CN109829658B - Parking berth distribution method based on different crowd demands - Google Patents
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Abstract
The invention discloses a parking berth distribution method based on different crowd requirements, which specifically comprises the following steps: classifying and dividing service crowds of a designated parking lot, then establishing a linear programming model according to parking requirements of different types of crowds, and solving the number of berths obtained by each type of crowds; sorting different crowds according to the number of the obtained berths from more to less, sequentially selecting the different crowds as objects, counting potential destinations of the objects, sequentially sorting the objects according to the occurrence frequency of the reached destinations from more to less, calculating the number of vehicles which each destination of a specific certain group of crowds should be provided with, and sequentially distributing the corresponding number of parking spaces according to the sorting order of the destinations and with the shortest distance between the parking spaces and the destinations as the basis; the invention is beneficial to reasonably distributing limited parking resources, can effectively limit the random parking behavior, meets the parking requirements of different service objects, not only can improve the parking environment, but also can improve the satisfaction degree of people on parking.
Description
Technical Field
The invention relates to the field of traffic planning and management research, in particular to a parking berth distribution method based on different crowd requirements.
Background
With the development of the economic society and the improvement of the living standard of people in China, the maintenance quantity of motor vehicles is rapidly increased, and the problems of insufficient total quantity of parking facilities, low resource allocation efficiency and the like are increasingly revealed. For the problem of supply and demand balance distribution of parking, mo Chongjie and Xu Jingfei establish a variable coefficient linear programming model to solve; xu Xiaodan and Chen Jun utilize a double-layer planning model of a sharing strategy to realize the supply and demand balance of parking; ma Liang and the like propose ideas of regional planning management; zhang Da and the like are used for intelligent management and allocation of the parking spaces by using the ZigBee technology. Although different scholars try to solve the problems from different angles, the service characteristics of different crowds are not clearly distinguished, and the parking requirements of different crowds are not considered, so that a series of problems such as insufficient parking space, environmental pollution, traffic safety accidents and the like are caused. In addition, some parking lots have a priority service principle (such as a hospital parking lot is used for emergency patient service and a school parking lot is used for education staff service), but in life, the lack of parking resources cannot be matched with the parking requirements expected by people due to the fact that social vehicles outside the service range are not limited. Therefore, in order to achieve the purpose of balancing parking supply and demand and preferentially serving a specific crowd, it is necessary to establish a new parking space allocation method from the viewpoint of the serving crowd.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art and provide a parking berth distribution method based on different crowd requirements; aiming at the problems in the background technology, the invention provides the method for dividing the service crowd and reallocating the existing parking resources according to the parking requirements of the service crowd, and properly opening a certain parking space to the social vehicle on the premise of guaranteeing the preferential service of the specific crowd.
The aim of the invention is achieved by the following technical scheme:
a parking berth distribution method based on different crowd requirements comprises the following steps:
s1, collecting information of people taking parking action in a research area, selecting one or more characteristics to divide the people according to the obtained information, and thus determining the class number m of the service people;
s2, respectively carrying out parking characteristic investigation on different service crowd categories, and acquiring parking characteristic indexes;
s3, constructing a linear programming model, selecting an adaptive objective function according to the relation between the demand and the supply, and solving to obtain the planned berth number C of each class of service crowd by combining constraint conditions i ；
S4, for planning berth number C i Sequencing, wherein the maximum planned berth number is used as a current service crowd i;
s5, counting all destinations which can be reached by the current service crowd i, and generating frequency F for the destinations ij Sorting, namely taking the destination with the largest occurrence frequency as the current destination j;
s6, calculating the number N of berths which should be configured by the current service crowd i at the current destination j ij ，N ij ＝C i ·F ij Recalculate the current garmentDistance D between parking space to be allocated by business crowd i and current destination j ijk Sequencing the calculated distances, and selecting the front N with shorter distance ij The berths are allocated to the current service crowd;
s7, judging whether all the destinations are traversed, if not, selecting the second appearance frequency as a new maximum appearance frequency, determining a new current destination, and turning to the step S6; if the traversing is finished, entering the next step;
s8, judging whether all the service groups are traversed, if not, selecting the second planned berth number as a new planned berth number, determining a new current service group, and turning to the step S5; and if the traversal is finished, outputting a result.
