Figure 804628DEST_PATH_IMAGE046
in order to allow the minimum bypass factor,
Figure 968893DEST_PATH_IMAGE047
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 the passenger based on travel chain data obtained by processing the passenger travel big data and the mobile phone signaling data, so that the departure place and the get-off destination of the passenger 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 taken:
(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, and a bus station generally serves a range covering 5-8 cells, so that the number of variables can be reduced after clustering. The parameter is 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 detail 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. 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, and the present invention may have 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:
s2.1, setting the walking limit distance of the passenger for selecting to sit on the bus as
Figure 210519DEST_PATH_IMAGE001
Rice;
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 circle
Figure 813539DEST_PATH_IMAGE001
A round range is defined by taking the radius of the meter as the radius, and a feasible site set is screened;
affected site
Figure 191430DEST_PATH_IMAGE002
With other sites
Figure 792176DEST_PATH_IMAGE003
Is a distance of
Figure 22562DEST_PATH_IMAGE004
Calculated according to the following formula:
Figure 366956DEST_PATH_IMAGE005
wherein: r represents the earth radius and takes the value of 6378.137,
Figure 864933DEST_PATH_IMAGE006
representing sites
Figure 371001DEST_PATH_IMAGE002
And site
Figure 649536DEST_PATH_IMAGE003
The difference in the latitude of (a) is,
Figure 532041DEST_PATH_IMAGE007
representing sites
Figure 884525DEST_PATH_IMAGE002
And site
Figure 623811DEST_PATH_IMAGE003
The difference in the longitude of (a) to (b),
Figure 61745DEST_PATH_IMAGE008
representing affected sites
Figure 747942DEST_PATH_IMAGE002
The latitude of (a) is determined,
Figure 17249DEST_PATH_IMAGE009
representing other sites
Figure 130699DEST_PATH_IMAGE003
The latitude of (d);
sample data of 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 labels
Figure 55929DEST_PATH_IMAGE010
Set of feasible sites
Figure 855738DEST_PATH_IMAGE011
，
Figure 182814DEST_PATH_IMAGE012
Is an empty set;
s3.2, randomly selecting sites in a feasible site set
Figure 467165DEST_PATH_IMAGE013
Extracting site-specific sums
Figure 614112DEST_PATH_IMAGE013
Distance is less than or equal to
Figure 704428DEST_PATH_IMAGE014
If the site is aggregated
Figure 886011DEST_PATH_IMAGE015
The number of the intermediate stations is less than that of the intermediate stations
Figure 341263DEST_PATH_IMAGE016
Then, mark
Figure 568982DEST_PATH_IMAGE013
Is a noise point; otherwise, mark
Figure 135092DEST_PATH_IMAGE013
The core sample point is marked with the cluster label
Figure 171181DEST_PATH_IMAGE017
Updating a set
Figure 859652DEST_PATH_IMAGE018
Wherein, in the step (A),
Figure 981191DEST_PATH_IMAGE019
is the inter-site distance threshold within a cluster,
Figure 350993DEST_PATH_IMAGE016
minimum number of cluster samples;
s3.3, for
Figure 70949DEST_PATH_IMAGE020
Repeating the step S3.2 until the number of the stations is less than or equal to
Figure 868004DEST_PATH_IMAGE019
In-range site aggregation
Figure 476840DEST_PATH_IMAGE015
Is empty;
s3.4, update
Figure 384753DEST_PATH_IMAGE021
Selecting a station which is not marked yet, repeating the step S3.2-3.3 to obtain a final station cluster label of
Figure 457751DEST_PATH_IMAGE017
The 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 second
Figure 425707DEST_PATH_IMAGE017
Site aggregation for clusters
Figure 521839DEST_PATH_IMAGE013
Calculating the linear coefficient and the length of the line of the sub-path formed by the upstream station and the downstream station in turn, selecting the representative station of each cluster according to the degree of straightness and the length of the line,obtaining a set of alternative stations;
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
Travel chain ID Line ID Travel chain index Starting position End point 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 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 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 clusters
Figure 295760DEST_PATH_IMAGE022
Clustering to obtain a passenger position cluster set of the affected station,wherein the content of the first and second substances,
Figure 426527DEST_PATH_IMAGE022
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:
Figure 565385DEST_PATH_IMAGE023
Sfor the set of alternative stations to be used,
Figure 945551DEST_PATH_IMAGE024
，
Figure 460845DEST_PATH_IMAGE025
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,
Figure 446119DEST_PATH_IMAGE026
，
Figure 21457DEST_PATH_IMAGE027
to be composed of
Figure 826602DEST_PATH_IMAGE001
The number of passenger position clusters in a circular range is defined by taking the radius of meter as the center,bany one of the set of passenger position 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 sites
Figure 880008DEST_PATH_IMAGE028
Whether 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 constraint of replacing the station:
Figure 719788DEST_PATH_IMAGE029
s5.3, setting a constraint condition 2 as an inter-station distance constraint:
Figure 764230DEST_PATH_IMAGE030
Figure 322250DEST_PATH_IMAGE031
wherein the content of the first and second substances,
Figure 913769DEST_PATH_IMAGE032
representing nodes
Figure 935951DEST_PATH_IMAGE028
The actual navigation distance to the upstream station,
Figure 587513DEST_PATH_IMAGE033
representing nodes
Figure 367250DEST_PATH_IMAGE028
The actual navigation distance to the downstream station,
Figure 824776DEST_PATH_IMAGE034
the maximum value of the upstream station spacing is,
Figure 639148DEST_PATH_IMAGE035
is the minimum value of the distance between the downstream stations;
s5.4, setting a constraint condition 3 as a linear coefficient constraint:
Figure 461611DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure 790961DEST_PATH_IMAGE037
in order to allow the minimum straight-line coefficient,
Figure 989861DEST_PATH_IMAGE038
in order to allow the maximum linear coefficient,
Figure 658740DEST_PATH_IMAGE039
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:
Figure 652104DEST_PATH_IMAGE040
wherein, the first and the second end of the pipe are connected with each other,
Figure 970215DEST_PATH_IMAGE041
is a node
Figure 972806DEST_PATH_IMAGE028
And node
Figure 496191DEST_PATH_IMAGE042
The time of the navigation travel in between,
Figure 722773DEST_PATH_IMAGE043
is a node
Figure 964398DEST_PATH_IMAGE028
And node
Figure 505101DEST_PATH_IMAGE044
The time of the navigation travel in between,
Figure 210889DEST_PATH_IMAGE045
is a node
Figure 811635DEST_PATH_IMAGE042
And node
Figure 274977DEST_PATH_IMAGE044
Navigation travel time therebetween，
Figure 681688DEST_PATH_IMAGE046
In order to allow the minimum bypass factor,
Figure 914086DEST_PATH_IMAGE047
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 implements the steps of the intelligent bus alternative station address selection method described in the first embodiment when executing the computer program.
