CN112381472B - Subway connection bus route optimization method and device and storage medium - Google Patents

Subway connection bus route optimization method and device and storage medium Download PDF

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CN112381472B
CN112381472B CN202110052984.8A CN202110052984A CN112381472B CN 112381472 B CN112381472 B CN 112381472B CN 202110052984 A CN202110052984 A CN 202110052984A CN 112381472 B CN112381472 B CN 112381472B
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张晓春
吴宗翔
王卓
朱远祺
陈振武
刘维怡
陶勰琨
黎旭成
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Shenzhen Urban Transport Planning Center Co Ltd
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Abstract

The invention provides a subway connection bus route optimization method, a device and a storage medium, wherein the method comprises the following steps: for any subway station in a calibration area, generating a plurality of initial lines on the basis of the subway station, wherein each initial line comprises a plurality of stations; a domain solution generation step, namely solving each initial line by adopting a preset algorithm to obtain a plurality of domain solutions of each initial line; determining a transfer subway station in the demand origin-destination for the demand origin-destination of any passenger, wherein the transfer subway station and the destination in the demand origin-destination form a demand pair; for each domain solution, randomly inserting the demand pairs into the domain solution for multiple times to obtain multiple new domain solutions; and optimizing each new field solution by adopting a preset algorithm, and determining an optimal solution, wherein the optimal solution is a connection bus line. The technical scheme of the invention combines the requirements of passengers to purposefully generate the connection bus route, and can greatly improve the operation efficiency and the service quality.

Description

Subway connection bus route optimization method and device and storage medium
Technical Field
The invention relates to the technical field of traffic planning, in particular to a subway connection bus route optimization method, a subway connection bus route optimization device and a storage medium.
Background
With the continuous development of rail transit and the continuous optimization of urban road network structure, people's mode of going out is more and more diversified. In order to solve the problems of unsmooth circulation at the tail end of public transport, difficult riding in the last kilometer and the like, the attraction of public transport to the traveling of residents is improved, and the subway connection bus is generated as soon as the transportation is carried out, so that the problem that the residents in blind spot areas which cannot be covered by rail transit and conventional bus lines are difficult to travel can be solved; on the other hand, passenger flow feeding can be performed for rail transit and the like, the coverage range of public transit is expanded, and the overall cooperative service level of the public transit is improved.
In order to improve the carrying capacity of subway-connected buses, the lines of the subway-connected buses need to be planned better, the currently common subway-connected bus line planning method is to preprocess original passenger trip data according to line targets, then perform time division and space aggregation on the preprocessed data, generate a line candidate set based on simple single-line operations such as line extension, merging and reduction, and finally select an optimal operation line from the line candidate set. However, the method generates all possible routes by adopting a recursive traversal method, selects the station closest to the last station each time, sequentially connects all the selected stations to generate the routes, and optimizes the obtained routes to possibly omit stations with large bus taking demands or increase stations with small bus taking demands, so that the method has low operation efficiency and poor service quality and can reduce the bus taking experience of passengers.
Disclosure of Invention
The invention solves the problem of how to improve the operation efficiency of the optimized subway connection bus line.
In order to solve the problems, the invention provides a subway connection bus route optimization method, a device and a storage medium.
In a first aspect, the invention provides a method for optimizing a subway connection bus line, which comprises the following steps:
for any subway station in a calibration area, generating a plurality of initial lines on the basis of the subway station, wherein each initial line comprises a plurality of stations;
a domain solution generation step, namely solving each initial line by adopting a preset line generation algorithm to obtain a plurality of domain solutions of each initial line;
determining a transfer subway station in the demand origin-destination for the demand origin-destination of any passenger, wherein the transfer subway station and the destination in the demand origin-destination form a demand pair;
for each domain solution, randomly inserting the demand pairs into the domain solution for multiple times to obtain multiple new domain solutions;
and optimizing each new field solution by adopting a preset optimization algorithm to determine an optimal solution, wherein the optimal solution is a connection bus line.
Optionally, the generating a plurality of initial lines based on the subway station includes:
determining all the stations in the calibration area, and randomly determining a plurality of first stations connected with the subway station in all the stations;
for any first station, sequentially determining the station closest to the current station as a next station by adopting a greedy algorithm, and sequentially connecting all stations determined by adopting the greedy algorithm to generate an initial line.
Optionally, the method for optimizing a subway connection bus route provided by the invention further comprises the following steps:
respectively acquiring the required origin-destination points of each passenger in the calibration area, wherein the required origin-destination points comprise a starting point and a terminal point;
dividing all the passengers into a plurality of samples according to the required origin-destination points of all the passengers, wherein all the passengers at the same starting point or the same destination point form one sample, and the starting point or the destination point corresponding to the sample is the position of the sample;
calculating the distance between every two samples according to the positions of the samples, and comparing the distance with a preset threshold value;
combining the two samples with the distance smaller than or equal to the preset threshold into a cluster until the distance between any two samples is larger than the preset threshold;
for any one of the clusters, generating one of the sites based on the location of each of the samples within the cluster.
Optionally, the generating one station according to the position of each sample in the cluster includes:
for all the samples in any one cluster, traversing all the bus stations in the calibration range of each sample;
for each bus station, calculating the walking distance from each sample to the bus station, and calculating the average walking distance from each sample to the bus station according to all the walking distances;
and determining the bus station with the shortest average walking distance as the station corresponding to the cluster.
Optionally, the calculating the distance between each two samples according to the positions of the samples comprises:
let any sample
Figure 588072DEST_PATH_IMAGE002
In the position of
Figure 880511DEST_PATH_IMAGE004
Another sample
Figure 36686DEST_PATH_IMAGE006
In the position of
Figure 240265DEST_PATH_IMAGE008
Determining a distance between two of said samples using a first formula, said first formula comprising:
Figure 52363DEST_PATH_IMAGE010
，
wherein the content of the first and second substances,
Figure 757014DEST_PATH_IMAGE012
is the distance between two of said samples.
