CN114783204A - Supply and demand matching method for shared parking under automatic parking background - Google Patents

Supply and demand matching method for shared parking under automatic parking background Download PDF

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CN114783204A
CN114783204A CN202210279378.4A CN202210279378A CN114783204A CN 114783204 A CN114783204 A CN 114783204A CN 202210279378 A CN202210279378 A CN 202210279378A CN 114783204 A CN114783204 A CN 114783204A
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CN114783204B (en
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何胜学
崔允汀
袁鹏程
梁士栋
张虎
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University of Shanghai for Science and Technology
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Abstract

The invention discloses a supply and demand matching method for shared parking under the background of automatic parking.A robot for automatic parking utilizes leisure hours to park by autonomously moving vehicles to meet the shared parking requirement. Splitting the shared parking demand time and the shared parking space supply time; defining a pairing scheme, a time interval pairing scheme and a pairing scheme through the supply and demand relationship of shared parking; defining the size of the neighborhood and the specified neighborhood of the matching scheme, and reducing the time of searching the algorithm neighborhood; variation operation is introduced in the algorithm execution process, and 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 least automatic parking and transferring times, and reduces the vehicle cost, resource waste and traffic accident occurrence rate while meeting the shared parking requirement and improving the parking utilization rate.

Description

Supply and demand matching method for shared parking under automatic parking background
Technical Field
The invention relates to the field of shared parking, in particular to a supply and demand matching method for shared parking under the background of automatic parking.
Background
The shared parking utilizes the idle time period of the parking space to park according to the parking demand time difference of travelers and the space-time difference of the parking space, and the lack of parking resources is relieved. The short-distance autonomous parking for the passengers is characterized in that an unmanned vehicle puts down passengers near the entrance of a parking lot and enters the parking lot for autonomous parking; when a passenger desires a vehicle, the vehicle will wait at the parking lot exit. The long-distance autonomous passenger-replacing means that an unmanned vehicle puts down a passenger near a passenger destination, 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. The autonomous valet parking is a new trend of unmanned driving, has the characteristic of 'empty driving', and can autonomously shift. Through the characteristic, the vehicle can be parked in different parking spaces in continuous time intervals, and the parking is carried out in leisure time intervals sharing the parking spaces, so that the problem of difficulty in parking is solved. Supply and demand matching method for shared parking under automatic parking background
The vehicle is shifted by automatic passenger-replacing parking, and the vehicle is parked in the fragmentation time of parking space idling, so that the traditional shared parking can be avoided, the defect caused by the fact that the vehicle cannot shift in the parking demand time is overcome, the shared parking demand is flexibly met to the maximum extent, and the parking utilization rate is improved. However, the improper vehicle layout may cause frequent vehicle displacement, which may increase the cost of using the vehicle and also cause frequent traffic accidents in the parking area. The invention designs an improved taboo search algorithm for matching shared parking supply and demand under an autonomous parking background, which aims to reduce the vehicle cost and potential traffic accident risk caused by frequent displacement of an unmanned vehicle while meeting the shared parking requirement by utilizing the 'empty driving' characteristic of autonomous passenger-assistant parking.
Disclosure of Invention
The invention aims to solve the problems and provides a supply and demand matching method for shared parking under the background of automatic parking, 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 supply time as division points;
s2: on the basis of time interval division, a pairing scheme, a time interval pairing scheme and a pairing scheme are defined through the relation between the shared parking demand and the supply parking space;
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 a tabu table is determined according to a first-in first-out principle, mutation operation is introduced when the optimal solution is unchanged after 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, t is usedv,sRepresenting a parking demand time start time; t is tv,eIndicating the stopping demand ending time; t is tp,sRepresenting the starting time of the open time of the berth; t is tp,eFor example, the parking space open time end time is represented, and the specific step of dividing the shared parking required time includes:
s11: all t are comparedv,s、tv,e、tp,s、tp,ePutting the set T;
s12: eliminating repeated elements in the set T, arranging the rest elements in an ascending order, and finally updating the set T;
s13: and dividing all the shared parking demand time and the shared parking space supply time into a plurality of small time intervals by taking the elements in the set T as dividing points.
