Macroscopic road network parking dynamic modeling and pricing optimization method

Abstract

The present application relates to the technical field of intelligent parking, and more particularly to a macroscopic road network parking dynamic modeling and pricing optimization method, which establishes a macroscopic parking dynamic model to quantify and evaluate the influence of parking cruising based on the expected cruising time of vehicles in the region; a feedback real-time pricing strategy based on fuzzy PID is proposed to control the on-street and off-street parking prices in two regions, and under the driving of parking occupancy, the parking prices change, the parking demand in the region is affected by the parking prices and the expected cruising time and thus changes, and by setting a target function to minimize the regional vehicle cruising time, the parking demand of the two regions is balanced; the present application more accurately describes the parking cruising behavior, formulates a real-time pricing strategy more suitable for rapidly changing parking conditions, optimizes the regional vehicle cruising time, balances the parking demand, relieves the parking pressure, and improves the parking resource allocation efficiency.

Description

Technical Field

[0001] This invention relates to the field of intelligent parking technology, and in particular to a method for dynamic modeling and pricing optimization of parking in a macroscopic road network. Background Technology

[0002] Parking, as a crucial component of static traffic, plays a vital role in urban transportation systems, and the resulting parking patrols are a significant cause of regional congestion. For parkers, finding available parking spaces in city centers is often difficult; for drivers, travel delays are largely attributed to the presence of patrolling vehicles searching for parking; for city administrators, the imbalance between parking supply and demand leads to persistent illegal and disorderly parking; and for parking lot managers, uneven utilization of parking facilities and low economic returns remain persistent problems. The current urban parking fee system is not entirely rational, and traditional pricing methods have limitations. Previously, the fees for commercial parking lots were primarily set or guided by the government, with a small portion determined by the market. Fixed parking fee policies have failed to effectively regulate traffic demand. Firstly, the development of parking fee systems lacks accurate research on parking dynamics, particularly regarding the impact of parking patrols, which has not been quantified and assessed. Secondly, pricing is often based on fixed prices for different time periods, inevitably resulting in prices that are either too high or too low, and the time period divisions are not suitable for rapidly changing parking conditions.

[0003] Chinese Patent Publication No. CN109416879A discloses a dynamic pricing method for priority short-term parking spaces, which classifies parking spaces in a parking area and prices premium parking spaces. The steps include: (1) creating a parking space information table; (2) classifying parking spaces and determining the number of premium parking spaces; (3) determining the unit billing time; (4) determining the parking fee price for premium parking spaces and adjusting it; (5) determining the real-time detection interval and performing real-time statistics and detection on the actual number of vehicles parked at premium parking spaces and the occupancy rate of premium parking spaces at each interval; (6) comparing the real-time detection data with the predicted data to determine the parking fee price for premium parking spaces in subsequent periods.

[0004] However, the following problems still exist in the existing technology:

[0005] (1) Existing studies on parking and cruising behavior have established parking models that typically use the Greenshields model to describe speed in parking dynamics. However, this linear relationship does not conform to actual traffic conditions, especially when approaching congestion.

[0006] (2) Existing studies on real-time pricing and charging mostly utilize the concept of feedback control to develop fixed-parameter feedback pricing control. However, good controller parameters require a lot of manual fine-tuning to determine, and fixed-parameter feedback control has poor adaptability and a large lag. Summary of the Invention

[0007] To address the above problems, this invention provides a method for dynamic modeling and pricing optimization of parking in a macroscopic road network, comprising:

[0008] Step S1: Establish a macroscopic road network parking dynamic model, in which,

[0009] Step S11: Obtain basic information about the macroscopic road network;

[0010] Step S12: Divide the macro road network area into two areas, including the in-road area and the out-of-road area, to initially divide the in-road demand and the out-of-road demand.