Optionally, the preset line generation algorithm includes a destroy operator, and the obtaining the multiple domain solutions of each initial line by solving each initial line with the preset line generation algorithm includes:
for any initial line, randomly destroying at least one site by the aid of the destroying operators, and randomly combining the rest sites to obtain a plurality of domain solutions.
Optionally, the determining a transfer subway station in the demand origin-destination comprises:
for any passenger, a connecting line between the required origin-destination point of the passenger and any subway station of the calibration area is a travel route of the passenger;
calculating generalized travel cost of each travel route according to predetermined unit time cost of the passenger;
calculating the probability of selecting the corresponding subway station as the transfer subway station by the passenger according to the generalized travel cost;
and determining the subway station with the maximum probability as the transfer subway station.
Optionally, the calculating the generalized travel cost of each travel route according to the predetermined cost per unit time of the passenger includes:
for any one of the travel routes, determining the generalized travel cost for the travel route using a second formula, the second formula comprising:
Figure 174220DEST_PATH_IMAGE014
，
wherein the content of the first and second substances,
Figure 673335DEST_PATH_IMAGE016
for any of the subway stations in the calibration area,
Figure 674527DEST_PATH_IMAGE018
for subway stations
Figure 651710DEST_PATH_IMAGE016
The generalized travel cost of the corresponding travel route,
Figure 923422DEST_PATH_IMAGE020
represents the penalty cost of the trip experience corresponding to each transfer,
Figure 265542DEST_PATH_IMAGE022
the number of transfers is indicated as such,
Figure 114549DEST_PATH_IMAGE024
which represents the cost per unit time of the passenger,
Figure 770790DEST_PATH_IMAGE026
indicating presence of passengers at said subway station
Figure 457861DEST_PATH_IMAGE027
The weight of the medium dwell time (tdr),
Figure 33198DEST_PATH_IMAGE029
indicating presence of passengers at said subway station
Figure 510447DEST_PATH_IMAGE030
The time of the residence in (a) is,
Figure 95012DEST_PATH_IMAGE032
it is meant to refer to any one of the vehicles,
Figure 138055DEST_PATH_IMAGE034
representing a set of vehicles riding in the travel route,
Figure 25239DEST_PATH_IMAGE036
representing vehicles
Figure 848839DEST_PATH_IMAGE032
The weight of the time-in-transmit,
Figure 610996DEST_PATH_IMAGE038
representing vehicles
Figure 836441DEST_PATH_IMAGE039
Time in transit.
Optionally, the calculating, according to the generalized travel cost, the probability that the passenger selects the corresponding subway station as the transfer subway station includes:
determining the probability that the passenger selects the corresponding subway station as the transfer subway station by adopting a third formula, wherein the third formula comprises the following steps:
Figure 628948DEST_PATH_IMAGE041
，
wherein the content of the first and second substances,
Figure 205423DEST_PATH_IMAGE043
indicating passenger selection of said subway station
Figure 7157DEST_PATH_IMAGE044
As the probability of the transfer to the subway station,
Figure 87108DEST_PATH_IMAGE046
indicating selection of said subway station
Figure 581674DEST_PATH_IMAGE016
The weight of (a) is determined,
Figure 556365DEST_PATH_IMAGE047
indicating selection of said subway station
Figure 286423DEST_PATH_IMAGE027
A corresponding generalized travel cost of the travel route.
Optionally, the domain solution includes a plurality of sites arranged in sequence, and for each domain solution, the inserting the demand pair into the domain solution for a plurality of times at random to obtain a plurality of new domain solutions includes:
and for any one of the domain solutions, randomly inserting the transfer subway station and the terminal point in the demand pair into the domain solution respectively, and repeating the operations for multiple times to obtain multiple new domain solutions.
Optionally, the step of optimizing each new-field solution by using a preset optimization algorithm, wherein the determining an optimal solution includes:
respectively calculating the generalized trip cost of each new domain solution for all new domain solutions corresponding to any one of the domain solutions, and determining the new domain solution with the lowest generalized trip cost as a complete domain solution corresponding to the domain solution;
determining a current solution in all the complete domain solutions by adopting a predetermined optimization model;
the determining a current solution among all the complete-domain solutions using a predetermined optimization model comprises: screening the complete domain solution according to the constraint condition of the optimization model to obtain a screened complete domain solution, and determining the current solution in all the screened complete domain solutions according to the objective function of the optimization model;
the objective function of the optimization model is represented by a fourth formula, the fourth formula comprising:
Figure 96247DEST_PATH_IMAGE049
，
wherein the content of the first and second substances,
Figure 620770DEST_PATH_IMAGE051
representing the number of bus lines included in any of the screened complete domain solutions,
Figure 781624DEST_PATH_IMAGE053
represents the cost per kilometer of operation of the bus,
Figure 49794DEST_PATH_IMAGE055
is shown as
Figure 776442DEST_PATH_IMAGE056
The number of buses equipped on a bus route,
Figure 111346DEST_PATH_IMAGE058
is shown as
Figure 618550DEST_PATH_IMAGE002
The operation mileage of the bus route is determined,
Figure 565778DEST_PATH_IMAGE060
is shown as
Figure 474828DEST_PATH_IMAGE002
The length of time for the operation of the bus route,
Figure 216519DEST_PATH_IMAGE062
is shown as
Figure 476599DEST_PATH_IMAGE056
The operation of a bus route to and from once is time-consuming,
Figure 961938DEST_PATH_IMAGE064
the price of the ticket is represented,
Figure 991074DEST_PATH_IMAGE066
is shown as
Figure 402202DEST_PATH_IMAGE056
Demand for riding a bus on a bus route.
