Urban intelligent parking planning method and system

Abstract

The invention relates to the technical field of intelligent parking, in particular to an urban intelligent parking planning method and system. According to the method, firstly, concert information and parking information of n public parking lots in a city are collected, and a concert information set and a parking information set are constructed respectively; secondly, according to the concert information set, the parking demand quantity of the venue where the concert is located is predicted; then, acquiring a real-time position of a user and road data from the real-time position to the venue, and predicting first time when the user arrives at the venue according to the real-time position, the road data, the parking demand quantity and the concert information; when the first time is greater than an expected value, matching the matching information of the user with the parking information of the public parking lot in the city; and finally, outputting a matching list according to a matching result. According to the urban intelligent parking planning method and system, the convenience of concert parking can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of smart parking technology, specifically to a method and system for urban smart parking planning. Background Technology

[0002] With economic development, the number of private cars is increasing, leading to parking difficulties. Parking problems, especially at large concerts and music festivals, have always been one of the main issues these events need to address.

[0003] There is limited research on parking planning for concert venues. Parking demand at concert venues is influenced by various factors, requiring adjustments based on available parking spaces. Parking planning for concert venues not only needs to calculate the venue's parking demand but also needs to match it with available parking spaces in surrounding public parking lots to address the inconvenience of parking at concert venues.

[0004] To address the problem of inconvenient parking at concert venues, a smart parking planning method and system for cities is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a smart parking planning method and system for cities. By using information about the concert venue and performers, the system predicts parking demand on the day of the concert and adjusts the venue's parking fees based on the prediction results. It then obtains users' parking reservation information and matches it with parking information from within the venue and from public parking lots throughout the city, outputting the matching results.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A smart parking planning method for cities includes:

[0008] Collect concert information and construct a concert information set; collect parking information from n public parking lots in the city and construct a parking information set.

[0009] Furthermore, the concert information includes venue information and performer information; the venue information includes the distance d of the venue from the city center. c The venue can accommodate a maximum number of spectators (num). c The venue's historical concert attendance rate (L) c and venue online popularity R c The singer information includes the number of concerts the singer has performed in the past. h y Average number of tickets sold per concert by the performer (s) s yAverage ticket price per concert; average attendance rate per concert (L) for each performer. y Singer's online popularity R y The parking information set includes n subsets of parking information from public parking lots; the parking information includes the number of parking spaces in the public parking lots, the location of the public parking lots, the parking space attributes, and the parking fee standards; the parking space attributes include the size and status of each parking space in the public parking lots; the parking space status includes unoccupied and occupied.

[0010] Furthermore, the average attendance rate L for concerts historically held at the venue c The calculation formula is:

[0011]

[0012] Where A represents the number of concerts held at the venue throughout its history; num c This represents the number of spectators the venue can accommodate; num ω L represents the actual number of audience members at the ωth concert; L represents the average attendance rate per concert for the performer. y The calculation formula is:

[0013]

[0014] in, This represents the number of concerts a singer has performed in their history. This represents the average number of tickets sold per concert by the performer; num σ This represents the number of audience members that can be accommodated at the σth concert of the aforementioned singer.

[0015] Furthermore, the venue's online popularity is calculated based on the number of followers of the venue's official account on different social media platforms and the number of times the venue's official account is accessed within a set time t; the formula for calculating the venue's online popularity is:

[0016]

[0017] Among them, R c The venue's online popularity is represented by n; n represents the number of social media platforms counted; p i Let f represent the number of followers of the official venue account on the i-th social media platform; ti This represents the number of visits to the official venue account on the i-th social media platform within time t.

[0018] Furthermore, the singer's online popularity is calculated based on the number of followers of the singer's account on different social media platforms, the number of times the singer's account is accessed within a set time t, and the number of times the singer's account is accessed within the set time t; the formula for calculating the singer's online popularity is:

[0019]

[0020] Wherein, the R y The online popularity of the singer is represented by ; n represents the number of social media platforms used in the statistics. This represents the number of followers of the singer's account on the i-th social media platform; This represents the number of times the singer's account on the i-th social media platform is accessed within time t.

[0021] Based on the concert information set, the parking demand of the venue where the concert is held is predicted.

[0022] Furthermore, an SVR model is used for parking demand prediction; the SVR model is trained using the city's historical concert information and corresponding parking demand; the concert information is input into the SVR model, and the predicted parking demand value C is output. xq .

[0023] Obtain the user's real-time location and road data from the real-time location to the venue; based on the real-time location, the road data, the predicted parking demand value, and the concert information, predict the first time the user will arrive at the venue.

[0024] Furthermore, the real-time location data from the venue includes the navigation software's predicted arrival time at the venue and the venue's historical traffic flow; the formula for calculating the first time is:

[0025]

[0026] Among them, T DC Indicates the first time; t dc This indicates the arrival time predicted by the navigation software. This indicates the start time of the concert; This represents the traffic flow at the venue for the time period corresponding to the 7*ith day before the concert begins; C xq Represented as the predicted parking demand; η c μ1, μ2, μ3 and μ4 are adjustment parameters.

