On-street parking reservation management system based on order degree

Abstract

This invention belongs to the field of on-street parking within intelligent parking systems and relates to an on-street parking reservation management system based on orderliness. The system comprises an information acquisition module, a parking reservation module, and an information feedback module. First, a parking orderliness model is designed and its feasibility verified. Second, the parking orderliness model is incorporated into the parking reservation module. The information acquisition module then acquires on-street parking system information, which is then used to determine the optimal parking space through the parking reservation module. Finally, the information feedback module provides feedback to departing vehicles. The technical solution employed in this invention can reduce on-street cruising time spent searching for parking spaces, improve parking space utilization, and, to a certain extent, reduce traffic congestion and road accidents caused by searching for parking spaces.

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, allowing vehicles to temporarily park without time or space restrictions. Sometimes, drivers spend a considerable amount of time searching for a parking space. This not only causes stress and frustration for the driver cruising for a spot, but also disrupts normal vehicle traffic. In serious cases, it can cause traffic congestion and accidents. Cruising parking reduces the efficiency of urban vehicle traffic and impacts road capacity. Research on on-street parking abroad focuses on using scientific management methods to alleviate parking difficulties. Domestic research primarily focuses on the design of parking mechanisms, typically tracked transmissions and curbside lifts. Some cities manage on-street parking through intelligent parking fee management platforms. In Shenzhen, vehicles can reserve parking locations and parking times through the platform, and pay parking fees upon departure. Existing research, whether on parking lots or other parking methods, has largely failed to consider the issue of space selection during parking, nor has it considered the impact of varying vacant parking space distribution on vehicle entry and exit times.

[0003] Current research on on-street parking has yielded some results in management methods, mechanical device design, and parking reservation platforms. However, issues remain, such as crude on-street parking management methods; inconvenient and limited mechanical devices; and single-function parking reservation platforms that lack comprehensive optimization of on-street parking resources in terms of time and space. Commonly used random parking involves vehicles selecting a parking space at random from among all available spaces, without considering other factors. This method of parking can sometimes be time-consuming. Therefore, ensuring orderly parking within on-street parking systems remains a pressing issue. Summary of the Invention

[0004] This invention aims to provide an on-street parking reservation management system based on orderliness. This system alleviates the current parking difficulty problem in on-street parking systems by reducing parking time by selecting different parking spaces. When a vehicle is searching for a parking space, the parking reservation management system calculates the most suitable on-street parking space for the user using the proposed method, thereby reducing vehicle cruising time.

[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 degree 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 a 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] Where, PO n Indicates the degree of order 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 exit 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 vehicle agent movement rules are as follows:

[0019]

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

[0021] Where, available(x x,y (t)) indicates whether there is an available parking space at the location (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 vehicle agent A k The direction of movement is used to find the nearest free parking space;

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

[0023]

[0024] Where, 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, calculate the order value PO of the empty parking space p according to the parking space usage. p The smaller the calculated order value, 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 current moment to the target parking space. 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 choose the parking space with the highest probability to park;

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

[0031] S2. Validate the model using Python;

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

[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: the average waiting time of a vehicle, 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 the system has n vehicles, 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] Where λ represents the vehicle arrival rate (the number of vehicles arriving per unit time), s represents the arrival rate at full load, μ s represents the service rate at full load, and 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 the 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 obeys the 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] Where, 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 the requirement 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, which is expressed as 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] Where, 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 model to the parking reservation module;

[0051] S4. Obtain the vehicle location 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 this 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%, where s represents the total number of parking spaces in the system and n represents the number of vehicles in the system at a given moment. This indicates that many parking spaces are already occupied and few are available, so it is not recommended that the owner park their vehicle in this on-street parking system. Instead, the owner is advised to select another on-street parking system. After selecting an on-street parking system, the system repeats the judgment process of the first case and recalculates the n / s value for that area until it finds an area with n / s ≤ 80%. Once this condition is met, the parking order degree is calculated.

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

[0060] After parking is completed, 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 violated the parking regulations by delaying parking. In this case, the system records the violation and calculates the parking fee. The user pays the fee and leaves the parking space.

