On-road parking reservation management system based on order degree

Abstract

The invention belongs to the field of curb parking in intelligent parking, and relates to an order-based curb parking reservation management system, which comprises an information acquisition module, a parking reservation module and an information feedback module, firstly, a parking order degree model is designed and feasibility of the model is verified; secondly, setting the parking order degree model in a parking reservation module; then, after the information of the on-road parking system is obtained through the information obtaining module, the optimal parking space is judged through the parking reservation module, and finally, the leaving vehicle is fed back through the information feedback module. According to the technical scheme adopted by the invention, the on-road cruise time for finding the parking space by the vehicle can be shortened, the utilization rate of the parking space is improved, and traffic congestion and road traffic accidents caused by finding the parking space can be reduced to a certain extent.

Description

Technical Field

[0001] The present invention relates to the field of smart parking, and in particular to an on-street parking reservation management system based on orderliness. Background Art

[0002] On-street parking refers to parking spaces marked on both sides of the road, which allow vehicles to park temporarily without time and space restrictions. Sometimes, car owners often need to spend a lot of time to find a parking space, which not only brings pressure and displeasure to the car owners who are cruising to find parking spaces, but also affects the normal driving of vehicles. In serious cases, it will cause traffic congestion and traffic accidents. Cruising parking reduces the efficiency of urban vehicle traffic and affects the road traffic capacity. For the research on on-street parking, foreign countries focus on using scientific management methods to alleviate the parking problem to a certain extent. Domestic research is mainly reflected in the design of parking mechanical devices, typically crawler transmission type, roadside lifting type, etc. Some cities manage on-street parking through intelligent toll parking management platforms. In Shenzhen, the platform can be used to make reservations for vehicle parking locations and parking times, and the parking fees are paid when the vehicle leaves. In existing studies, whether it is research on parking lots or other parking methods, most of them do not consider the problem of parking space selection when vehicles are parked, and do not consider the impact of different empty parking space distribution conditions on vehicle entry and exit time when vehicles choose parking spaces.

[0003] At present, the research on on-street parking has achieved certain results in management methods, mechanical device design, parking reservation platform, etc., but there are still problems such as extensive on-street parking management methods; poor convenience of mechanical devices and not widely used; single function of parking reservation platform and lack of comprehensive optimization of spatiotemporal resources of on-street parking spaces. Common random parking means that the vehicle randomly selects a parking space from all available parking spaces without considering other factors when choosing a parking space. This parking method sometimes takes more time. Therefore, orderly parking in the on-street parking system is a problem that needs to be solved. Summary of the invention

[0004] The present invention aims to provide an on-street parking reservation management system based on order degree, which can alleviate the parking difficulty problem in the current on-street parking system by reducing parking time by selecting different parking spaces. When a vehicle is looking for a parking space, the parking reservation management system will calculate according to the method proposed by the present invention to find an on-street parking space that is most suitable for the user, thereby reducing the cruising time of the vehicle.

[0005] The on-street parking reservation management system based on order degree includes the following steps:

[0006] S1. Establish parking order model;

[0007] The parking order model includes: order PO value, cellular automaton state update rules, vehicle agent movement rules and vehicle parking rules;

[0008] Specifically, the parking order degree is defined to obtain the order degree PO value, and the vehicle parking rules are established using cellular automata and agent-based models;

[0009] The concept of parking order is: the degree of orderliness of the spatial distribution of vehicles in parking spaces within a certain period of time;

[0010] The steps to establish the parking order model are as follows:

[0011] S1.1, establish the order PO value;

[0012] The order PO value expression is as follows:

[0013]

[0014] In the formula, PO n Indicates the order degree when there are n vehicles in the system; Indicates the average time it takes for a vehicle to enter a parking space; represents the average time it takes for a vehicle to leave a parking space; s represents the total number of parking spaces in the system;

[0015] S1.2, establish the cellular automaton state update rules and vehicle agent movement rules;