[0027] When the first time is greater than the expected value T QW At that time, the user's matching information is matched with the parking information of public parking lots in the city.

[0028] Furthermore, the matching information is matched with the parking space sizes in each public parking lot parking information subset of the parking information set, and the unoccupied parking spaces in the public parking lot parking information subset that satisfy the longitude rule are calculated. w -long l ≥0.5 and weight w -weight l The first quantity ≥ 0.5; long w and weight w These represent the length and width of the parking space, respectively; long l and weight l These represent the length and width of the vehicle, respectively; the first subset of public parking information that has a quantity of 0 is removed from the parking information set, and the first parking information set is output.

[0029] Furthermore, the first parking information set is divided into a second parking information set and a third parking information set according to the location of the public parking lot; the second parking information set contains n 1 A subset of parking information from public parking lots; the third parking information set contains n 2 A subset of parking information from public parking lots; n 1 +n 2 ≤n; n represents the number of public parking lot parking information subsets included in the parking information set; when the location difference between the public parking lot and the concert venue... When, the public parking lot is classified as the second parking information set; when At that time, the public parking lot will be classified as the third parking information set; This is represented as the set position difference.

[0030] Further, the second arrival time of the user at the venue is predicted based on the parking information in the second parking information set and the third parking information set, and the matching information; the calculation formula for predicting the second arrival time of the user at the venue based on the parking information in the second parking information set and the matching information is as follows:

[0031]

[0032] in, This indicates the predicted time for the user to park in the kth public parking lot in the second parking information set and then return to the venue; This represents the time predicted by the navigation software for the user to arrive at the k-th public parking lot in the second parking information set; This indicates the time required to park in the k-th public parking lot within the second parking information set; β1 represents the location difference between the kth public parking lot in the second parking information set and the venue where the concert is held; β1 is an adjustment parameter.

[0033] Furthermore, the formula for predicting the second arrival time of the user at the venue based on the parking information in the third parking information set and the matching information is as follows:

[0034]

[0035] in, This indicates the predicted time for the user to park in the kth public parking lot in the third parking information set and then return to the venue; This represents the time predicted by the navigation software for the user to arrive at the k-th public parking lot in the third parking information set; This represents the time required to park in the k-th public parking lot in the third parking information set. β1 represents the time required to travel by public transportation from the kth public parking lot in the third parking information set to the concert venue; β2 is an adjustment parameter.

[0036] Output a list of matches based on the matching results.

[0037] Furthermore, by removing the subset of public parking information in the second parking information set where the second time is greater than the first time, a fourth parking information set is obtained; by removing the subset of public parking information in the third parking information set where the second time is greater than the first time, a fifth parking information set is obtained.

[0038] Furthermore, the second time corresponding to each public parking information subset in the fourth parking information set and the fifth parking information set is sorted in ascending order, and the parking information corresponding to the first m public parking information subsets is output to obtain a matching list.

[0039] A smart parking planning system for cities, comprising:

[0040] Data collection unit: Used to collect concert information and build a concert information set; collect parking information from public parking lots in the city and build a parking information set;

[0041] Prediction unit: used to predict the parking demand of the concert venue, the first time the user arrives at the venue, and the second time the user arrives at the venue;

[0042] Matching unit: Used to match the user's matching information with the parking information set;

[0043] Output unit: Used to output the matching list.

[0044] Furthermore, the concert information includes venue information and performer information; the venue information includes the distance d of the venue from the city center. c The venue can accommodate a maximum number of spectators (num). c The venue's historical concert attendance rate (L) c and the online popularity R of the venue c .

[0045] Furthermore, the singer information includes the number of concerts the singer has performed in the past. The average number of tickets sold per concert by the singer The average ticket price per concert and the average attendance rate per concert for the performer (L) y The singer's online popularity R y .

[0046] Furthermore, the parking information set includes a subset of parking information from public parking lots within the city and a subset of parking information from parking lots within venues; the parking information of the parking lot includes the number of parking spaces, the location of the parking lot, the attributes of the parking spaces, and the parking fee standard; the parking space attributes include the size of the parking space and whether it is occupied.

[0047] Furthermore, the venue's online popularity is calculated based on the number of followers of the venue's official account on different social media platforms and the number of times the venue's official account is accessed within a set time t; the formula for calculating the venue's online popularity is:

[0048]

[0049] Among them, R c The venue's online popularity is represented by n; n represents the number of social media platforms counted; p i Let f represent the number of followers of the official venue account on the i-th social media platform; ti This represents the number of visits to the official venue account on the i-th social media platform within time t.

[0050] Furthermore, the singer's online popularity is calculated based on the number of followers of the singer's account on different social media platforms, the number of times the singer's account is accessed within a set time t, and the number of times the singer's account is accessed within a set time t.

[0051] The formula for calculating the singer's online popularity is:

[0052]

[0053] Wherein, the R yThe online popularity of the singer is represented by ; n represents the number of social media platforms used in the statistics. This represents the number of followers of the singer's account on the i-th social media platform; This represents the number of times the singer's account on the i-th social media platform is accessed within time t.

