WO2018122813A1

WO2018122813A1 - Method for premium parking space reservation prioritizing short-term parking and for dynamic pricing

Info

Publication number: WO2018122813A1
Application number: PCT/IB2017/058543
Authority: WO
Inventors: 杜豫川; 王晨薇; 蒋盛川; 王金栋; 施曙东
Original assignee: 同济大学; 上海浦东路桥建设股份有限公司; 许军
Priority date: 2016-12-30
Filing date: 2017-12-30
Publication date: 2018-07-05
Also published as: CN109416879B; CN109661693B; WO2018122587A1; WO2018122812A1; CN109416879A; CN110337680A; GB201909413D0; GB201711410D0; CN109661693A

Abstract

A method for premium parking space reservation prioritizing short-term parking and for dynamic pricing. By means of time and spatially differentiated pricing for different parking resources, management and guidance with respect to parking demand are implemented, and a parking space reservation service is provided to a person who is parking. Specifically, by means of incrementally progressive fees, prices charged for premium parking spaces are finely set and dynamically adjusted on the basis of actual conditions, thus achieving the goal of prioritizing the provision of limited premium parking spaces to vehicles parking for shorter durations.

Description

High-quality berth reservation and dynamic pricing method with priority short stop

The invention relates to a high quality berth reservation and dynamic pricing method with priority short stop. Drivers tend to choose high-quality parking spaces that are convenient and safe to park. They are not willing to be remote, parking difficult, and difficult to find ordinary berths. Due to the shortage of quality berths, congestion and emissions will increase. In particular, the present invention considers the number of high-quality parking berths, the characteristics of regional parking demand, the occupancy rate of parking spaces, etc., by means of incremental progressive charging, finely setting the charging price of high-quality berths and providing berth booking services to achieve limited The high quality berths are preferentially provided for the purpose of vehicles with shorter parking lengths, so that more drivers can get comfortable and convenient parking services and shorter walking time, which improves the overall efficiency of the society.

Background technique

Prior art 1

A U.S. Patent Application Serial No. US20140122375 discloses a method for dynamically adjusting parking pricing based on real-time parking occupancy of a parking lot. This pricing method needs to detect the real-time occupancy rate of the parking space through the smart sensor. The comparison module compares the current occupancy rate with the target occupancy rate, and realizes the feedback control of the parking demand by adjusting the parking pricing in real time. Figure 1 shows the flow chart for the implementation of this dynamic pricing method. Figure 2 shows the change in parking demand, parking space occupancy, and set parking pricing for an implementation of this pricing method.

It can be seen from Fig. 2 that this pricing method improves the pricing of parking charges when it detects that the occupancy rate of the parking space exceeds the set target value, that is, when the parking demand is large, and suppresses the demand, thereby reducing the occupancy rate of the parking space to Set below the 85% threshold. However, in this pricing method, the system provides berth resources to the parking lot according to the principle of first-come first-served service, and does not consider the difference between the advantages and disadvantages of the berth resource conditions, and does not distinguish and select the parking time of the parking users. Therefore, the maximum utilization of high quality berth resources has not been realized.

Prior Art 2 A US patent application, US20110213672, discloses a differential pricing method for berths under high demand conditions. This method divides the available berths in the parking lot into "classic berths", "one of the last reserved berths", and "the only last berths reserved". The concept of berth "contribution value" is used for reference, according to different categories. The berths have different contribution values and are priced differently in order to maximize the operator's profits.

Figure 3 shows the division of the berth category using this method in a parking lot. The mark L is "large size berth", the mark S is "safe berth", and the unmarked is the ordinary parking space. The following table shows the classification of berths and pricing rules in one implementation of this method. Identification classification price (S) duration limit (h)

0 Ordinary berths 1.00 2.5

L Last reserved berth 3.00 3.0

NL last reserved adjacent berths for parking spaces 2.00 3.0

S safe berth 10.00 10.0 It can be seen that although this pricing method differentiates and differentiates the berth, it does not make a reasonable choice for the parking time of the parking lot. The purpose of this pricing method is to maximize the operator's profit rather than optimize the social efficiency. Therefore, it cannot guarantee that its high quality berth can serve more drivers to the greatest extent. Therefore, there is also a certain amount of waste of high quality berth resources.

Prior Art 3 A Chinese patent application, CNid 00000063094751, discloses a method of regulating a parking guidance system that considers parking time. The inductive system described in the method displays the parking condition of the area in the form of a road network diagram, and the inducing screen includes the berth information and the driving route of each parking lot in the area, and dynamically displays the road traffic condition of the road network and the parking lot of each parking lot. Information on the difficulty level of parking. This difficulty level information is given by different color identifications depending on the parking time range. The control method of the parking guidance system is to calculate the parking time required by the driver in different parking lots in the current location selection area according to the parking time and select an appropriate manner for release, and the parking time of the parking lot considers the road section reaching the parking lot. Travel time, queued into the parking lot and the time inside the parking lot to find the parking space. However, the driver's induction is determined only according to the current parking occupancy rate of different parking lots. The convenience of parking resources is not distinguished, and the quality parking resources are not fully utilized.

Prior art 4

A Chinese patent application, CN201510448131, discloses a parking dynamic pricing method based on demand characteristics and parking lot utilization. By analyzing the time-sequence analysis of the parking lot utilization data of the parking lot vehicle collected by the detector to determine whether the parking lot or the parking lot area needs to be adjusted for the parking price, and setting the dynamic pricing target and Cycle; RP survey through the mobile APP to construct the parking lot probability equation of the parking lot or the parking lot area, to establish the relationship between parking utilization and parking lot attributes (including price), and then combined with the detector The regional parking flow data can optimize the parking lot utilization rate of the parking lot by adjusting the parking price of the parking lot, and reach the previously set target, thereby realizing the reasonable dynamic pricing of the parking lot.

In this dynamic pricing method, the prices of all berths within a particular parking lot are the same, ie, the convenience of different berths inside the parking lot is not differentiated.

Prior Art 5 A Chinese patent application, CNid00000071874281, discloses an intelligent parking space parking mechanism algorithm based on an optimal berth model. The method includes the determination of the optimal berth model of the parking lot, the drawing of the weight map of the road network and the design and programming of the parking space induction algorithm.