[0016] Each cell represents a parking space, and its status includes: free (0) and occupied (1). x,y (t) represents the state of the cell at position (x, y) at time t, then S x,y (t)∈{0,1};

[0017] Each agent represents a vehicle, and the vehicle agent A k The properties include: current position (x k (t),y k (t)), destination location and driving speed v k ;

[0018] The cellular automaton state update rules and the movement rules of the vehicle agent are as follows:

[0019]

[0020] (x k (t+1),y k (t+1))=(x k (t),y k (t))+v k ·dk

[0021] In the formula, available(x x,y (t)) indicates whether there is an available parking space at the position (x, y); S x,y (t+1) represents the cell state at the position (x, y) at time t+1; (x k (t),y k (t)) represents the position of the vehicle at time t; (x k (t+1),y k (t+1)) represents the position of the vehicle at time t+1; v k Indicates the vehicle speed; d k Is a vehicle agent A k The moving direction is used to find the nearest free parking space;

[0022] Vehicle Agent A k The decision process for finding the target parking space is as follows:

[0023]

[0024] In the formula, Indicates the destination location; (x k ,y k ) represents the current vehicle position; (x, y) represents the target parking space position; S x,y (t) represents the state of the cell at position (x, y) at time t;

[0025] S1.3, Establishing vehicle docking rules using cellular automata and agent-based models;

[0026] For each free parking space p, the order value PO of the free parking space p is calculated according to the parking space usage. p The smaller the calculated order value is, the higher the probability that the vehicle will choose the parking space. In addition, whether the vehicle chooses the parking space is also related to the distance d from the vehicle to the target parking space at the current moment. The expression is as follows:

[0027] P * =α·PO p +β·d x,y

[0028] Where P * represents the probability that the vehicle will eventually choose a parking space; α, β represent the weight coefficients under the influence of order and distance respectively; PO p represents the order value of the empty parking space p; d x,y Indicates the distance between the current position and the target parking space;

[0029] Calculate the probability of all available parking spaces in the on-street parking system being selected, and the vehicle will park in the parking space with the highest probability.

[0030] In order to balance the influence of order and distance on the probability of a vehicle finally selecting a parking space, the values ​​of α and β are limited between (0, 1), and α+β=1.

[0031] S2. Verify the model through python;

[0032] The verification method is to calculate and compare the parking delay with and without the parking order model through Python;

[0033] Specifically, parking delays were determined by adding three identical experimental conditions to the two models in Python each time;

[0034] The three identical experimental conditions included: average vehicle waiting time, the probability of a vehicle arriving at a parking space, and the probability of a vehicle leaving a parking space;

[0035] Parking delays include: start parking delay, end parking delay and total delay;

[0036] Furthermore, the average waiting time of vehicles, the probability of vehicle arrival and the probability of leaving the parking space are calculated by inputting the queuing theory model into Python;

[0037] In the queuing theory model, when there are n vehicles in the system, the steady-state probability of the system is P n The calculation formula is as follows:

[0038]

[0039] Where P0 represents the steady-state probability when there is no car in the system; λ n represents the arrival rate of vehicles when there are n vehicles in the system; μ n represents the service rate when there are n vehicles in the system; s represents the arrival rate of vehicles when there are s vehicles in the system, that is, the arrival rate when the system is fully loaded; μ s represents the service rate when there are s vehicles in the system, that is, the service rate at full load; ρ n represents the utilization rate of the system when there are n vehicles in the system; s represents the total number of parking spaces in the system; n represents the number of vehicles in the system at a certain moment;

[0040] In the average waiting time of vehicles, when n≤s, the number of vehicles in the system does not exceed the number of parking spaces, and vehicles do not need to queue up; when n>s, the number of vehicles in the system exceeds the number of parking spaces, and vehicles must queue up to park. At this time, there are ns vehicles in the queue. These situations need to be considered and accumulated to W. qIt represents the average waiting time of vehicles. The calculation of the average waiting time satisfies the following formula:

[0041]