[0054] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0055] 1. This invention calculates the venue's online popularity by combining the number of followers on the venue's social media accounts and the number of visits within a set time period, and calculates the singer's online popularity by combining the number of followers on the singer's social media accounts and the number of visits within a set time period. Then, by combining the venue's online popularity and the singer's online popularity with relevant venue and singer information, the invention predicts the parking demand for the venue on the day of the concert. This method comprehensively considers both venue and singer information, which can improve the accuracy of parking demand prediction, thereby enhancing parking convenience.

[0056] 2. This invention combines the arrival time predicted by navigation software, the venue's online popularity, the singer's online popularity, predicted parking demand, and historical traffic flow data when predicting the user's arrival time at the concert venue. This method integrates multiple pieces of information, improving the accuracy of predicting the user's arrival time at the concert venue.

[0057] 3. When matching matching information with parking information in public parking lots within the city, this invention uses different calculation methods to calculate the second time from the real-time location to parking in a public parking lot and then to the venue, taking into account the different distances between the parking lot and the venue. The matching result is then output based on this second time. This method can match matching information with parking lot information according to different matching methods, thereby improving parking convenience. Attached Figure Description

[0058] Figure 1 This is a schematic diagram of a smart parking planning method for cities provided in an embodiment of the present invention;

[0059] Figure 2 A comparison chart of arrival time prediction results provided in embodiments of the present invention;

[0060] Figure 3 A comparison chart of parking demand prediction accuracy provided in embodiments of the present invention;

[0061] Figure 4 A flowchart of parking information matching provided in an embodiment of the present invention;

[0062] Figure 5This is a schematic diagram of a smart parking planning system for cities, provided as an embodiment of the present invention. Detailed Implementation

[0063] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0064] This invention provides a method and system for urban smart parking planning, the technical solution of which is as follows:

[0065] Reference Figure 1 A smart parking planning method for cities includes: S10. Collecting concert information and constructing a concert information set; collecting parking information from n public parking lots in the city and constructing a parking information set; S20. Predicting the parking demand of the venue where the concert is held based on the concert information set, and outputting a predicted parking demand value; S30. Obtaining the user's real-time location and road data from the real-time location to the venue; S40. Predicting the first time the user arrives at the venue based on the real-time location, the road data, the predicted parking demand value, and the concert information; S50. When the first time is greater than the expected value T... QW S60. Match the user's matching information with the parking information of public parking lots in the city; S61. Output a matching list based on the matching results.

[0066] Example 1:

[0067] As one embodiment of the present invention, see [reference]. Figure 1 As mentioned in S10, collect concert information and construct a concert information set; collect parking information from n public parking lots in the city and construct a parking information set.

[0068] Furthermore, this embodiment collected venue information and performer information for a certain Olympic Sports Center Gymnasium; the performers were Li Moumou and Xu Mou. The concert information included venue information and performer information; the venue information included the distance d of the venue from the city center. c The venue can accommodate a maximum number of spectators (num). c The venue's historical concert attendance rate (L) c and the online popularity R of the venue c The singer information includes the number of concerts the singer has performed in the past. The average number of tickets sold per concert by the singer The average ticket price per concert and the average attendance rate per concert for the performer (L) y The singer's online popularity R y .

[0069] Furthermore, the Olympic Sports Center stadium is located a distance d from the city center. c = 8.8km; Venue capacity: num c = 8,000 people; the venue's historical concert attendance rate L c =91%; the venue's online popularity R c The venue's online popularity is calculated based on the number of followers of the venue's official account on different social media platforms and the number of visits to the official account within a set time t; the different social media platforms include Douyin, Kuaishou, Xiaohongshu, and Weibo; the formula for calculating the venue's online popularity is:

[0070]

[0071] Among them, R c This is represented by the venue's online popularity; p i Let f represent the number of followers of the venue's official account on the i-th social media platform; the number of followers of the venue's official accounts on Douyin, Kuaishou, Xiaohongshu, and Weibo are 0.0017 billion, 0.0011 billion, 0.0003 billion, and 0.0035 billion, respectively; ti Let R represent the number of visits to the official account of the venue on the i-th social media platform within time t. In this embodiment, time t is set to 4 hours before the start of the concert. The number of visits to the official accounts of the venue on Douyin, Kuaishou, Xiaohongshu, and Weibo within time t are 0.0013 billion, 0.0007 billion, 0.0001 billion, and 0.0010 billion, respectively. Based on the above data, the online popularity R of the venue is calculated. c =0.0077.

[0072] Furthermore, the venue's historical average attendance rate for concerts is L c The calculation formula is:

[0073]

[0074] Where A represents the number of concerts held at the venue throughout its history; num ω This represents the actual number of audience members at the ωth concert.

