CN114842668A - Multi-scene parking space guiding method based on analytic hierarchy process - Google Patents
Multi-scene parking space guiding method based on analytic hierarchy process Download PDFInfo
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Abstract
The invention relates to the technical field of parking stall guidance, in particular to a multi-scene parking stall guidance method based on an analytic hierarchy process, which comprises the following steps of: the data acquisition unit acquires the number and position of vacant parking spaces in the parking lot and the license plate number of the vehicle to be parked, and sends the number and position to the central processing unit through the transmission module to prepare for vehicle parking path planning; the central processing unit receives a planning mode signal sent by a user and plans a parking path according to a planning mode; and the central processing unit transmits the planned parking path to a user terminal through a transmission module, and performs real-time path navigation on the user to complete the guidance of the parking space. The invention provides a parking space guiding method with the shortest distance under the subjective condition of a client and a parking space guiding method with the shortest time based on an analytic hierarchy process under the non-subjective condition, solves the problem of low efficiency of the existing parking space guiding technology, can be applied to a plurality of parking scenes, and has wide adaptability.
Description
Technical Field
The invention relates to the technical field of parking stall guidance, in particular to a multi-scene parking stall guidance method based on an analytic hierarchy process.
Background
In recent years, with the blowout type increase of the number of motor vehicles, the traffic condition is rapidly worsened, and the problem of increasingly short parking spaces is particularly serious in large and medium cities. The traffic management department takes measures in many times to build underground parking lots and three-dimensional garages, night parking lots on two sides of roads are opened up, all-dimensional parking facilities are constructed with the intention, and the problem of difficulty in parking parts is solved. However, it is also statistically inefficient in parking facilities that have already been put into use.
At present, parking space guiding technology research is mainly based on the attribute of a static road network in a parking lot, road conditions in the parking lot are time-varying, and vehicle detention in the parking lot in peak hours can influence target parking space selection. After the motor vehicle enters a field, a user does not know where the vacant parking space is specifically located, and in the process of searching the vacant parking space, inefficient tour operation can be generated, so that traffic jam is caused, and parking of other vehicles is influenced.
The method for guiding the multi-scene parking space under the subjective condition has the shortest distance and the non-subjective condition has the shortest time based on the analytic hierarchy process, so that a vehicle space guiding path is obtained, and the problem of low efficiency of the existing space guiding technology is solved.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a multi-scene parking space guiding method based on an analytic hierarchy process, provides a parking space guiding method with the shortest distance under the subjective condition of a client, provides a parking space guiding method based on the analytic hierarchy process with the shortest time under the non-subjective condition, and solves the problem of low efficiency of the existing parking space guiding technology.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme.
The invention provides a multi-scene parking space guiding method based on an analytic hierarchy process, which is realized based on a parking space guiding system.
The parking stall guidance system comprises a data acquisition unit, an image processing module, a transmission module, a central processing unit and a user terminal;
the data acquisition unit comprises a parking space detector module and an identification camera module, the parking space detector module is used for acquiring vacant parking space image information, and the identification camera module is used for acquiring license plate number images;
the image processing module processes the image information of the vacant parking spaces to obtain the positions and the number of the vacant parking spaces and processes the license plate number image to obtain the license plate number of the vehicle to be parked;
The transmission module is used for transmitting the positions and the number of the vacant parking spaces and the planning mode signals to the central processing unit; the transmission module is also used for transmitting the path planned by the central processing unit to a user terminal corresponding to the license plate number of the vehicle to be parked;
the central processing unit is used for planning paths according to the positions and the number of the vacant parking spaces and sending the planned paths to the transmission module;
the user terminal is used for sending a planning mode signal to the transmission module and receiving the planned path sent by the transmission module.
The invention discloses a multi-scene parking space guiding method based on an analytic hierarchy process, which comprises the following steps of:
step S1: the data acquisition unit acquires the number and position of vacant parking spaces in the parking lot and the license plate number of the vehicle to be parked, and sends the number and position to the central processing unit through the transmission module to prepare for vehicle parking path planning;
step S2: the central processing unit receives a planning mode signal sent by a user and plans a parking path according to a planning mode;
step S3: and the central processing unit transmits the planned parking path to a user terminal through a transmission module, and performs real-time path navigation on the user to complete the guidance of the parking space.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a parking space guiding method with the shortest distance under the subjective condition of a client and a parking space guiding method with the shortest time based on an analytic hierarchy process under the non-subjective condition, and solves the problem of low efficiency of the existing parking space guiding technology. The parking space guiding method can be applied to a plurality of parking scenes and has good adaptability.
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The invention is described in further detail below with reference to the figures and specific embodiments.
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of the architecture of the present invention;
FIG. 3 is a schematic view of a parking space of a parking lot according to an embodiment of the present invention;
FIG. 4 is a parking lot weighted directed graph of the present invention;
FIG. 5 is a diagram of a hierarchy of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to examples, but it will be understood by those skilled in the art that the following examples are only illustrative of the present invention and should not be construed as limiting the scope of the present invention.