Further, in the step S2, when e (v, p, i) indicates that the vehicle v is parked in one pair of the parking spaces p in the time period i, e (0, p, i) indicates that the vehicle is not parked in the parking spaces p in the time period i; all vehicles with parking requirements on time period i form a set ViAll parking spaces supporting sharing form a set Pi；
With E (V)i,Pi)＝{e1,…,enH, i e {1, …, n } represents a timeslot pair for which all vehicles with shared parking requirements are parked at the appropriate parking space for timeslot i; ei＝E(Vi,Pi) N represents the number of divided periods; m represents a pairing scheme that all vehicles with shared parking requirements are parked in appropriate parking spaces, MaAnd MbTwo different pairing schemes are shown.
Further, in the step S3, the known pairing scheme M is matchedaThe pairing scheme with the least number of shifts is M in the pairing schemes with only one different period pairingaAll the pairing schemes satisfying the above conditions constitute MaWhile the pairing scheme satisfying the above condition over the period i is called MaThe i period of time of (1) is adjacent.
Further, in the step S3, the step of determining the i-period neighbor of the given pairing scheme includes the following steps:
and S31, calculating the vehicle displacement cost. For each pairing, the following operations are performed: for the pair e (v, p, i), the vehicle moving cost of the vehicle v between the time periods i-1 and i and the time period i and i +1 is calculated, and the sum of the two costs is recorded as cv,p,v∈Vi,p∈Pi；
And S32, constructing a cost matrix. With cv,pIs an element, ViAnd PiThe inner elements form a cost matrix for the row and column elements
Figure BDA0003556407280000031
S33, constructing transportation problems. By a cost matrix
Figure BDA0003556407280000032
On the basis, the parking demand and the parking space supply quantity of the shared parking i period are the production place goods production quantity and the consumption place goods demand of the transportation problem, and a classical transportation problem is constructed;
and S34, solving the transportation problem. Solving the transportation problem by using a classical Hungarian algorithm to obtain a matching relation between the shared parking demand and the shared berth, and reasonably combining the matching relation to obtain MaThe i period of time of (1) is adjacent.
Further, in step S4, when the number of iterations in which the optimal solution is not changed reaches a set value, a mutation operation is performed to jump out of the current solution. The improved tabu search algorithm comprises the following steps:
s41: and initializing parameters. Tabu length L, maximum number of iterations τmaxMaximum number of searches mu for which the current solution remains constantmaxNeighborhood maximum size nbCoefficient of variation α. The current iteration time tau is equal to 0, and the current optimal solution unchanged iteration time mu is equal to 0;
s42: a feasible pairing scheme is randomly generated as an initial solution. Time period pairing E for randomly generating n time periodsiAnd i belongs to {1, …, n }, reasonably combining to obtain an initial pairing scheme M0As the optimal pairing scheme M*And current pairing scheme Ming；
S43: and updating the taboo table. Will MingAdding a tabu table, and deleting the earliest element in the tabu table according to a first-in first-out rule to enable tau to be tau + 1;
s44: a neighborhood of the current solution is determined. Randomly determining nbEach divided period is determined by using the neighborhood search algorithm in step S3ingN of (A) to (B)bThe neighbors constitute the neighborhood of the current solution. Selecting the matching scheme which is not in the tabu table and has the minimum number of the moving cars as Ming；
S45: and updating the optimal solution. If M isingThe number of times of moving is less than the current optimal solution M*The number of times of moving the vehicle is M*：＝MingGo to step S47. Otherwise, M*Held constant, let μ: μ +1, γ: γ +1 and go to step S46;
s46: when mu is mumaxTime period pairing E on time period j is randomly generatedjReplace the current pairing scheme MingThe j time period in the step (2) is paired to obtain a new pairing scheme MnewLet M stand foring：＝MnewAnd proceeds to step S45; when mu ≠ mumaxEntering 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 and the optimal solution is output and the algorithm is ended.
Further, the termination condition is: the maximum iteration times of the algorithm or the optimal matching scheme vehicle moving times are 0.