[0075] Furthermore, this embodiment collects the number of concerts Li Moumou has held historically. The average number of tickets sold per concert by Li Moumou. 10,000; the average ticket price per concert by Li Moumou was 413 yuan; the average attendance rate per concert by Li Moumou was L. y =87%; Li Moumou's online popularity R y The online popularity of Li Moumou is calculated based on the number of followers of Li Moumou's account on different social media platforms and the number of visits to Li Moumou's account within a set time t; the different social media platforms include Douyin, Kuaishou, Xiaohongshu, and Weibo; the formula for calculating Li Moumou's online popularity is:

[0076]

[0077] Wherein, the R y This refers to Li Moumou's online popularity; Let represent the number of followers of Li Moumou's account on the i-th social media platform; the number of followers of Li Moumou's accounts on Douyin, Kuaishou, Xiaohongshu and Weibo are 0.1014 billion, 0.0726 billion, 0.0194 billion and 0.2415 billion respectively; Let R represent the number of visits to the singer's account on the i-th social media platform within time t. In this embodiment, time t is set to 4 hours before the start of the concert. The number of visits to Li Moumou's accounts on Douyin, Kuaishou, Xiaohongshu, and Weibo within time t are 0.0728 billion, 0.0443 billion, 0.0121 billion, and 0.1467 billion, respectively. Based on the above data, Li Moumou's online popularity R is calculated. y =0.6466.

[0078] Furthermore, this embodiment also collected data on the number of concerts Xu had previously held. The average number of tickets sold per concert by Mr. Xu The average ticket price for each of Wan and Xu's concerts was 298 yuan, and the average attendance rate for each of Xu's concerts was L. y =94%, Xu's online popularity R y The online popularity of Xu was calculated based on the number of followers of Xu's account on different social media platforms and the number of times Xu's account was accessed within a set time period t; the different social media platforms include Douyin, Kuaishou, Xiaohongshu, and Weibo; the formula for calculating Xu's online popularity is:

[0079]

[0080] Wherein, the R y This refers to Xu's online popularity; Let represent the number of followers of Xu's account on the i-th social media platform; the number of followers of Xu's official accounts on Douyin, Kuaishou, Xiaohongshu and Weibo are 0.1376 billion, 0.0346 billion, 0.0075 billion and 0.1843 billion respectively. Let R represent the number of visits to the singer's account on the i-th social media platform within time t. In this embodiment, time t is set to 4 hours before the start of the concert. The number of visits to Xu's accounts on Douyin, Kuaishou, Xiaohongshu, and Weibo within time t are 0.0425 billion, 0.0157 billion, 0.029 billion, and 0.1352 billion, respectively. Based on the above data, Xu's online popularity R is calculated. y =0.4926.

[0081] Furthermore, the average attendance rate for each concert by Li and Xu was L. y The calculation formula is:

[0082]

[0083] Where, num σ This indicates the number of audience members that the σ-th concert by the aforementioned singer can accommodate.

[0084] See Figure 1 S20. Based on the concert information set, predict the parking demand of the venue where the concert is held, and output the predicted parking demand value.

[0085] Furthermore, an SVR model is used for parking demand prediction; the SVR model is trained using the city's historical concert information and corresponding parking demand; the concert information is input into the SVR model, and the predicted parking demand value C is output. xq In this embodiment, the predicted parking demand for Li's concert is 0.11 million vehicles, and the predicted parking demand for Xu's concert is 0.07 million vehicles, based on the SVR model.

[0086] Furthermore, this embodiment also collected concert information from 18 different concerts held at the venue, and output predicted parking demand values, as shown in the following figures. Figure 2 As shown, Model 1 uses concert information as the independent variable to predict parking demand accuracy; Model 2 uses historical traffic flow as the independent variable; Model 3 uses performer information as the independent variable; and Model 4 uses venue information as the independent variable. To more intuitively display the prediction results, Table 1 shows the parking demand prediction accuracy based on the output predicted parking demand value and the actual parking demand. As can be seen from Table 1, compared with other models, this embodiment effectively improves the prediction accuracy by using concert information as the independent variable to predict parking demand.

[0087] Table 1 Accuracy of Parking Demand Forecast

[0088] Model Model 1 Model 2 Model 3 Model 4 Prediction accuracy 92.81％ 85.59％ 88.66％ 83.82％

[0089] This embodiment calculates the venue's online popularity by combining the number of followers on the venue's social media accounts and the number of visits within a set time period, and calculates the singer's online popularity by combining the number of followers on the singer's social media accounts and the number of visits within a set time period. Then, by combining the venue's online popularity and the singer's online popularity with relevant venue and singer information, the model predicts the parking demand on the day of the concert. Compared to Models 2, 3, and 4, this model comprehensively considers both venue and singer information, which improves the accuracy of parking demand prediction and thus enhances parking convenience.

[0090] Example 2:

[0091] Reference Figure 1 In steps S30 and S40 of this embodiment, the real-time locations of 50 users who want to reach the venue of Li Moumou's concert and 50 users who want to reach the venue of Xu Mou's concert are collected. The first time each user arrives at the corresponding concert is calculated using the following formula:

[0092]

[0093] Among them, T DC Indicates the first time; t dc This indicates the arrival time predicted by the navigation software. This indicates the start time of the concert; This represents the traffic flow at the venue on the 7*ith day before the concert, where i is a positive integer, and in this embodiment, N = 5; C xq Represented as the predicted parking demand value in Example 1; η c μ1, μ2, μ3 and μ4 are adjustment parameters.