Among them, the optimal berth is determined according to the driving distance of the vehicle entering the parking space, the walking distance from the parking lot and the personal safety. By quantitatively representing these three distances, a mathematical model is established with the shortest path method in which the sum of the three distances is the shortest, and the optimal berth is thus determined. According to the optimal berth model, the parking lot road network can be abstracted into the weighted graph solution in the graph theory, so that the optimal berth problem can be converted into the shortest distance calculation problem on the weighted graph. In the optimal berth selection, the improved floyd algorithm with better performance is used, and finally verified by Matlab simulation. This method distinguishes different berths in the parking lot and determines the optimal berth, but only uses the algorithm for parking induction, does not involve parking pricing, and does not regulate parking demand through differentiated pricing. Summary of the invention

The use of limited quality berths to best meet the parking needs of vehicles with shorter parking hours and improve the turnover rate of high quality berths is the key to improving the overall efficiency of society. This idea can be illustrated with the following example:

Assume that there is a convenient high-quality berth, and the walking distance of the final destination that the traveler wants to reach after parking is 2 minutes; at the same time, there is a normal parking space with a far distance, and the walking distance to the final destination is 5 minutes. Suppose that two drivers, A and B, need to stop at the same time and arrive at this destination. The parking time of A is 6 hours, the parking time of B is 2 hours, and the driver C is 2 hours later. After the hour, the driver D also needs to stop and arrive at the same destination. The parking time is also 2 hours. Under the existing technical methods, the possible situations are:

Driver A parks the car in a premium parking space and walks for 2 minutes to reach the destination; at the same time, driver B parks the car in the regular parking space and walks for 5 minutes to reach the destination. After 2 hours, the B car leaves, and the C that arrives can only park the car in the ordinary parking space (because the high quality berth is still occupied by the A car), and it takes 5 minutes to walk to the destination after parking. Similarly, After 2 hours, C will leave, and the D that arrives at this time can only park the car in the ordinary parking space (because the high quality berth is still occupied by the A car), and it takes 5 minutes to walk to the destination after parking. In this case, the total time spent by four drivers in the entire system is 2+5+5+5=17 minutes. However, if the idea of the present invention is adopted to prioritize the high quality berth to meet the short stop vehicle, the situation will become:

Driver A parked the car in the regular parking space and walked for 5 minutes after parking; at the same time, driver B parked the car in a quality parking space and walked for 2 minutes after stopping. After 2 hours, when C arrives, the B on the premium parking space has already left, so C will park the car in the quality parking space. Similarly, when the arrival of 2 hours D, the C on the premium parking space has already left, so D will park the car in a quality parking space, so the walking time required for the CD is 2 minutes. In this case, the total time spent by four drivers in the entire system is 5+2+2+2=11 minutes. Figure 4 compares the two scenarios. Through this example, it can be found that by preferentially assigning premium parking spaces to vehicles with short parking periods (hereinafter referred to as "short stop vehicles"), in this system, the overall walking time of the system is significantly reduced, and the efficiency is greatly improved. How to stop the short stop vehicle to stop at the high quality berth and stop the vehicle to the ordinary parking by reasonable pricing of the high quality berth is the problem to be solved by the present invention.

The invention provides a method for preferential berth reservation and dynamic pricing based on priority berth quantity limitation and parking demand feature distribution, and obtains parking time control threshold and charging standard by obtaining parking parking behavior characteristics in the region, thereby realizing induction Transfer long-term vehicles to ordinary berths, and dynamically adjust prices according to actual demand status. At the same time, we provide high-quality berth reservation service to improve the utilization of quality berths and reduce the total cruise time and walking time of the system.

The high quality berth described here refers to a parking space with higher convenience. The obvious feature is that the distance from the final destination of the driver to the parking area is relatively short, and the walking time required by the driver after parking is short. The ordinary berths described here refer to berths that are less convenient than high quality berths, and are characterized by relatively remote locations, and the walking time required for the driver to walk to the final destination in the parking area after parking is longer. When high quality berths and ordinary berths are present in a certain area, the high quality berths therein can be pre-arranged and priced by the method of the present invention.

The invention specifically includes the following steps: (1) Establish a parking area geometry information table. The information contained includes the number m parking garage entrance area, distance between the motor car dealers distance d _n between the respective inlet and garage parking area quality berths, each parking area and a general parking garage entrance and ordinary quality Berth The walking distance between the berths Ad, these data are obtained by field measurements. An example of the established parking area geometry information table is as follows:

(2) Determine the unit billing time t _Q . The unit billing time length t _Q may be any length of time longer than the required price period. If the parking fee policy within 3 hours is to be established, it shall satisfy ^ < 3 hours. The portion of the parking duration that is less than one t _Q is calculated by pressing a ^ during billing. In particular, the values proposed in the method should satisfy 1 minute ≤ to ≤ 20 minutes. This is because the larger the to, the more obvious the step-by-step mutation of the parking charge with the increase of the parking time, and the user whose time is near the sudden change threshold is more sensitive to the change of the charge, thereby increasing the user's time anxiety and reducing the parking user's Satisfaction with parking services.

Figure 5 shows that in the case where a user is parked for 2 hours and the total cost of the final payment is the same, the unit billing time is set to = 1 hour and to = 10 minutes. The parking fee is 2 hours. Changes in internal growth over time. As can be seen from Figure 5, in the case of t _Q = l hours, the charging increase has a significant step change, which makes the parking person have obvious psychological anxiety when parking for nearly 2 hours, because the worry time is more than 2 Hours, the cost will increase suddenly. In the case of to = 10 minutes, the fee growth is more gradual, and users do not have to worry about a large increase in costs due to exceeding a certain time limit, thereby improving the user's parking experience.

(3) Determine the parking feature data in the parking area. These data include the number Q of vehicles entering the parking area with the parking demand, the parking time t of the vehicle with the parking demand, the proportion of the vehicles entering the parking area in the parking area /? _n , the parking user in the parking area The travel time value α, the average speed in the parking area, the average walking speed in the parking area of 17 _W, and the price sensitivity coefficient μ of the parking user in the parking area.

Wherein, the ratio of the vehicles entering the parking area from the entrance of each parking lot /? _η , the travel time value α of the parking user in the parking area, the average speed in the parking area, the average walking speed of the parking users in the parking area 17 _W, and the parking The price sensitivity coefficient μ of the parking user in the area can be obtained by field sampling survey in the parking area. In determining the two data of the number Q of vehicles in the parking area and the parking time t of the vehicle having the parking demand, it is necessary to utilize at least one of the following four related data: a) parking at the same time The number of parking vehicles Q _τ and the historical experience value t _{τ of the} parking time in the area. Through intelligent parking facilities Stored data or manual records, get the number of arrivals of high-quality berths and parking vehicles at ordinary berths, and sum, and record the parking time of each vehicle. Randomly extract the recorded values of multiple days and take the average value, which is the parking area. The historical experience value of the number of vehicles parked inside and the historical experience value of the length of parking. In particular, when the historical data is extracted and counted, the selected date is divided into three categories: the working day, the weekend, and the special holiday. b) Real-time traffic flow Q _{n of} surrounding roads. Refers to real-time traffic flow data published by the traffic management department or related professional third parties around the road network around the parking area. c) Berth reservation data ρ _ΙΙΡ t _m in the berth reservation system. Refers to the user who has the parking demand in advance through the berth system, including the website, mobile APP, WeChat public number, etc., to make an appointment for the high quality berth in the parking area, and to inform the required parking time. The number of premium berths reserved during the reservation system and the appointment period can be obtained in real time from the application background. d) The induced parking demand for temporary activities in known parking areas is t _ni . Temporary activities that will occur in the parking area will increase the parking demand in the parking area, so it is necessary to know the number of people participating in the event and the time when the event is held. Using one or more of the above related data, the number of vehicles Q having parking demand and the parking time t of the vehicle having parking demand in the parking area are predicted by one of the following three methods: a) the parking Number of vehicles with parking demand in the area Q = historical experience value of the number of parking vehicles in the parking area at the same time