The invention provides a multi-scene parking space guiding method based on an analytic hierarchy process, which is based on a parking space guiding system, wherein the parking space guiding system comprises a data acquisition unit, an image processing module, a transmission module, a central processing unit and a user terminal, and is shown in figure 2.
The data acquisition unit comprises a parking space detector module and an identification camera module, the parking space detector module is used for acquiring vacant parking space image information, and the identification camera module is used for acquiring license plate number images;
the image processing module processes the image information of the vacant parking spaces to obtain the positions and the number of the vacant parking spaces and processes the license plate number image to obtain the license plate number of the vehicle to be parked;
the transmission module is used for transmitting the positions and the number of the vacant parking spaces and the planning mode signals to the central processing unit; the transmission module is also used for transmitting the path planned by the central processing unit to a user terminal corresponding to the license plate number of the vehicle to be parked;
the central processing unit is used for planning paths according to the positions and the number of the vacant parking spaces and sending the planned paths to the transmission module;
the user terminal is used for sending a planning mode signal to the transmission module and receiving the planned path sent by the transmission module.
Specifically, the data measured by the data acquisition unit are transmitted to the image processing module through the transmission module, so that the position and quantity data of the vacant parking spaces are obtained and transmitted to the central processing unit. The central processing unit plans a path according to the positions and the number of the vacant parking spaces and sends the planned path to the transmission module; and the transmission module sends the planned path to the client terminal.
Referring to fig. 1, a flow chart of the present invention is shown. The invention discloses a multi-scene parking space guiding method based on an analytic hierarchy process, which comprises the following steps of:
step S1: the data acquisition unit acquires the number and position of vacant parking spaces in the parking lot and the license plate number of the vehicle to be parked, and sends the number and position to the central processing unit through the transmission module to prepare for vehicle parking path planning;
step S2: and the central processing unit receives a planning mode signal sent by a user and plans a parking path according to the planning mode. The planning modes include a subjective mode and a non-subjective mode.
Further, the parking path planning method specifically comprises the following steps:
if the user selects the subjective mode, corresponding parking path planning is carried out according to the selected sub-mode;
the subjective mode comprises a walking distance shortest sub-mode and a driving distance shortest sub-mode; under the sub-mode of shortest walking distance, the central processing unit adopts the Dijkstra algorithm of shortest walking distance to plan the path of the parking space, and the shortest walking distance path from the parking space to the destination selected by the user is obtained; under the sub-mode of the shortest driving distance, the central processing unit adopts a Dijkstra algorithm of the shortest driving path to plan the path of the parking space, and the shortest driving distance from the entrance of the parking lot to the parking space is obtained;
Referring to fig. 3, a distribution diagram of parking spaces in a parking lot according to the present invention is shown. The path planning of the parking space is carried out through a Dijkstra algorithm with the shortest walking distance, and the path planning method comprises the following steps:
substep 2.101: searching within a radius range of 40m of walking distance by taking an entrance x as a center, and gradually increasing the radius by 20m for searching until an empty parking space appears if no empty parking space exists; recording the searched empty parking space set as Pi, wherein i belongs to [0, n-1], and n is the number of searched empty parking space nodes; setting X as a set of all paths with the shortest driving distance taking the entrance of the parking lot as a starting point, setting P as a set of all empty parking spaces, and setting P as Pi;
substep 2.102: initializing X and its corresponding shortest path weight D (i), i.e. D (i) ═ D M (0, i), M ═ X }; where D (i) is the weight of the shortest path from entry x to Pi, D M An adjacency matrix representing an empty parking space as a node in the calculation of X, and an adjacency matrix D M Each element D in M (i, j) represents each node (P) i ，P j ) A weight value of;
substep 2.103: selecting P K So that P is K ＝{min D(i)|P i E is equal to P-X, then P is K Is the shortest path from entry x, node P K Adding to the set X;
substep 2.104: updating from entry x to P K Let D (i) min { D (k) } D (i, k);
Substep 2.105: repeating substep 2.13 and substep 2.14 until all nodes in set P are included in set X;
substep 2.106: setting the exit of the parking lot as Y, setting Y as a path set with the shortest driving distance taking the exit of the parking lot as a starting point, and initializing Y and a corresponding shortest path weight d (i), namely d (i) ═ d N (0, i), N ═ Y }; wherein d is N An adjacency matrix representing the nodes with vacant parking spaces in Y calculation, and an adjacency matrix d N Each element d of N (i, j) represents a weight value between ∞ of each node, if P i ，P j If not, setting the elements of the adjacent matrix as ∞;
substep 2.107: selecting P L So that P is L ＝{min D(i)|P i E is equal to P-Y, then P is L Is the shortest path from the exit y, and connects the node P L Adding into the set Y;
substep 2.108: updating from egress y to P L Let D (i) min { D (k), D (i, k) };
substep 2.109: repeating substep 2.17 and substep 2.18 to include all nodes in set P in Y;