[0094] Furthermore, in this embodiment, the expected value is set as the concert start time, and the selected navigation software is Baidu Maps. The prediction results show that 56 users' first time did not exceed the expected value T. QW The actual arrival times of 56 users, the arrival times predicted by navigation software, and the predicted arrival times were compared. Five users whose actual arrival times were close to those of the users arriving at Li Moumou's concert were selected, and the following analysis was performed: Figure 3 The bar chart shown is based on Figure 3 The results show that the predicted arrival time is more accurate than that predicted by navigation software. Furthermore, this embodiment also selected Gaode Maps and Tencent Maps as navigation software to predict the arrival time, and compared the predicted arrival time with the actual arrival time, calculating the average error between the two. The results are shown in Table 2.

[0095] Table 2 Prediction Comparison Table

[0096]

[0097] in, and These represent the arrival times predicted by Baidu Maps, Gaode Maps, and Tencent Maps, respectively. and The values ​​represent the predicted arrival times using Baidu Maps, Gaode Maps, and Tencent Maps as navigation software, respectively. Table 2 shows that this embodiment predicts the user's arrival time at the venue more accurately. This embodiment combines the arrival time predicted by Baidu Maps, the venue's online popularity, the performer's online popularity, predicted parking demand, and historical traffic flow data for the same period when predicting the user's arrival time. This method integrates information from multiple sources, improving the accuracy of the predicted user arrival time at the venue.

[0098] Example 3:

[0099] Reference Figure 1 In steps S10, S40, S50, and S60, this embodiment collects parking information from 1324 public parking lots in the city to construct a parking information set. This embodiment also collects matching information between one user wanting to reach Li Moumou's concert and another user wanting to reach Xu Mou's concert, based on... Figure 4 As shown, the user matching information to the concert is matched with the parking space size of each public parking lot parking information subset in the parking information set, and the unoccupied parking spaces in the public parking lot parking information subset that satisfy the longitude rule are calculated. w -long l ≥0.5 and weight w -weight l The first quantity ≥ 0.5; long w and weight w These represent the length and width of the parking space, respectively; long l and weight l These represent the length and width of the vehicle, respectively; the first subset of public parking information that has a quantity of 0 is removed from the parking information set, and the first parking information set is output.

[0100] Furthermore, the first parking information set is divided into a second parking information set and a third parking information set according to the location of the public parking lot; the second parking information set contains n 1 A subset of parking information from public parking lots; the third parking information set contains n 2 A subset of parking information from public parking lots; n 1 +n2 ≤1324; when the location difference between the public parking lot and the concert venue When, the public parking lot is classified as the second parking information set; when At that time, the public parking lot will be classified as the third parking information set; This is represented as a set position difference, in this embodiment.

[0101] Further, the second arrival time of the user at the venue is predicted based on the parking information in the second parking information set and the third parking information set, and the matching information; the calculation formula for predicting the second arrival time of the user at the venue based on the parking information in the second parking information set and the matching information is as follows:

[0102]

[0103] in, This indicates the predicted time for the user to park in the kth public parking lot in the second parking information set and then return to the venue; This represents the time predicted by the navigation software for the user to arrive at the k-th public parking lot in the second parking information set; This indicates the time required to park in the k-th public parking lot within the second parking information set; β1 represents the location difference between the kth public parking lot in the second parking information set and the venue where the concert is held; β1 is an adjustment parameter.

[0104] Furthermore, the formula for predicting the second arrival time of the user at the venue based on the parking information in the third parking information set and the matching information is as follows:

[0105]

[0106] in, This indicates the predicted time for the user to park in the kth public parking lot in the third parking information set and then return to the venue; This represents the time predicted by the navigation software for the user to arrive at the k-th public parking lot in the third parking information set; This represents the time required to park in the k-th public parking lot in the third parking information set. β1 represents the time required to travel by public transportation from the kth public parking lot in the third parking information set to the concert venue; β2 is an adjustment parameter.

[0107] Furthermore, by removing the subset of public parking information in the second parking information set where the second time is greater than the first time, a fourth parking information set is obtained; by removing the subset of public parking information in the third parking information set where the second time is greater than the first time, a fifth parking information set is obtained.

[0108] Furthermore, the second time corresponding to each public parking information subset in the fourth parking information set and the fifth parking information set is sorted in ascending order, and the parking information corresponding to the first m public parking information subsets is output to obtain a matching list.

[0109] Furthermore, this embodiment outputs a matching list consisting of parking information corresponding to the first five subsets of parking information from the public parking lots based on the second time calculation result. The first matching list generated based on the user matching information to reach Li Moumou's concert is shown in Table 3.