[0011] Step S13: Establish the traffic dynamic model for the two regions;

[0012] Step S14: Group the vehicles in the two areas according to their status;

[0013] Step S15: Calculate the total vehicle accumulation in the two regions;

[0014] Step S16: Establish a dynamic model of traffic and parking in the vicinity;

[0015] Step S17: Determine the objective function as minimizing the vehicle cruising time in the region;

[0016] Step S2: Parking price optimization strategy, where,

[0017] Step S21: The fuzzy controller receives parking occupancy rate data and calculates the deviation E between the parking occupancy rate at step K of price control and the expected parking occupancy rate. K The time deviation of price control at step K and the time deviation of price control at step K-1, E K The change ΔE;

[0018] Step S22: Obtain the adjustment value of the PID controller parameters through the fuzzy inference system, and adjust the gain parameter of the PID controller in real time.

[0019] Step S23: The price controller adjusts the parking price based on the parking occupancy rate.

[0020] Furthermore, in step S13, a traffic dynamic model for the two regions is established according to equation (1).

[0021] In equation (1), k represents the time step of the simulation, Δt represents the time length of the simulation time step, and n i (t k ) represents the total number of vehicles traveling in region i within the k-th time step, n ij (t k ) represents the number of vehicles traveling in region i with destination region j within the k-th time step, q ij (t k () represents the traffic demand in region i with destination region j at time step k, where n i,max M represents the vehicle capacity of region i. ij (t k G represents the transfer flow from region i to region j within the k-th step. i (n i (t k )) represents the MFD of the defined region i.

[0022] Further, in step S14, the vehicles in the two areas are grouped according to their vehicle status, including...

[0023] Determine the vehicle status of vehicles in both areas, and classify vehicles with the same status into the same vehicle category. This vehicle category includes vehicles intended for on-street parking but not yet in the patrol area. Vehicles intended for off-street parking but not yet arrived Passing vehicles crossing the road n vehicles patrolling the roadside c n vehicles already parked on the road on n vehicles already parked in off-street parking lots off .

[0024] Further, in step S15, the total vehicle accumulation in the two regions is calculated according to equation (2).

[0025]

[0026] In equation (2), n(t) k () represents the accumulation of vehicles moving within the k-th step. This represents the number of vehicles that intend to park on the road but have not yet started cruising within the k-th time step. This represents the number of vehicles that intended to park off-street but have not yet arrived within the k-th time step. n represents the number of vehicles crossing the road in the k-th time step. c (t k ) represents the number of vehicles patrolling the roadside during the k-th step.

[0027] Furthermore, in step S16, a dynamic model of traffic and parking in the vicinity is established according to equation (3).

[0028]

[0029] In equation (3), This represents the number of vehicles crossing the road during the (k-1)th step. This represents the inflow of vehicles during the k-th time step. This represents the traffic flow of vehicles re-entering off-street parking spaces within the k-th time step. This represents the traffic flow that resumes from on-street parking spaces within the k-th time step. This indicates the vehicle that abandons its patrol at time k. This represents the outflow of vehicles in the k-th time step, where n is the number of vehicles passing through. c (t k-1 () represents the number of vehicles patrolling the roadside during the (k-1)th step. This represents vehicles that attempted to park off-road within the k-th time step but failed and instead cruised on-road. This represents the number of vehicles that want to park on the road within the k-th time step, o c (k) represents the outflow of vehicles that successfully stop on the road within the k-th step time, n off (t k ) represents the accumulation of vehicles that have stopped off the road within the k-th step, where n is the number of vehicles. off (t k-1 The ) represents the accumulation of vehicles that have stopped off the road within the (k-1)th step. This represents the number of vehicles that want to park off-road within the k-th time step, where n is the number of vehicles. on (t k ) represents the accumulation of vehicles that have stopped on the road within the k-th step, where n is the number of vehicles. on (t k-1 This represents the accumulation of vehicles that have stopped on the road within the (k-1)th step. This represents the number of vehicles that intend to park on the road but have not yet started cruising within the (k-1)th step. This represents the number of vehicles that have intended to park off-street but have not yet arrived within the (k-1)th step.

[0030] Furthermore, in step S17, the objective function is determined according to equation (4) to be minimizing the regional vehicle cruising time.

[0031]

[0032] In equation (4), v on (t) represents the cruising speed within the road. v represents the ideal cruising speed under conditions of no congestion. m N represents the vehicle speed at the maximum traffic volume in the area. j This indicates the number of vehicles accumulated when congestion begins in a region.