substep 2.110: calculating the final weight of all empty parking space nodes in the set P, and then calculating the parking space P corresponding to min { D (i) + d (i) } a For the optimal parking space, the path corresponding to D (a) is the entrance to P a The optimal parking path of (a);
Referring to fig. 3, a parking space distribution diagram of a parking lot according to the present invention, which performs path planning of parking spaces by using Dijkstra algorithm with shortest driving path, includes the following steps:
substep 2.201: taking a population x as a center, firstly searching within a radius range of 50m of a driving distance, if no empty parking space exists, gradually increasing the radius by 20m until the empty parking space appears, and recording a set of searched empty parking spaces as Pi, wherein i belongs to [0, n-1], and n is the number of searched empty parking space nodes; setting X as a path set with the shortest driving distance taking the entrance of the parking lot as a starting point; p is the set of all empty parking spaces, and P is Pi;
substep 2.202: initializing X and its corresponding shortest path weight D (i), i.e. D (i) ═ D M (0, i), M ═ X }; wherein D (i) is the shortest distance from inlet x to PiWeight of the path, D M An adjacency matrix representing an empty parking space as a node in the calculation of X, and an adjacency matrix D M Each element D in M (i, j) represents each node (P) i ，P j ) A weight value of;
substep 2.203: selecting P M So that P is M ＝{min D(i)|P i E is equal to P-X, then P is M Is the shortest path from entry x, node P M Adding into the set X;
substep 2.204: updating from entry x to P M Let D (i) min { D (k) } D (i, k);
Substep 2.205: repeating substep 2.23 and substep 2.24 until all nodes in set P are included in set X;
substep 2.206: setting the exit of the parking lot as Y, setting Y as a path set with the shortest driving distance taking the exit of the parking lot as a starting point, and initializing Y and a corresponding shortest path weight d (i), namely d (i) ═ d N (0, i), N ═ Y }; wherein d is N An adjacency matrix representing the nodes with vacant parking spaces in Y calculation, and an adjacency matrix d N Each element d of N (i, j) represents a weight value between ∞ of each node, if P i ，P j If not, setting the elements of the adjacent matrix as ∞;
substep 2.207: selecting P N So that P is N ＝{min D(i)|P i E is equal to P-Y, then P is N Is the shortest path from the exit y, and connects the node P N Adding into the set Y;
substep 2.208: updating from egress y to P N Let D (i) min { D (k) } D (i, k);
substep 2.209: repeating substep 2.27 and substep 2.28 to include all nodes in set P in Y;
substep 2.210: calculating the final weight of all empty parking space nodes in the set P, and then calculating the parking space P corresponding to min { D (i) + d (i) } a For the optimal parking space, the path corresponding to D (a) is the entrance to P a The optimal parking path of (a);
If the user selects the non-subjective mode, the central processing unit plans the path with the shortest parking time;
firstly, establishing a hierarchical structure diagram for factors influencing parking time by utilizing an analytic hierarchy process to obtain the weight of each factor influencing parking difficulty;
the analytic hierarchy process specifically comprises the following substeps:
substep 2.301, four factors that affect the length of parking time for a parking lot: the parking space number, the number of vehicles to be parked, the parking difficulty and the passing rule are combined to establish a hierarchical structure chart, and a paired comparison matrix table is established by combining the hierarchical structure chart;
the structure of the established hierarchy is shown in fig. 5.
Specifically, one layer element is one layer element above another layer element as an index. The evaluation scales are divided into equally important, slightly important, very important and absolutely important, and four evaluation scales are arranged between five evaluation scales. The nine scales are respectively assigned as 1, 2, … and 9, and the element i and the element j have the same importance on the last level factor when Aij is 1; aij-3 element i is slightly more important than element j; aij is 5 elements i more important than element j; aij-7 element i is much more important than element j; aij is 9 elements i more important than element j; the importance of the Aij-2 n, n-1, 2, 3, 4 elements i and j is between Aij-2 n-1 and Aij-2 n + 1. The measurement results of the elements being compared with each other are placed in the upper triangular part of the pair of comparison matrices a, wherein, 
A ij The elements of the matrix A are compared in pairs, the priority ratio of Xi to Xj is represented, the priority is a scoring judgment matrix which is established by comparing two indexes with each other in advance through multiple experts and according to the relative importance of factors influencing the parking time of the parking lot. The diagonal of the pair comparison matrix A is the element self-comparison, so the values are all 1; the lower triangle part value of the paired comparison matrix A is the reciprocal of the relative position of the triangle part on the matrix, i.e. the reciprocal
The pairwise comparison matrix A is listed below:
wherein a is a pair-wise comparison matrix,
A i n, (i ═ 1.. n): n elements of a certain level i;
X i n, (i ═ 1.. n): the priority or contribution degree of n elements of a certain level i to a certain element of the previous level is a scoring judgment matrix which is established in advance by comparing two indexes with each other through multiple experts and according to the relative importance of factors influencing the parking time of the parking lot.
Substep 2.302: and solving the characteristic vector value of the matrix A, namely the corresponding factor weight value.
Substep 2.303: consistency check, namely checking whether the tested person is consistent before and after according to the consistency index C.I or the consistency ratio C.R; C.I. ≦ 0.1, C.R. ≦ 0.1 are tolerances, representing that the subject's responses are consistent within this tolerance range.