Further, the service crowd category number m is a positive integer, and i=1, 2, …, m.
Further, the information includes gender, age, occupation, and month income.
Further, the parking characteristic index includes obtaining the total number Z of the existing parking spaces, and the parking demand N of the i-th class personnel i Turnover rate alpha of parking berth i Shortage of berths L i And berth idle amount M i 。
Further, the parking space turnover rate alpha i The method comprises the following steps:
α i ＝S i /T i ，
wherein S is i To investigate the actual parking amount of class i personnel, T i Actual number of parking places for group i.
Further, the berth lacks a small amount L i The method comprises the following steps:
L i ＝α i ·N i -C i ，
wherein a is i A is the turnover rate of the parking berth i ＝S i /T i ；S i To investigate the actual parking amount of the class i person of the period, T i Actual number of parking positions for i-th group of people, N i Parking demand for class i personnel;
the berth idle quantity M i The method comprises the following steps:
M i ＝max{C i -N i ，0}，
wherein C is i Planning the number of berths for the group i crowd.
Further, the step S3 specifically includes:
in the step S3 of the process,
the objective function of the linear programming model is:
wherein the constraint conditions are:
wherein m is the total number of service crowd categories, m is more than or equal to 1 and m is a positive integer; i is the group i, i is more than or equal to 1 and less than or equal to m; c (C) i Planning the number of berths for the i-th group of people, C i Not less than 0; z is the total number of the existing parking positions.
Further, the objective function of the linear programming model is selected according to the supply-demand relationship, specifically: when the demand is greater than or equal to the supply, the objective function is:
when the demand is smaller than the supply, the objective function is:
compared with the prior art, the invention has the following advantages and beneficial effects:
according to the invention, the parking spaces are arranged according to the service characteristics and the parking demands of people, the existing parking resources can be more fully utilized, the current parking environment is improved, the balance of supply and demand and the limitation of social vehicles outside the service range are facilitated, a new thought is provided for the planning design of the parking lot and the management of the parking demands, and particularly, the allocation of the parking resources has practical significance.
Drawings
FIG. 1 is a flow chart of a method for parking lot allocation based on crowd demand according to the present invention;
FIG. 2 (a) is a parking lot profile in the embodiment of the present invention;
FIG. 2 (b) is a graph of the calculation of the distance between the current crowd and the current destination 3 in the embodiment of the invention;
FIG. 2 (c) is a diagram of the allocation of the parking spaces between the current crowd and the current destination 3 according to the embodiment of the invention;
FIG. 2 (d) is a graph of the calculation of the distance between the current crowd and the current destination 1 in the embodiment of the invention;
FIG. 2 (e) is a diagram of the allocation of the parking spaces between the current crowd and the current destination 1 according to the embodiment of the invention;
fig. 2 (f) is a schematic diagram of final allocation in the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
Examples:
a parking berth distribution method based on crowd demand is shown in fig. 1, and specifically comprises the following steps:
firstly, a linear programming model established by the invention is provided, and the variables are defined as follows, assuming that m groups of people exist:
z-total number of existing parking spaces
C i -planned number of parking positions for group i, i=1, 2, …, m
N i Parking demand for group i, i=1, 2, …, m
α i -parking turnover rate, alpha for group i i ＝S i /T i ，S i To investigateActual parking amount of group i, T i Actual number of parking positions for group i, i=1, 2, …, m
L i -lack of class i berths, i.e. difference in demand and supply, L i ＝α i ·N i -C i ，i＝1,2,…,m
M i -number of idle class i berths, M i ＝max{C i -N i ，0}，i＝1,2,…,m
The available models are as follows:
the objective function of the model is selected according to the supply and demand relation obtained by investigation. When the demand obtained by investigation is greater than or equal to the supply, the demand of a certain group of people or a plurality of groups of people is not or just satisfied, so the objective function adopts
The lack of berths should be as small as possible, and as many people as possible can park. If the demand obtained by investigation is smaller than the supply, the supply is over and demand, and the rest is not utilized, so that the resource is wasted, and the +.>
As an objective function, the idle berths are as few as possible, and the utilization rate of resources is improved. For constraint condition->
And C i Planned parking berth number C of ith group of people not less than 0 i An integer of 0 or more should be used, and the sum should be less than the total number Z of existing parking spaces. In addition, the optimization model belongs to a linear programming model, and the model is solved because the rank of the constraint matrix is smaller than or equal to the number of constraint variables.
