CN114783204B - Supply and demand matching method for shared parking in automatic parking background - Google Patents
Supply and demand matching method for shared parking in automatic parking background Download PDFInfo
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- CN114783204B CN114783204B CN202210279378.4A CN202210279378A CN114783204B CN 114783204 B CN114783204 B CN 114783204B CN 202210279378 A CN202210279378 A CN 202210279378A CN 114783204 B CN114783204 B CN 114783204B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
- G08G1/148—Management of a network of parking areas
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0605—Supply or demand aggregation
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
Abstract
The invention discloses a supply and demand matching method for shared parking in an automatic parking background, wherein an automatic parking robot parks by using idle small-period through autonomous moving vehicles to meet the demand of shared parking, and the invention comprises an improved tabu search algorithm which comprises the following steps: splitting the shared parking demand time and the shared parking space supply time; defining pairing, time interval pairing and pairing schemes through the supply-demand relationship of shared parking; defining a neighborhood of the pairing scheme and prescribing the scale of the neighborhood, and reducing the time of algorithm neighborhood searching; and variation operation is introduced in the algorithm execution process, so that the algorithm is prevented from falling into a local optimal solution. The algorithm provided by the invention aims to maximally meet the shared parking requirement by the minimum automatic parking number of vehicles, and reduce the cost of vehicles, the resource waste and the traffic accident occurrence rate while meeting the shared parking requirement and improving the berth utilization rate.
Description
Technical Field
The invention relates to the field of shared parking, in particular to a supply and demand matching method for shared parking in an automatic parking background.
Background
The shared parking utilizes the idle period of the parking space to park according to the difference of the parking demand time of the traveler and the space-time difference of the parking space, and the shortage of parking resources is relieved. Short-distance autonomous passenger-carrying parking refers to that an unmanned vehicle puts off passengers near the entrance of a parking lot and enters the parking lot for autonomous parking; when a passenger needs a vehicle, the vehicle will wait at the exit of the parking lot. The long-distance autonomous passenger-carrying means that an unmanned vehicle puts off passengers near the destination of the passengers and autonomously searches a parking lot for parking; when a passenger needs to use the vehicle, the vehicle will wait for the passenger at the designated location. Autonomous passenger parking is a new trend of unmanned, has the characteristic of 'empty driving', and can be automatically shifted. Through this characteristic, the vehicle parks in different parking stalls on continuous period, utilizes the leisure period of sharing parking stall to park, solves the difficult problem of parking. Supply and demand matching method for shared parking in automatic parking background
The vehicle is shifted by automatic passenger-substituting parking, the vehicle is parked by utilizing the idle fragmentation time of the parking space, the traditional shared parking can be avoided, the defects caused by the incapability of shifting the vehicle in the parking demand time are overcome, the shared parking demand is met to the greatest extent and flexibly, and the parking space utilization rate is improved. However, an unreasonable vehicle layout may also cause frequent displacement of the vehicle, resulting in increased cost of the vehicle and frequent traffic accidents in the parking area. The invention designs an improved tabu search algorithm for matching the supply and demand of shared parking in an autonomous parking background, which aims at reducing the cost of the vehicle and the potential traffic accident risk caused by frequent displacement of an unmanned vehicle while meeting the demand of shared parking by utilizing the characteristic of 'empty driving' of autonomous passenger parking.
Disclosure of Invention
The invention aims to solve the problems, and provides a supply and demand matching method for shared parking in an automatic parking background, which comprises an improved tabu search algorithm and specifically comprises the following steps:
s1: dividing the shared parking demand time by taking the first and last moments of the shared parking demand and the supply time as dividing points;
s2: on the basis of time interval segmentation, a pairing scheme and a time interval pairing scheme are defined through the relationship between the shared parking requirement and the supplied berth;
s3: on the premise of ensuring the optimal solution quality, analyzing the characteristics of a tabu search algorithm, and designing the neighborhood and the neighbor of a pairing scheme
S4: the length of the tabu list is determined according to a first-in first-out principle, variation operation is introduced when the optimal solution is unchanged for multiple iterations of the algorithm, and an improved tabu search algorithm for sharing parking supply and demand matching under the automatic parking condition is designed and solved.
Further, in the step S1, at t v,s Indicating the start time of the parking demand time; t is t v,e Indicating the stopping demand end time; t is t p,s Indicating the starting time of the berth opening time；t p,e The specific steps of dividing the shared parking demand time include:
s11: let all t v,s 、t v,e 、t p,s 、t p,e Putting the set T;
s12: removing the repeated elements in the set T, arranging the residual elements in ascending order, and finally updating the set T;
s13: and taking elements in the set T as division points, and dividing all the shared parking demand time and the shared berth supply time into a plurality of small time periods.
