WO2018122812A1 - Grading and dynamic pricing method for parking spaces with priority given to short-term parking - Google Patents

Abstract

A grading and dynamic pricing method for parking spaces with priority given to short-term parking, wherein parking spaces in a parking area are graded and premium parking spaces in the area are priced. The method comprises the steps of: (1) establishing a parking space information table; (2) grading parking spaces, and determining a premium parking spaces number; (3) determining a parking fee unit metering duration; (4) determining a premium parking space parking fee price, and adjusting said price; (5) determining a real-time detection interval duration, and performing real-time counting and detection on an actual number of vehicles parked in premium parking spaces and a premium parking space occupancy rate at each interval; (6) comparing real-time detection data and forecast data, and determining a premium parking space parking fee price for a subsequent time period. The method manages and guides parking demands by carrying out differentiated pricing in terms of time and space for different parking resources. A progressively increasing metering means is used to set fee prices for premium parking spaces in a precise manner, and dynamic adjustments are made according to actual circumstances, in order to achieve the goal of preferentially supplying limited premium parking spaces to vehicles parking for short periods of time.

Description

Boost grading dynamic pricing method with priority short stop

The invention relates to a berth hierarchical dynamic pricing method with priority short stop. Usually, when driving, the driver prefers to choose a high-quality berth with convenient location and safe parking. He is not willing to be remote or park a difficult berth. Therefore, a large number of vehicles will cruise for high-quality berth resources, resulting in emissions. increase. The prioritized short-stop berth grading dynamic pricing method proposed by the invention can realize the management and guidance of parking demand by time and space differential pricing of different parking resources. In particular, the present invention considers the number of berths, the characteristics of the parking parking demand, the occupancy rate of the parking space, etc., and by means of incremental progressive charging, finely sets the charging price of the premium berth and dynamically adjusts according to the actual situation, so as to achieve limited The high-quality berths are preferentially provided for the purpose of providing vehicles with shorter parking hours, improving the turnover rate of high-quality berths, enabling more drivers to get comfortable and convenient parking services and shorter walking times.

Background technique

At present, the contradiction between parking supply and demand in many cities is prominent, and the parking problem has become a serious urban traffic problem. Toll collection is one of the most direct and effective means of regulating market supply and demand. Therefore, parking pricing is an important content and key measure of parking management.

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 berth categories using this method in a parking lot. Among them, the mark L is the "large size berth", the mark S is the "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

 N L Lastly reserved adjacent berths for parking spaces 2.00 3.0

 S Safety berths 10.00 10.0 It can be seen that although this pricing method differentiates the berths, it does not make a reasonable choice for the parking time of the parking lots. 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 solved. The problem is calculated by converting to the shortest distance 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 prioritizing short-stay berth grading dynamic pricing based on the berth number limitation and the parking demand feature distribution, which can obtain the parking behavior characteristics in the area, calculate the parking time control threshold and the charging standard, and realize the induced transfer long stop. Vehicles to ordinary berths, and can dynamically adjust prices according to actual demand status. Achieve the goal of improving the utilization of high quality berths and reducing the total cruise time and walking time of the system. The invention specifically includes the following steps: (1) Establish a berth information table. Establish a berth information table to collect information about all berths in the parking area, including the berth number, the average vehicular distance d _e from the parking lot entrance and exit of the parking area, the berth distance from the nearest elevator d _w , and the nearest berth distance The distance of the self-service payment machine, the number n of cameras in the area that can monitor the berth, and the area A of the berth. The established berth information is shown in the following table.

In this step, when the self-service payment machine is not set in the parking area under consideration, the statistical item is deleted in the berth information table; when the camera system for monitoring is not installed in the parking area under consideration, the berth information table is Delete the statistic.