[0033] Furthermore, in step S17, the cruising speed is calculated according to equation (5).

[0034]

[0035] In equation (5), v(t) represents the cruising speed.

[0036] Further, in step S21, the deviation E between the parking occupancy rate and the expected parking occupancy rate at the Kth step of the price control time is calculated according to equation (6). K The time deviation of price control at step K and the time deviation of price control at step K-1, E K The change ΔE,

[0037]

[0038] In equation (6), K represents the time step of price control, and E K E represents the deviation between the parking occupancy rate at step K of the price control period and the expected parking occupancy rate. K-1 ΔE represents the deviation between the parking occupancy rate at time step K-1 of the price control period and the expected parking occupancy rate, where ΔE represents the difference between the current deviation and the previous deviation E. K Changes, This indicates the average parking occupancy rate, whether on or off the street. This represents the expected on-street and off-street parking occupancy rate.

[0039] Furthermore, in step S23, the price controller adjusts the parking price based on the parking occupancy rate, wherein...

[0040] If the parking occupancy rate is higher than expected, parking prices will increase.

[0041] If the parking occupancy rate is less than or equal to the expected value, the parking price will decrease.

[0042] Further, in step S23, the parking occupancy rate is calculated according to equation (7).

[0043]

[0044] In equation (7), O x (t k ), x∈{on,off} represents the parking occupancy rate on or off the road, N x x∈{on,off} represents the parking capacity on or off the road.

[0045] Compared with existing technologies, this invention establishes a macroscopic parking dynamic model to quantify and evaluate the impact of parking patrols using the expected cruising time of vehicles within a region. It proposes a feedback real-time pricing strategy based on fuzzy PID to control on-street and off-street parking prices in two regions. Driven by parking occupancy rates, parking prices change, and parking demand within and outside the region changes accordingly, influenced by parking prices and expected cruising times. By setting the objective function to minimize the regional vehicle cruising time, the parking demand in the two regions is balanced. This invention provides a more accurate description of parking patrol behavior, formulates a real-time pricing strategy more suitable for rapidly changing parking conditions, optimizes regional vehicle cruising times, balances parking demand, alleviates parking pressure, and improves the efficiency of parking resource allocation.

[0046] In particular, this invention establishes an improved regional macro-road network parking dynamic model, which uses the velocity-accumulation MFD in the form of the Greenberg model to describe the system parking dynamics. The description of velocity is more accurate and more in line with traffic reality under congested conditions with high traffic density.

[0047] In particular, this invention proposes an integration of a fuzzy PID-based feedback pricing optimization strategy with a parking dynamic model. The fuzzy PID-based feedback controller parameters are time-varying, making it more suitable for rapidly changing parking conditions, and the pricing system is more effective and stable. Attached Figure Description

[0048] Figure 1 A logical execution structure diagram of the macroscopic road network parking dynamic modeling and pricing optimization method in an embodiment of the invention;

[0049] Figure 2 This is a two-area traffic network diagram of an embodiment of the invention;

[0050] Figure 3 This is a flow transfer diagram between different types of vehicles in a parking model of an embodiment of the invention. Detailed Implementation

[0051] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0052] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0053] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0054] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0055] Please see Figure 1 As shown, Figure 1 This is a logic execution structure diagram of the macro-level road network parking dynamic modeling and pricing optimization method according to an embodiment of the invention. The macro-level road network parking dynamic modeling and pricing optimization method of the present invention includes:

[0056] Step S1: Establish a macroscopic road network parking dynamic model, in which,

[0057] Step S11: Obtain basic information about the macroscopic road network;

[0058] Step S12: Divide the macro road network area into two areas, including the in-road area and the out-of-road area, to initially divide the in-road demand and the out-of-road demand.