Further, in the step S2, when e (v, p, i) indicates that the vehicle v is parked at the berth p for the period i, e (0, p, i) indicates that the vehicle is not parked at the berth p for the period i; all vehicles with parking requirements over period i form set V i All parking spaces supporting sharing form set P i ；
By E (V) i ,P i )＝{e 1 ,…,e n I e {1, …, n } represents a period pairing over period i where all vehicles with shared parking needs are parked at the appropriate berth; e (E) i ＝E(V i ,P i ) N represents the number of divided periods; m represents a pairing scheme for all vehicles with shared parking requirements to be parked in a proper parking space, M a And M b Two different pairing schemes are represented.
Further, in said step S3, the known pairing scheme M will be followed a Among the pairing schemes in which there is only one pairing of different periods, one pairing scheme with the smallest number of moving times is M a All pairing schemes satisfying the above conditions constitute M a Meanwhile, the pairing scheme meeting the above condition in the period i is called M a I period neighbors of (a).
Further, in the step S3, determining the i-period neighbor of the given pairing scheme includes the following steps:
s31, calculating the vehicle shift cost. The following is performed for each pairing: for pairing e (v, p, i), calculating a cost of moving vehicle v between periods i-1 and i, periods i and i +1,let the sum of the two costs be c v,p ,v∈V i ,p∈P i ；
S32, constructing a cost matrix. C is set forth in v,p Is an element, V i And P i The internal elements form a cost matrix for the line elements
S33, constructing a transportation problem. In a cost matrixOn the basis, the parking demand and berth supply quantity in the period of sharing parking i are the production place cargo production quantity and the consumption place cargo demand quantity of the transportation problem, and a classical transportation problem is constructed;
s34, solving the transportation problem. Solving the transportation problem by using a classical Hungary algorithm to obtain a matching relation between the shared parking requirement and the shared berth, and reasonably combining the matching relation to obtain M a I period neighbors of (a).
Further, in the step S4, when the number of iterations of the optimal solution unchanged reaches the set value, the mutation operation is implemented to jump out of the current solution. Wherein, the improved tabu search algorithm comprises the following steps:
s41: initializing parameters. Length L of tabu table, maximum iteration number τ max Maximum search number mu, where the current solution remains unchanged max Neighborhood maximum scale n b Coefficient of variation α. The current iteration number tau=0, and the unchanged iteration number mu=0 of the current optimal solution;
s42: a feasible pairing scheme is randomly generated as an initial solution. Randomly generating a period pairing E of n periods i I epsilon {1, …, n }, reasonably combining to obtain an initial pairing scheme M 0 As the optimal pairing scheme M * And current pairing scheme M ing ；
S43: the tabu table is updated. Will M ing Adding a tabu table, and deleting the earliest element in the tabu table according to a first-in first-out rule, so that τ is =τ+1;
s44: determining the neighborhood of the current solutionDomain. Randomly determining n b Determining M by using the neighborhood searching algorithm in step S3 ing N of (2) b The neighbors constitute the neighborhood of the current solution. Selecting the pairing scheme which is not in the tabu list and has the minimum number of times of moving as M ing ；
S45: updating the optimal solution. If M ing The number of times of the shift is smaller than the current optimal solution M * Make M * ：＝M ing Step S47 is performed. Otherwise, M * Hold unchanged, let μ: =μ+1, γ: = γ+1 and step S46;
s46: when μ=μ max Time period pairing E over random generation period j j Replacement of the current pairing scheme M ing The j time periods in the two are paired to obtain a new pairing scheme M new Let M ing ：＝M new And proceeds to step S45; when mu is equal to mu max Entering the next step;
s47: and judging whether the current data meets the termination condition. If yes, outputting an optimal solution; if not, the process proceeds to step S43 until the termination condition is satisfied, the optimal solution is output, and the algorithm is ended.
Further, the termination condition is: the maximum iteration number of the algorithm or the number of times of vehicle moving of the optimal pairing scheme is 0.
Compared with the prior art, the invention has the beneficial effects that: focusing on the parking space matching of the shared parking; the autonomous shifting process of the automatic bus-substituting parking vehicle in the sharing parking process is optimized; the neighbor of the designed pairing scheme reduces the neighbor range of the algorithm solution, and the algorithm is prevented from being trapped into local optimum through mutation operation.