The consistency index formula is as follows:
λ max ＝Max[λ 1 …λ n ]
wherein λ is 1 ，λ 2 ，…，λ n Comparing the eigenvectors of matrix A for pairs
When n is less than 3, judging that the matrix has complete consistency,
the ratio of the matrix consistency index C.I. to the average random consistency index R.I. of the same order is called the random consistency ratio C.R.
The average random consistency index R.I. is obtained by repeatedly carrying out random judgment matrix characteristic root calculation for multiple times and then taking an arithmetic mean value.
When the C.R. is less than or equal to 0.1, judging that the matrix has acceptable consistency;
substep 2.304: overall hierarchical weight computation
Calculating the average weight value of each factor under the same influence factor; the individual importance of each finger criterion is averaged to obtain the average relative weight value of each finger criterion.
Secondly, under the parking lot passing rule, building a parking lot weighted directed graph;
in the parking lot traffic rule, the shortest route is the route with the smallest weight between two specified network nodes, and the weight takes time as a main factor.
The parking area road network is composed of nodes such as loops, intersections and parking spaces, and is abstracted into a weighted directed graph G (P, D, T), as shown in fig. 4.
P denotes a parking point in the parking lot road network, D denotes an intra-site directed road segment, T is a weight whose value is a travel time of the vehicle between road nodes i and j:
Where c (i, j) and v (i, j) are the distance between nodes i to j and the travel speed, respectively. Taking the Manhattan distance as the distance between the nodes of the parking lot, and calculating the distance in the Manhattan distance: c ═ x 1 -x 2 |+|y 1 -y 2 L, |; wherein c is the Manhattan distance of two points, the position of the parking space is approximately regarded as one point, and the coordinate is (x) 2 ，y 2 ) The parking lot entrance position is also approximated to be a point with coordinates of (x) 1 ，y 1 )。
Specifically, the weight between two adjacent nodes in the parking lot weighted directed graph is the time required for the vehicle to travel on the road section. Generally, the driving speed Δ t of a road is relatively stable and inversely proportional to the number of vehicles on the road. The larger the number of vehicles, the more crowded the road and the slower the traveling speed. The time required for a vehicle to travel on an on-site road for a unit length is set to Δ t, and a delay coefficient K is defined in consideration of the mutual influence between a plurality of vehicles on the same route. According to field and field statistics, when 2 vehicles exist on one path, the running time per unit length is prolonged to 1.9 delta t; when 3 vehicles are on one path, the running time per unit length is prolonged to 2.7 delta t; when the number of vehicles on one route is greater than 4, the travel time per unit length is extended to 3.1 Δ t. Namely:
Wherein, the first and the second end of the pipe are connected with each other,
and thirdly, according to the weight of each factor obtained by the analytic hierarchy process and in combination with the parking lot weighted directed graph, selecting different path planning strategies to obtain the parking path with the shortest parking time under the different path planning strategies.
If the parking space number or the number of the vehicles to be parked has a great weight, selecting a peak balancing time period strategy or a peak time period strategy; and if the parking difficulty weight is great, selecting a parking difficulty strategy.
And (3) a peak-smoothing period strategy, based on the peak-smoothing period user optimal model, adopting a heuristic A path planning algorithm to plan the parking path in the peak-smoothing period.
And in the peak-balancing period, a user optimal model is established according to a user optimal strategy, the user optimal strategy aims to maximize the benefit of each parking person, the utilization rate of the whole parking resource is not considered, and factors such as the extension of parking time caused by the congestion of channels generated by the same target areas of a plurality of vehicles are not considered. And (3) establishing a user optimal model, and researching that a parking person can know the conditions of all parking spaces through a parking guidance system under the condition that parking guidance exists, wherein the decision principle is the minimization of the parking time of the parking person.
The heuristic a algorithm cost function is: and f (i) ═ g (i) + h (i), wherein i represents the target node to be solved, g (i) is the actual cost from the starting point to the target node i along the generated path, and the value of the actual cost is determined according to the metric selected by the road network, and h (i) is the estimated cost value from the current node i to the end point.