[0059] Step S13: Establish the traffic dynamic model for the two regions;

[0060] Step S14: Group the vehicles in the two areas according to their status;

[0061] Step S15: Calculate the total vehicle accumulation in the two regions;

[0062] Step S16: Establish a dynamic model of traffic and parking in the vicinity;

[0063] Step S17: Determine the objective function as minimizing the vehicle cruising time in the region;

[0064] Step S2: Parking price optimization strategy, where,

[0065] Step S21: The fuzzy controller receives parking occupancy rate data and calculates the deviation E between the parking occupancy rate at step K of price control and the expected parking occupancy rate. KThe time deviation of price control at step K and the time deviation of price control at step K-1, E K The change ΔE;

[0066] Step S22: Obtain the adjustment value of the PID controller parameters through the fuzzy inference system, and adjust the gain parameter of the PID controller in real time.

[0067] Step S23: The price controller adjusts the parking price based on the parking occupancy rate.

[0068] Specifically, this embodiment selects an actual road network area and obtains basic information such as lane length, number of lanes, lane function, number of on-street and off-street parking spaces, and average distance between adjacent parking spaces for each road segment through Baidu Maps and on-site surveys. It also obtains regional status information such as free-flow speed, number of vehicles accumulated during congestion, and parking occupancy rate. This invention selects a portion of the Yizhuang area in Beijing, and based on the obtained data, sets the following parameters: v f =50km / h, v off =10km / h, v m =25km / h, d off =1.5m, d on =10m, N off =100veh, N on =1200veh, N j =2000veh,l m =1km, ΔT=0.5h

[0069] Among them, v f v represents the speed of free-flowing vehicles within the area. off Indicates the speed of off-road patrol, v m d represents the vehicle speed at the maximum traffic volume in the area. on d represents the distance between two consecutive parking spaces on the road. off N represents the distance between two consecutive parking spaces outside the road. off N represents the off-street parking capacity. on N represents the parking capacity within the road. j This indicates the number of vehicles accumulated when congestion begins in a region. m This indicates the average distance traveled by vehicles in the area. This represents the departure rate of vehicles that have parked in on-street parking spaces and then restarted within the k-th time step. This represents the departure rate of vehicles that have parked in off-street parking spaces and restarted within the k-th time step. ΔT represents the departure rate of vehicles that abandon the cruise within the k-th step, and ΔT represents the duration of the price control step. This indicates the ideal cruising speed under conditions of no congestion.

[0070] Please see Figure 2 This is a two-area traffic network diagram according to an embodiment of the present invention.

[0071] Specifically, this invention divides the demand in the selected two regional transportation networks into internal demand q within region 1. 11 (t k ), the transportation demand q in region 1 destined for region 2 12 (t k ), Internal demand q in Region 2 21 (t k And the traffic demand q in region 2 destined for region 1. 22 (t k In this context, region R1 is the main region, and R2 is the network perimeter.

[0072] Specifically, in step S13, a traffic dynamic model for the two regions is established according to equation (1).

[0073] In equation (1), k represents the time step of the simulation, Δt represents the time length of the simulation time step, and n i (t k ) represents the total number of vehicles traveling in region i within the k-th time step, n ij (t k ) represents the number of vehicles traveling in region i with destination region j within the k-th time step, q ij (t k () represents the traffic demand in region i with destination region j at time step k, where n i,max M represents the vehicle capacity of region i. ij (t k G represents the transfer flow from region i to region j within the k-th step. i (n i (t k )) represents the MFD of the defined region i.

[0074] Specifically, this invention divides the vehicles traveling in each region into two parts: vehicles with internal destinations and vehicles with external destinations. The traffic dynamics model of the two regions includes a flow conservation equation, which states that the change in the number of vehicles traveling in each region is equal to the total inflow minus the outflow. The MFD of regions 1 and 2 is defined by the trip completion flow. The MFD of regions 1 and 2 defined by the trip completion flow is the sum of the trip completion flow within region 1 and the trip completion flow with region 2 as the destination, and the sum of the trip completion flow within region 2 and the trip completion flow with region 1 as the destination.

[0075] Specifically, this implementation assumes that each area has a certain number of evenly distributed on-street parking spaces and off-street parking lots with a fixed capacity.

[0076] Specifically, in order to reasonably define the problem and considering that the parked vehicles are not permanently parked, this implementation makes the following assumptions:

[0077] Assumption 1: Parking demand is sufficient to trigger vehicle cruising behavior and will not far exceed the capacity of on-street parking spaces and off-street parking lots;

[0078] Assumption 2: On-street parking and off-street parking are managed by the same agency, they have the same goal, and there is no competition between them;

[0079] Hypothesis 3: Drivers can learn about parking prices and expected cruising time for different time periods through daily experience or online information.