[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 and establishes an order degree model. When a vehicle selects a parking space, it gives priority to areas with larger spaces and the distance between the vehicle and the target parking space. The most favorable parking space is selected by combining the two conditions. Selecting an area with larger spaces allows the vehicle to enter the parking space more conveniently, while 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 Schematic diagram of on-street parking optimization method;

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

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

[0071] Figure 5 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 orderliness, thereby alleviating the parking difficulty problem and reducing time waste. In order to better explain and describe the present invention, the present invention will be further described herein with reference to 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 parking order model;

[0078] The parking order model includes: order degree 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 a 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] Where, PO n Indicates the degree of order 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 exit 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 a vehicle to exit a parking space

[0087] Specifically, m represents the number of vacant parking spaces between occupied parking spaces in the actual sampling. When m = 1, the time required for a vehicle to enter the parking space is 51 seconds; when m = 2, the time required for a vehicle to enter the 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 the 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 the parking space is 18 seconds, as shown below:

[0088]

[0089] When m=1, the time required for a vehicle to exit the parking space is 33 seconds. When m≥2, if there is a vehicle in front of the parking space, the time required for a vehicle to exit the parking space is 22 seconds. If there is at least one empty parking space in front of the parking space, the time required for a vehicle to exit the parking space is 12 seconds. This is 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 vehicle agent movement rules are as follows:

[0095]

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

[0097] Where, available(x x,y (t)) indicates whether there is an available parking space at the location (x, y); S x,y (t+1) represents the cell state at 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 vehicle agent A k The direction of movement is used to find the nearest free parking space;

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

[0099]

[0100] Where, 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, calculate the order value PO of the empty parking space p according to the parking space usage. p The smaller the calculated order value, 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 current moment to the target parking space. 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 choose the parking space with the highest probability to park;

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

[0107] S2. Validate the model using Python;

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

[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: the average waiting time of a vehicle, 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 the system has n vehicles, 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] Where λ represents the vehicle arrival rate (the number of vehicles arriving per unit time), s represents the arrival rate at full load, μ s represents the service rate at full load, and 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 the 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 obeys the 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] Where, 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 the requirement 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, which is expressed as 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] Where, e is a natural constant; μ represents the service rate of the system;

[0125] Numerical simulation verification is performed through python;

[0126] The simulation started with 100 parking spaces, numbered 0-99. The number of occupied spaces, the number of available spaces, and the parking time for each vehicle were all randomly generated, a setting that more closely resembles real-world scenarios. A total of 100 experiments were conducted, the code was run 100 times, and 100 different results were obtained.

[0127] Parking order model 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 remaining empty parking spaces and no order. Later, it is difficult for vehicles to enter the parking space and it takes a long time; with the parking order model, vehicles are parked in an orderly manner and later vehicles can enter the parking space more easily.

[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] As can be seen from the graph, the delay curve and the orderliness PO value curve for random parking are all above those for orderly parking. The variance section of the table also shows that random parking has a larger variance, indicating that the delay and orderliness PO values ​​for orderly parking are more stable than those for random parking. Based on the experimental results, it can be concluded that random parking requires longer time than the orderly parking method proposed by this invention, indicating that using orderly parking can reduce parking delays and shorten the time required for vehicles to enter and exit parking spaces.

[0132] S3. Setting the verified parking order model to the parking reservation module;

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

[0134] S4. Obtain the vehicle location 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 this 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%, where s represents the total number of parking spaces in the system and n represents the number of vehicles in the system at a given moment. This indicates that many parking spaces are already occupied and few are available, so it is not recommended that the owner park their vehicle in this on-street parking system. Instead, the owner is advised to select another on-street parking system. After selecting an on-street parking system, the system repeats the judgment process of the first case and recalculates the n / s value for that area until it finds an area with n / s ≤ 80%. Once this condition is met, the parking order degree is calculated.

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

[0143] After parking is completed, 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 violated the parking regulations by delaying parking. In this case, the system records the violation and calculates the parking fee. The user pays the fee and leaves the parking space.

[0146] The reservation management system is implemented as a 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] Download and install the WeChat developer tools from the WeChat public platform.

[0149] The WeChat applet front end involved in the present invention is developed using 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: Home and My.