Based on the above discussion and fig. 1, the parking space allocation method based on different crowd requirements includes the following steps:
And determining the class number m of the service crowd according to the formulated target, wherein the class number m is any positive integer and is set by a user. For example, on a campus, the crowd may be divided into teaching staff, students and guests, i.e. determine m=3; for example, in hospitals, the population can be divided into medical staff, emergency patients, general patients and exploratory families, i.e. m=4 is determined.
And (5) carrying out parking characteristic investigation on each group of people and obtaining a series of indexes. Comprises obtaining the total number Z of the existing parking spaces and the parking demand N of the ith class personnel i Turnover rate alpha of parking berth i Shortage of berths L i And berth idle amount M i . Wherein i is a positive integer, and the value of i is between 1 and m.
The total number Z of existing parking spaces represents the maximum capacity that can be accommodated in the parking lot, and the unit is one, and the data can be directly obtained from the site.
Planned number of berths C for group i crowd i The unit is one, which is a non-negative integer, is an unknown quantity to be solved. C (C) i Number of actual parking positions T with group i i The parking lot has the substantial difference, the value is calculated according to the parking requirements of various groups of people, the importance of different groups of people can be distinguished by the value, and a basis is provided for the allocation of the following parking spaces.
Parking demand N for class i group i The method mainly reflects the parking requirements of different crowds, the unit is that the method can be solved by adopting a common parking incidence model, a related analysis model, a motor vehicle OD prediction method, a traffic volume-parking requirement model and the like, but no matter what method is used, the important point is that different target crowds can be distinguished before analysis.
Parking turnover rate alpha for class i group of people i The average number of vehicles with berths repeatedly stopped in the observation period is shown, the utilization degree of the parking facility is reflected, and the calculation formula is alpha i ＝S i /T i . Wherein S is i To investigate the actual parking amount of class i personnel, T i The number of actual parking positions is the number of the i-th group of people, and the two units are the same, and can acquire data from the site.
Berth shortage L for i-th group of people i In units of one, the calculation formula is L i ＝α i ·N i -C i 。
Berth idling amount M for i-th group of people i In units of one, the calculation formula of which is M i ＝max{C i -N i ，0}。
Solving planned berth number C of each group of people i . Substituting the data in the steps 1 and 2 into the step (1), and obtaining the parking number C matched with each group of people through manual calculation or special mathematical solving software (such as Matlab and Lingo) i 。
Planning the number of berths C from big to small i Ordering by C i The maximum value is the current value and the current service crowd i is determined.
Counting all the destinations j which can be reached by the current crowd i, and counting the occurrence frequency F of the destinations from large to small ij Ordering by F ij The maximum value is the current value and the current destination j is determined.
Calculating the number N of parking spaces which should be allocated to the current crowd i in the current destination j ij Then, respectively calculating the distance D between all unallocated parking spaces and the destination j ijk (k represents the number of each parking space), sorting from small to large, and selecting the front N with shorter distance ij The individual parking spaces are given to the service group i. The specific calculation process is as follows:
first, the number of poise N that the current service crowd i should configure at the current destination j ij ＝C i ·F ij ；
Then, for the service crowd i, the distances between all unallocated parking spaces and the current destination j are calculated,specifically, according to the provided or designed plane drawing, firstly marking the geometric centers of a parking space to be allocated and a destination, then connecting the geometric centers of the parking space to be allocated and the destination, wherein the length of an obtained line segment is the distance between the parking space and the current destination j, the process can be realized through computer or manual measurement, after the distances between all unallocated parking spaces and the current destination j are obtained, the distances are sorted from small to large according to the numerical values, and the D is determined ijk (wherein k=1 for the shortest distance, k=2 for the next shortest distance, k=3 for the third shortest distance, and so on);
finally, according to the sorting result, selecting the front N with shorter distance ij And the unassigned berths are assigned to the current service group i.