Drawings
FIG. 1 is a flow chart of an algorithm of the present invention;
fig. 2 is a schematic diagram of a segmentation period according to the present invention.
Detailed Description
A more detailed description of a method for matching supply and demand for shared parking in an automatic parking context of the present invention will be presented below in conjunction with a schematic diagram, wherein preferred embodiments of the present invention are shown, it being understood that one skilled in the art may modify the invention described herein while still achieving the advantageous effects of the invention, and therefore the following description should be construed as broadly known to those skilled in the art and not as limiting the invention.
In the description of the present invention, it should be noted that, for the azimuth words such as "center", "lateral", "longitudinal", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., the azimuth and positional relationships are based on the azimuth or positional relationships shown in the drawings, it is merely for convenience of describing the present invention and simplifying the description, and it is not to be construed as limiting the specific scope of protection of the present invention that the device or element referred to must have a specific azimuth configuration and operation.
As shown in fig. 1, a method for matching supply and demand of shared parking in an automatic parking background includes an improved tabu search algorithm, and the overall flow of the algorithm comprises the following specific steps:
s1, dividing the shared parking requirement time by taking the first time and the last time of the shared parking requirement and the supply time as dividing points. t is t v,s Indicating the start time of the parking demand time, t v,e Indicating the stop demand end time, t p,s Indicating the starting time, t, of the berth opening time p,e Indicating the end time of the berth opening time.
S11, all t v,s 、t v,e 、t p,s 、t p,e Put into the set T.
S12, eliminating repeated elements in the T, arranging the rest elements in ascending order, and updating the set T.
S13, taking elements in the set T as division points, and dividing all the shared parking demand time and the shared berth supply time into finer and more small time periods.
The open time of 8 shared berths in a shared parking area is p1 (7:30-12:00, 13:00-16:30), p2 (6:00-11:30), p3 (8:30-14:15, 16:00-20:00), p4 (7:15-11:45, 13:30-18:15), p5 (9:30-16:00, 16:45-19:30), p6 (6:45-12:45, 14:45-18:15), p7 (9:00-13:45, 15:00-18:30), p8 (8:00-15:15, 6:00-18:45), and the visual display is shown in FIG. 2. In fig. 2, the time of day is 24 parts on a 1h scale, the hatched portion indicates that the berths do not support sharing, and the blank portion indicates that the berths are open during this period. And dividing the berth opening time by taking the starting and ending moments of the 8 berth sharing time as break points. After division, the berths p1, p2, p3, p4 are respectively changed from original 2, 1, 2 time periods to 15, 7, 17, 15 sharing time periods. For simplicity, the present example only considers the start and end times of the shared berth opening time.
S2, on the basis of time interval segmentation, a pairing scheme, a time interval pairing scheme and a pairing scheme are defined through the relationship between the shared parking requirements and the supply berths.
Further, in step S2, e (v, p, i) represents a pairing that the vehicle v is parked at the berth p over the period i. e (0, p, i) indicates that period i berth p does not park the vehicle. All vehicles with parking requirements over period i form set V i All parking spaces supporting sharing form set P i . By E (V) i ,P i )＝{e 1 ,…,e n I e {1, …, n } represents a period pairing over period i where all vehicles with shared parking needs are parked at the appropriate berth; e (E) i ＝E(V i ,P i ) N represents the number of divided periods. M represents a pairing scheme for all vehicles with shared parking requirements to be parked in a proper parking space, M a And M b Two different pairing schemes are represented.
And S3, analyzing the characteristics of a tabu search algorithm on the premise of ensuring the optimal solution quality, and designing the neighborhood and the neighbor of the pairing scheme.
Further, in step S3, the known pairing scheme M will be followed a Among pairing schemes in which there is only one pairing of different periods, one pairing scheme with the smallest number of moves is defined as M a All pairing schemes satisfying the above conditions constitute M a Is a neighborhood of (c). Meanwhile, a pairing scheme satisfying the above condition over the period i is referred to as M a I period neighbors of (a).
S4, determining the length of the tabu table according to a first-in first-out principle, introducing variation operation when the optimal solution is unchanged for multiple iterations of the algorithm, and designing an improved tabu search algorithm for solving the shared parking supply and demand matching under the automatic parking condition.