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 the computer program, when being executed by a processor, implements an intelligent bus alternative station addressing method according to one of the specific embodiments.
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 content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by 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 proposed in this patent can be solved by other precise numerical solution or heuristic, and it should belong to the scope of patent protection 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 phrase "comprising a … …" does not exclude the presence of another identical element 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 (7)

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 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 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;
the specific implementation method of the step S5 comprises 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:
Figure FDA0003909377060000011
s is an alternative station set, S = {1,2, …, m }, m is the number of alternative stations, and S is any one of the alternative station set; b is a passenger position cluster set of the affected station, B = { m +2, …, m + n +1}, n is the number of passenger position clusters in a circular range defined by taking alpha meters as a radius, B is any one of the passenger position cluster sets of the affected station, u is the number of the passenger position clusters of the affected station, and u is the number of the passenger position clusters of the affected station b Number of persons getting on the bus, clustering cluster b for passenger position, d b Number of people alighting, x, clustering cluster b for passenger position s A variable of 0 or 1, x s Whether the station s is selected as a final station or not is represented, 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:
Figure FDA0003909377060000012
s5.3, setting a constraint condition 2 as an interstation distance constraint:
Figure FDA0003909377060000013
Figure FDA0003909377060000014
wherein l s，0 Represents the actual navigation distance, l, from node s to the upstream site s，m+1 Representing the actual navigation distance of node s to the downstream site,
Figure FDA0003909377060000015
the maximum value of the upstream station spacing is,lis the minimum value of the distance between the downstream stations;
s5.4, setting a constraint condition 3 as a linear coefficient constraint:
Figure FDA0003909377060000021
wherein the content of the first and second substances,αin order to allow the minimum straight-line coefficient,
Figure FDA0003909377060000022
to the maximum linear coefficient allowed, e 0，m+1 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:
Figure FDA0003909377060000023
wherein, t s，0 Is the navigation travel time, t, between node s and node 0 s，m+1 Is the navigation travel time, t, between node s and node m +1 0，m+1 For the navigation travel time between node 0 and node m +1,βin order to allow for the minimum bypass factor,
Figure FDA0003909377060000024
the maximum allowable detour coefficient;
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:
s2.1, setting the walking limit distance of the passenger for selecting to sit on the bus as alpha meters;
s2.2, based on the data of the affected stations of the bus route and the data of other bus stations in the bus route area acquired in the step S1, carrying out range division by using a straight line distance, dividing a circular range by using the geographical position of the affected stations as the center of a circle and alpha meter as the radius, and screening a feasible station set;
affected site s 1 With other sites s 2 Is a distance of
Figure FDA0003909377060000025
Calculated according to the following formula:
Figure FDA0003909377060000026
wherein: r represents the earth radius and takes 6378.137, and theta represents a site s 1 And site s 2 The difference in latitude of (a), gamma, the site s 1 And site s 2 Difference in longitude of (a), y 1 Representing affected sites s 1 Latitude, y of 2 Representing other sites s 2 The 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, setting a cluster label k =0, and setting a feasible site set N k Phi, phi is an empty set;
s3.2, randomly selecting site S in a feasible site set k Extracting the sum s in the site set k Site set E with distance less than or equal to epsilon k If E is k If the number of the medium stations is less than lambda, marking s k Is a noise point; otherwise, mark s k Updating the set N for the core sample point and marking the cluster label k thereof k ＝N k ∪E k Wherein epsilon is a distance threshold value between stations in the cluster, and lambda is a minimum cluster sample number;
s3.3 for N k Repeating the step S3.2 until the station set E in the range of epsilon is less than or equal to k Is empty;
s3.4, updating k = k +1, selecting a station which is not marked, and repeating the step S3.2-3.3 to obtain a DBSCAN algorithm cluster set with a final station cluster label of k;
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 site set S of the kth cluster k And 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 stop 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 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 data of the passenger getting on and off the bus at the affected station;
and S4.4, clustering the getting-on initial position and the getting-off final position of the passengers on the affected station by using a KMeans clustering algorithm to obtain h clusters, and obtaining a passenger position clustering cluster set of the affected station, wherein the value of h is 5-8.
5. The intelligent bus alternative station site selection method according to claim 4, characterized in that: 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.
6. Electronic equipment, characterized by comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the intelligent bus alternative station site selection method according to any one of claims 1-5 when executing the computer program.
7. 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 5.
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