The constraints of the optimization model are represented by a fifth formula, which includes:
Figure 821682DEST_PATH_IMAGE068
，
wherein the content of the first and second substances,
Figure 235345DEST_PATH_IMAGE070
representing the maximum number of kilometers allowed for the bus route,
Figure 994354DEST_PATH_IMAGE072
indicating that the passenger is able toThe maximum of the coefficients of detour that are tolerated,
Figure 468061DEST_PATH_IMAGE074
is shown as
Figure 312520DEST_PATH_IMAGE076
The corresponding time in transit of each passenger sitting in the bus,
Figure 264295DEST_PATH_IMAGE078
is shown as
Figure 641925DEST_PATH_IMAGE076
The corresponding time in transit for each passenger to sit in the car,
Figure 20954DEST_PATH_IMAGE080
represents a set of bus routes in the complete domain solution,
Figure 87130DEST_PATH_IMAGE082
representing a collection of passengers.
Judging whether a preset termination condition is reached, if so, determining that the current solution is the optimal solution; if not, returning to execute the field solution generation step according to the parameters of the preset line generation algorithm of the current demodulation.
Optionally, the termination condition includes that the iteration number reaches the calibration number or the current solution obtained by iterating the calibration number has no change.
In a second aspect, the invention provides a subway connection bus route optimization device, which includes:
the system comprises a line generation module, a line detection module and a line identification module, wherein the line generation module is used for generating a plurality of initial lines on the basis of any subway station in a calibration area, and each initial line comprises a plurality of stations;
the domain solution generating module is used for solving each initial line by adopting a preset line generating algorithm to obtain a plurality of domain solutions of each initial line;
the demand processing module is used for determining a transfer subway station in the demand origin-destination point for the demand origin-destination point of any passenger, and the transfer subway station and the destination in the demand origin-destination point form a demand pair;
the restoration module is used for randomly inserting the demand pairs into the domain solutions for multiple times to obtain multiple new domain solutions;
and the optimization module is used for optimizing each new field solution by adopting a preset optimization algorithm to determine an optimal solution, and the optimal solution is the connection bus route.
In a third aspect, the invention provides a subway connection bus route optimization device, which comprises a memory and a processor;
the memory for storing a computer program;
the processor is used for realizing the optimization method of the subway connection bus line when executing the computer program.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method for optimizing a subway docking bus line is implemented.
The subway connection bus line optimization method, the subway connection bus line optimization device and the storage medium have the beneficial effects that: for each subway station, all initial lines connected with each subway station are generated firstly, and each initial line is decomposed to obtain a plurality of domain solutions. The method comprises the steps of obtaining required origin-destination points of passengers, wherein the required origin-destination points comprise a starting point and a destination point, determining transfer subway stations which the passengers want to select according to the required origin-destination points, randomly inserting the demand pair consisting of the transfer subway stations and the destination point into each field solution, obtaining a plurality of new field solutions, enabling each new field solution to correspond to a bus line, generating the line by combining the required origin-destination points of the passengers, optimizing the line in a targeted manner, and greatly improving the operation efficiency of the subway bus transfer. And determining an optimal solution in all new field solutions by adopting a preset optimization algorithm, determining that the optimal line in all generated lines is a subway connection bus line, and the optimal line can be a line with the lowest cost and the like. The technical scheme of the invention combines the requirements of passengers to generate the connection bus route in a targeted manner, can greatly improve the operation efficiency and the service quality, can reduce detour and the like, and improves the riding experience of the passengers.
Drawings
Fig. 1 is a schematic flow chart of a subway connection bus route optimization method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a subway connection bus route optimization method according to another embodiment of the present invention;
fig. 3 is a schematic flow chart of a subway connection bus route optimization method according to still another embodiment of the present invention;
fig. 4 is a schematic structural diagram of a subway connection bus route optimization device according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.
The subway connection bus line is optimized on the basis of the following assumptions:
the transit time cost between a station and a stop in a bus line is calculated based on the normal travel speed in the uncongested period. Although the transit time of the buses at the same distance may be influenced by the reasons of the type of the bus, the number of vehicles on the travelable road section, the number of vehicles at the same intersection and the like, the influence can be improved by various policies and technical means such as ensuring the right of the bus road, adding a special lane for the bus or the cooperation of the bus road and the like.
Assuming that the passengers are rational people, the passing cost information of each bus line can be obtained when the bus line is selected, and only the bus line with the lowest passing cost is considered.
After the subway connection bus line is formed, the buses operate at a fixed departure frequency, so that the speed of passengers arriving at the subway station is only linearly related to the demand in the subway connection bus line.
The capacity limit exists in the stations such as the subway station, and after the traffic threshold value of unit time is exceeded, the queuing waiting time of passengers is linearly related to the capacity overrun value.
The selection of passengers for transferring subway stations is limited and conservative, and passengers can only select among subway stations within a calibrated straight line range of a departure point or a destination.
As shown in fig. 1, a method for optimizing a subway connection bus route according to an embodiment of the present invention includes determining a station in a calibration area according to a demand origin-destination of each passenger, and specifically includes:
respectively acquiring the required origin-destination points of each passenger in the calibration area, wherein the required origin-destination points comprise a starting point and a terminal point;
dividing all the passengers into a plurality of samples according to the required origin-destination points of all the passengers, wherein all the passengers at the same starting point or the same destination point form one sample, and the starting point or the destination point corresponding to the sample is the position of the sample;
and calculating the distance between every two samples according to the positions of the samples, and comparing the distance with a preset threshold value.