The peak-flattening period strategy comprises the following sub-steps:
the objective function of the optimal model of the user in the peak-smoothing period is as follows: average parking time of parking person
Minimum;
substep 2.401: dividing the parking area into n subareas, wherein the number of parking spaces distributed by each subarea is m 1 ，m 2 ，…，m n ；
Substep 2.402: the parking time of each vehicle in each subarea is the vehicle running time from the entrance to the center of the subarea
And parking time in parking spaces
I 1, n; the parking time of the jth vehicle in each subarea is respectively recorded as t 1j ，…，t mj (ii) a The average parking time of the vehicle in each subarea is respectively
Substep 2.403: parking time on parking spaces
Is fixed, independent of the parking area, the objective equation is obtained:
wherein the content of the first and second substances,
the target equation then translates into:
due to total parking number m 1 +…+m n Is fixed, and the total parking time of the vehicle in the parking space is a fixed value
Independent of the parking area, only the parking demand; the objective equation is equivalent to solving
M of 1 、...、m n The value is that the number of the parking spaces distributed to each subarea which minimizes the target equation is solved;
substep 2.404: solving the user optimal model in the peak flattening period: setting the distance between the parking space of the parking lot and the entrance to be consistent with the serial number, namely t 1j1 ＜…＜t nj1 To minimize the objective equation, a minimum t is required 1j1 The coefficient of (1) is maximized, namely, a parking area nearest to the entrance is allocated first, and the area nearest to the entrance is allocated to be full of m 1 Redistributing sub-near region m 2 And so on; the constraint conditions are as follows:
wherein: the maximum number of parking spaces available in each community is recorded as m 1 ′，...，m n ', the total number of available parking spaces of the whole parking lot is marked as m General assembly ，
The average value of the parking space occupancy of each partition is obtained;
substep 2.405, combine heuristic A to get parking stall route planning under the peak period user optimal tactics condition of flat peak, including the following substep:
substep 2.501: when a lot of vehicles enter the parking lot in a short time, guiding the vehicles to enter different areas of the parking lot, and then determining the weight of each area, so that the smallest weight obtained by comparison is the optimal parking space;
substep 2.502, calibration of road weight: the road weight is dynamically calculated by considering two indexes of a traffic rule and a parking travel distance, and the formula is as follows:
W＝c 1 *s(p i ，e)+c 2 *d(p i )
wherein, s (p) i ，e)、d(p i ) Representing the distance covered and the traffic rules, respectively, constant c 1 、c 2 Calculating the reference weight corresponding to each attribute by an analytic hierarchy process or subjective assignment;
substep 2.503: and calculating the expenditure of each idle node by using a heuristic A-x algorithm, determining the optimal parking space, if the expenditure corresponding to a certain parking space is minimum, determining the parking space as the optimal parking space, and if the expenditure of a plurality of parking spaces is the same and is the minimum, selecting the parking space with the shortest driving path as the optimal parking space to obtain the optimal parking path.
And (3) a peak period strategy, based on the optimal model of the peak period system, adopting a heuristic A path planning algorithm to plan the parking path in the peak period.
The rush hour strategy is a strategy for maximizing the resources of the whole parking lot through system analysis and corresponding optimization technology, which may cause local individuals to fail to achieve the current optimization, but realizes the optimization of the parking resource utilization and the average parking time as a whole. And a system optimal model is established, and the problem that when all vehicles are simultaneously distributed to the same area according to the user optimal strategy, a local road section congestion phenomenon is generated, so that the parking time is prolonged is solved.
A rush hour policy comprising the sub-steps of:
substep 2.601, determining an objective equation for the optimal model of the peak hour system: the occupancy balance of each subarea is a target, namely the variance of the parking space occupancy of each subarea is minimum;
wherein R is j Representing the occupancy of the j-th partition;
the calculation formula of the parking space occupancy of each subarea is as follows:
average parking space occupancy of each partition:
wherein the constraint condition is as follows:
m 1 Total +…+m 2 Total ＝m General assembly
m 1 account for ′＜m 1 Total ，…，m n accounts for ′＜m n is total
m 1 ＜(m 1 Total -m 1 account for ′)，…，m n ＜(m n total -m n accounts for ′)
Substep 2.602, solving the peak period system optimal model: due to the fact that
So when the objective equation takes a minimum value, i.e.
Namely, if and only if the parking space occupancy of each area is equal, the optimal model target equation of the system is optimal in the peak period; and under the optimal model target, the partition with the largest difference value of the average values of the occupancy rates of the parking spaces is allocated, and then the partition with the small difference value of the average values of the occupancy rates is allocated.
Substep 2.603, combine heuristic A to get the parking stall route planning under the optimal tactics condition of the peak period system, including the following substep:
substep 2.701: when a lot of vehicles enter the parking lot in a short time, guiding the vehicles to enter different areas of the parking lot, and then determining the weight of each area, so that the smallest weight obtained by comparison is the optimal parking space;
substep 2.702, calibration of road weight: the road weight is dynamically calculated by considering two indexes of a traffic rule and a parking travel distance, and the formula is as follows:
W＝c 1 *s(p i ，e)+c 2 *d(p i )
wherein, s (p) i ，e)、d(p i ) Representing the distance covered and the traffic rules, respectively, constant c 1 、c 2 Calculating the reference weight corresponding to each attribute by an analytic hierarchy process or subjective assignment;
substep 2.703: and calculating the expenditure of each idle node by using an A-x algorithm, determining the optimal parking space, if the expenditure corresponding to a certain parking space is minimum, determining the parking space as the optimal parking space, and if the expenditure of a plurality of parking spaces is the same and is the minimum, selecting the parking space with the shortest driving path as the optimal parking space to obtain the optimal parking path.
And (3) a parking difficulty strategy, namely establishing parking path planning under the parking difficulty constraint based on a Dijkstra algorithm under the parking difficulty constraint condition.