[0042] In the formula, λ represents the vehicle arrival rate (the number of vehicles arriving per unit time), λ s represents the arrival rate at full load, μ s represents the service rate at full load, P0 represents the steady-state probability when there is no car in the system;

[0043] The probability of a vehicle arriving at a parking space is that in a parking event, the arrival of each vehicle is an independent event, and the average rate of vehicle arrival per unit time can be regarded as a constant. Therefore, the probability of vehicle arrival usually follows a Poisson distribution with parameter λ, expressed as P' arrival (t) represents the probability of vehicle arrival at time t. The vehicle arrival rate after correction combined with parking order can be expressed as:

[0044]

[0045] In the formula, e is a natural constant; λ represents the vehicle arrival rate;

[0046] The probability of a vehicle leaving a parking space is that, since the departure of a vehicle satisfies that the service time of each vehicle is an independent and identically distributed random variable, and the average rate of vehicle departure can also be regarded as a constant, the probability of a vehicle leaving a parking space follows an exponential distribution with parameter μ, denoted by P' departure (t) represents the probability of the vehicle leaving at time t. The probability of the vehicle leaving the parking space after correction of parking order can be expressed as:

[0047]

[0048] In the formula, e is a natural constant; μ represents the service rate of the system;

[0049] Numerical simulation verification is performed through python;

[0050] S3, setting the verified parking order degree model to the parking reservation module;

[0051] S4, obtaining the location of the vehicle and all parking space information of the on-street parking system through the information acquisition module;

[0052] The method of obtaining the vehicle's location is: obtaining the vehicle's GPS location data through the WeChat applet;

[0053] The on-street parking system is the on-street parking system where the vehicle is located;

[0054] Parking space information includes: parking space with car and parking space without car;

[0055] S5. Determine the parking conditions of the on-street parking system through the parking reservation module;

[0056] Determining the parking conditions of the on-street parking system includes: the first case and the second case;

[0057] The first case is n / s≤80%, where s represents the total number of parking spaces in the system, and n represents the number of vehicles in the system at a certain moment. If the parking conditions are met, the parking order degree is calculated;

[0058] The second case is n / s>80%, s represents the total number of parking spaces in the system, and n represents the number of vehicles in the system at a certain moment. At this time, it means that a large number of parking spaces have been used and there are few remaining parking spaces. It is not recommended for car owners to park their vehicles in this on-street parking system. Instead, it is recommended that car owners select another on-street parking system. After selecting an on-street parking system, the system repeats the judgment process of the first case and calculates the value of n / s in the area again until an area with n / s≤80% is found. After this condition is met, the parking order degree is calculated.

[0059] S6. Provide information feedback to the vehicle leaving the on-street parking system through the information feedback module.

[0060] After parking, the vehicle leaves the parking space, including two types of information feedback: the vehicle leaves the parking space before the expected stay time and the vehicle continues to park after the expected stay time;

[0061] In the first case, the user has not committed any violation. In this case, the system calculates the parking fee, and the user pays the parking fee and drives away.

[0062] In the second case, the user has delayed parking violations. In this case, the system records the vehicle's violation and calculates the parking fee. The user leaves the parking space after paying the fee.

[0063] The implementation carrier of the appointment management system is WeChat applet.

[0064] Beneficial effects of the present invention

[0065] The present invention takes into account the usage of parking spaces in the entire parking system, establishes an order degree model, and gives priority to areas with larger spaces and the distance between the vehicle and the target parking space when the vehicle selects a parking space. The most favorable parking space is selected by combining the two conditions. Selecting an area with larger spaces can make the vehicle enter the parking space more conveniently, and considering the distance between the vehicle and the target parking space can shorten the cruising time.