Optionally, the calculating the distance between each two samples according to the positions of the samples comprises:
let any sample
Figure 842596DEST_PATH_IMAGE083
In the position of
Figure 576197DEST_PATH_IMAGE084
In addition, anotherA sample
Figure 391706DEST_PATH_IMAGE006
In the position of
Figure 641116DEST_PATH_IMAGE085
Determining a distance between two of said samples using a first formula, said first formula comprising:
Figure 137957DEST_PATH_IMAGE086
，
wherein the content of the first and second substances,
Figure 726064DEST_PATH_IMAGE087
is the euclidean distance between two of the samples,
Figure 446895DEST_PATH_IMAGE089
representing a sample
Figure 986199DEST_PATH_IMAGE002
The longitude of the location of the mobile terminal,
Figure 958834DEST_PATH_IMAGE091
representing a sample
Figure 260503DEST_PATH_IMAGE092
The latitude of the location at which the vehicle is located,
Figure 293181DEST_PATH_IMAGE094
representing a sample
Figure 211458DEST_PATH_IMAGE006
The longitude of the location of the mobile terminal,
Figure 722205DEST_PATH_IMAGE096
representing a sample
Figure 409538DEST_PATH_IMAGE006
The latitude of the location.
Combining the two samples with the distance smaller than or equal to the preset threshold into a cluster until the distance between any two samples is larger than the preset threshold.
Alternatively, the method of average-linking agglomerative hierarchical clustering (HAC) may be used to score the similarity of each sample, and combine two samples with similar similarity into a cluster until the similarity difference between any two samples exceeds a certain threshold, and the samples in each cluster have the same cluster label, that is, each cluster includes a plurality of samples with similar starting points or similar end points.
For any one of the clusters, generating one of the sites based on the location of each of the samples within the cluster.
Optionally, the generating one station according to the position of each sample in the cluster includes:
for all the samples in any one cluster, traversing all the bus stations within the calibration range of each sample, wherein the calibration range can be a walking range of 500 meters;
for each bus station, calculating the walking distance from each sample to the bus station, and calculating the average walking distance from each sample to the bus station according to all the walking distances;
and determining the bus station with the shortest average walking distance as the station corresponding to the cluster.
In this alternative embodiment, the areas with concentrated start points or end points are aggregated into a cluster, and a bus stop with a suitable distance from each passenger in the cluster is selected as the stop. The passenger has higher timeliness requirement on the bus to be plugged, and can accept walking within a certain range, so that the requirement origin-destination point of the user is obtained, the bus station with moderate distance and concentrated demand from each passenger in an area is set as a station, the search time for generating the initial line can be reduced, the generation efficiency of the bus line to be plugged is greatly improved, and the generated line can reduce the time for people to walk around.
As shown in fig. 2 and fig. 3, a method for optimizing a subway connection bus route according to an embodiment of the present invention includes:
step S110, for any subway station in a calibration area, generating a plurality of initial lines connected with the subway station on the basis of the subway station, wherein each initial line comprises a plurality of stations.
Optionally, determining all the stations in the calibration area, and randomly determining a plurality of first stations connected with the subway station in all the stations;
for any first station, sequentially determining the station closest to the current station as a next station by adopting a greedy algorithm, and sequentially connecting all the determined stations to generate the initial line.
And step S120, solving each initial line by adopting a preset line generation algorithm to obtain a plurality of field solutions of each initial line.
Optionally, the preset line generation algorithm includes a destroy operator, and the obtaining the multiple domain solutions of each initial line by solving each initial line with the preset line generation algorithm includes:
for any initial line, randomly destroying at least one site by the aid of the destroying operators, and randomly combining the rest sites to obtain a plurality of domain solutions.
In this optional embodiment, the preset line generation algorithm may include an aln (Adaptive Large neighbor Search algorithm) algorithm. For example, for an original line ABCD, if there are E, F sites near the original line, all the sites A, B, C, D, E, F are randomly combined to obtain a plurality of new lines, such as AEF and ACDB, where each new line is a domain solution and the set of all the domain solutions is a domain space. The ALNS algorithm comprises various destroying operators and inserting operators, parameters of the line generation algorithm comprise weights of the destroying operators and weights of the inserting operators, in the embodiment, the destroying operators are adopted to destroy partial sites, the rest sites are randomly combined, the sequence of each site is changed, the sites after the sequence is changed are sequentially connected, and a plurality of field solutions are generated.
Step S130, determining a transfer subway station T in a demand Origin-Destination OD (Origin Destination ) for any passenger, where the transfer subway station T and an end point D in the demand Origin-Destination OD constitute a demand pair TD.
The passenger can select most to be for transferring to the subway station from its nearer subway station in position, but along with the passenger's of this website a large amount of gathers, if the passenger need queue up, must increase passenger's trip time cost, and then can influence follow-up passenger and select the decision of transferring to the subway station.
Optionally, for any passenger, a connection line between the demand origin-destination OD of the passenger and any subway station in the calibration area is a travel route of the passenger;
and calculating the generalized travel cost of each travel route according to the unit time cost of the passenger determined in advance.
For any one of the travel routes, determining the generalized travel cost for the travel route using a second formula, the second formula comprising:
Figure 111653DEST_PATH_IMAGE097
，
wherein the content of the first and second substances,
Figure 251647DEST_PATH_IMAGE016
for any of the subway stations in the calibration area,
Figure 628402DEST_PATH_IMAGE098
for subway stations
Figure 780029DEST_PATH_IMAGE016
The generalized travel cost of the corresponding travel route,
Figure 642592DEST_PATH_IMAGE099
represents the penalty cost of the trip experience corresponding to each transfer,
Figure 4303DEST_PATH_IMAGE100
the number of transfers is indicated as such,
Figure 856853DEST_PATH_IMAGE102
for representing the impact value of transfer on the ride experience of the user, for example: the penalty cost of the riding experience corresponding to each transfer is 6, and the transfer is performed for 1 time in the travel route, so that the influence value on the riding experience of the user is 1 multiplied by 6= 6;
Figure 518778DEST_PATH_IMAGE103
which represents the cost per unit time of the passenger,
Figure 64160DEST_PATH_IMAGE104
indicating presence of passengers at said subway station
Figure 850851DEST_PATH_IMAGE030
The weight of the medium dwell time (tdr),
Figure 631725DEST_PATH_IMAGE105
indicating presence of passengers at said subway station
Figure 522058DEST_PATH_IMAGE106
The time of the residence in (a) is,
Figure 362975DEST_PATH_IMAGE107
it is meant to refer to any one of the vehicles,
Figure 840224DEST_PATH_IMAGE108
representing a set of vehicles riding in the travel route,
Figure 736374DEST_PATH_IMAGE109
representing vehicles
Figure 717099DEST_PATH_IMAGE110
The weight of the time-in-transmit,
Figure 401021DEST_PATH_IMAGE111
representing vehicles
Figure 959042DEST_PATH_IMAGE032
Time in transit.