[0110] Table 3 First Matching List

[0111]

[0112] Furthermore, based on the user matching information for those who want to attend Xu's concert, the generated second matching list is shown in Table 4.

[0113] Table 4 Second Matching List

[0114]

[0115] In this embodiment, when matching information with parking information from public parking lots within the city, different calculation methods are used to calculate the second time from the real-time location to parking in a public parking lot and then to the venue, taking into account the different distances between the parking lot and the venue. The matching result is then output based on this second time. This method can match information with parking lot information according to different matching methods, thereby improving parking convenience.

[0116] Example 4:

[0117] As one embodiment of the present invention, refer to Figure 5 A smart parking planning system for cities includes: S210. A data acquisition unit: used to collect concert information and construct a concert information set; and to collect parking information from public parking lots within the city and construct a parking information set; S220. A prediction unit: used to predict the parking demand at the concert venue, the first time a user arrives at the venue, and the second time a user arrives at the venue; S230. A matching unit: used to match user matching information with parking information from the parking information set; and S240. An output unit: used to output a matching list.

[0118] Furthermore, the concert information includes venue information and performer information; the venue information includes the distance d of the venue from the city center. c The venue can accommodate a maximum number of spectators (num). c The venue's historical concert attendance rate (L) c and venue online popularity R c .

[0119] Furthermore, the singer information includes the number of concerts the singer has performed in the past. The average number of tickets sold per concert by the singer The average ticket price per concert and the average attendance rate per concert for the performer (L) y Singer's online popularity R y .

[0120] Furthermore, the parking information set includes a subset of parking information from public parking lots within the city and a subset of parking information from parking lots within venues; the parking information of the parking lot includes the number of parking spaces, the location of the parking lot, the attributes of the parking spaces, and the parking fee standard; the parking space attributes include the size of the parking space and the status of the parking space; the parking space status includes unoccupied and occupied.

[0121] Furthermore, the venue's online popularity is calculated based on the number of followers of the venue's official account on different social media platforms and the number of times the venue's official account is accessed within a set time t; the different social media platforms include Douyin, Kuaishou, Xiaohongshu, and Weibo; the formula for calculating the venue's online popularity is:

[0122]

[0123] Among them, R c This is represented by the venue's online popularity; p i f represents the number of followers of the venue's official account on the i-th social media platform; the number of followers is in the hundreds of millions; ti This represents the number of visits to the official venue account on the i-th social media platform within time t; the number of visits is in the hundreds of millions.

[0124] Furthermore, the singer's online popularity is calculated based on the number of followers of the singer's account on different social media platforms and the number of times the singer's account is accessed within a set time t; the different social media platforms include Douyin, Kuaishou, Xiaohongshu, and Weibo; the formula for calculating the singer's online popularity is:

[0125]

[0126] Wherein, the R yThis is represented by the singer's online popularity; This represents the number of followers of the singer's account on the i-th social media platform; the number of followers is in the hundreds of millions. This represents the number of times the singer's account on the i-th social media platform is accessed within time t; the number of accesses is in the hundreds of millions.

[0127] Furthermore, the formula for calculating the first time is:

[0128]

[0129] Among them, T DC Indicates the first time; t dc This indicates the arrival time predicted by the navigation software. This indicates the start time of the concert; This represents the traffic flow at the venue for the time period corresponding to the 7*ith day before the concert begins; C xq Represented as the predicted parking demand; η c μ1, μ2, μ3 and μ4 are adjustment parameters.

[0130] Furthermore, when the first time is greater than the expected value T QW At that time, the user's matching information is matched with the parking information of public parking lots in the city. The matching information is then matched with the parking space size of each public parking lot parking information subset in the parking information set, and the number of unoccupied parking spaces in the public parking lot parking information subset that satisfy the longitude rule is calculated. w -long l ≥0.5 and weight w -weight l The first quantity ≥ 0.5; long w and weight w These represent the length and width of the parking space, respectively; long l and weight l These represent the length and width of the vehicle, respectively; the first subset of public parking information that has a quantity of 0 is removed from the parking information set, and the first parking information set is output.

[0131] Furthermore, the first parking information set is divided into a second parking information set and a third parking information set according to the location of the public parking lot; the second parking information set contains n 1 A subset of parking information from public parking lots; the third parking information set contains n 2 A subset of parking information from public parking lots; n 1 +n 2≤n; n represents the number of public parking lot parking information subsets included in the parking information set; when the location difference between the public parking lot and the concert venue... When, the public parking lot is classified as the second parking information set; when At that time, the public parking lot will be classified as the third parking information set; This is represented as a set position difference; the second time calculation formula is:

[0132]

[0133] in, This indicates the predicted time for the user to park in the kth public parking lot in the second parking information set and then return to the venue; This represents the time predicted by the navigation software for the user to arrive at the k-th public parking lot in the second parking information set; This indicates the time required to park in the k-th public parking lot within the second parking information set; β1 represents the location difference between the kth public parking lot in the second parking information set and the venue where the concert is held; β1 is an adjustment parameter.