Judging whether all j are traversed, if yes, turning to step 8, otherwise, selecting and sorting to be only inferior to the current F ij New F of (2) ij As current value and determines a new destination j, and then goes to step 6.
Judging whether all i are traversed, if yes, outputting a result, ending the whole iteration process, and if no, selecting and sorting to be inferior to the current C i New C of (2) i As current value and determine a new service group i, and then go to step 5.
Assuming that there is a group of 2 people, i.e. i=1, 2, the resulting C i Respectively C 1 ＝10，C 2 =5; group 1 has 3 different destinations, i.e. j=1, 2,3, frequency F after statistics ij Respectively F 11 ＝0.3，F 12 ＝0.2，F 13 =0.5; group 2 has 2 destinations, one of which is the same as destination 2 of group 1 and the other is different from the previous destination, so for group 2, j=2, 4, then the counted frequencies are F respectively 22 ＝0.4，F 24 =0.6; to visually represent the above data, table 1 can be obtained.
According to step 4, determine C i The current value is 10, and the current service crowd is the class 1 crowd. In general, the resulting C is dispensed i The larger the crowd, the more important the crowd should be, i.e. the crowd should be the subject of the priority service, so the group 1 crowd should be served before the group 2 crowd. Therefore, the next allocation of the parking spaces begins with the group 1 crowd, and the group 1 crowd is allocated and then the group 2 crowd is allocated.
According to step 5, the destinations reachable by the current crowd (class 1 crowd) are counted. In general, an individual should have one or more intended destinations, and the frequency of the different destinations can be obtained by counting the different individuals, but in order to better allocate the parking space, the frequency is then converted into frequency. The higher the frequency, the greater the demand for that class of people to that destination, e.g., F in the example above 13 At maximum, it is stated that the demand of crowd 1 to destination 3 will be higher than to destination 1 and destination 2, so that the demand of crowd 1 to that destination should be satisfied first when a specific parking space is allocated.
According to step 6, for group 1 people, destination 3 should be equipped with a number of N 13 =10×0.5=5, and then the distance between each parking space and the destination, i.e. the distance between the geometrical center point of the parking space and the geometrical center point of the destination, is calculated according to the parking space distribution and the destination position of the specific parking lot (as in fig. 2 (a)), as in fig. 2 (b). Since the parking lot has 15 parking spaces, k=1, 2, …,15, and the iterative code is not repeated. As can be seen from FIG. 2 (b), D 136 <D 131 <D 137 <D 132 <D 138 <D 133 <D 139 <D 134 <D 1310 <D 135 <D 1314 <D 1315 <D 1312 <D 1311 <D 1313 Thus, 5 spaces 6, 1, 7, 2, 8 are selected for allocation to group 1 people, as shown in fig. 2 (c).
Not traversed (destination left) per step 7,j1 and destination 2), because of F 11 Next to F 13 Therefore, the destination 1 is selected as the next destination, and the step 6 is executed again, so that the number of vehicles to be equipped with the destination 1 can be calculated as N 13 =10×0.3=3, and D 1111 <D 1112 <D 1113 <D 1114 <D 119 <D 1115 <D 1110 <D 113 <D 114 <D 115 (as in fig. 2 (d)), therefore, 3 parking spaces 11, 12, 13 are selected to be allocated to the group 1 crowd, as in fig. 2 (e). Since no traversal has been completed, destination 2 is selected as the current destination and step 6 is performed again, and finally 14, 15 may be assigned to the group 1 crowd.