The method comprises the steps of obtaining experimental data from a plurality of users, installing a measuring device on a vehicle of an experimental user, wherein the measuring device is used for obtaining the experimental data, including regional attributes of parking areas, parking data at each historical moment and attribute information of parking spots in the parking areas. After enough experimental data are obtained, the parking space vacancy rate at each historical moment is compared with the vacancy threshold value, and therefore the parking difficulty condition of each parking space is obtained. Divide the parking stall degree of difficulty into difficult, medium, simple, if the parking stall vacancy rate is higher, then this parking stall degree of difficulty is difficult, if the parking stall vacancy rate is lower, then the vehicle degree of difficulty of parking is simple. And quantifying the parking passing difficulty to obtain quantification coefficients of parking under different difficulties.
In the traditional Dijkstra algorithm, < V i ，V j Weight d on arc is more than V i ，V j The distance between two nodes is represented, each parking space is used as a vertex in a Dijkstra algorithm, the distance between each parking space and the entrance of the parking lot, the parking difficulty and other factors are considered, the Dijkstra algorithm is combined, and the weighted average value of the parking difficulty is adopted to determine the weight:
Wherein T is a quantization coefficient of parking difficulty, and di represents an influence factor of a selected path; wherein d1 is V i ，V j Distance between two fixed points, d2 being from V i Go to V j Based on the actual driving distance under the parking lot passing rule; pi is the weight of the parking distance; and then determining the optimal path for parking space guidance by utilizing a Dijkstra algorithm, and applying the algorithm to a space guidance method to obtain the optimal parking path under the constraint of parking difficulty.
Step S3: and the central processing unit transmits the planned parking path to a user terminal through a transmission module, and performs real-time path navigation on the user to complete the guidance of the parking space.
The multi-scene parking space guiding method based on the analytic hierarchy process can be applied to various parking scenes including shopping mall parking lots, residential area parking lots and airport parking lots.
Although the present invention has been described in detail in this specification with reference to specific embodiments and illustrative embodiments, it will be apparent to those skilled in the art that modifications and improvements can be made thereto based on the present invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (7)
1. A multi-scene parking space guiding method based on an analytic hierarchy process is characterized by comprising the following steps:
step S1: the data acquisition unit acquires the number and position of vacant parking spaces in the parking lot and the license plate number of the vehicle to be parked, and sends the number and position to the central processing unit through the transmission module to prepare for vehicle parking path planning;
step S2: the central processing unit receives a planning mode signal sent by a user and plans a parking path according to a planning mode;
step S3: and the central processing unit transmits the planned parking path to a user terminal through a transmission module, and performs real-time path navigation on the user to complete the guidance of the parking space.
2. The analytic hierarchy process-based multi-scene parking space guidance method of claim 1, wherein in the step S2, the specific method for parking path planning is as follows:
the planning mode comprises a subjective mode and a non-subjective mode;
if the user selects the subjective mode, corresponding parking path planning is carried out according to the selected sub-mode;
the subjective mode comprises a walking distance shortest sub-mode and a driving distance shortest sub-mode; under the sub-mode of shortest walking distance, the central processing unit adopts the Dijkstra algorithm of shortest walking distance to plan the path of the parking space, and the shortest walking distance path from the parking space to the destination selected by the user is obtained; under the sub-mode of the shortest driving distance, the central processing unit adopts a Dijkstra algorithm of the shortest driving path to plan the path of the parking space, and the shortest driving distance from the entrance of the parking lot to the parking space is obtained;
If the user selects the non-subjective mode, the central processing unit plans the path with the shortest parking time;
firstly, establishing a hierarchical structure diagram for factors influencing parking time by utilizing an analytic hierarchy process to obtain the weight of each factor influencing parking difficulty;
secondly, under the parking lot passing rule, a parking lot weighted directed graph is established;
and thirdly, according to the weight of each factor obtained by the analytic hierarchy process and in combination with the parking lot weighted directed graph, selecting different path planning strategies to obtain the path with the shortest parking time under the different path planning strategies.
3. The analytic hierarchy process-based multi-scene parking space guidance method of claim 2, wherein the routing strategy is:
if the parking space number or the number of the vehicles to be parked has a great weight, selecting a flat peak time period strategy or a peak time period strategy; if the parking difficulty weight is large, selecting a parking difficulty strategy;
the peak-smoothing period strategy is based on the peak-smoothing period user optimal model, and adopts a heuristic A path planning algorithm to plan the parking path in the peak-smoothing period;
the peak period strategy adopts a heuristic A route planning algorithm to plan the parking route in the peak period based on the optimal model of the peak period system;
And the parking difficulty strategy is based on a Dijkstra algorithm under the parking difficulty constraint condition, and a parking path plan under the parking difficulty constraint condition is established.