Q! +The number of participants in the temporary parking activity in the parking area QjyX car travel ratio; the sharing ratio of car travel is greater than 0.1 less than 0.3, obtained by field sampling survey; parking time t in the parking area is the length of parking The historical experience value tj and the parking time distribution tjy of the temporary parking activity in the parking area are superimposed. b) The number of vehicles with parking demand in the parking area Q = the number of reserved berths in the reservation system Q _m + the historical experience value of the number of parking vehicles in the parking area at the same time! x (l - the proportion of reserved users to all parking users) + the number of participants in the temporary parking activities in the parking area QjyX car travel sharing ratio; the proportion of reserved users to all users is obtained through sample survey, car travel sharing The ratio of the ratio is greater than 0.1 and less than 0.3, which is obtained by field sampling survey; the parking duration t in the parking area is the historical experience value tj of the parking time and the parking time t _m of the parking demand determined by the historical data and the reservation system reservation data. It is superimposed on the parking time t _ni of the parking demand that is induced by temporary activities in the parking area. c) Number of vehicles with parking demand in the parking area Q = real-time traffic flow Q _Ti x of surrounding roads At the same time, the historical experience of stopping the number of noon cars in the parking area

The total duration of the parking time t distribution and the historical data of the parking time history value ~ the historical average of the real-time traffic flow of the surrounding roads ~

The distribution t _{T is} consistent. Method a) should be used when determining the premium berth price offered to the booking user in the reservation system _; method b) or method c) should be used when real-time dynamic adjustment of the premium berth price is made. However, the price adjusted in real time is only applied to non-reserved users who enter the berth after the price is released. For the parking users who have made reservations on the APP, the charging standard is still executed according to the charging standard notified at the time of the reservation.

Determine the parking duration control threshold t _m . Follow these steps:

From the data obtained in the step (3), the total number of vehicles Q having parking demand in the parking area is grouped according to the parking time length t, and the group distance is the unit charging time period t _Q . That is, the parking duration of the i-th data is = iXt _Q , and the number of vehicles in the group is i = 1, 2, 3, T/t ₀ , where T is the total pricing duration; by the i-th group Number of vehicles, calculate the average number of arrivals per hour of the i-th group of vehicles q _Qi = ^-; Calculate the i-th of the arrival amount q of the i-th group of vehicles per t _Q duration ₍ ^ and the parking length t of the i-th group of vehicles) The amount of parking space and time resources required for a group of vehicles = i^ Xt _{i ;}

Calculate the number of parking space-time resources required for the vehicles in the former group i by the number of parking space resources S ₂ , S _t required by each group of vehicles ∑s = S _x + S ₂ +... + Sr'

Calculated by the high number of berths s berth which can provide temporal resources parking S _p = 0.85X5X t _0; the ΣSi, ΣS ₂ Σ berth and can provide high spatial and temporal resources parking S _p is compared, to find a i', so that ∑S is closest but not exceeding S _p , and the parking duration corresponding to the group i' is the parking time control threshold t _m . Figure 6 shows the calculation flow of the parking duration control threshold t _m . This calculation process can be calculated using the parking demand statistics table. The following table is an example of a parking demand statistics table.

Parking time ^ Average arrival amount q _oi Required parking resources accumulation required parking space and time resources 辆 Number of vehicles

(lOmin) (cars / lOmin ) (one 'hours' (one hour)

1 6 0.333 0.056 0.056

2 1 0.056 0.019 0.074

3 1 0.056 0.028 0.102

4 1 0.056 0.037 0.139

5 3 0.167 0.139 0.278

6 2 0.111 0.111 0.389

7 3 0.167 0.194 0.583

8 1 0.056 0.074 0.657

9 3 0.167 0.250 0.907

Determine the price of premium berth parking. Follow these steps:

From the known ordinary berth parking charge policy, calculate the parking charge price P _t ' of the ordinary berth when the parking time is the parking time control threshold 1 _∞ _;

The free parking time t of the high quality berth is set _/ t is not charged when the vehicle is parked at the high quality berth; the value of t _f may be 0, that is, the vehicle starts to charge from a high quality berth;

Cost pricing method to determine the quality berth ^ price floor in the length of time, the first after the end of a long ^ as a high-quality berths free parking ^ price charged p ₁ within the length of _time; a time when the parking duration is 1 _∞, parking ordinary berth The price P _t 'and the data in the parking area geometric information table are calculated according to the formula ( 1 ). When the parking time is t _m , the parking fee of the vehicle parked at the high quality berth /

( 1 )

, _d v _w where:

P _t ' represents the parking fee required to park the vehicle at the ordinary berth when the parking time is equal to the duration control threshold t _m ;

a represents the travel time value of the parking user in the parking area;

Ad means walking distance between high quality berth and ordinary berth;

d' denotes the distance between the ordinary berth and the entrance of the parking area. If there are multiple entrances in the parking area, a weighted average of the travel distance between the ordinary berth and each of the vehicle entrances is used, and the weight is the ratio of the vehicles entering the parking area from the respective entrances. Expressed by (2):

d' =∑^ ₌₁ β _η - ά _η ' ( 2 ) where:

m represents the total number of vehicle entrances in the area; β _η represents the proportion of vehicles entering the area from the nth car line entrance in the parking area;

d indicates the distance between the nth car entrance and the ordinary berth.

d represents the distance between the high quality berth and the parking area entrance of the parking area. If there are multiple car park entrances in the area, the weighted average of the distance between the high quality berth and each car line entrance is used, and the weight is the ratio of the vehicles entering the parking area from each entrance/? _n . Expressed by (3):

(3) among them:

d _n represents the distance between the nth car line entrance and the premium berth. The rest is the same as above. v _c represents the average traveling speed of the vehicle in the parking area; v _w represents the average walking speed of the traveler in the parking area.