[0080] Assumption 4: The driver has already determined the parking choice before entering the traffic area, that is, the vehicle entering will either park on the road, park in an off-street parking lot, or be a passing vehicle;

[0081] Assumption 5: When choosing a parking location, drivers mainly consider the expected cruising time and parking price;

[0082] Assumption 6: Drivers whose goal is to park on the street will either start parking and cruise or leave the area; drivers whose goal is to park off the street will continue to cruise on the street after failing to find a parking space during an off-street cruise.

[0083] Assumption 7: The relationship between the accumulation and the cruise velocity is a constant ideal velocity system under free flow conditions and a decreasing velocity system that conforms to MFD under congested conditions;

[0084] Assumption 8: The system dynamics are described using a velocity-accumulation MFD in the form of a Greenberg model.

[0085] Specifically, in this embodiment, the cruising distance is calculated based on equation (8).

[0086]

[0087] In equation (8), L represents the cruising distance, d represents the distance between two consecutive parking spaces, and O represents the parking occupancy rate. The speed represents the ideal cruising speed under no congestion conditions. a and b are parameters that need to be estimated. In this embodiment, a = 0.013 and b = 11.199.

[0088] Specifically, in this embodiment, the demand is divided into two categories. The first category is exogenous demand for entering the area, including parking demand. and transit demand Parking demand and transit demand The demand for parking varies over time, is inflexible, and includes on-street parking. off-street parking demand On-street parking demand off-street parking demand Affected by the expected cruising time and parking price, the on-street parking demand is shown in Equation (9), and the off-street parking demand is shown in Equation (10). The second type is the endogenous demand generated by vehicles parked in the area leaving again or cruising vehicles abandoning their cruising. In this embodiment, it is set as vehicles leaving at a given departure rate.

[0089]

[0090] In equations (9) and (10), This indicates the inflow of vehicles intended for off-street parking. This indicates an influx of vehicles with parking needs. Indicates the expected cruising time within the route. Indicates the expected time for off-road cruising, τ on (k) represents the parking price within the road, τ off (k) represents the off-street parking price. Vehicles intended for on-street parking. This indicates the inflow of vehicles intended for off-street parking.

[0091] Specifically, the logistic model coefficient β T β c The time value of parking (VOT) is determined jointly, as shown in formula (11). In this embodiment, the model assumes that 20% of parkers have a VOT of 40 RMB / h and 80% of parkers have a VOT of 20 RMB / h.

[0092]

[0093] Where VOT represents the value of time, β T β c These are the coefficients of the logical model.

[0094] Specifically, in this embodiment, the on-street parking price τ is set respectively. on Off-street parking prices τ off The on-street parking price τ on Off-street parking prices τ off A price range is set.

[0095] Specifically, in this embodiment, the cruise speed v is represented by a piecewise function of n(t). on (t), as in formula (12),

[0096]

[0097] in, This indicates the ideal cruising speed under conditions of no congestion. It is based on the decreasing rate of MFD.

[0098] Please see Figure 3 This is a flow transfer diagram between different types of vehicles in the parking model of this invention.

[0099] Specifically, in step S14, the vehicles in the two areas are grouped according to their vehicle status, including:

[0100] Determine the vehicle status of vehicles in both areas, and classify vehicles with the same status into the same vehicle category. This vehicle category includes vehicles intended for on-street parking but not yet in the patrol area. Vehicles intended for off-street parking but not yet arrived Passing vehicles crossing the road n vehicles patrolling the roadside c n vehicles already parked on the road on n vehicles already parked in off-street parking lots off .

[0101] Specifically, in step S15, the total vehicle accumulation in the two regions is calculated according to equation (2).

[0102]

[0103] In equation (2), n(t) k () represents the accumulation of vehicles moving within the k-th step. This represents the number of vehicles that intend to park on the road but have not yet started cruising within the k-th time step. This represents the number of vehicles that intended to park off-street but have not yet arrived within the k-th time step. n represents the number of vehicles crossing the road in the k-th time step. c (t k ) represents the number of vehicles patrolling the roadside during the k-th step.