[0066] Compared with random parking, the present invention can reduce traffic congestion and exhaust emissions caused by cruise parking. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 is a flow chart of the present invention;

[0068] Figure 2 A schematic diagram of the on-street parking optimization method;

[0069] Figure 3 is a parking order model diagram, where 1 is a random parking model; 2 is an order parking model;

[0070] Figure 4 The comparison results of the start parking delay between random parking and orderly parking;

[0071] Figure 5 The comparison results of the end parking delay between random parking and orderly parking;

[0072] Figure 6 The comparison results of total delay between random parking and orderly parking;

[0073] Figure 7 The PO value calculation results of random parking and orderly parking;

[0074] Figure 8 This is a working principle diagram of the on-street parking reservation management system. DETAILED DESCRIPTION

[0075] The present invention provides an on-street parking reservation management system based on order, thereby alleviating the parking difficulty problem and reducing the waste of time. In order to better explain and describe the present invention, the present invention will be further described here in conjunction with the accompanying drawings and embodiments.

[0076] like Figure 1 , Figure 2 As shown, the on-street parking reservation management system based on order degree includes the following steps:

[0077] S1. Establish a parking order model;

[0078] The parking order model includes: order PO value, cellular automaton state update rules, vehicle agent movement rules and vehicle parking rules;

[0079] Specifically, the parking order degree is defined to obtain the order degree PO value, and the vehicle parking rules are established using cellular automata and agent-based models;

[0080] The concept of parking order is: the degree of orderliness of the spatial distribution of vehicles in parking spaces within a certain period of time;

[0081] The steps to establish the parking order model are as follows:

[0082] S1.1, establish the order PO value;

[0083] The order PO value expression is as follows:

[0084]

[0085] In the formula, PO n Indicates the order degree when there are n vehicles in the system; Indicates the average time it takes for a vehicle to enter a parking space; represents the average time it takes for a vehicle to leave a parking space; s represents the total number of parking spaces in the system;

[0086] In this embodiment, the average time for a vehicle to enter a parking space is determined by actual sampling. and the average time it takes for vehicles to exit a parking space

[0087] Specifically, m represents the number of vacant parking spaces between used parking spaces in the actual sampling. When m=1, the time required for a vehicle to enter a parking space is 51 seconds; when m=2, the time required for a vehicle to enter a parking space is 39 seconds; when m≥3 and there is a car in front of or behind the parking space, the time required for a vehicle to enter a parking space is 25 seconds; when m≥3 and there is no car in front of or behind the parking space, the time required for a vehicle to enter a parking space is 18 seconds; as shown below:

[0088]

[0089] When m=1, the time required for the vehicle to drive out of the parking space is 33 seconds; when m≥2, if there is a vehicle in front of the parking space, the time required for the vehicle to drive out of the parking space is 22 seconds, and if there is at least one empty space in front of the parking space, the time required for the vehicle to drive out of the parking space is 12 seconds; as shown below:

[0090]

[0091] S1.2, establish the cellular automaton state update rules and vehicle agent movement rules;

[0092] Each cell represents a parking space, and its status includes: free (0) and occupied (1). x,y (t) represents the state of the cell at position (x, y) at time t, then S x,y (t)∈{0,1};

[0093] Each agent represents a vehicle, and the vehicle agent A k The properties include: current position (x k (t),y k (t)), destination location and driving speed v k ;

[0094] The cellular automaton state update rules and the movement rules of the vehicle agent are as follows:

[0095]

[0096] (x k (t+1),y k (t+1))=(x k (t),y k (t))+v k ·d k

[0097] In the formula, available(x x,y (t)) indicates whether there is an available parking space at the position (x, y); S x,y (t+1) represents the cell state at the position (x, y) at time t+1; (x k (t),y k (t)) represents the position of the vehicle at time t; (x k (t+1),y k (t+1)) represents the position of the vehicle at time t+1; v k Indicates the vehicle speed; d k Is a vehicle agent A k The moving direction is used to find the nearest free parking space;

[0098] Vehicle Agent A k The decision process for finding the target parking space is as follows:

[0099]

[0100] In the formula, Indicates the destination location; (x k ,y k ) represents the current vehicle position; (x, y) represents the target parking space position; S x,y (t) represents the state of the cell at position (x, y) at time t;