Compared with the prior art, the invention has the following beneficial effects: focusing on the matching of the parking spaces for shared parking; the autonomous shifting process of the automatic valet parking vehicle in the shared parking process is optimized; the neighborhood range of the algorithm solution is reduced by designing the neighbors of the pairing scheme, and the algorithm is prevented from falling into local optimum by mutation operation.
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FIG. 1 is a flow chart of the algorithm of the present invention;
FIG. 2 is a schematic diagram of the time division according to the present invention.
Detailed Description
The method for matching supply and demand for shared parking in the context of automated parking according to the present invention will be described in more detail below with reference to schematic drawings, in which preferred embodiments of the present invention are shown, it being understood that a person 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 taken as being widely known to a person skilled in the art and not as limiting the invention.
In the description of the present invention, it should be noted that, for the terms of orientation, such as "central", "lateral", "longitudinal", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., it indicates that the orientation and positional relationship shown in the drawings are based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, but does not indicate or imply that the device or element referred to must have a specific orientation, be constructed in a specific orientation, and be operated without limiting the specific scope of protection of the present invention.
As shown in fig. 1, a supply and demand matching method for shared parking under the background of automatic parking includes an improved taboo search algorithm, and the overall flow of the algorithm includes the following specific steps:
and S1, dividing the shared parking demand time by taking the first and last moments of the shared parking demand and supply time as dividing points. t is tv,sIndicating the start of the parking demand time, tv,eIndicating the end of the parking demand, tp,sIndicates the starting time of the parking space open time, tp,eIndicating the end of the berth opening time.
S11, dividing all tv,s、tv,e、tp,s、tp,ePut into the set T.
And S12, removing repeated elements in the T, arranging the rest elements in an ascending order, and updating the set T.
And S13, dividing all the shared parking demand time and the shared parking space supply time into thinner and more small time periods by taking the elements in the set T as division points.
The open time of 8 shared parking spaces in one 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), and 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 on the time scale of 1h, the shaded portion indicates that the parking space does not support sharing, and the blank portion indicates that the parking space is open in this period. The open time of the berthage is divided by taking the starting and ending moments of the shared time of the 8 berthages as break points. After division, the berths p1, p2, p3 and p4 are changed from the original 2, 1, 2 and 2 time periods to 15, 7, 17 and 15 sharing time periods respectively. For simplicity, this example only considers the beginning and end times of the shared parking space open time.
And S2, on the basis of time interval division, defining pairing, time interval pairing and pairing schemes by sharing the relation between the parking demand and the supply parking space.
Further, in step S2, e (v, p, i) represents that the vehicle v is parked in a pair of the berths p over the time period i. e (0, p, i) indicates that the vehicle is not parked at the parking space p for the period i. All vehicles with parking requirements over time period i form a set ViAll parking spaces supporting sharing form a set Pi. With E (V)i,Pi)＝{e1,…,enH, i e {1, …, n } represents a timeslot pair for which all vehicles with shared parking requirements are parked at the appropriate parking space for timeslot i; ei＝E(Vi,Pi) And n represents the number of divided periods. M represents a pairing scheme in which all vehicles with shared parking requirements are parked in the appropriate parking space, MaAnd MbTwo different pairing schemes are shown.
And 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.
Further, in step S3, the known pairing scheme M is matchedaOf the pairing schemes which exist and are paired only in one different time period, the pairing scheme with the least number of the moving times is defined as MaAll pairing schemes satisfying the above conditions constitute MaOf the neighborhood of (c). Meanwhile, a pairing scheme satisfying the above condition over the period i is referred to as MaThe i period of time of (c) is adjacent.
And S4, determining the length of a taboo table according to a first-in first-out principle, introducing mutation operation when the optimal solution of the algorithm is not changed through multiple iterations, and designing and solving an improved taboo search algorithm for sharing parking supply and demand matching under the automatic parking condition.
Further, in step S4, by using the pairing scheme neighborhood and the neighbor defined in step S3, a certain time can be saved when the algorithm neighborhood search obtains a new solution. However, when the number n of divided periods is large, obtaining all neighbors of a pairing scheme still requires a lot of computation, thus limiting the maximum size of the neighborhood. And when the unchanged iteration number of the optimal solution reaches a set value, carrying out mutation operation to jump out the current solution. The steps of improving the tabu search algorithm are as follows:
and S41, initializing parameters. Tabu length L, maximum number of iterations τmaxMaximum number of searches mu for which the current solution remains constantmaxNeighborhood maximum size nbCoefficient of variation α. The current iteration time τ is 0, and the current optimal solution invariant iteration time μ is 0.