4. The analytic hierarchy process-based multi-scene parking space guidance method of claim 3, wherein the peak-smoothing period strategy comprises the following sub-steps:
the objective function of the optimal model of the user in the peak-smoothing period is as follows: average length of time of parking for the parking lot
Minimum;
substep 2.401: dividing the parking area into n subareas, wherein the number of parking spaces distributed by each subarea is m 1 ，m 2 ，…，m n ；
Substep 2.402: the parking time of each vehicle in each subarea is the vehicle running time from the entrance to the center of the subarea
And parking time in parking spaces
I 1, n; the parking time of the jth vehicle in each subarea is respectively recorded as t 1j ，…，t mj (ii) a The average parking time of the vehicle in each subarea is respectively
Substep 2.403: parking time on parking spaces
Is fixed, independent of the parking area, the objective equation is obtained:
wherein the content of the first and second substances,
the target equation is equivalent to:
substep 2.404: solving the user optimal model in the peak flattening period: setting t 1j1 ＜…＜t nj1 To minimize the objective equation, a minimum t is required 1j1 The coefficient of (1) is maximized, namely, a parking area nearest to the entrance is allocated first, and the area nearest to the entrance is allocated to be full of m 1 Redistributing the sub-near region m 2 And so on; the constraint conditions are as follows:
wherein: the maximum number of parking spaces available in each community is recorded as m 1 ′，...，m n ', the total number of available parking spaces of the whole parking lot is marked as m General assembly ，
The average value of the parking space occupancy of each partition is obtained;
and substep 2.405, combining a heuristic A-x algorithm to obtain the parking path under the optimal strategy condition of the user in the peak flattening period.
5. The analytic hierarchy-based multi-scenario parking space guidance method of claim 3, wherein the rush hour strategy comprises the following sub-steps:
substep 2.601, determining an objective equation for the optimal model of the peak hour system: the occupancy rates of all the subareas are balanced, namely the variance of the occupancy rates of the parking spaces of all the subareas is minimum;
wherein R is j Representing the occupancy of the j-th partition;
the calculation formula of the parking space occupancy of each subarea is as follows:
average parking space occupancy of each partition:
wherein the constraint condition is as follows:
m 1 Total +…+m 2 Total ＝m General assembly
m 1 account for ′＜m 1 Total ，…，m n accounts for ′＜m n total
m 1 ＜(m 1 Total -m 1 account for ′)，…，m n ＜(m n total -m n accounts for ′)
Substep 2.602, solving the peak period system optimal model: due to the fact that
So when the objective equation takes a minimum value, i.e.
Namely, if and only if the parking space occupancy of each area is equal, the optimal model target equation of the system is optimal in the peak period; under the optimal model target, firstly distributing the partition with the largest difference value of the average values of the occupancy rates of the parking spaces, and then distributing the partition with the small difference value of the average values of the occupancy rates;
And substep 2.603, combining a heuristic A-star algorithm to obtain the parking path under the optimal strategy condition of the peak period system.
6. The analytic hierarchy process-based multi-scene parking space guidance method of claim 3, wherein the parking difficulty strategy specifically comprises:
determining the weight value by adopting the weighted average value of the parking difficulty as follows:
wherein T is a quantization coefficient of parking difficulty, and di represents an influence factor of a selected path; wherein d1 is V i ，V j Distance between two fixed points, d2 being from V i Go to V j Based on the actual driving distance under the parking lot passing rule; pi is the weight of the parking distance;
and obtaining the parking path under the parking difficulty constraint by using the optimal path determined by the Dijkstra algorithm.
7. A parking space guidance system for implementing the method of claim 1, comprising a data acquisition unit, an image processing module, a transmission module, a central processing unit and a user terminal;
the data acquisition unit comprises a parking space detector module and an identification camera module, the parking space detector module is used for acquiring vacant parking space image information, and the identification camera module is used for acquiring license plate number images;
the image processing module processes the image information of the vacant parking spaces to obtain the positions and the number of the vacant parking spaces and processes the license plate number image to obtain the license plate number of the vehicle to be parked;
The transmission module is used for transmitting the positions and the number of the vacant parking spaces and the planning mode signals to the central processing unit; the transmission module is also used for transmitting the path planned by the central processing unit to a user terminal corresponding to the license plate number of the vehicle to be parked;