e) ratio of when the parking length 1 _∞ when the vehicle is parked in a parking high berth at / by the formula (4) is calculated to obtain high berth price increment variance Δρ, i.e., high berth of η length t _Q when units charging The part of the (n-1)th unit billing time t _Q charge rises:

2( ^ _{( 4 )} where:

Ν indicates the number of unit billing durations ^ in the parking duration control threshold 1 _,, that is, Ν =

f) When the free parking from high quality berths on a long time t _Q the price charged increment and the price of high-quality berths ₁ after the end of a long ^ variance Δρ, after the end of a long ^ time to get high-quality berths free parking according to equation (5) to calculate _{the n-th} t _Q The price of the time ρ _{η :}

n ⁼ Pi + ( ^{η —} 1) " ( 5 ) where steps b), c), d) can be performed simultaneously, and Figure 7 shows a flow chart for calculating the premium berth parking charge price. In this step, A possible implementation is for a parking user who makes a reservation for a premium berth using the mobile terminal application APP, and the system provides a charging plan at the time of reservation, and at the time of final charging, a certain degree of discount is provided on the basis of the charging scheme. .

In the parking reservation service, the user should first provide the estimated parking time, calculate the parking fee for different berths according to the estimated parking time, and inform the user.

In the reservation system, two prices, the retractable reservation price and the irrevocable reservation price, should be provided for the user to select. The retractable reservation price means that the appointment can be cancelled free of charge before the agreed time ^ hour, and the value of t _e ranges from 0.1 to 5. The smaller the value of t _e , the higher the price of the reservation can be revoked. The irrevocable reservation price means that once the reservation is completed, it cannot be revoked free of charge, and if the reservation is cancelled, the prescribed default deduction is required, and the amount of the default deduction is set to 1% to 100% of the total price of the reservation order, and the time distance is revoked. The closer the appointment time, the higher the amount of default deduction. The parking reservation system is composed of three modules, including:

i) a statistical module for counting the number and distribution of all available berths in the parking area when the user makes an appointment;

i) a calculation module for calculating the parking charge when parking on different berths according to the attribute of the available parking space and the estimated parking time of the user; Iii) A publishing module for generating and publishing parking space information based on available berths and calculated parking charge prices for the reserved user to select a parking space.

In this step, one possible implementation is to consider the price sensitivity coefficient μ of the parking user in the parking area. That is, when the parking user in the parking area has a small response to the price change of the premium berth charge, the calculated parking charge price may be multiplied by a coefficient μ, 1 < μ < 1.5 to achieve a certain expansion to achieve the purpose of effective splitting. . The price sensitivity coefficient μ can be set to take into account user loyalty and user coupon usage. Among them, user loyalty is measured by the user's repurchase rate, that is, the number of repeated parkings in a month. The higher the user loyalty, the less sensitive the price is, and the corresponding price sensitivity coefficient μ is larger. The higher the user coupon usage rate, the more sensitive the user is to the price and the smaller the price sensitive factor μ.

(6) Determine the real-time detection interval duration ^, and perform real-time statistics and detection on the actual number of vehicles parked at high-quality berths and the real-time occupancy rate of high-quality berths. Use intelligent gates, video parking detectors, infrared parking detectors, microwave parking detectors or geomagnetic coils to count the actual number of vehicles parked at high quality berths from the beginning of the pricing period to the current time. The real-time occupancy of the berth and report the data to the system.

(7) Compare the real-time detection data with the forecast data to determine the premium berth parking fee price for the subsequent period. Calculating the predicted demand from the start of the pricing period to the current time from the number of vehicles Q in the parking area determined in the step (3), from the start of the pricing period to the current time

Q _v = QX - Compared with the actual number of vehicles parked at the high quality berth, if 0.85 ≤ Q _r ≤ i ^pv the total length of the pricing period τ ''"' ^/u '^

1.15 and 0.7 ≤ O _r ≤ 0.9, the original charging plan is not

Update relevant parameters, and publish and release new charging plans.

The above symbols and their meanings are summarized in the following table: Symbol Meaning

m Total number of car entrances in the parking area

d _n parking area between the nth car line entrance and the quality berth

d The distance between the nth car entrance and the ordinary berth in the parking area

Ad quality berth and regular berth walk

To unit billing time

Q Number of vehicles with parking demand in the parking area

t Parking time of vehicles with parking demand in the parking area

Historical experience of parking time in parking area Historical experience of number of parking vehicles in parking area Real-time traffic flow of surrounding roads tIII Parking time of reserved berths on mobile terminal APP Qui berth reservation number on the mobile terminal APP The number of parking hours for the temporary parking activity in the parking area

Q N The amount of parking demand for temporary activities in the parking area βη The proportion of vehicles entering from the nth car entrance in the parking area a The travel time value of parking users in the parking area

Average speed of vehicles in the parking area

Average walking speed of parking users in the parking area

μ Price sensitivity coefficient of parking users in parking area

Parking time control threshold

Ti parking time of the i-group of vehicles in the parking demand statistics table

q; Number of vehicles in the i-group of the parking demand statistics table

q ₀ i The average amount of arrival of the i-th group of vehicles per t _Q duration in the parking demand statistics table

T total pricing duration

Number of parking space-time resources required for the i-th vehicle in the Si Parking Demand Statistics

The number of parking time and space resources required for the first group of vehicles in the parking demand statistics table

S Number of berths for premium berths

s Number of parking space resources available in premium berths

V Parking demand statistics table, the parking time control threshold 1 „ ₁ corresponds to the group

Parking Pt length equal to t _m, the vehicle is parked in the general parking is required to pay parking fees parking duration equal to p _t t _m, the vehicle is parked in high-berth required to pay parking fees

Free parking time for premium berths

Pi high quality berth free parking time ^ the first ^ length of the price after the end d the weighted average of the quality berth and parking area between the car park entrance d' ordinary berth and parking area between the car line entrance Weighted average

Δρ high quality berth price increment variance

N When the parking time is t _m , the number of unit billing time t _Q

Pn premium berth free parking duration ^ the price of the nth ^ duration after the end

Yi rank factor of high quality berth with grade i

Ai The distance from a high quality berth to the nearest entrance of the parking area a ₀ The distance from a high quality berth to the nearest exit of the parking area

CLf A high-quality berth is the distance from the nearest self-service payment machine in the parking area. ac The distance from a high-quality berth to the nearest surveillance camera in the parking area is calculated as a + a ₀ + for all the quality parking spaces in the parking area where a high-quality berth is located. a _f +

The minimum value obtained after ^ s berth area of a quality berth

The berth area of the highest quality berth in the parking area where a high quality berth is located c The total parking cost when the parking user selects a quality berth

P Parking fee paid by parking users when selecting premium berths

Td The travel time required for the parking user to travel from the parking area entrance to the premium berth. The required walking time between the parking user and the high quality berth and the destination. c' The total parking cost when the parking user selects the ordinary berth.