And the travel time period of each passenger can be obtained, the landing number of each station can be determined according to the travel time period and the required origin-destination of the passenger, for example, if one route is ABCD in sequence, the required origin-destination of the passenger 1 is BD, and the required origin-destination of the passenger 2 is BC, the passenger 1 and the passenger 2 are considered to be served by the route. The boarding amount of a station is the sum of the numbers of passengers of which the starting point is the station in a calibration period, and the descent amount of the station is the sum of the numbers of passengers of which the ending point is the station in the calibration period, for example, the boarding amount of the station B is 2, namely, passenger 1+ passenger 2, and the descent amount of the station C is 1, namely, passenger 2. For a station, the sum of the login amount and the descent amount of the station is the number of the login and descent persons of the station, and the residence time of the passengers at the station can be calculated according to the number of the login and descent persons and the pre-determined per-person residence time.
And calculating the probability that the passenger selects the corresponding subway station as the transfer subway station T according to the generalized travel cost.
The calculating the probability that the passenger selects the corresponding subway station as the transfer subway station according to the generalized travel cost comprises:
determining the probability that the passenger selects the corresponding subway station as the transfer subway station by adopting a third formula, wherein the third formula comprises the following steps:
Figure 222664DEST_PATH_IMAGE112
，
wherein the content of the first and second substances,
Figure 353169DEST_PATH_IMAGE113
indicating passenger selection of said subway station
Figure 535888DEST_PATH_IMAGE016
As the probability of the transfer to the subway station T,
Figure 784467DEST_PATH_IMAGE114
indicating selection of said subway station
Figure 586201DEST_PATH_IMAGE016
I.e. passenger selects subway station
Figure 666152DEST_PATH_IMAGE016
The preference degree of the subway station can obtain the weight of each subway station based on the trip preference of passengers by means of questionnaire survey and the like,
Figure 160719DEST_PATH_IMAGE115
indicating selection of said subway station
Figure 958911DEST_PATH_IMAGE027
The generalized travel cost of the corresponding travel route and denominator representing the corresponding travel cost of each subway station
Figure 486973DEST_PATH_IMAGE117
And (4) summing.
And determining the subway station with the maximum probability as the transfer subway station T.
And S140, for each domain solution, inserting the TD of the demand pair into the domain solution for multiple times at random, and repairing the domain solution to obtain multiple new domain solutions.
Optionally, the domain solution includes a plurality of stations arranged in sequence, and for any domain solution, the transfer subway station T and the destination D in the demand pair are respectively inserted into the domain solution at random, and are repeated for a plurality of times to obtain a plurality of new domain solutions. Wherein, the insertion sequence corresponding to the new domain solution with the minimum cost is the optimal insertion sequence.
And S150, optimizing each new field solution by adopting a preset optimization algorithm, and determining an optimal solution, wherein the optimal solution is a connection bus line.
Optionally, for all new domain solutions corresponding to any one of the domain solutions, calculating the generalized trip cost of each new domain solution, and determining that the new domain solution with the lowest generalized trip cost is a complete domain solution corresponding to the domain solution;
determining a current solution among all the complete-domain solutions using a predetermined optimization model.
Optionally, the determining a current solution in all the complete-domain solutions by using a predetermined optimization model includes:
screening the complete domain solution according to the constraint condition of the optimization model to obtain a screened complete domain solution, and determining the current solution in all the screened complete domain solutions according to the objective function of the optimization model;
the objective function of the optimization model is represented by a fourth formula, the fourth formula comprising:
Figure 687010DEST_PATH_IMAGE049
，
wherein the content of the first and second substances,
Figure 352478DEST_PATH_IMAGE118
representing the number of bus routes included in any of the complete domain solutions,
Figure 575649DEST_PATH_IMAGE119
represents the cost per kilometer of operation of the bus,
Figure 483300DEST_PATH_IMAGE120
is shown as
Figure 537844DEST_PATH_IMAGE056
The number of buses equipped on a bus route,
Figure 843054DEST_PATH_IMAGE121
is shown as
Figure 615838DEST_PATH_IMAGE056
The operation mileage of the bus route is determined,
Figure 359803DEST_PATH_IMAGE122
is shown as
Figure 472116DEST_PATH_IMAGE056
The length of time for the operation of the bus route,
Figure 276124DEST_PATH_IMAGE123
is shown as
Figure 175684DEST_PATH_IMAGE056
The operation of a bus route to and from once is time-consuming,
Figure 926603DEST_PATH_IMAGE124
the price of the ticket is represented,
Figure 690159DEST_PATH_IMAGE125
is shown as
Figure 602752DEST_PATH_IMAGE126
Demand for riding a bus on a bus route. The objective function includes the per-person social travel cost of taking and connecting the public transport passengers.