Further, in step S4, a certain time can be saved when the algorithm neighborhood searches to obtain a new solution through the pairing scheme neighborhood and the neighbor defined in step S3. However, when the number of divided periods n is large, a large amount of calculation is still required to acquire all neighbors of one pairing scheme, thus defining the maximum size of the neighborhood. When the unchanged iteration number of the optimal solution reaches a set value, implementing mutation operation to jump out of the current solution. The improved tabu search algorithm steps are as follows:
s41, initializing parameters. Length L of tabu table, maximum iteration number τ max Maximum search number mu, where the current solution remains unchanged max Neighborhood maximum scale n b Coefficient of variation α. The current iteration number τ=0, and the current optimal solution has unchanged iteration number μ=0.
S42, randomly generating a feasible pairing scheme as an initial solution. Randomly generating a period pairing E of n periods i I epsilon {1, …, n }, reasonably combining to obtain an initial pairing scheme M 0 As the optimal pairing scheme M * And current pairing scheme M ing 。
S43, updating the tabu list. Will M ing Adding the tabu list, and deleting the earliest element in the tabu list according to the first-in first-out rule. Let τ =τ+1.
S44, determining the neighborhood of the current solution. Randomly determining n b A dividing period, M is determined by the following method ing Up to determining n b The neighbors constitute the neighborhood of the current solution. Selecting the pairing scheme which is not in the tabu list and has the minimum number of times of moving as M ing 。
S441, calculating the vehicle moving cost. For the pairing e (v, p, i), in the case that the time interval pairing of the front and rear time intervals i-1, i+1 is given, calculating the vehicle moving cost of the vehicle v between the time intervals i-1 and i and between the time intervals i and i+1, and recording the sum of the two costs as c v,p ,v∈V i ,p∈P i 。
S442, c v,p Is an element, V i And P i The elements forming a cost matrix for the line elements
S443, using cost matrixOn the basis, the parking demand and berth supply in the shared parking i period constitute a transportation problem for the production-place cargo throughput and the consumption-place cargo demand of the transportation problem.
S444, solving the transportation problem by using a classical Hungary algorithm to obtain a matching relation between the shared parking requirement and the shared berth, and reasonably combining the matching relation to obtain M ing Is a neighbor M of (1) new Let M ing ：＝M new 。
S45, updating the optimal solution. If M ing The number of times of the shift is smaller than the current optimal solution M * Make M * ：＝M ing Step S47 is performed. Otherwise, M * Hold unchanged, let μ: =μ+1, γ: =γ+1, go to step S46.
S46, when μ=μ max The following mutation operation is performed, otherwise, the process goes to step S47. Period pairing E over random generation period j j Replacement of the current pairing scheme M ing The j time periods in the two are paired to obtain a new pairing scheme M new Let M ing ：＝M new Step S45 is performed.
S47, terminating judgment. If the maximum iteration number of the algorithm or the number of times of the optimal pairing scheme is 0, outputting an optimal solution and ending the algorithm; otherwise, go to step S4.
The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any person skilled in the art will make any equivalent substitution or modification to the technical solution and technical content disclosed in the invention without departing from the scope of the technical solution of the invention, and the technical solution of the invention is not departing from the scope of the invention.
Claims (3)
1. The supply and demand matching method for shared parking in the automatic parking background is characterized by comprising an improved tabu search algorithm, and specifically comprises the following steps:
s1: dividing the shared parking supply and demand time by taking the first and last moments of the shared parking demand and the supply time as dividing points;
s2: on the basis of time interval segmentation, a pairing scheme and a time interval pairing scheme are defined through the relationship between the shared parking requirement and the supplied berth;
s3: on the premise of ensuring the quality of the optimal solution, analyzing the characteristics of a tabu search algorithm, and designing a neighborhood and a neighbor of a feasible solution;
s4: determining the length of a tabu table according to a first-in first-out principle, introducing mutation operation when the optimal solution is unchanged for multiple iterations of the algorithm, and designing and solving an improved tabu search algorithm for sharing parking supply and demand matching under the automatic parking condition;
in step S1, toIndicating the start time of the parking demand time; />Indicating the stopping demand end time; />Representing the starting time of the berth opening time; />The specific steps of dividing the shared parking supply and demand time include:
s11: all are put together、/>、/>、/>Put in the collection->；
S12: rejecting the collectionRepeating the elements in the set, arranging the rest elements in ascending order, and finally updating the set +.>；
S13: to be assembled intoThe inner element is a division point, and all the shared parking demand time and the shared berth supply time are divided into a plurality of small time periods;