 (2) Grading the berths, the number of quality berths is indeed s. According to the berth information table established in step (1), the grading coefficient γ of each berth is calculated according to the following steps:

a) Calculate the convenience of the berth, c _e , according to formula (1):

among them:

 ^ indicates the average distance of the berth from the entrances and exits of each parking lot in the parking area; indicates the average of the average vehicular distances of all berths in the parking area from the entrances and exits of the parking areas;

b) Calculate the pedestrian convenience c _{w of the} berth, and calculate according to formula (2):

among them:

d _w indicates the walking distance of the berth from the nearest elevator in the parking area;

 Means the average of all berths in the parking area from the nearest elevator in the parking area;

c) Calculate the convenience of the berth payment _C/ , calculated according to formula (3):

among them: ^ indicates the distance of the berth from the nearest self-service payment machine;

 ^ indicates the average distance of all berths in the parking area from the nearest self-service payment machine;

d) Calculate the dimensional convenience of the berth, c _a , and calculate according to formula (4): c _a = - ( 4 ) where:

 4 indicates the area of the berth;

 Indicates the average of all berth areas in the parking area;

e) calculate the safety level c _s of the berth, when the berth can only be monitored by a surveillance camera, ^ = 1; when the berth can be monitored by two surveillance cameras at the same time, it = 1.25; when the berth can When monitored by three or more surveillance cameras at the same time, ^ = 1.5; when the berth is in the blind zone of all cameras and cannot be monitored, it = 0. f) Calculate the gradation coefficient γ of the berth, Equation (5) for calculation:

among them:

γ represents the rank coefficient of the berth, and the larger the γ, the better the comprehensive condition indicating the berth;

Γο characterizes the minimum standard for the set of high quality berth conditions, with a range of 12 . _The larger the value of _Yq , the better the condition of high quality berth in this area, and vice versa. The γο can be set by the parking facility operator, and can take values of 1, 1.2, 1.25, 1.4, 1.5, 1.75; the rest of the symbols have the same meaning as before.

g) Determine the number s of premium berths. When the berth convenience line c _e , pedestrian convenience c _w , payment convenience _{C /} , berth size convenience and safety degree ^ one of the five items is greater than or equal to 1.5, or the berth grade coefficient γ is greater than At 1 o'clock, the berth is a high quality berth and the remaining berths are ordinary berths. The calculation results of all berths in the parking area are counted, and the number s of high quality berths in the parking area is obtained.

In this step, another possible implementation manner is that when the number of berths is small, the number of berths can be roughly graded according to the position, safety and size conditions of different berths, and the quality is different grades. The berth sets a fixed grade factor γ. The better the conditions of the quality berth, the higher the level, and the corresponding level coefficient γ. Figure 8 shows a possible grading scheme for multiple berths located in the same parking lot. The grading factors are set as shown in the table below.

Determine the unit billing time t _Q . The unit billing time length t _Q can be any length of time longer than the required price period, if The parking charge policy within 3 hours should be met for ^ < 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.

 (4) Determine the parking feature data in the parking area. These data include the number of vehicles entering the parking area with parking demand Q, and the parking time t of vehicles with parking demand.

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 historical experience value tj of the number of parking vehicles Q j and the length of parking in the area. Through the data or manual records stored in the intelligent parking facilities, the number of arrivals of the parking vehicles of the high quality berths and the ordinary berths is obtained and summed, and the parking time of each vehicle is recorded, and the recorded values of multiple days are randomly selected and averaged. That is, the historical experience value of the number of parking vehicles in the parking area and the historical experience value of the parking time. 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 _Ι , t _m on the mobile terminal APP. Refers to users with parking needs in advance through relevant mobile terminal applications

APP, makes an appointment for the premium berth in the parking area, and informs the time period of the required parking. The number of premium berths reserved for the APP 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) Number of vehicles with parking demand in the parking area Q = Number of reserved berths in APP Q _m + Historical experience value of number of parking vehicles in the parking area at the same time! x(l -

APP booking users accounted for the proportion of all parking users) + The number of participants in the parking area QjyX car travel sharing ratio; The proportion of APP booking users to all users is obtained through sample survey, the contribution ratio of car travel The value is greater than 0.1 and less than 0.3, obtained by field sampling survey; the parking time t in the parking area is the historical experience value tj of the parking time and the parking time required by the parking data determined by the historical data, the APP reservation data! The temporary parking activity induced by the temporary parking demand for the parking time t _ni three items are superimposed. 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 value of the parking time history data 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 subscriber in the APP; 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 the steps below: a) From the data obtained in step (3), group the total number of vehicles Q with parking demand in the parking area according to the parking time t, and the group distance is the unit charging time 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; b) by the i number of vehicle group, the i-th group of vehicles calculated average to the duration amount reaches q _Qi = ^ -; c) t to the average stop each to the length of the arrival quantity q _Qi and the i-th group of the vehicle group i of the vehicle, is calculated The amount of parking space and time resources required for the i-th vehicle = i^ Xt _{i ;}

d) the number of parking by the hourly space required for the vehicle resource group Si, S ₂ S _t, i is calculated before the set vehicle parking time and space resources for accumulated number _{ΣS = S x + S 2 +} ... + S ;; e) calculated from the number of berths berth s quality which can provide temporal resources parking _{S p = 0.85X5X t 0; f} ) S _p to the parking time and space resources ΣSi, ΣS ₂ Σ and can provide high compared berth Find an 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.