The constraints of the optimization model are represented by a fifth formula, which includes:
Figure 350128DEST_PATH_IMAGE068
，
wherein the content of the first and second substances,
Figure 435895DEST_PATH_IMAGE127
representing the maximum number of kilometers allowed by the bus line, may be set to 5km,
Figure 693439DEST_PATH_IMAGE072
which represents the maximum bypass factor that can be tolerated by the passenger, may be set to 1.5,
Figure 901567DEST_PATH_IMAGE128
is shown as
Figure 746026DEST_PATH_IMAGE076
The corresponding time in transit of each passenger sitting in the bus,
Figure 963381DEST_PATH_IMAGE078
is shown as
Figure 576896DEST_PATH_IMAGE076
And (4) the corresponding time in transit of each passenger in the automobile. The constraint condition indicates that the maximum length of the generated line is not more than 5 kilometers, and the maximum bus transit time of the passenger taking the bus is not more than 1.5 times of the direct driving time of the bus,
Figure 221504DEST_PATH_IMAGE129
represents a set of bus routes in the complete domain solution,
Figure 287680DEST_PATH_IMAGE130
representing a collection of passengers.
In the optional embodiment, due to the intensive attribute of the bus, huge social travel cost can be saved, but meanwhile, some passengers who are inconvenient to take the bus area inevitably sacrifice travel convenience due to the reasons of detour, overlong walking distance and the like. Therefore, the optimal route is screened by comprehensively combining the average social trip cost of the passengers taking the buses and the average social trip cost of the passengers taking the automobiles, and the average riding experience of the passengers can be improved.
Judging whether a preset termination condition is reached, if so, determining that the current solution is the optimal solution; if not, returning to execute the field solution generation step according to the parameters of the preset line generation algorithm of the current demodulation.
In this optional embodiment, the parameters in the ALNS algorithm are adjusted, that is, the weight of the destroy operator and the weight of the insert operator are adjusted in each iteration process, where the weight of the destroy operator refers to the probability of executing the destroy operator, and the weight of the insert operator refers to the probability of executing the insert operator. The weights of the destroy operator and the insert operator change with the progress of the iteration, and in the total K iterations, the weights can be calculated according to the target value of the current solution obtained by the current K-th iteration, namely the corresponding value in the target function, and the target value from the first iteration to the (K-1) th iteration in the past, and the calculation process is the prior art and is not repeated herein.
For example: for any operator
Figure 43146DEST_PATH_IMAGE083
The operator may be adjusted based on a sixth formula
Figure 281141DEST_PATH_IMAGE126
The sixth formula includes:
Figure 768755DEST_PATH_IMAGE132
，
wherein the content of the first and second substances,
Figure 446861DEST_PATH_IMAGE134
representing the effect of the current iteration result on the weights of the next iteration,
Figure 881384DEST_PATH_IMAGE136
in time, the weight of the operator in the next iteration is not influenced by the current iteration result,
Figure 594125DEST_PATH_IMAGE138
then, the new weight is completely adopted in the next iteration, the current iteration result is the target value of the current solution,
Figure 190323DEST_PATH_IMAGE140
representation operator
Figure 621304DEST_PATH_IMAGE002
First, the
Figure 92474DEST_PATH_IMAGE142
The weight at the time of the sub-iteration,
Figure 659722DEST_PATH_IMAGE144
representation operator
Figure 692400DEST_PATH_IMAGE002
First, the
Figure 345098DEST_PATH_IMAGE146
The weight at the time of the sub-iteration,
Figure 121424DEST_PATH_IMAGE148
representation operator
Figure 808758DEST_PATH_IMAGE056
The weight corresponding to the current iteration result can be calculated by adopting the following method:
if the iteration obtains a new optimal solution
Figure 12337DEST_PATH_IMAGE150
；
If the complete domain solution obtained by the iteration is better than the current solution, the current solution is obtained
Figure 152331DEST_PATH_IMAGE152
；
If the complete domain solution obtained by the iteration is accepted and does not appear in the past iteration process, the complete domain solution is obtained by the iteration, and the complete domain solution is obtained by the iteration
Figure 762042DEST_PATH_IMAGE154
；
Wherein the content of the first and second substances,
Figure 179248DEST_PATH_IMAGE156
、
Figure 412783DEST_PATH_IMAGE158
and
Figure DEST_PATH_IMAGE160
three different parameters can be set according to the requirement.
And the termination condition comprises that the iteration times reach the calibration times or the current solution obtained by the iteration calibration times has no change.
In the optimization method for the subway connection bus line, for each subway station, all initial lines connected with each subway station are generated at first, and each initial line is decomposed to obtain a plurality of field solutions. The method comprises the steps of obtaining required origin-destination points of passengers, wherein the required origin-destination points comprise a starting point and a destination point, determining transfer subway stations which the passengers want to select according to the required origin-destination points, randomly inserting the demand pair consisting of the transfer subway stations and the destination point into each field solution, obtaining a plurality of new field solutions, enabling each new field solution to correspond to a bus line, generating the line by combining the required origin-destination points of the passengers, optimizing the line in a targeted manner, and greatly improving the operation efficiency of the subway bus transfer. And determining an optimal solution in all new field solutions by adopting a preset optimization algorithm, determining that the optimal line in all generated lines is a subway connection bus line, and the optimal line can be a line with the lowest cost and the like. The technical scheme of the invention combines the requirements of the passengers to generate the connection bus line in a targeted manner, has high efficiency, can greatly improve the operation efficiency and the service quality of the connection bus, can reduce detour and the like, and improves the riding experience of the passengers.
Through tests, the calculation efficiency of the route screened and determined according to the optimized model is high, the total social trip cost in the calibration area can be reduced by about 10%, the service rate of passengers in the calibration area reaches about 95%, the time for taking the bus for connection is less than 1.5 times of the time for taking the automobile for direct running, the riding experience of the passengers is improved, and the service quality is greatly improved.