[0104] Specifically, in step S16, a dynamic model of traffic and parking in the vicinity is established according to equation (3).

[0105]

[0106] In equation (3), This represents the number of vehicles crossing the road during the (k-1)th step. This represents the inflow of vehicles during the k-th time step. This represents the traffic flow of vehicles re-entering off-street parking spaces within the k-th time step. This represents the traffic flow that resumes from on-street parking spaces within the k-th time step. This indicates the vehicle that abandons its patrol at time k. This represents the outflow of vehicles in the k-th time step, where n is the number of vehicles passing through. c (t k-1 () represents the number of vehicles patrolling the roadside during the (k-1)th step. This represents vehicles that attempted to park off-road within the k-th time step but failed and instead cruised on-road. This represents the number of vehicles that want to park on the road within the k-th time step, o c (k) represents the outflow of vehicles that successfully stop on the road within the k-th step time, n off (t k ) represents the accumulation of vehicles that have stopped off the road within the k-th step, where n is the number of vehicles. off (t k-1 The ) represents the accumulation of vehicles that have stopped off the road within the (k-1)th step. This represents the number of vehicles that want to park off-road within the k-th time step, where n is the number of vehicles. on (t k ) represents the accumulation of vehicles that have stopped on the road within the k-th step, where n is the number of vehicles. on (t k-1 This represents the accumulation of vehicles that have stopped on the road within the (k-1)th step. This represents the number of vehicles that intend to park on the road but have not yet started cruising within the (k-1)th step. This represents the number of vehicles that have intended to park off-street but have not yet arrived within the (k-1)th step.

[0107] Specifically, in step S17, the objective function is determined according to equation (4) to be minimizing the regional vehicle cruising time.

[0108]

[0109] In equation (4), v on (t) represents the cruising speed within the road. v represents the ideal cruising speed under conditions of no congestion. m N represents the vehicle speed at the maximum traffic volume in the area. j This indicates the number of vehicles accumulated when congestion begins in a region.

[0110] Specifically, in step S17, the cruising speed is calculated according to equation (5).

[0111]

[0112] In equation (5), v(t) represents the cruising speed.

[0113] Specifically, this invention improves the mathematical description of cruising speed. Previous models used the Greenshields model to define the speed-accumulation MFD, but this linear relationship does not conform to actual traffic conditions. Therefore, this solution adopts the Greenberg model, which is more accurate in describing speed under congested conditions with high traffic density and is more in line with reality.

[0114] Specifically, this invention proposes a real-time feedback pricing strategy based on fuzzy PID and integrates it with a parking model. A fuzzy PID controller is developed based on the traditional PID algorithm. The controller adjusts the parking price according to the changes in parking occupancy rate in real time, thereby affecting the parking demand in the area and optimizing the parking patrol time. This invention sets the ideal parking occupancy rate at 90% and adjusts the parking price every 30 minutes, taking into account the driver's acceptance of the speed of price changes.

[0115] Specifically, in step S21, the deviation E between the parking occupancy rate and the expected parking occupancy rate at the Kth step of the price control time is calculated according to equation (6). K The time deviation of price control at step K and the time deviation of price control at step K-1, E K The change ΔE,

[0116]

[0117] In equation (6), K represents the time step of price control, and E K E represents the deviation between the parking occupancy rate at step K of the price control period and the expected parking occupancy rate. K-1 ΔE represents the deviation between the parking occupancy rate at time step K-1 of the price control period and the expected parking occupancy rate, where ΔE represents the difference between the current deviation and the previous deviation E. K Changes, This indicates the average parking occupancy rate, whether on or off the street. This represents the expected on-street and off-street parking occupancy rate.

[0118] Specifically, this invention designs a fuzzy inference system, setting the domain, value, and membership function of each linguistic variable; it sets fuzzy inference rules, fuzzifying the upper and lower limits of the deviation E, dividing the interval E into 8 parts, separated by NB, NM, NS, ZO, PS, PM, and PB, which represent negative large, negative medium, negative small, 0, positive small, positive medium, and positive large, respectively. The designed fuzzy rules are shown in Table 1; finally, fuzzy inference is performed and clarified.