[0134] Furthermore, the formula for predicting the second arrival time of the user at the venue based on the parking information in the third parking information set and the matching information is as follows:

[0135]

[0136] in, This indicates the predicted time for the user to park in the kth public parking lot in the third parking information set and then return to the venue; This represents the time predicted by the navigation software for the user to arrive at the k-th public parking lot in the third parking information set; This represents the time required to park in the k-th public parking lot in the third parking information set. β1 represents the time required to travel by public transportation from the kth public parking lot in the third parking information set to the concert venue; β2 is an adjustment parameter.

[0137] In summary, this embodiment calculates the venue's online popularity by combining the number of followers on the venue's social media accounts and the number of visits within a set time period, and calculates the singer's online popularity by combining the number of followers on the singer's social media accounts and the number of visits within a set time period. Then, by combining the venue's online popularity and the singer's online popularity with relevant venue and singer information, the parking demand at the venue on the day of the concert is predicted. This method comprehensively considers venue and singer information, improving the accuracy of parking demand prediction and thus enhancing parking convenience. Next, when predicting the user's first arrival time at the venue, the arrival time predicted by the navigation software, the venue's online popularity, the singer's online popularity, the predicted parking demand, and historical traffic flow at the same time are combined for prediction. This method integrates multiple sources of information, improving the accuracy of predicting the user's first arrival time at the venue. Finally, when matching the matching information with parking information from public parking lots in the city, different calculation methods are used depending on the distance between the parking lot and the venue to calculate the second time from the real-time location to parking in a public parking lot in the city and then to the venue, and the matching result is output based on the second time. This method can match information with parking lot information according to different matching methods, which can improve parking convenience.

Claims

1. A smart parking planning method for cities, characterized in that, include: Collect concert information and build a concert information set; Collect parking information from n public parking lots within the city and construct a parking information set; The concert information includes venue information and performer information; The venue information includes the venue's distance d from the city center. c The venue can accommodate a maximum number of spectators (num). c The venue's historical concert attendance rate (L) c and venue online popularity R c ; The singer information includes the number of concerts the singer has performed in the past. Average number of tickets sold per concert by the performer Average ticket price per concert, average attendance per concert (L) y And the singer's online popularity R y ; Based on the concert information set, predict the parking demand of the venue where the concert is held and output the predicted parking demand value. Obtain the user's real-time location and road data from that location to the venue; Based on the real-time location, the road data, the predicted parking demand, and the concert information, predict the first time the user will arrive at the venue; The formula for calculating the first time is: Among them, T DC Indicates the first time; t dc This indicates the arrival time predicted by the navigation software. This indicates the start time of the concert; This represents the traffic flow at the venue for the period corresponding to the 7*ith day before the concert begins, where i is a positive integer; C xq Represented as the predicted parking demand; η c μ1, μ2, μ3, and μ4 are adjustment parameters; When the first time is greater than the expected value T QW At that time, the user matching information is matched with the parking information set; Output a list of matches based on the matching results.

2. The urban smart parking planning method according to claim 1, characterized in that: The parking information set includes n subsets of parking information from public parking lots; The parking information includes the number of parking spaces in public parking lots, the location of public parking lots, the attributes of parking spaces, and the parking fee standards; The parking space attributes include the size and status of each parking space in the public parking lot; The parking space status includes unoccupied and occupied; The real-time location data from the venue includes the navigation software's predicted arrival time at the venue and the venue's historical traffic flow. The user matching information includes the user's real-time location and vehicle size.

3. The urban smart parking planning method according to claim 1, characterized in that: The venue's historical average attendance rate for concerts is L c The calculation formula is: Where A represents the number of concerts held at the venue throughout its history; num c This represents the number of spectators the venue can accommodate; num ω Let ω represent the actual number of audience members at the ωth concert. The average attendance rate L of each concert by the singer y The calculation formula is: in, This represents the number of concerts a singer has performed in their history. This represents the average number of tickets sold per concert by the performer; num σ This represents the number of audience members that can be accommodated at the σth concert of the aforementioned singer.

4. The urban smart parking planning method according to claim 1, characterized in that, The online popularity of the venue and the online popularity of the singer include: The venue's online popularity is calculated based on the number of followers of the venue's official account on different social media platforms and the number of times the venue's official account is accessed within a set time t; The formula for calculating the venue's online popularity is: Among them, R c The venue's online popularity is represented by n; n represents the number of social media platforms counted; p i Let f represent the number of followers of the official venue account on the i-th social media platform; ti This represents the number of visits to the official venue account on the i-th social media platform within time t; The singer's online popularity is calculated based on the number of followers of the singer's account on different social media platforms, the number of times the singer's account is accessed within a set time t, and the number of times the singer's account is accessed within a set time t. The formula for calculating the singer's online popularity is: Wherein, the R y The online popularity of the singer is represented by ; n represents the number of social media platforms used in the statistics. This represents the number of followers of the singer's account on the i-th social media platform; This represents the number of times the singer's account on the i-th social media platform is accessed within time t.