As shown in fig. 4, an optimization device for a subway connection bus route provided in an embodiment of the present invention includes:
the system comprises a line generation module, a line detection module and a line identification module, wherein the line generation module is used for generating a plurality of initial lines on the basis of any subway station in a calibration area, and each initial line comprises a plurality of stations;
the domain solution generating module is used for solving each initial line by adopting a preset line generating algorithm to obtain a plurality of domain solutions of each initial line;
the demand processing module is used for determining a transfer subway station in the demand origin-destination point for the demand origin-destination point of any passenger, and the transfer subway station and the destination in the demand origin-destination point form a demand pair;
the restoration module is used for randomly inserting the demand pairs into the domain solutions for multiple times to obtain multiple new domain solutions;
and the optimization module is used for optimizing each new field solution by adopting a preset optimization algorithm to determine an optimal solution, and the optimal solution is the connection bus route.
The subway connection bus route optimization device provided by another embodiment of the invention comprises a memory and a processor; the memory for storing a computer program; the processor is used for realizing the optimization method of the subway connection bus line when executing the computer program. The device comprises a computer, a server and the like.
A further embodiment of the present invention provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the method for optimizing a subway docking bus line is implemented.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like. In this application, the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
Although the present disclosure has been described above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be effected therein by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present disclosure, and these changes and modifications are intended to be within the scope of the present disclosure.

Claims (14)

1. A subway connection bus route optimization method is characterized by comprising the following steps:
for any subway station in a calibration area, generating a plurality of initial lines on the basis of the subway station, wherein each initial line comprises a plurality of stations;
a domain solution generating step, in which the sites in each initial line are recombined by adopting a preset line generating algorithm to obtain a plurality of domain solutions of each initial line, wherein a new line obtained after the sites are recombined is one domain solution;
determining a transfer subway station in the demand origin-destination for the demand origin-destination of any passenger, wherein the transfer subway station and the destination in the demand origin-destination form a demand pair;
for each domain solution, randomly inserting the demand pairs into the domain solution for multiple times to obtain multiple new domain solutions;
optimizing each new field solution by adopting a preset optimization algorithm to determine an optimal solution, wherein the optimal solution is a connection bus line and comprises the following steps: respectively calculating generalized trip cost of each new domain solution for all new domain solutions corresponding to any one of the domain solutions, and determining the new domain solution with the lowest generalized trip cost as a complete domain solution corresponding to the domain solution; screening the complete domain solution according to the constraint condition of an optimization model to obtain a screened complete domain solution, and determining a current solution in all the screened complete domain solutions according to an objective function of the optimization model; judging whether a preset termination condition is reached, if so, determining that the current solution is the optimal solution; if not, adjusting the parameters of the line generation algorithm according to the current solution, and returning to execute the field solution generation step.
2. A method as claimed in claim 1, wherein said generating a plurality of initial routes on the basis of said subway stations comprises:
determining all the stations in the calibration area, and randomly determining a plurality of first stations connected with the subway station in all the stations;
for any first station, sequentially determining the station closest to the current station as a next station by adopting a greedy algorithm, and sequentially connecting all stations determined by adopting the greedy algorithm to generate an initial line.
3. A method as claimed in claim 1, wherein before said generating a plurality of initial lines based on said subway stations, further comprising:
respectively acquiring the required origin-destination points of each passenger in the calibration area, wherein the required origin-destination points comprise a starting point and a terminal point;
dividing all the passengers into a plurality of samples according to the required origin-destination points of all the passengers, wherein all the passengers at the same starting point or the same destination point form one sample, and the starting point or the destination point corresponding to the sample is the position of the sample;
calculating the distance between every two samples according to the positions of the samples, and comparing the distance with a preset threshold value;
combining the two samples with the distance smaller than or equal to the preset threshold into a cluster until the distance between any two samples is larger than the preset threshold;
for any one of the clusters, generating one of the sites based on the location of each of the samples within the cluster.
4. A method as claimed in claim 3, wherein said generating one of said stations according to the position of each of said samples in said cluster comprises:
for all the samples in any one cluster, traversing all the bus stations in the calibration range of each sample;
for each bus station, calculating the walking distance from each sample to the bus station, and calculating the average walking distance from each sample to the bus station according to all the walking distances;
and determining the bus station with the shortest average walking distance as the station corresponding to the cluster.
5. The method for optimizing a metro connection bus line according to claim 1, wherein the preset line generation algorithm includes a destruction operator, and the recombining the stations in each of the initial lines by using the preset line generation algorithm to obtain the plurality of domain solutions of each of the initial lines includes:
for any initial line, randomly destroying at least one site by the aid of the destroying operators, and randomly combining the rest sites to obtain a plurality of domain solutions.
6. A method as claimed in claim 1, wherein said determining a transfer subway station in said demand origin-destination comprises:
for any passenger, a connecting line between the required origin-destination point of the passenger and any subway station of the calibration area is a travel route of the passenger;
calculating generalized travel cost of each travel route according to predetermined unit time cost of the passenger;
calculating the probability of selecting the corresponding subway station as the transfer subway station by the passenger according to the generalized travel cost;
and determining the subway station with the maximum probability as the transfer subway station.
7. The method of claim 6, wherein the calculating the generalized travel cost for each of the travel routes according to the predetermined cost per unit time of the passenger comprises:
for any one of the travel routes, determining the generalized travel cost for the travel route using a second formula, the second formula comprising:
Figure DEST_PATH_IMAGE002
，
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE004
for any of the subway stations in the calibration area,
Figure DEST_PATH_IMAGE006
for subway stations
Figure 853909DEST_PATH_IMAGE004
The generalized travel cost of the corresponding travel route,
Figure DEST_PATH_IMAGE008
represents the penalty cost of the trip experience corresponding to each transfer,
Figure DEST_PATH_IMAGE010
the number of transfers is indicated as such,
Figure DEST_PATH_IMAGE012
which represents the cost per unit time of the passenger,
Figure DEST_PATH_IMAGE014
indicating presence of passengers at said subway station
Figure 630104DEST_PATH_IMAGE004
The weight of the medium dwell time (tdr),
Figure DEST_PATH_IMAGE016
indicating presence of passengers at said subway station
Figure 347524DEST_PATH_IMAGE004
The time of the residence in (a) is,
Figure DEST_PATH_IMAGE018
it is meant to refer to any one of the vehicles,
Figure DEST_PATH_IMAGE020
representing a set of vehicles to be taken in the travel route,
Figure DEST_PATH_IMAGE022
representing vehicles
Figure 368832DEST_PATH_IMAGE018
The weight of the time-in-transmit,
Figure DEST_PATH_IMAGE024
representing vehicles
Figure DEST_PATH_IMAGE025
Time in transit.