(6) Determine the price of premium berth parking fees. Follow these 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 time control threshold of 1 _{∞ ;}

b) Set the free parking time of the high quality berth t _/ that is, when the vehicle is parked at the high quality berth for no longer than ^, no charge will be made; t _f can be 0, that is, the vehicle starts to charge from a high quality berth. ;

c) Determine the lower price limit of the high quality berth in the duration of the time by the cost pricing method, as the free parking time for the high quality berth, and the charging price p _{1 in the} first time period after the end _;

d) Calculated by the parking charge price P _t ' of the ordinary berth when the parking time is 1 _和 and the grade factor γ of the high quality berth, according to formula (6), when the parking time is 1 _∞ , the vehicle is parked at a high quality berth. Parking charge /

P _t = Pt' xy ( 6 ) In the formula (6), when the grade of the high quality berth is 3⁄4γ < 1, it is calculated as 1.

 Step c) and step d) can be performed simultaneously.

e) from 1 _∞ length when the vehicle is parked in a parking berth quality at / by the formula (7) calculated quality obtained when the parking berth The price increase variance Δρ, that is, the charge of the nth unit billing time length t _Q of the high quality berth is higher than the (n-1) unit billing time length t _Q charge:

among them:

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

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 long after the end of the variance Δρ, calculated according to formula (8) to obtain long-quality berths free parking ^ long t _{Q n} th after the end Charge price ρ _{η :}

 n = Pi + (η - 1) ■ Δρ ( 8 ) where, steps b), c), d) can be performed simultaneously, and Figure 7 shows the flow chart for calculating the premium parking price. 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, and the value is μ=1.0, 1.1, 1.2, 1.3, 1.4, 1.5, etc., to carry out certain expansion, in order to achieve the purpose of effective diversion. 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.

The parking charge price charging rule for a calculated premium berth can be represented by a charging matrix. An example of a high quality berth charging matrix is as follows:

(7) 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, every inch length statistics from the beginning of the pricing period to the current moment, the actual number of vehicles parked at the high quality berth and the quality at this time The real-time occupancy of the berth and report the data to the system.

(8) Compare the real-time detection data with the predicted data to determine the premium berth parking charge price for the subsequent period. By step (4) The number Q of vehicles in the parking area determined to have parking demand is determined, and the predicted demand amount from the start of the pricing period to the current time is calculated = ^^ 1 ^, compared with the actual number of vehicles parked at the high quality berth, if ₀ . _{85 ≤ ≤}

1.15Q _p and 0.7 ≤ 0^ ≤ 0.9, the original charging plan is unchanged; if not, the steps (4) to (6) need to be re-executed, the relevant parameters are updated, and a new charging plan is issued.

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

The average distance from the berth to the entrance and exit of each parking lot in the parking area. d _w berth is the distance from the nearest elevator.

 Df berth distance from the nearest self-service paying machine

 The number of cameras that can monitor a berth

 A berth area

C _e berth car convenience

d _e 'Potential convenience of the average berth of all the berths in the parking area from the average car route of the parking lot entrances and exits in the parking area

 The average of all the berths in the parking area from the nearest elevator. cf The convenience of payment for berths

 Df' Average of the distance from all berths in the parking area to the nearest self-service payment machine

Ca berth size convenience

 A' average of all berth areas in the parking area

Safety of c _s berth

 Y berth grade factor

 Yo The minimum standard for high quality berths

 s Number of high quality berths in the parking area

 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 value of parking time in parking area Historical experience value of number of parking vehicles in parking area Real-time traffic flow of surrounding road tIII Mobile terminal Parking time of reserved berth on APP