P' Parking fee paid by parking users when choosing a normal berth

The travel time required for the parking user to travel from the entrance of the parking area to the ordinary berth t' The walking time required for the parking user to walk between the ordinary berth and the destination t _{r The} real-time detection interval of the high quality berth

Qr The actual number of vehicles parked at high quality berths

o _r real-time occupancy of premium berths

QP Predicted value of the number of vehicles with parking demand from the beginning of the pricing period to the detection time.

1 is a flow chart showing the implementation of the prior art 1.

Fig. 2 is an illustration of an embodiment of the prior art 1. Fig. 3 is an example of berth classification in the prior art 2.

Figure 4 is a comparison of the existing situation and the optimization situation.

Figure 5 is a graph of the change in charging for different unit billing durations t _Q .

Figure 6 is a flow chart for calculating the parking duration control threshold 1 _∞ . Figure 7 is a flow chart for calculating the premium berth parking charge price. Figure 8 is a possible classification of multiple high quality berths in a parking lot.

Figure 9 is a flow chart of the implementation of the high-quality berth dynamic pricing method with priority short stop.

Fig. 10 is a schematic diagram of a parking area in the embodiment. Figure 11 is an internal plan view of the parking lot where the premium berth is located in the embodiment.

Concrete 3⁄4 3⁄4r

Specific embodiment 1

In this embodiment, a possible implementation manner of the above invention is provided. The overview of the parking area in this example is as shown in FIG. There are two entrances El and E2, the on-street parking space PI is a high-quality parking resource, there are 100 parking spaces; the off-street parking lot P' is an ordinary parking resource, and the charging research time is from 07:00 to 24:00 on a certain day. The user can reserve the premium berth in the parking area in advance through the relevant mobile terminal application APP. At the time of the reservation, the APP will inform the user of the premium price of the premium berth, and finally charge the user who made the reservation according to the price. Now, the high-quality berth dynamic pricing method with priority short stop is used to make parking pricing for APP reservation users for the high quality berths in the parking area. The implementation process is as follows:

1. Through the field measurement, establish the geometric information table of the parking area as follows:

Set the unit billing time to 10 minutes, and the part that stops for less than 10 minutes is charged for 10 minutes. According to the field sampling survey, the number of vehicles entering the parking area from E1 every day in the parking area is 400, and the number of vehicles entering the parking area from E2 is 200; the travel time per person in the parking area is 25 yuan/hour; The average speed of vehicles in the area is 10km/h, and the per capita walking speed is 5km/h. From the high-quality berth parking lot P and the intelligent berth data at the ordinary berth parking lot P', the historical experience value of the number of parking vehicles in the parking area is 500, and the parking time distribution is known. At the same time, the parking area is known to be in this day. An event is scheduled to be held. The number of participants is expected to be 300, and the event time is from 9:00 to 11:00. The proportion of people who participated in the event before driving was about 20%. Because it is the high quality berth parking pricing for the APP reservation user, according to the method a) in the step (3), the number of vehicles having the parking demand in the parking area is predicted during the day: the parking demand vehicle in the parking area Number Q

= historical experience value of parking vehicle arrival rate

+Number of participants in temporary parking activities in the parking area X Sharing ratio of car travel = 500 + 300 X 20O

= 560 vehicles

The number of vehicles Q with parking demand in the area is grouped according to their parking time t, and the parking demand statistics table is as follows: Parking time length ^ Average arrival amount q _oi Required parking resources accumulation required parking space and time resources 辆 Number of vehicles

(lOmin) (cars / lOmin ) (one 'hours' (one hour)

1 10 0.1190 0.0198 0.0198

2 11 0.1310 0.0437 0.0635

3 14 0.1667 0.0833 0.1468

4 5 0.0595 0.0397 0.1865

5 9 0.1071 0.0893 0.2758

Cl7S8S0/Z.l0Zai/X3d £18 8Ϊ0Ζ OAV //3u O 3s8soZJOSI1d 28siAV

80 2 0.0238 0.3175 36.4226

81 3 0.0357 0.4821 36.9048

82 1 0.0119 0.1627 37.0675

83 2 0.0238 0.3294 37.3968

84 0 0.0000 0.0000 37.3968 At the same time, because the number of high quality berths is s=100, the parking space and time resources it can provide S _p = 0.85 X5Xt ₀ =

0.85x l00x (^) = 14.1667 (one hour). By comparing with the accumulated parking space and time resources ∑S of each group in the parking demand statistics table, it is found that in the 42nd group data, that is, when the parking time is = 42 X 10 = 420 min, the accumulated parking space and time resources are required. S = 13.7698 hours, which is the closest group that does not exceed S _p = 14.1667 hours. Therefore, the parking time control threshold t _m = 420 min of the parking area is determined. It is known that the parking fee for the ordinary berth in the parking area is 5 yuan/h, and the portion for less than 1 hour is calculated as 1 hour. When the parking time is the parking time control threshold t _m = 420min, the parking fee for parking in the off-street parking lot is = 7h X5 yuan / h = 35 yuan. High quality parking

The weighted average of the distance between the 400 200 and the parking lot entrances in the parking area d = ∑ TM i /? _n ■ d _n = X0.5 + ■ X

400+200 400+200

400

2 = 1 km; weighted average of the travel distance between the ordinary berth and the parking lot entrance of the parking area = ∑^ ₌₁ β _η ■ d = X

400+200

200

L.2 +■ x l.2 = 1.2 km. According to formula (1), when the parking time is t _m , the parking of the vehicle at the high quality berth is calculated.