And S42, randomly generating a feasible pairing scheme as an initial solution. Time period pairing E for randomly generating n time periodsiI belongs to {1, …, n }, and an initial pairing scheme M is obtained by reasonable combination0As the optimal pairing scheme M*And current pairing scheme Ming。
And S43, updating the taboo table. Will MingAdding a tabu table, and deleting the earliest elements in the tabu table according to a first-in first-out rule. Let τ ═ τ + 1.
And S44, determining the neighborhood of the current solution. Randomly determining nbA divided period of time, M is determined by the following methodingUntil n is determinedbThe neighbors constitute the neighborhood of the current solution. Selecting the matching scheme which is not in the tabu table and has the minimum number of the moving cars as Ming。
And S441, calculating the vehicle moving cost. For the pair e (v, p, i), under the condition that the time period pair of the front time period and the rear time period i-1 and i +1 is given, the vehicle moving cost of the vehicle v between the time periods i-1 and i and the time period i and i +1 is calculated, and the sum of the two costs is cv,p,v∈Vi,p∈Pi。
S442, with cv,pIs element, ViAnd PiElement forming a cost matrix for row and column elements
Figure BDA0003556407280000071
S443, using the expense matrix
Figure BDA0003556407280000072
On the basis, the parking demand and the parking space supply quantity of the shared parking i period form the production place goods production quantity and the consumption place goods demand quantity of the transportation problem, and a transportation problem is constructed.
S444, solving the transportation problem by a classical Hungarian algorithm to obtain a matching relation between the shared parking demand and the shared berth, and reasonably combining the matching relation to obtain MingA neighbor M ofnewLet M stand foring：＝Mnew。
And S45, updating the optimal solution. If M isingThe number of times of moving is less than the current optimal solution M*Number of times of moving the vehicle, order M*：＝MingGo to stepStep S47. Otherwise, M*Keep it unchanged, let μ: μ +1, γ: go to step S46 when γ + 1.
S46, when mu equals to mumaxOtherwise, go to step S47. Randomly generating a period pairing E over a period jjReplace the current pairing scheme MingThe j time period in the step (2) is paired to obtain a new pairing scheme MnewLet M stand foring：＝MnewGo to step S45.
And S47, termination judgment. If the maximum iteration times of the algorithm or the optimal matching scheme moving times are 0, outputting an optimal solution and finishing the algorithm; otherwise, go to step S4.
The above description is only a preferred embodiment of the present invention, and does not limit the present invention in any way. It will be understood by those skilled in the art that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (7)

1. A supply and demand matching method for shared parking under the background of automatic parking is characterized by comprising an improved tabu search algorithm, and specifically comprising 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 supply time as division points;
s2: on the basis of time interval division, a pairing scheme, a time interval pairing scheme and a pairing scheme are defined through the relation between the shared parking demand and the supply parking space;
s3: on the premise of ensuring the optimal solution quality, analyzing the characteristics of a tabu search algorithm, and designing neighborhoods and neighbors of feasible solutions;
s4: the length of a tabu table is determined according to a first-in first-out principle, mutation operation is introduced when the optimal solution is unchanged after 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.
2. The supply and demand matching method according to claim 1, wherein in the step S1, t is usedv，sRepresenting a parking demand time start time; t is tv，eIndicating the stopping demand ending time; t is tp，sRepresenting the starting time of the open time of the berth; t is tp，eFor example, the parking space open time end time is represented, and the specific step of dividing the shared parking required time includes:
s11: all t are comparedv，s、tv，e、tp，s、tp，ePutting the set T;
s12: eliminating repeated elements in the set T, arranging the rest elements in an ascending order, and finally updating the set T:
s13: and dividing all the shared parking demand time and the shared parking space supply time into a plurality of small time intervals by taking the elements in the set T as dividing points.