[0101] S1.3, Establishing vehicle docking rules using cellular automata and agent-based models;

[0102] For each free parking space p, the order value PO of the free parking space p is calculated according to the parking space usage. p The smaller the calculated order value is, the higher the probability that the vehicle will choose the parking space. In addition, whether the vehicle chooses the parking space is also related to the distance d from the vehicle to the target parking space at the current moment. The expression is as follows:

[0103] P * =α·PO p +β·d x,y

[0104] Where P * represents the probability that the vehicle will eventually choose a parking space; α, β represent the weight coefficients under the influence of order and distance respectively; PO p represents the order value of the empty parking space p; d x,y Indicates the distance between the current position and the target parking space;

[0105] Calculate the probability of all available parking spaces in the on-street parking system being selected, and the vehicle will park in the parking space with the highest probability.

[0106] In order to balance the influence of order and distance on the probability of the vehicle finally selecting a parking space, the values ​​of α and β are limited to between (0, 1), and α+β=1. Considering the influence of order and distance, α=β=0.5 is taken in this embodiment;

[0107] S2. Verify the model through python;

[0108] The verification method is to calculate and compare the parking delay with and without the parking order model through Python;

[0109] Specifically, parking delays were determined by adding three identical experimental conditions to the two models in Python each time;

[0110] The three identical experimental conditions included: average vehicle waiting time, the probability of a vehicle arriving at a parking space, and the probability of a vehicle leaving a parking space;

[0111] Parking delays include: start parking delay, end parking delay and total delay;

[0112] Furthermore, the average waiting time of vehicles, the probability of vehicle arrival and the probability of leaving the parking space are calculated by inputting the queuing theory model into Python;

[0113] In the queuing theory model, when there are n vehicles in the system, the steady-state probability of the system is P n The calculation formula is as follows:

[0114]

[0115] Where P0 represents the steady-state probability when there is no car in the system; λ n represents the arrival rate of vehicles when there are n vehicles in the system; μ n represents the service rate when there are n vehicles in the system; s represents the arrival rate of vehicles when there are s vehicles in the system, that is, the arrival rate when the system is fully loaded; μ s represents the service rate when there are s vehicles in the system, that is, the service rate at full load; ρ nrepresents the utilization rate of the system when there are n vehicles in the system; s represents the total number of parking spaces in the system; n represents the number of vehicles in the system at a certain moment;

[0116] In the average waiting time of vehicles, when n≤s, the number of vehicles in the system does not exceed the number of parking spaces, and vehicles do not need to queue up; when n>s, the number of vehicles in the system exceeds the number of parking spaces, and vehicles must queue up to park. At this time, there are ns vehicles in the queue. These situations need to be considered and accumulated to W. q It represents the average waiting time of vehicles. The calculation of the average waiting time satisfies the following formula:

[0117]

[0118] In the formula, λ represents the vehicle arrival rate (the number of vehicles arriving per unit time), λ s represents the arrival rate at full load, μ s represents the service rate at full load, P0 represents the steady-state probability when there is no car in the system;

[0119] The probability of a vehicle arriving at a parking space is that in a parking event, the arrival of each vehicle is an independent event, and the average rate of vehicle arrival per unit time can be regarded as a constant. Therefore, the probability of vehicle arrival usually follows a Poisson distribution with parameter λ, expressed as P' arrival (t) represents the probability of vehicle arrival at time t. The vehicle arrival rate after correction combined with parking order can be expressed as:

[0120]

[0121] In the formula, e is a natural constant; λ represents the vehicle arrival rate;

[0122] The probability of a vehicle leaving a parking space is that, since the departure of a vehicle satisfies that the service time of each vehicle is an independent and identically distributed random variable, and the average rate of vehicle departure can also be regarded as a constant, the probability of a vehicle leaving a parking space follows an exponential distribution with parameter μ, denoted by P' departure (t) represents the probability of the vehicle leaving at time t. The probability of the vehicle leaving the parking space after correction of parking order can be expressed as:

[0123]