400+200

Fee

Id'- d Ad\ L.2 - 1 1.4\ 兀一

P _t = P _t + α + 2 - 1 = 35 + 25x _{+ 2} x ) = ₄₉ .5

V v _d v _w J

The free parking time for setting high-quality berths is to = 30min, that is, there is no charge when parking on-street parking is no more than 30min. Then, when the parking duration is the parking duration control threshold t _m = 420 min, the number of unit billing durations t _Q = lOmin included therein is N = t _m -tf _ 420-30

39 c

10 At the same time, according to the cost pricing method, the price limit of the on-street parking space at P1 is 2 yuan/h, that is, the charge for the first unit billing time t _Q = lOmin after the end of the free parking time to = 30min _Pl = 0.5 yuan. Therefore, find _Δ ρ = 3⁄4^ = according to (4)

W(W_1) ^2X( 39 ⁹ x' ⁵ ' ⁵ ) = 0.040 yuan

(39 ³ -l) Therefore, the parking price for PI premium berths is free for the first 30 minutes, and the first lOmin for the first time is 0.5 yuan. After that, the price of each lOmin is 0.040 yuan higher than the previous lOmin, that is, the second one. lOmin charges 0.540 yuan, the third lOmin charges 0.580 yuan, the fourth lOmin charges 0.620 yuan ... and so on, as shown in the following table: Period (min) 0-30 30-40 40-50 50-60 (30+n X lO)- [30+(n+l)X lO] Charge (yuan) 0 0.5 0.540 0.580 0.5+Π Χ0.040 At the same time, in order to encourage parking users to make reservations for high-quality berths through the APP, the final parking charge for users who come to the premium berth parking after the advance reservation through the APP is calculated at 90% of the above calculated price, that is, enjoy a 10% discount. On the day of parking, if the price of the premium berth is adjusted in real time, the fee for the reserved user does not change, and it is still executed according to the charging standard notified by the system at the time of the reservation.

Specific embodiment 2

In this embodiment, a possible implementation of the above invention is provided. Also in the parking area described in the first embodiment, it is now known that the number of high quality berths reserved by the APP is used for the quality berth in the parking area. The same day applies to the parking fee standard for unreserved vehicles, and is dynamically adjusted according to the actual parking situation. The implementation process is as follows:

1. By field measurement, the parking area geometry information table is established as follows, and the results are the same as in the first embodiment.

2. Set the unit billing time to 10 minutes, and the part that stops for less than 10 minutes will be charged for 10 minutes.

3. According to the field sampling survey, the number of vehicles entering the parking area from E1 every day in the parking area is 400, and the number of vehicles entering the parking area from E2 is 200; the travel time per person in the parking area is 25 yuan/hour. The average speed of vehicles in the parking area is 10km/h, and the per capita walking speed is 5km/h.

It is known that in the relevant mobile terminal APP, the number of high-quality berths in the parking area is 60, and the number of parking users who use the APP to reserve high-quality parking spaces here accounts for about the parking users here. 10% of the total number of people, the parking time is filled in at the time of booking; the historical experience value of the number of parking vehicles in the parking area is 500 by the intelligent gate data at the parking lot P, and the parking length is known. It is known that there will be an event on this day in the parking area. It is expected that the number of participants will be 300, and the event time will be from 9:00 to 11:00. The proportion of people who participate in the event is about 20%.

Follow the method in step (3) b) predict the number of vehicles with parking demand in the parking area during the day. The number of vehicles with parking demand in the parking area Q = the number of reserved berths in the parking area

+ Historical experience value of parking vehicle arrival rate x (l - 比例 the ratio of reserved users to all users) + number of participants in temporary parking activities in the parking area X Sharing ratio of car travel

= 60 + 500x(l - 10%) + 300x20% = 570 (ft)

4. The number of vehicles with parking demand in the area is grouped according to their parking time t. The parking demand statistics are as follows:

(lOmin) (cars / lOmin ) (one 'hours' (one hour)

1 7 0.0833 0.0139 0.0139

76 2 0.0238 0.3016 33.6389

77 5 0.0595 0.7639 34.4028

78 1 0.0119 0.1548 34.5575

79 1 0.0119 0.1567 34.7143

80 1 0.0119 0.1587 34.8730

81 0 0.0000 0.0000 34.8730

82 0 0.0000 0.0000 34.8730

83 2 0.0238 0.3294 35.2024

84 3 0.0357 0.5000 35.7024 At the same time, because the number of high quality berths is s=100, the parking space and time resources that can be provided are S _p = 0.85X5Xt ₀ =

0.85xl00x(^) = 14.1667 (one hour). By comparing with the accumulated parking space and time resources ∑S of each group in the parking demand statistics table, it is found that in the 41st group data, that is, when the parking time length = 41X10 = 410min, the accumulated parking space and time resources ∑S = 14.0357, hours, is the group that is closest and does not exceed S _p = 14.1667 hours. Therefore, the parking time control threshold t _m = 410 min of the parking area is determined. It is known that the parking fee for the ordinary berth in the parking area is 5 yuan/h, and the portion for less than 1 hour is calculated as 1 hour. Then when the parking time is the parking time control threshold t _m = 410min, parked in the off-street parking lot

Yuan / h = 35 yuan. The weighted average of the quality berths and the distance between the car park entrances in the parking area d = ∑ TM ₌₁ /? _n ■ d _n = 7^^X0.5 + ^^^x

2 = 1 km; weighted average of the distance between the ordinary berth and the parking lot entrance of the parking area cf = ι^ιβη■ d = -^-x

!- ² +7^1^x1-2 = 1-2 km. Calculate the parking charge P _{t of the} vehicle parked at the high quality berth when the parking time is t _m according to formula (1):

P _t = P + α

The free parking time for setting high quality berths is = 30min, ie no charge when parking on the road is no more than 30min. Then, when the parking duration is the parking duration control threshold t _m = 410 min, the number of unit billing durations t _Q = lOmin included therein is N = tm-tf 410-30 „ ₀

= = oo

To 10 At the same time, according to the cost pricing method, the lower limit of the on-street parking space at P1 is 2 yuan/h, that is, the first unit billing time t _Q = lOmin after the end of the free parking time to = 30min. Charge _Pl = 0.5 yuan. Therefore, press (4) ^ _Δ ρ = ^ = β ^ = 0.043^ Therefore, the P1 premium berth is suitable for unreserved users. The parking charge price is free for the first 30 minutes, after the first lOmin The charge is 0.5 yuan. After that, the price of each lOmin is 0.043 yuan higher than the previous lOmin, that is, the second lOmin charge is 0.543 yuan, the third lOmin charge is 0.586 yuan, and the fourth lOmin charge is 0.629 yuan... By analogy, as shown in the following table:

6. Set the system real-time detection interval length = lh, that is, every 1 hour, the actual number of vehicles parking at the high-quality berth and the real-time occupancy rate of the high-quality berth at this time are automatically detected by the intelligent gate and video parking detector. And reported to the system, compared with the forecast data and target values. Take the test result of this time 09:00 this time as an example to explain the comparison process.