5. The urban smart parking planning method according to claim 1, characterized in that, The prediction of parking demand at the concert venue includes: Using the SVR model for parking demand forecasting; The SVR model was trained using the city's historical concert information and corresponding parking demand. The concert information is input into the SVR model, and the predicted parking demand value C is output. xq .

6. The urban smart parking planning method according to claim 1, characterized in that, The process of matching the user's matching information with the parking information of public parking lots in the city includes: The matching information is matched with the parking space sizes in each public parking lot parking information subset of the parking information set, and the unoccupied parking spaces in the public parking lot parking information subset that satisfy the longitude rule are calculated. w -long l ≥0.5 and weight w -weight l The first quantity ≥ 0.5; long w and weight w These represent the length and width of the parking space, respectively; long l and weight l These represent the length and width of the vehicle, respectively. Remove the first subset of public parking information in the parking information set whose first quantity is 0, and output the first parking information set; The first parking information set is divided into a second parking information set and a third parking information set based on the location of the public parking lot; the second parking information set contains n 1 A subset of parking information from public parking lots; the third parking information set contains n 2 A subset of parking information from public parking lots; n 1 +n 2 ≤n; n represents the number of public parking lot parking information subsets included in the parking information set; When the location difference between the public parking lot and the concert venue When, the public parking lot is classified as the second parking information set; when At that time, the public parking lot will be classified as the third parking information set; This is represented as a set position difference; Based on the parking information in the second parking information set and the third parking information set, and the matching information, predict the second time when the user arrives at the venue; The formula for predicting the second arrival time of the user at the venue based on the parking information in the second parking information set and the matching information is as follows: in, This indicates the predicted time for the user to park in the kth public parking lot in the second parking information set and then return to the venue; This represents the time predicted by the navigation software for the user to arrive at the k-th public parking lot in the second parking information set; This indicates the time required to park in the k-th public parking lot within the second parking information set; This represents the location difference between the k-th public parking lot in the second parking information set and the concert venue; β1 is an adjustment parameter. The formula for predicting the second arrival time of the user at the venue based on the parking information in the third parking information set and the matching information is as follows: in, This indicates the predicted time for the user to park in the kth public parking lot in the third parking information set and then return to the venue; This represents the time predicted by the navigation software for the user to arrive at the k-th public parking lot in the third parking information set; This represents the time required to park in the k-th public parking lot in the third parking information set. β1 represents the time required to travel by public transportation from the kth public parking lot in the third parking information set to the concert venue; β2 is an adjustment parameter.

7. The urban smart parking planning method according to claim 6, characterized in that: By removing the subset of public parking information in the second parking information set where the second time is greater than the first time, a fourth parking information set is obtained; By removing the subset of public parking information in the third parking information set where the second time is greater than the first time, a fifth parking information set is obtained; Sort the second time corresponding to each public parking information subset in the fourth parking information set and the fifth parking information set in ascending order, and output the parking information corresponding to the first m public parking information subsets to obtain a matching list.

8. A smart urban parking planning system, characterized in that, include: Data Acquisition Unit: Used to collect concert information and build a concert information set; Collect parking information from public parking lots within the city to construct a parking information set; Prediction unit: used to predict the parking demand of the concert venue, the first time the user arrives at the venue, and the second time the user arrives at the venue; Matching unit: Used to match the user's matching information with the parking information set; Output unit: Used to output the matching list.

9. A smart urban parking planning system according to claim 8, characterized in that: The concert information includes venue information and performer information; The venue information includes the venue's distance d from the city center. c The venue can accommodate a maximum number of spectators (num). c The venue's historical concert attendance rate (L) c and the online popularity R of the venue c ; The singer information includes the number of concerts the singer has performed in the past. The average number of tickets sold per concert by the singer The average ticket price per concert and the average attendance rate per concert for the performer (L) y The singer's online popularity R y ; The parking information set includes n subsets of parking information from public parking lots; The parking information includes the number of parking spaces in the public parking lot, the location of the public parking lot, the parking space attributes, and the parking fee standard; The parking space attributes include the size of each parking space in the public parking lot and whether it is occupied; The matching information includes the user's real-time location and vehicle dimensions.

10. A smart urban parking planning system according to claim 9, characterized in that, The network popularity includes: The venue's online popularity is calculated based on the number of followers of the venue's official account on different social media platforms and the number of times the venue's official account is accessed within a set time t; The formula for calculating the venue's online popularity is: Among them, R c The venue's online popularity is represented by n; n represents the number of social media platforms counted; p i Let f represent the number of followers of the official venue account on the i-th social media platform; ti This represents the number of visits to the official venue account on the i-th social media platform within time t; The singer's online popularity is calculated based on the number of followers of the singer's account on different social media platforms, the number of times the singer's account is accessed within a set time t, and the number of times the singer's account is accessed within a set time t. The formula for calculating the singer's online popularity is: Wherein, the R y The online popularity of the singer is represented by ; n represents the number of social media platforms used in the statistics. This represents the number of followers of the singer's account on the i-th social media platform; This represents the number of times the singer's account on the i-th social media platform is accessed within time t.

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