[0124] In the formula, e is a natural constant; μ represents the service rate of the system;

[0125] Numerical simulation verification is performed through python;

[0126] The initial conditions of the simulation set 100 parking spaces, numbered 0-99. The number of used parking spaces, the number of vacant parking spaces, and the parking time of each vehicle in the parking system at the initial moment were randomly generated. This setting is more in line with the real scene. A total of 100 experiments were conducted, the code was run 100 times, and 100 sets of different results were obtained;

[0127] The parking order model is as follows Figure 3 As shown, 1 is without using the parking order model; 2 is with using the parking order model; without using the parking order model, vehicles are parked randomly, resulting in uneven distribution of the remaining empty parking spaces, no order, and it is difficult for vehicles to enter the parking spaces later, which takes a long time; with the parking order model, vehicles are parked in an orderly manner, and vehicles can enter the parking spaces more easily later;

[0128] The experimental results are as follows Figure 4 , Figure 5 , Figure 6 , Figure 7 , integrating the experimental data, we get the following results:

[0129]

[0130]

[0131] It can be seen from the image that the delay result curve and the order PO value curve of random parking are all above the order parking; it can also be seen from the variance part of the table that the variance of random parking is larger, indicating that the delay and order PO value of order parking are more stable than random parking. Comprehensive experimental results can be concluded that the time required for random parking is greater than the order parking proposed by the present invention, indicating that the use of order parking can reduce parking delays and reduce the time required for vehicles to enter and exit parking spaces;

[0132] S3, setting the verified parking order degree model to the parking reservation module;

[0133] like Figure 8 As shown, the on-street parking reservation management system based on order degree includes: an information acquisition module, a parking reservation module, and an information feedback module;

[0134] S4, obtaining the location of the vehicle and all parking space information of the on-street parking system through the information acquisition module;

[0135] The method of obtaining the vehicle's location is: obtaining the vehicle's GPS location data through the WeChat applet;

[0136] The on-street parking system is the on-street parking system where the vehicle is located;

[0137] Parking space information includes: parking space with car and parking space without car;

[0138] S5. Determine the parking conditions of the on-street parking system through the parking reservation module;

[0139] Determining the parking conditions of the on-street parking system includes: the first case and the second case;

[0140] The first case is n / s≤80%, where s represents the total number of parking spaces in the system, and n represents the number of vehicles in the system at a certain moment. If the parking conditions are met, the parking order degree is calculated;

[0141] The second case is n / s>80%, s represents the total number of parking spaces in the system, and n represents the number of vehicles in the system at a certain moment. At this time, it means that a large number of parking spaces have been used and there are few remaining parking spaces. It is not recommended for car owners to park their vehicles in this on-street parking system. Instead, it is recommended that car owners select another on-street parking system. After selecting an on-street parking system, the system repeats the judgment process of the first case and calculates the value of n / s in the area again until an area with n / s≤80% is found. After this condition is met, the parking order degree is calculated.

[0142] S6. Provide information feedback to the vehicle leaving the on-street parking system through the information feedback module.

[0143] After parking, the vehicle leaves the parking space, including two types of information feedback: the vehicle leaves the parking space before the expected stay time and the vehicle continues to park after the expected stay time;

[0144] In the first case, the user has not committed any violation. In this case, the system calculates the parking fee, and the user pays the parking fee and drives away.

[0145] In the second case, the user has delayed parking violations. In this case, the system records the vehicle's violation and calculates the parking fee. The user leaves the parking space after paying the fee.

[0146] The implementation carrier of the reservation management system is WeChat applet, which contains four types of files: json, wxml, wxss and js;

[0147] The WeChat public platform comes from the browser. You need to search from the browser, apply for it, and then register. After registration, you will have your own AppID.

[0148] WeChat developer tools are downloaded and installed from the WeChat public platform.

[0149] The WeChat applet front end involved in the present invention is developed through JavaScript, and the back end of the applet is implemented through cloud development.