The system detects the data from the gate detection at 09:00 in the morning. During the period from 07:00 to 09:00, the total number of actual vehicles parked at the high quality berth is = 120, by the video parking detector at 09:00. The test data shows that there are a total of 79 high quality berths at this time, that is, the real-time occupancy rate of high quality berths at this time is ^=0.79 ί IB / ί I r> r»r> Π A ;^ * # , μ r,n M,| From the start of the 3⁄4 price period to the current time « Duration from high quality parking from 07:00 as of the beginning of the As Palace at 09:00, although the forecast demand (3⁄4 = QX - m ^ T =

560x ^ = 80 cars, Bay IJ has 120> 1.15x80 = 92, that is, it does not satisfy 0.85 (3⁄4 ≤ Q _r ≤ 1.15 (3⁄4. Therefore, it is necessary to re-predict the number of vehicles Q in the parking area for the remaining time of the day, That is, step (3) to step (5) are re-calculated, the charged price of the premium berth is recalculated according to the new parameters, and the updated parking charge price is released. The parking from the high quality berth is from 09:00 to the next update. For non-reserved users, the parking fee will be charged according to the updated charging standard. However, the parking price of the parking users who have entered the market will still be charged according to the charging standard announced at the time of entry; the reserved user will still inform the APP according to his appointment. The fee is charged.

Embodiment 3

In this embodiment, a possible embodiment of the above invention is provided, and the parking area and known conditions are the same as those described in the second embodiment. Further, the high quality berths in the high quality berth parking lot P are graded according to the location, size and other conditions, and differentiated pricing is implemented for different grades of high quality berths. The specific implementation process is as follows: Since the parking area and the implementation conditions are the same as those in the second embodiment, the preliminary steps and results are the same as those in the second embodiment, and the price changes with the parking time of the high quality berth under the non-grading condition are as follows: Show:

The quality berths are now graded. The internal plan view of the parking lot P where the premium berth is located is shown in Fig. 14. The berth of the sign 1 in the figure is a first-class high-quality berth located close to the entrance and exit, the elevator or the paying machine and larger than the remaining berths. The berth of the sign 2 is the same size as the majority berth but close to the entrance and exit, the elevator or the paying machine. High quality berths, unmarked berths are three quality berths. The high quality berth grade is divided as shown in Figure 8. The grade factor of different grades of high quality berths is set as shown in the following table: High quality berth level 1st level 2rd level 3 sign 1 2 No sign level factor)^ 1.5 1.2 1.0 The charging standard for different grades of high quality berths in the parking lot is expressed in the form of a high quality berth charging matrix as shown in the following table:

According to the grade factor and the high-quality berth charge price calculated in the non-grading case calculated in the second embodiment, the parking area can be obtained.

In the real-time detection of the actual number of vehicles parked at 13 high quality berths and the real-time occupancy rate of high quality berths on the same day, both indicators satisfy 0.85ρ _ρ ≤ Q _r ≤ 1.15ρ _ρ and 0.7 ≤ 0 _r ≤ 0.9 ο Therefore, the premium berth for the unreserved users on the same day has been executed at the above price and has not been changed.

Claims

Claim

1. A priority short-stop high-quality berth reservation and dynamic pricing method, the steps of which include:

1) to establish a parking area geometry information table, the table comprising a number m of garage parking area inlet, garage parking area from among dealers with high inlet berth d _n, each parking area between the common inlet and dealers berth The route of the car and the walking distance between the premium berth and the ordinary berth, these data are obtained by field measurement;

2) Determine the unit billing time to ;

3) determining parking characteristic data in the parking area, including the number Q of vehicles having parking demand in the parking area, the parking time t of the vehicle having the parking demand, the proportion of the vehicle entering the parking area in the parking area, parking The travel time value α of the parking user in the area, the average speed i7 _d in the parking area, the average walking speed u _w in the parking area, and the price sensitivity coefficient μ of the parking user in the parking area;

4) Determine the parking time control threshold t _{m ;}

5) Determining the premium berth parking charge price p _n and adjusting it by adjusting the price sensitivity coefficient μ of the parking user in the parking area that is multiplied by the value to satisfy 1 ≤ μ ≤ 1.5, and Discounted parking users are offered discounts;

6) Determine the real-time detection interval duration ^, and perform real-time statistics and detection on the actual number of vehicles parked at the high quality berth and the real-time occupancy rate of the premium berths every inch length;

7) Compare the real-time test data with the forecast data to determine the premium berth parking charge price for the subsequent time period.

2. The priority short stop high quality berth reservation and dynamic pricing method according to claim 1, wherein the unit billing time to value should satisfy 1 minute ≤ to ≤ 30 minutes.

3. The priority short stop high quality berth reservation and dynamic pricing method according to claim 1, wherein a ratio of vehicles entering from each vehicle entrance in the parking area/? _n , and a parking user travel time in the parking area. The value α, the average speed in the parking area, the average walking speed of the parking user in the parking area of 17 _W, and the price sensitivity coefficient μ of the parking user in the parking area are obtained by field sampling surveys in the parking area.

4. The priority short stop high quality berth reservation and dynamic pricing method according to claim 1, wherein the number Q of vehicles having parking demand in the parking area and the parking time t of a vehicle having parking demand are determined. , first get at least two of the following four related data:

a) The historical experience value of the number of parking vehicles in the parking area at the same time and the length of the parking time tj: refers to the data or manual records stored by the intelligent parking facilities, respectively, and the high-quality berths and the common berths at the same time. Number, summing the two to obtain the number of parking vehicles in the parking area; simultaneously recording the parking time of each vehicle; randomly extracting the recorded values of multiple days and taking the average value, that is, the historical experience of the number of parking vehicles in the parking area Value and The historical experience value of the length of the parking period; in particular, when the historical data is extracted and counted, the selected three periods of the working period, the weekend, and the special holiday should be separately counted; b) the surrounding roads Real-time traffic flow Q _n : refers to real-time traffic flow data published by the traffic management department or related professional third parties around the road network around the parking area;

c) berth reservation data Q _m , t _m in the berth reservation system; means that the user with parking demand advances through the berth system, including the website, mobile APP, WeChat public number, etc., to the high quality berth in the parking area. Appointment, and inform the time period of parking required; the number of high quality berths reserved on the reservation system and the appointment time period can be obtained in real time from the application background;

d) the amount of trapped parking for temporary activities in known parking areas, t _ni : the number of participants in the temporary activities that will occur in the parking area and the time of the event, the length of parking for the induced parking demand and The duration of the event is consistent;

Using the acquired data, the number Q of vehicles having parking demand in the parking area and the parking time of vehicles having parking demand are calculated according to one of the following three methods: i) parking demand in the parking area Number of vehicles Q = Historical experience value of the number of vehicles parked in the parking area at the same time Q! +The number of participants in the parking area for the number of participants in the car travel; the sharing ratio of the car trip is greater than 0.1 and less than 0.3, obtained by field sampling survey; the parking time in the parking area is the history of the parking time The experience value tj is superimposed on the parking time distribution of the parking demand induced by the temporary activity in the parking area; ϋ) the number of vehicles having parking demand in the parking area Q = the number of reserved parking spaces in the APP ^^^ + Historical experience value of the number of parking vehicles in the parking area Q! x(l -