[0119] Table 1

[0120]

[0121] Specifically, in step S23, the price controller adjusts the parking price based on the parking occupancy rate, wherein...

[0122] If the parking occupancy rate is higher than expected, parking prices will increase.

[0123] If the parking occupancy rate is less than or equal to the expected value, the parking price will decrease.

[0124] Specifically, in this embodiment, the price is controlled as shown in equation (13) under the influence of parking occupancy rate.

[0125]

[0126] Where, τ x (K+1) represents the average parking occupancy rate in the (K+1)th control time step, τ x (K) represents the average parking occupancy rate in the Kth control time step. It is a pre-set, expected critical parking occupancy rate. It is the average parking occupancy rate in the Kth control time step. It is the average parking occupancy rate in the (K-1)th control time step. It is the average parking occupancy rate in the (K-2)th control time step, and α1 is the PID controller parameter K. p α2 is the PID controller parameter K i α3 is the PID controller parameter K d The initial values ​​are determined by manual fine-tuning. In this embodiment, the initial values ​​are set to 0.2, 0.1, and 1, respectively. The fuzzy controller will adjust the parameters in real time to improve its self-adaptive ability.

[0127] Specifically, in step S23, the parking occupancy rate is calculated according to equation (7).

[0128]

[0129] In equation (7), O x (tk x∈{on,off} represents the on-street or off-street parking occupancy rate at time step k, N x x∈{on,off} represents the parking capacity within or outside the road during the k-th step.

[0130] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for dynamic modeling and pricing optimization of parking in a macroscopic road network, characterized in that, include: Step S1: Establish a macroscopic road network parking dynamic model, in which, Step S11: Obtain basic information about the macroscopic road network; Step S12: Divide the macro road network area into two areas, including the in-road area and the out-of-road area, to initially divide the in-road demand and the out-of-road demand. Step S13: Establish a traffic dynamic model for the two regions; Step S14: Group the vehicles in the two areas according to their status; Step S15: Calculate the total vehicle accumulation in the two regions; Step S16: Establish a dynamic model of traffic and parking in the vicinity; Step S17: Determine the objective function as minimizing the vehicle cruising time in the region; Step S2: Parking price optimization strategy, where, Step S21: The fuzzy controller receives parking occupancy rate data and calculates the deviation E between the parking occupancy rate at step K of price control and the expected parking occupancy rate. K The time deviation of price control at step K and the time deviation of price control at step K-1, E K The change ΔE; Step S22: Obtain the adjustment value of the PID controller parameters through the fuzzy inference system, and adjust the gain parameter of the PID controller in real time. Step S23: The price controller adjusts the parking price based on the parking occupancy rate; In step S16, a dynamic model of traffic and parking in the vicinity is established according to equation (3). In equation (3), This represents the number of vehicles crossing the road during the (k-1)th step. This represents the inflow of vehicles during the k-th time step. This represents the traffic flow of vehicles re-entering off-street parking spaces within the k-th time step. This represents the traffic flow that resumes from on-street parking spaces within the k-th time step. This indicates the vehicle that abandons its patrol at time k. This represents the outflow of vehicles in the k-th time step, where n is the number of vehicles passing through. c (t k-1 () represents the number of vehicles patrolling the roadside during the (k-1)th step. This represents vehicles that attempted to park off-road within the k-th time step but failed and instead cruised on-road. This represents the number of vehicles that want to park on the road within the k-th time step, o c (k) represents the outflow of vehicles that successfully stop on the road within the k-th step time, n off (t k ) represents the accumulation of vehicles that have stopped off the road within the k-th step, where n is the number of vehicles. off (t k-1 The ) represents the accumulation of vehicles that have stopped off the road within the (k-1)th step. This represents the number of vehicles that want to park off-road within the k-th time step, where n is the number of vehicles. on (t k ) represents the accumulation of vehicles that have stopped on the road within the k-th step, where n is the number of vehicles. on (t k-1 This represents the accumulation of vehicles that have stopped on the road within the (k-1)th step. This represents the number of vehicles that intend to park on the road but have not yet started cruising within the (k-1)th step. This represents the number of vehicles that have intended to park off-street but have not yet arrived within the (k-1)th step.