[0150] After the user enters the mini program, the mini program displays the menus of the two initial interfaces: the home page and my interface.

[0151] The homepage of the mini program includes three functions: parking navigation, parking reservation, and parking fee payment. Users can use various functions such as parking navigation, parking reservation, and payment.

[0152] Click on the parking reservation to jump to the destination selection interface. After selecting the destination, you can follow the navigation instructions to reach the destination;

[0153] After clicking on parking reservation, you will be redirected to the parking reservation interface. At this time, you only need to select the destination, estimated arrival time, and estimated stay time to query nearby parking spaces. The system will recommend parking spaces to users based on the query interface.

[0154] In my interface, the two main functions are set up: "Register | Login". When users use the mini program for the first time, they can click Register to register as a user. Each subsequent entry only requires clicking Login to jump to the login interface to log in.

[0155] Since on-street parking spaces are different from parking lots, in order to achieve data acquisition, existing map data is used to mark and obtain the location information of parking spaces, and WeChat mini-programs are used to obtain vehicle GPS location data, parking time, arrival time, estimated parking time and other data.

[0156] The present invention is mainly implemented through an on-street parking optimization method and reservation management system based on parking order. Through the destination, estimated arrival time, estimated parking time and other information input by the user, the system automatically recommends an on-street parking space for the car owner, and the user can choose to accept or reject the recommendation. The method and system proposed by the present invention are used to select and reserve parking spaces, which can prevent the user from not being able to find a suitable parking space or having no parking space after arriving at the destination, and can reduce the on-street cruising time of the user looking for a parking space. It can also alleviate traffic congestion caused by a large number of parking users looking for parking spaces, reduce the impact of parking on other traffic flows, improve road traffic capacity, and reduce the incidence of accidents caused by parking.

[0157] Finally, it should be noted that the present invention includes but is not limited to the above embodiments, and the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

Claims

1. An on-street parking reservation management system based on order degree, characterized in that: The following steps are involved: S1. Establish parking order model; The parking order model includes: order PO value, cellular automaton state update rules, vehicle agent movement rules and vehicle parking rules; S2. Verify the model through python; The verification method is to calculate and compare the parking delay with and without the parking order model through Python; S3, setting the verified parking order degree model to the parking reservation module; S4, obtaining the location of the vehicle and all parking space information of the on-street parking system through the information acquisition module; The on-street parking system is the on-street parking system where the vehicle is located; Parking space information includes: parking space with car and parking space without car; S5. Determine the parking conditions of the on-street parking system through the parking reservation module; S6. Provide information feedback to the vehicle leaving the on-street parking system through the information feedback module.

2. The on-street parking reservation management system based on orderliness according to claim 1, characterized in that: The steps of establishing the parking order degree model are as follows: S1.1, establish the order PO value; The order PO value expression is as follows: In the formula, PO n Indicates the order degree when there are n vehicles in the system; Indicates the average time it takes for a vehicle to enter a parking space; represents the average time it takes for a vehicle to leave a parking space; s represents the total number of parking spaces in the system; S1.2, establish the cellular automaton state update rules and vehicle agent movement rules; Each cell represents a parking space, and its status includes: free (0) and occupied (1). x,y (t) represents the state of the cell at position (x, y) at time t, then S x,y (t)∈{0,1}; Each agent represents a vehicle, and the vehicle agent A k The properties include: current position (x k (t),y k (t)), destination location and driving speed v k ; The cellular automaton state update rules and the movement rules of the vehicle agent are as follows: (x k (t+1),y k (t+1))=(x k (t),y k (t))+v k ·d k In the formula, available(x x,y (t)) indicates whether there is an available parking space at the position (x, y); S x,y (t+1) represents the cell state at the position (x, y) at time t+1; (x k (t),y k (t)) represents the position of the vehicle at time t; (x k (t+1),y k (t+1)) represents the position of the vehicle at time t+1; v k Indicates the vehicle speed; d k Is a vehicle agent A k The moving direction is used to find the nearest free parking space; Vehicle Agent A k The decision process for finding the target parking space is as follows: In the formula, Indicates the destination location; (x k ,y k ) represents the current vehicle position; (x, y) represents the target parking space position; S x,y (t) represents the state of the cell at position (x, y) at time t; S1.3, Establishing vehicle docking rules using cellular automata and agent-based models; For each free parking space p, the order value PO of the free parking space p is calculated according to the parking space usage. p The smaller the calculated order value is, the higher the probability that the vehicle will choose the parking space. Whether the vehicle chooses the parking space is also related to the distance d from the vehicle to the target parking space at the current moment. The expression is as follows: P * =α·PO p +β·d x,y Where P * represents the probability that the vehicle will eventually choose a parking space; α, β represent the weight coefficients under the influence of order and distance respectively; PO p represents the order value of the empty parking space p; d x,y Indicates the distance between the current position and the target parking space; Calculate the probability of all available parking spaces in the on-street parking system being selected, and the vehicle will park in the parking space with the highest probability. In order to balance the influence of order and distance on the probability of a vehicle finally selecting a parking space, the values ​​of α and β are limited between (0, 1), and α+β=1.