8. The method for optimizing a subway transfer bus line according to claim 7, wherein said calculating the probability that a passenger selects the corresponding subway station as the transfer subway station according to the generalized travel cost comprises:
determining the probability that the passenger selects the corresponding subway station as the transfer subway station by adopting a third formula, wherein the third formula comprises the following steps:
Figure DEST_PATH_IMAGE027
，
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE029
indicating passenger selection of said subway station
Figure 307838DEST_PATH_IMAGE004
As the probability of the transfer to the subway station,
Figure DEST_PATH_IMAGE031
indicating selection of said subway station
Figure DEST_PATH_IMAGE032
The weight of (a) is determined,
Figure DEST_PATH_IMAGE033
indicating selection of said subway station
Figure DEST_PATH_IMAGE034
A corresponding generalized travel cost of the travel route.
9. A method as claimed in any one of claims 1 to 8, wherein the domain solution includes a plurality of stations arranged in sequence, and for each domain solution, inserting the demand pair into the domain solution a plurality of times at random to obtain a plurality of new domain solutions includes:
and for any one of the domain solutions, randomly inserting the transfer subway station and the terminal point in the demand pair into the domain solution respectively, and repeating the operations for multiple times to obtain multiple new domain solutions.
10. A method as claimed in claim 9, wherein the objective function of the optimization model is represented by a fourth formula, the fourth formula comprising:
Figure DEST_PATH_IMAGE036
，
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE038
representing the number of bus lines included in any of the screened complete domain solutions,
Figure DEST_PATH_IMAGE040
represents the cost per kilometer of operation of the bus,
Figure DEST_PATH_IMAGE042
is shown as
Figure DEST_PATH_IMAGE044
The number of buses equipped on a bus route,
Figure DEST_PATH_IMAGE046
is shown as
Figure DEST_PATH_IMAGE047
The operation mileage of the bus route is determined,
Figure DEST_PATH_IMAGE049
is shown as
Figure 504202DEST_PATH_IMAGE047
The length of time for the operation of the bus route,
Figure DEST_PATH_IMAGE051
is shown as
Figure 771235DEST_PATH_IMAGE047
The operation of a bus route to and from once is time-consuming,
Figure DEST_PATH_IMAGE053
the price of the ticket is represented,
Figure DEST_PATH_IMAGE055
is shown as
Figure 94769DEST_PATH_IMAGE047
Demand for a bus on a bus route;
the constraints of the optimization model are represented by a fifth formula, which includes:
Figure DEST_PATH_IMAGE057
，
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE059
representing the maximum number of kilometers allowed for the bus route,
Figure DEST_PATH_IMAGE061
represents the maximum bypass factor that the passenger can tolerate,
Figure DEST_PATH_IMAGE063
is shown as
Figure DEST_PATH_IMAGE065
The corresponding time in transit of each passenger sitting in the bus,
Figure DEST_PATH_IMAGE067
is shown as
Figure 573767DEST_PATH_IMAGE065
The corresponding time in transit for each passenger to sit in the car,
Figure DEST_PATH_IMAGE069
represents a set of bus routes in the complete domain solution,
Figure DEST_PATH_IMAGE071
representing a collection of passengers.
11. The method of claim 10, wherein the termination condition includes that the iteration number reaches the calibration number or the current solution obtained by the iteration calibration number has no change.
12. The utility model provides a subway bus route optimization device of plugging into which characterized in that includes:
the system comprises a line generation module, a line detection module and a line identification module, wherein the line generation module is used for generating a plurality of initial lines on the basis of any subway station in a calibration area, and each initial line comprises a plurality of stations;
a domain solution generation module, configured to recombine the sites in each initial line by using a preset line generation algorithm to obtain multiple domain solutions of each initial line, where one new line obtained after the sites are recombined is one domain solution;
the demand processing module is used for determining a transfer subway station in the demand origin-destination point for the demand origin-destination point of any passenger, and the transfer subway station and the destination in the demand origin-destination point form a demand pair;
the restoration module is used for randomly inserting the demand pairs into the domain solutions for multiple times to obtain multiple new domain solutions;
the optimization module is used for optimizing each new field solution by adopting a preset optimization algorithm to determine an optimal solution, wherein the optimal solution is a connection bus line and comprises: respectively calculating generalized trip cost of each new domain solution for all new domain solutions corresponding to any one of the domain solutions, and determining the new domain solution with the lowest generalized trip cost as a complete domain solution corresponding to the domain solution; screening the complete domain solution according to the constraint condition of an optimization model to obtain a screened complete domain solution, and determining a current solution in all the screened complete domain solutions according to an objective function of the optimization model; judging whether a preset termination condition is reached, if so, determining that the current solution is the optimal solution; if not, adjusting the parameters of the line generation algorithm according to the current solution, and returning to be executed by the field solution generation module.
13. A computer device comprising a memory and a processor;
the memory for storing a computer program;
the processor, when executing the computer program, is configured to implement the method for optimizing a subway docking bus route according to any one of claims 1 to 11.
14. A computer-readable storage medium, characterized in that the medium has stored thereon a computer program which, when being executed by a processor, carries out the method for optimizing a subway docking bus line according to any one of claims 1 to 11.
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