3. The supply and demand matching method according to claim 2, wherein in the step S2, when e (v, p, i) indicates that the vehicle v is parked in one pair of the berths p in the time period i, e (0, p, i) indicates that the vehicle is not parked in the berths p in the time period i; all vehicles with parking requirements on time period i form a set ViAll parking spaces supporting sharing form a set Pi；
With E (V)i，Pi)＝{e1，...，enThe time interval i belongs to a time interval pair of the proper berths of all vehicles with shared parking requirements on the time interval i; ei＝E(Vi，Pi) N represents the number of divided periods; m represents a pairing scheme in which all vehicles with shared parking requirements are parked in the appropriate parking space, MaAnd MbTwo different pairing schemes are indicated.
4. The supply and demand matching method according to claim 3, wherein in the step S3, the matching scheme M is matched with a known matching schemeaThe pairing scheme with the least number of moving times is M in the pairing schemes with only one different time period pairingaOf (2)All pairing schemes satisfying the above conditions constitute MaWhile the pairing scheme satisfying the above condition over the period i is called MaThe i period of time of (c) is adjacent.
5. The supply and demand matching method according to claim 4, wherein the step S3, determining the i-period neighbors of the given pairing scheme comprises the steps of:
s31: vehicle displacement costs are calculated. For each pairing, the following operations are performed: for the pair e (v, p, i), the vehicle moving cost of the vehicle v between the time periods i-1 and i and the time period i and i +1 is calculated, and the sum of the two costs is recorded as cv，p，v∈Vi，p∈Pi；
S32: constructing a cost matrix with cv，pIs element, ViAnd PiThe inner elements form a cost matrix for the row and column elements
Figure FDA0003556407270000021
S33: constructing transportation problems in terms of cost matrices
Figure FDA0003556407270000022
On the basis, the parking demand and the parking space supply quantity of the shared parking i period are the production place goods production quantity and the consumption place goods 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 Hungarian algorithm to obtain a matching relation between the shared parking demand and the shared berth, and reasonably combining the sharing relation and the sharing berth to obtain MaThe i period of time of (1) is adjacent.
6. The supply and demand matching method according to claim 5, wherein in the step S4, when the number of iterations for which the optimal solution is unchanged reaches a set value, a mutation operation is performed to jump out of the current solution, wherein the improved tabu search algorithm comprises the following steps:
s41: initialization parameters, tabu table length L, maximum number of iterations τmaxMaximum number of searches mu for which the current solution remains constantmaxNeighborhood maximum size nbThe coefficient of variation α, the current iteration number τ is 0, and the current optimal solution invariant iteration number μ is 0;
s42: randomly generating a feasible pairing scheme as an initial solution and taking the feasible pairing scheme as an optimal pairing scheme M*And current pairing scheme Ming；
S43: update the taboo table, and compare MingAdding a tabu table, and deleting the earliest elements in the tabu table according to a first-in first-out rule, wherein the ratio of tau: τ + 1;
s44: determining the neighborhood of the current solution, and randomly determining nbEach divided period is determined by using the neighborhood search algorithm in step S3ingN of (a)bThe neighbors form the neighborhood of the current solution, and a pairing scheme which is not in a tabu table and has the minimum number of the moving times is selected as Ming；
S45: update the optimal solution if MingThe number of the shift times is less than the current optimal solution M*The number of times of moving the vehicle is M*：＝MingGo to step S47, otherwise, M*Keep it unchanged, let μ: μ +1, γ: go to step S46, γ + 1;
s46: when mu is mumaxTime period pairing E on time period j is randomly generatedjReplace the current pairing scheme MingThe j time period in the step (2) is paired to obtain a new pairing scheme MnewLet M stand foring：＝MnewAnd proceeds to step S45; when mu ≠ mumaxEntering the next step;
s47: judging whether the current data meets a termination condition, and if so, outputting an optimal solution; if not, the process proceeds to step S43 until the termination condition is satisfied and the optimal solution is output and the algorithm is ended.
7. The supply and demand matching method according to claim 6, wherein the termination condition is: the maximum iteration times of the algorithm or the vehicle moving times of the optimal matching scheme are 0.
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