the central processing unit is used for planning paths according to the positions and the number of the vacant parking spaces and sending the planned paths to the transmission module;
the user terminal is used for sending a planning mode signal to the transmission module and receiving the planned path sent by the transmission module.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115662183A (en) * | 2022-12-22 | 2023-01-31 | 深圳市博铭维***工程有限公司 | Method and system for allocating parking spaces in parking lot |
| CN115798254A (en) * | 2022-11-23 | 2023-03-14 | 武汉大学 | Park parking guiding method based on minimum adjacent road closing unit |
| CN116167536A (en) * | 2022-12-07 | 2023-05-26 | 江苏巨楷科技发展有限公司 | Intelligent parking management method based on time period learning optimization |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102853841A (en) * | 2012-05-08 | 2013-01-02 | 龚䶮 | Man-machine interaction method of navigator |
| KR20170062247A (en) * | 2015-11-27 | 2017-06-07 | 충북대학교 산학협력단 | Method for searching path for minimal risk, and recording medium thereof |
| CN108615396A (en) * | 2018-04-26 | 2018-10-02 | 中设设计集团股份有限公司 | Stopping guide, reverse vehicle searching system and method based on bluetooth positioning |
| CN109242153A (en) * | 2018-08-16 | 2019-01-18 | 青岛华通石川岛停车装备有限责任公司 | A kind of sky parking's pick-up paths planning method |
| CN109949604A (en) * | 2019-04-01 | 2019-06-28 | 南京邮电大学 | A kind of large parking lot scheduling air navigation aid, system and application method |
| CN110503848A (en) * | 2019-07-01 | 2019-11-26 | 浙江科技学院 | A kind of best truck space guiding system in parking lot based on Internet of Things |
| KR20200036232A (en) * | 2018-09-28 | 2020-04-07 | 홍익대학교 산학협력단 | Real-time optimal parking route guidance system and method |
| CN111967811A (en) * | 2020-07-08 | 2020-11-20 | 吉林大学 | Urban traffic environment-oriented hybrid logistics vehicle path planning method and system |
| CN112556714A (en) * | 2020-12-08 | 2021-03-26 | 武汉理工光科股份有限公司 | Fire-fighting rescue intelligent path planning method and system |
| CN113506457A (en) * | 2021-07-08 | 2021-10-15 | 湖南大学 | Device and method for searching parking space based on large parking lot scene |
-
2022
- 2022-04-08 CN CN202210366918.2A patent/CN114842668B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102853841A (en) * | 2012-05-08 | 2013-01-02 | 龚䶮 | Man-machine interaction method of navigator |
| KR20170062247A (en) * | 2015-11-27 | 2017-06-07 | 충북대학교 산학협력단 | Method for searching path for minimal risk, and recording medium thereof |
| CN108615396A (en) * | 2018-04-26 | 2018-10-02 | 中设设计集团股份有限公司 | Stopping guide, reverse vehicle searching system and method based on bluetooth positioning |
| CN109242153A (en) * | 2018-08-16 | 2019-01-18 | 青岛华通石川岛停车装备有限责任公司 | A kind of sky parking's pick-up paths planning method |
| KR20200036232A (en) * | 2018-09-28 | 2020-04-07 | 홍익대학교 산학협력단 | Real-time optimal parking route guidance system and method |
| CN109949604A (en) * | 2019-04-01 | 2019-06-28 | 南京邮电大学 | A kind of large parking lot scheduling air navigation aid, system and application method |
| CN110503848A (en) * | 2019-07-01 | 2019-11-26 | 浙江科技学院 | A kind of best truck space guiding system in parking lot based on Internet of Things |
| CN111967811A (en) * | 2020-07-08 | 2020-11-20 | 吉林大学 | Urban traffic environment-oriented hybrid logistics vehicle path planning method and system |
| CN112556714A (en) * | 2020-12-08 | 2021-03-26 | 武汉理工光科股份有限公司 | Fire-fighting rescue intelligent path planning method and system |
| CN113506457A (en) * | 2021-07-08 | 2021-10-15 | 湖南大学 | Device and method for searching parking space based on large parking lot scene |
Non-Patent Citations (6)
| Title |
|---|
| HAN ZHANG 等: "A Model of Flight Safety Situation Evaluation Based on Fuzzy Matter-element", PROCEEDINGS OF SPIE, pages 1 - 8 * |
| 吴若伟 等: "基于Dijkstra算法的大型停车场最优泊车路径规划", 工业控制计算机, vol. 26, no. 5, pages 93 - 95 * |
| 段满珍 等: "个性化诱导下的居住区共享停车泊位分配模型", 东北大学学报(自然科学版), vol. 38, no. 2, 28 February 2017 (2017-02-28), pages 174 - 179 * |
| 袁琳 等: "基于权值的Dijkstra停车路径规划算法的优化与实现", 湖北大学学报(自然科学版), vol. 39, no. 3, pages 279 - 284 * |
| 贝振权 等: "层次分析法在泊车诱导中的应用", 交通科技与经济, vol. 15, no. 6, pages 27 - 31 * |
| 陈緋: "面向停车场应用的多属性决策与最短路径算法研究", 中国优秀硕士学位论文全文数据库 基础科学辑, no. 3, 15 March 2022 (2022-03-15), pages 23 - 29 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115798254A (en) * | 2022-11-23 | 2023-03-14 | 武汉大学 | Park parking guiding method based on minimum adjacent road closing unit |
| CN115798254B (en) * | 2022-11-23 | 2024-04-12 | 武汉大学 | Park parking guiding method based on minimum adjacent road closing unit |
| CN116167536A (en) * | 2022-12-07 | 2023-05-26 | 江苏巨楷科技发展有限公司 | Intelligent parking management method based on time period learning optimization |
| CN116167536B (en) * | 2022-12-07 | 2023-08-04 | 江苏巨楷科技发展有限公司 | Intelligent parking management method based on time period learning optimization |
| CN115662183A (en) * | 2022-12-22 | 2023-01-31 | 深圳市博铭维***工程有限公司 | Method and system for allocating parking spaces in parking lot |
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