in step S2, whenRepresentation period->Go up vehicle->Park in berth->Is a pairing of->Representing a time periodBerth->The vehicle is not parked; period->All vehicles with parking requirements above form a set +.>All parking spaces supporting sharing form a set +.>；
By usingRepresentation period->Pairing all vehicles with shared parking requirements at one time period when the vehicles are parked at the proper berth; />，/>Representing the number of divided periods; />A pairing scheme indicating that all vehicles with shared parking requirements are parked in the appropriate parking space, +.>And->Representing two different pairing schemes;
in step S3, the known pairing scheme is to be followedAmong the pairing schemes in which there is only one pairing at different time periods, one pairing scheme with the smallest number of moving times is +.>All pairing schemes satisfying the above conditions constitute +.>Is to simultaneously add period->The pairing scheme satisfying the above conditions is called +.>Is->A time period neighbor;
in step S4, when the number of iterations of the optimal solution is unchanged reaches a set value, performing a mutation operation to jump out of the current solution, where the improved tabu search algorithm includes the following steps:
s41: initializing parameters, tabu table lengthMaximum number of iterations->Maximum search number +.>Neighborhood maximum size->Coefficient of variation->Current iteration number +.>Unchanged iteration number of current optimal solution；
S42: randomly generating a feasible pairing scheme as an initial solution and taking the initial solution as an optimal pairing schemeAnd the current pairing scheme->；
S43: update the tabu listAdding the tabu list, deleting the earliest element in the tabu list according to the first-in first-out rule, and enabling the +.>；
S44: determining a neighborhood of a current solution, randomly determiningA plurality of divided periods, which are determined by using the neighborhood searching algorithm in step S3Is->The neighbors form the neighborhood of the current solution, and a pairing scheme which is not in the tabu list and has the minimum number of times of moving is selected as +.>；
S45: updating optimal solutionsIf (3)The number of times of the move is smaller than the current optimal solution +.>Number of times of moving car, orderStep S47 is proceeded to, otherwise, < >>Keep unchanged, let->And a step S46;
s46: when (when)Random generation period->Time period pairing->Replacement of the current pairing scheme->Is->Time period pairing, new pairing scheme is obtained>Let->And proceeds to step S45; when->Entering the next step;
s47: judging whether the current data meets the termination condition, and if so, outputting an optimal solution; if not, the process proceeds to step S43 until the termination condition is satisfied, the optimal solution is output, and the algorithm is ended.
2. The supply and demand matching method according to claim 1, wherein in the step S3, a given pairing scheme is determinedThe period neighbor comprises the following steps:
s31: calculating a vehicle shift cost, and performing the following operation on each pairing: for pairingCalculating the vehicle +.>In period->And->Period->And->The cost of the vehicle moving between the two costs is recorded as +.>；
S32: constructing a cost matrix toIs an element (I)>And->The inner elements form a cost matrix for the row and column elements>；
S33: constructing transportation problems to cost matrixBased on, shared parking->The parking demand and the berth supply quantity in the time period are the production place cargo production quantity and the consumption place cargo demand of the transportation problem, and a classical transportation problem is constructed;
s34: solving the transportation problem, solving the transportation problem by using a classical Hungary algorithm to obtain a matching relation between the shared parking requirement and the shared berth, and reasonably combining the matching relation to obtainIs->Time period neighbors.
3. The supply and demand matching method according to claim 1, wherein the termination condition is: the maximum iteration number of the algorithm or the number of times of vehicle moving of the optimal pairing scheme is 0.
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| CN113516868A (en) * | 2021-03-25 | 2021-10-19 | 杭州昊恒科技有限公司 | Intelligent parking system based on big data |
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| US11341431B2 (en) * | 2017-12-28 | 2022-05-24 | Pied Parker, Inc. | Systems and methods for parking space selection based on weighted parameter comparison |
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| CN106097771A (en) * | 2016-08-24 | 2016-11-09 | 上海理工大学 | Application way is shared in the comment of urban parking area resource |
| CN108417080A (en) * | 2018-04-10 | 2018-08-17 | 华南理工大学 | A kind of timesharing divides the shared parking stall unattended system and method for position valuation |
| CN109544962A (en) * | 2018-12-11 | 2019-03-29 | 青岛大学 | A kind of parking stall is shared and dispatches system and implementation method |
| CN111985835A (en) * | 2020-08-31 | 2020-11-24 | 盐城工学院 | Distribution method for shared parking berths in residential area |
| CN113516868A (en) * | 2021-03-25 | 2021-10-19 | 杭州昊恒科技有限公司 | Intelligent parking system based on big data |
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