The ratio of APP reservation users to all parking users) + The number of participants in the parking area for the number of participants in the parking area; the ratio of APP reservation users to all users is obtained through sample survey, and the contribution ratio of car travel is More than 0.1 is less than 0.3, obtained by field sampling survey; parking time t in the parking area is the historical experience value tj of the parking time and the parking time of the parking demand determined by historical data, APP reservation data 3⁄4 and temporary activities in the parking area The number of parking hours for the induced parking demand is increased _{by the} three items; iii) the number of vehicles with parking demand in the parking area Q = the real-time traffic flow of the surrounding roads Q _TI x Historical experience value of the number of parking vehicles in the parking area at the same time / historical average value of real-time traffic flow of surrounding roads

The distribution of the total demand t time t is consistent with the distribution tj of the historical data of the parking time history data; when determining the price of the premium berth provided to the reserved user, method i) should be used _; when the real-time dynamic adjustment of the premium berth price is performed, Method ii) or method iii) should be used; the price adjusted in real time is only applicable to non-reserved users who enter the berth after the price is released. For the parking users who have made the reservation, the price charged is still executed according to the charging standard notified at the time of the reservation. .

5. The priority short stop high quality berth reservation and dynamic pricing method according to claim 4, wherein in the reservation system, two prices of the revocable reservation price and the irrevocable reservation price are provided for the user to select; The withdrawal of the reservation price means that the appointment can be cancelled free of charge before the agreed time ^ hour, the range of t _e is from 0.1 to 5; ^ the smaller the value, the higher the reservation price can be revoked; the irrevocable The reservation price means that once the reservation is completed, it cannot be revoked free of charge. If you need to cancel the reservation, you need to pay the specified default deduction. The amount of the default deduction is set to 1% to 100% of the total price of the reservation order. The closer the withdrawal time is to the appointment time. The higher the amount of the default deduction.

6. preferentially stopped short of the claims 1 to 5 berth reservation and high dynamic pricing method, characterized in that the length of the control threshold determination parking _∞ 1 the following steps:

(1) The total number of vehicles Q with parking demand in the parking area is grouped according to the parking time t of the vehicle with parking demand. The group distance is the unit charging time t _Q , and the parking time of the data of the i-th group is = Ix t _Q , the number of vehicles is qi, and the range of i is i = 1, 2, 3 T/to, where T is the total pricing duration;

(2) From the number of vehicles in the i-th group, the average arrival amount per hour of the i-th group of vehicles is calculated q _Qi = _qi /(r/t ₀ );

(3) Calculate the amount of parking space-time resources required by the i-th group of vehicles = q _oi Xt _r from the arrival amount _{q of} the i-th group of vehicles per t _Q duration ₍ ^ and the parking length t of the i-th group of vehicles).

(4) Calculate the number of parking space-time resources required for the accumulation of vehicles in the former i group by the number of parking space and time resources S ₂ S _t required by each group of vehicles ∑ = Si + S ₂ +... + S;

(5) Calculate the parking space-time resources that can be provided by the berths of high-quality berths S _p = 0.85X5X t ₀ ;

(6) The ΣS ₁ ΣS ₂ ΣS berth and can provide high spatial and temporal resources parking S _p is compared, to find a i ', such that Σ nearest to but not more than S _p, in its category i' The corresponding parking duration is the parking duration control threshold t _m .

The high quality berth reservation and dynamic pricing method for priority short stop according to any one of claims 1 to 5, wherein the high quality berth parking charge price is determined by the following steps:

(a) From the known general berth parking charge policy, calculate the parking charge price P _t ' of the ordinary berth when the parking time is the parking duration control threshold t _m _; (b) Set the free parking time for quality berths t _{/ ;}

(c) Determining the free parking time for high quality berths by cost pricing method ^ The price of the first t _Q period after the end of the charging price p _{1 ;}

(d) Calculated by = P _t ' + α (― + 2■ ^^ when the parking time is 1 _,, the parking charge of the vehicle parked at the high quality berth where P _t 'is the normal berth when the parking time is 1 _∞ The parking charge price, cf is calculated by cf = ∑ ^ ₌₁ /? _η · d, d is calculated by d 2 ^ ^ / ^ ^ ^;

(e) Calculate the price increase variance Δρ of the high quality berth by Δρ = [2(P _t - N■ _Pl ]/[N(N - 1)], where P _t is the vehicle parked in the high quality berth when the parking time is 1 _∞ Parking charges, N = (t _m - t _f ) / t ₀ , the meaning of each symbol is shown in the table in the manual;

(f) Calculate the free parking time of the high quality berth by = _Pl + (n - 1) ■ Δρ ^ The charging price ρ _η of the _ηth time length after the end, where _Pl is the quality parking berth free parking time ^ the first t _Q after the end The price of the time, Δρ is the price increase variance of the premium berth.

The high quality berth reservation and dynamic pricing method for priority short stop according to any one of claims 1 to 5, wherein the high quality berth parking charge is based on different high quality berths. The difference in location, facilities, size, etc., the quality berth is graded; the quality berth fee matrix is obtained by multiplying the calculated parking charge price by a different high quality berth grade factor ^.

The high quality berth reservation and dynamic pricing method for priority short stop according to any one of claims 1 to 5, characterized in that: the actual number of vehicles parked at the high quality berth and the real-time occupancy rate of the high quality berth are utilized Real-time information collected by at least one intelligent parking facility of intelligent gates, video parking detectors, infrared parking spaces detectors, microwave parking spaces detectors or geomagnetic coils, which are collected every inch of length; said high quality berths The actual number of vehicles parked refers to the actual number of vehicles parked at the high quality berth from the start time of the pricing period to the current time of real-time detection; the real-time occupancy rate of high-quality berths refers to the number of high-quality berths and high-quality berths occupied at the current time. The ratio of the total amount.

The high quality berth reservation and dynamic pricing method for priority short stop according to any one of claims 1 to 5, wherein the real-time detection data is compared with the predicted data, and when > 10, the subsequent time period is determined. The criteria for judging the price of premium berth parking are:

0.85 ≤ Q _r ≤ 1.15 and 0.7 ≤ O _r ≤ 0.9, where the predicted demand from the start of the pricing period to the current time

Q _p = total duration T from the start of the pricing period to the current time / pricing period; if this criterion is met, the original charging plan is unchanged; if not, the method described in claim 1 is re-executed Step 3) to step 5), update the relevant parameters, and develop and release a new charging plan.