3. The on-street parking reservation management system based on orderliness according to claim 1, characterized in that: The parking delay with and without the parking order degree model is calculated by Python, that is, the parking delay is determined by adding three identical experimental conditions to the two models in Python each time; The three identical experimental conditions include: the average waiting time of vehicles, the probability of vehicles arriving at a parking space, and the probability of vehicles leaving a parking space; Parking delay includes: start parking delay, end parking delay and total delay.

4. The on-street parking reservation management system based on orderliness according to claim 1, characterized in that: The average waiting time of the vehicle, the probability of the vehicle arriving at the parking space and the probability of the vehicle leaving the parking space are calculated by inputting the queuing theory model into Python; The queuing theory model expression is as follows: Where P0 represents the steady-state probability when there is no car in the system; λ n represents the arrival rate of vehicles when there are n vehicles in the system; μ n represents the service rate when there are n vehicles in the system; s represents the arrival rate of vehicles when there are s vehicles in the system, that is, the arrival rate when the system is fully loaded; μ s represents the service rate when there are s vehicles in the system, that is, the service rate at full load; ρ n represents the utilization rate of the system when there are n vehicles in the system; s represents the total number of parking spaces in the system; n represents the number of vehicles in the system at a certain moment; The expression of the average waiting time of vehicles is as follows: In the formula, λ represents the vehicle arrival rate, λ s represents the arrival rate at full load, μ s represents the service rate at full load, P0 represents the steady-state probability when there is no car in the system; The probability of a vehicle arriving at a parking space is expressed as follows: In the formula, e is a natural constant; λ represents the vehicle arrival rate; The probability of a vehicle leaving a parking space is expressed as follows: Where, e is a natural constant; μ represents the service rate of the system.

5. The on-street parking reservation management system based on orderliness according to claim 1, characterized in that: In the determining the parking condition of the on-street parking system by the parking reservation module, determining the parking condition of the on-street parking system includes: a first situation and a second situation; The first case is n / s≤80%, where s represents the total number of parking spaces in the system, and n represents the number of vehicles in the system at a certain moment. If the parking conditions are met, the parking order degree is calculated; The second case is n / s>80%, where s represents the total number of parking spaces in the system, and n represents the number of vehicles in the system at a certain moment. At this time, it means that a large number of parking spaces have been used and a small number of remaining parking spaces exist. It is not recommended for car owners to park their vehicles in this on-street parking system.

6. The on-street parking reservation management system based on orderliness according to claim 1, characterized in that: The information feedback module provides information feedback to the vehicle leaving the on-street parking system including two situations: the vehicle leaves the parking space before the expected stay time and the vehicle continues to park after the expected stay time.

7. The on-street parking reservation management system based on orderliness according to claim 1, characterized in that: The information acquisition module, parking reservation module and information feedback module are deployed on the WeChat applet.

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