As step 8,i is not completed, the group 2 crowd is selected for the allocation as above, and after i is completed, the final output result is shown in fig. 2 (f), so that the whole allocation process is finished.
The above examples are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above examples, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principle of the present invention should be made in the equivalent manner, and the embodiments are included in the protection scope of the present invention.
Claims (4)
1. The parking berth distribution method based on different crowd requirements is characterized by comprising the following steps of:
s1, collecting information of people taking parking action in a research area, selecting one or more characteristics to divide the people according to the obtained information, and thus determining the class number m of the service people;
s2, respectively carrying out parking characteristic investigation on different service crowd categories, and acquiring parking characteristic indexes;
s3, constructing a linear programming model, selecting an adaptive objective function according to the relation between the demand and the supply, and solving to obtain the planned berth number C of each class of service crowd by combining constraint conditions i ；
S4, for planning berth number C i Sequencing, wherein the maximum planned berth number is used as a current service crowd i;
s5, counting all destinations which can be reached by the current service crowd i, and generating frequency F for the destinations ij Sorting, namely taking the destination with the largest occurrence frequency as the current destination j;
s6, calculating the number N of berths which should be configured by the current service crowd i at the current destination j ij ，N ij ＝C i ·F ij Calculating the distance D between the parking space to be allocated by the current service crowd i and the current destination j ijk Sequencing the calculated distances, and selecting the front N with shorter distance ij The berths are allocated to the current service crowd;
s7, judging whether all the destinations are traversed, if not, selecting the second appearance frequency as a new maximum appearance frequency, determining a new current destination, and turning to the step S6; if the traversing is finished, entering the next step;
s8, judging whether all the service groups are traversed, if not, selecting the second planned berth number as a new planned berth number, determining a new current service group, and turning to the step S5; if the traversal is finished, outputting a result;
the parking characteristic indexes comprise the total number Z of the existing parking spaces, the parking demand Ni of the ith class of personnel, the turnover rate alpha i of the parking spaces, the shortage quantity Li of the parking spaces and the idle quantity Mi of the parking spaces;
the berth shortage amount Li is as follows:
Li＝αi·Ni-Ci，
wherein a is i A is the turnover rate of the parking berth i ＝S i /T i ；S i To investigate the actual parking amount of the class i person of the period, T i Actual number of parking positions for i-th group of people, N i Parking demand for class i personnel;
the berth idling quantity Mi is as follows:
Mi＝max{Ci-Ni，0}，
wherein Ci is the planned berth number of the ith group of people;
in the step S3 of the process,
the objective function of the linear programming model is:
wherein the constraint conditions are:
wherein m is the total number of service crowd categories, m is more than or equal to 1 and m is a positive integer; i is the group i, i is more than or equal to 1 and less than or equal to m; c (C) i Planning the number of berths for the i-th group of people, C i Not less than 0; z is the total number of the existing parking berths;
the objective function of the linear programming model is selected according to the supply-demand relation, and specifically comprises the following steps: when the demand is greater than or equal to the supply, the objective function is:
when the demand is smaller than the supply, the objective function is:
2. the method of claim 1, wherein the number of classes of service groups is m, m is a positive integer, and i=1, 2, …, m.
3. A method of parking space allocation based on different crowd requirements according to claim 1, wherein the information comprises gender, age, occupation, month income.
4. The method for allocating parking spaces based on different crowd requirements according to claim 1, wherein the turnover rate alpha of parking spaces i The method comprises the following steps:
α i ＝S i /T i ，
wherein S is i For investigating the actual parking amount of the i-th group of people in period T i Actual number of parking places for group i.
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| CN112652174B (en) * | 2020-12-18 | 2021-08-31 | 中标慧安信息技术股份有限公司 | Parking service control method and system based on face verification |
| CN113593289B (en) * | 2021-06-25 | 2023-02-28 | 桂林电子科技大学 | Method and system for sensing in-road parking conflict avoidance scheduling based on available parking position state |
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