2. The method for dynamic modeling and pricing optimization of parking in macroscopic road networks according to claim 1, characterized in that, In step S13, a traffic dynamic model for the two regions is established according to equation (1). In equation (1), k represents the time step of the simulation, Δt represents the time length of the simulation time step, and n i (t k ) represents the total number of vehicles traveling in region i within the k-th time step, n ij (t k ) represents the number of vehicles traveling in region i with destination region j within the k-th time step, q ij (t k () represents the traffic demand in region i with destination region j at time step k, where n i,max M represents the vehicle capacity of region i. ij (t k G represents the transfer flow from region i to region j within the k-th step. i (n i (t k )) represents the MFD of the defined region i.

3. The method for dynamic modeling and pricing optimization of parking in macroscopic road networks according to claim 1, characterized in that, In step S14, the vehicles in the two areas are grouped according to their vehicle status, including: Determine the vehicle status of vehicles in both areas, and classify vehicles with the same status into the same vehicle category. This vehicle category includes vehicles intended for on-street parking but not yet in the patrol area. Vehicles intended for off-street parking but not yet arrived Passing vehicles crossing the road n vehicles patrolling the roadside c n vehicles already parked on the road on n vehicles already parked in off-street parking lots off .

4. The method for dynamic modeling and pricing optimization of macro-road network parking according to claim 3, characterized in that, In step S15, the total vehicle accumulation in the two regions is calculated according to equation (2). In equation (2), n(t) k () represents the accumulation of vehicles moving within the k-th step. This represents the number of vehicles that intend to park on the road but have not yet started cruising within the k-th time step. This represents the number of vehicles that intended to park off-street but have not yet arrived within the k-th time step. n represents the number of vehicles crossing the road in the k-th time step. c (t k ) represents the number of vehicles patrolling the roadside during the k-th step.

5. The method for dynamic modeling and pricing optimization of parking in macroscopic road networks according to claim 1, characterized in that, In step S17, the objective function is determined according to equation (4) to be minimizing the regional vehicle cruising time. In equation (4), v on (t) represents the cruising speed within the road. v represents the ideal cruising speed under conditions of no congestion. m N represents the vehicle speed at the maximum traffic volume in the area. j This indicates the number of vehicles accumulated when congestion begins in a region.

6. The method for dynamic modeling and pricing optimization of parking in macroscopic road networks according to claim 5, characterized in that, In step S17, the cruising speed is calculated according to equation (5). In equation (5), v(t) represents the cruising speed.

7. The method for dynamic modeling and pricing optimization of parking in macroscopic road networks according to claim 1, characterized in that, In step S21, the deviation E between the parking occupancy rate at the Kth step of the price control period and the expected parking occupancy rate is calculated according to formula (6). K The time deviation of price control at step K and the time deviation of price control at step K-1, E K The change ΔE, In equation (6), K represents the time step of price control, and E K E represents the deviation between the parking occupancy rate at step K of the price control period and the expected parking occupancy rate. K-1 ΔE represents the deviation between the parking occupancy rate at time step K-1 of the price control period and the expected parking occupancy rate, where ΔE represents the difference between the current deviation and the previous deviation E. K Changes, This indicates the average parking occupancy rate, whether on or off the street. This represents the expected on-street and off-street parking occupancy rate.

8. The method for dynamic modeling and pricing optimization of parking in macroscopic road networks according to claim 1, characterized in that, In step S23, the price controller adjusts the parking price based on the parking occupancy rate. If the parking occupancy rate is higher than expected, parking prices will increase. If the parking occupancy rate is less than or equal to the expected value, the parking price will decrease.

9. The method for dynamic modeling and pricing optimization of parking in macroscopic road networks according to claim 8, characterized in that, In step S23, the parking occupancy rate is calculated according to formula (7). In equation (7), O x (t k ), x∈{on,off} represents the parking occupancy rate on or off the road, N x x∈{on,off} represents the parking capacity on or off the road.

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