[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 primarily implemented through an on-street parking optimization method and reservation management system based on parking orderliness. Based on user input of information such as the destination, estimated arrival time, and expected parking time, the system automatically recommends an on-street parking space for the driver, who can then accept or reject the recommendation. The proposed method and system for selecting and reserving parking spaces prevents users from arriving at their destination without a suitable parking space or finding no parking available. This reduces the time users spend searching for parking spaces on the street, alleviates traffic congestion caused by large numbers of users searching for spaces, reduces the impact of parking on other traffic flows, improves road capacity, and reduces 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 optimization and reservation management system based on parking order, characterized in that: The following steps are involved: S1. Establish parking order model; Parking order degree model includes: order degree Values, cellular automaton state update rules and vehicle agent movement rules and vehicle parking rules; The steps of establishing the parking order degree model are as follows: S1.

1. Establishing order value; Order The value expression is as follows: ； Where, Indicates that there are The degree of orderliness when driving a car; Indicates the average time it takes for a vehicle to enter a parking space; Indicates the average time it takes for a vehicle to exit a parking space; Indicates 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). express Always at the location The cell state of ; Each agent represents a vehicle, vehicle agent The properties include: current location , destination location and driving speed ; The cellular automaton state update rules and the vehicle agent movement rules are as follows: ； ； ； Where, Indicates location Are there any available parking spaces? express Time position The cell state of express The vehicle's position at the moment; express The vehicle's position at the moment; Indicates the vehicle's speed; Is a vehicle agent The direction of movement is used to find the nearest free parking space; Vehicle Agency The decision-making process for finding the target parking space is as follows: ； Where, Indicates the destination location; Indicates the current vehicle position; Indicates the target parking space location; express Always at the location The cell state of S1.

3. Establishing vehicle docking rules using cellular automata and agent-based models; For each vacant parking space , calculate the empty parking spaces based on the parking space usage The order value 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 affected by the distance from the vehicle to the target parking space at the current moment. related; The expression is as follows: ； Where, represents the probability that the vehicle will eventually choose a parking space; Represent the weight coefficients under the influence of order and distance respectively; Indicates an empty parking space The order value of 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 choose the parking space with the highest probability to park; In order to balance the impact of order and distance on the probability of a vehicle finally choosing a parking space, and The value is limited to Between, and ; S2. Validate the model using Python; The verification method is to use Python to calculate and compare the parking delay with and without the parking order model; S3. Setting the verified parking order model to the parking reservation module; S4. Obtain the vehicle location 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 this 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 vehicles leaving the on-street parking system through the information feedback module.

2. The on-street parking optimization and reservation management system based on parking order 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 a vehicle, the probability of a vehicle arriving at a parking space, and the probability of a vehicle leaving a parking space; Parking delays include: start parking delay, end parking delay and total delay.

3. The on-street parking optimization and reservation management system based on parking order according to claim 2, characterized in that: The average waiting time of vehicles, the probability of vehicles arriving at a parking space, and the probability of vehicles leaving a parking space are calculated by inputting a queuing theory model into Python; The queuing theory model expression is as follows: ； ； Where, represents the steady-state probability when there is no car in the system; Indicates that there are The arrival rate of vehicles when ; Indicates that there are service rate per vehicle; Indicates that there are The arrival rate of vehicles when there are 100 vehicles, that is, the arrival rate when the vehicle is fully loaded; Indicates that there are The service rate when the vehicle is fully loaded; Indicates that there are The utilization rate of the system when the vehicle is Indicates the total number of parking spaces in the system; Indicates the number of vehicles in the system at a certain moment; The expression of the average waiting time of vehicles is as follows: ； Where, represents the vehicle arrival rate, represents the arrival rate at full load, Indicates the service rate at full load. 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: ； Where, e is a natural constant; represents the vehicle arrival rate; The probability of a vehicle leaving a parking space is expressed as follows: ； Where, e is a natural constant; Indicates the service rate of the system.

4. The on-street parking optimization and reservation management system based on parking order according to claim 1, characterized in that: In the determining of the parking conditions of the on-street parking system by the parking reservation module, the determining of the parking conditions of the on-street parking system includes: a first case and a second case; The first case is n / s≤80%, represents the total number of parking spaces in the system, Indicates 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%, represents the total number of parking spaces in the system, Indicates 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 the number of remaining parking spaces is small. It is not recommended for car owners to park their vehicles in the on-street parking system.

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

6. The on-street parking optimization and reservation management system based on parking order 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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