Qui Number of berth reservations on the mobile terminal APP Temporary parking in the parking area, the demand for parking, the parking time, the temporary parking activity in the parking area, the amount of parking demand μ, the price sensitivity coefficient of the parking user in the parking area, tm, the parking time control threshold

Ti parking stop time q of the i-th group of vehicles in the parking demand statistics table; number of vehicles of the i-th group in the parking demand statistics table q ₀ i the average number of arrivals per-t _Q of the i-th group of vehicles in the parking demand statistics table

T total pricing duration

 The number of parking space-time resources required for the i-th vehicle in the Si parking demand statistics table. The number of parking space-time resources required for the first group of vehicles in the parking demand statistics table s The number of parking space-time resources that the high-quality berth can provide

V Parking demand statistics table, the parking time control threshold 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 quality long time tf berth

 Pi premium berth free parking duration ^ 1st ^ duration price after the end

Δρ high quality berth price increment variance

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

Pn high quality berth free parking time ^ the end of the nth ^ time charge price t _r high quality berth real time detection interval

The actual number of vehicles in the parking Qr high-quality real-time berth occupancy rate at o _r quality 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.

Figure 10 is a schematic view showing the distribution of parking spaces in the parking area of the embodiment.

Concrete 3⁄4 3⁄4r

In this embodiment, a possible implementation of the above invention is provided. The parking area parking map in this example is shown in FIG. There are 36 berths in the parking area, and the berth numbers are as shown. The fee study time is from 07:00 to 24:00 on a certain day. The berth grading dynamic pricing method based on the priority short stop is used to dynamically priced the berth rating for the APP reservation users in the parking area. The implementation process is as follows: Establish the parking area berth information table as follows: The berth is from the car line. The berth is the nearest berth.

 Berth nnigong ί double 1豕 berth area S port average distance of entrances walking distance of elevators

The number of heads η (m from d _e (m) Cheng d _w (m) df (m)

 1 25 4 19 1 14.84

2 27.8 6.6 21.8 1 13.78

3 30.4 9.2 24.4 1 13.78

4 33 11.8 27 1 13.78

5 35.6 14.4 29.6 1 13.78

6 38.2 17 27 1 13.78

7 40.8 19.6 24.4 1 13.78

8 43.4 22.2 21.8 1 13.78

9 46.4 25.2 18.8 1 15.90

10 36.4 30.2 8.8 1 15.90

11 33.4 27.2 11.8 1 13.78

12 30.8 24.6 14.4 2 13.78

13 28.2 22 17 2 13.78

14 25.6 19.4 19.6 2 13.78

15 23 16.8 17 1 13.78

16 20.4 14.2 14.4 2 13.78

17 17.8 11.6 11.8 1 13.78

18 15 8.8 9 1 15.90

19 10 13.8 9 1 15.90

20 13 16.8 11.8 1 13.78

21 15.6 19.4 14.4 2 13.78

22 18.2 22 17 3 13.78

23 20.8 24.6 19.6 1 13.78 24 23.4 27.2 17 2 13.78

25 26 29.8 14.4 1 13.78

26 28.6 32.4 11.8 1 13.78

27 31.6 35.4 8.8 1 13.78

28 28.4 45.4 18.8 1 15.90

29 25.4 42.4 21.8 1 13.78

30 22.8 39.8 24.4 1 13.78

31 20.2 37.2 27 1 13.78

32 17.6 34.6 29.6 1 13.78

33 15 32 27 1 13.78

34 12.4 29.4 24.4 1 13.78

35 9.8 26.8 21.8 1 13.78

36 7 24 19 1 14.84 Average 24.92 23.27 18.76 _ 14.13 The berth is graded, indeed the number of quality berths s. According to the data in the berth information table, take γο = 1.2, calculate the car c _{e of} each berth, pedestrian convenience c _w , payment convenience _{C /} , size convenience c _a , safety degree c _s and grade coefficient γ as shown in the following table Show:

When the berth convenience line c _e , pedestrian convenience c _w , payment convenience _{C /} , berth size convenience ^ and safety degree ^ one of the five items is greater than or equal to 1.5, or the berth grade coefficient γ When it is greater than 1, the berth is a high quality berth and the remaining berths are ordinary berths. The calculation results of all the berths in the parking area are counted, and the number of the high quality berths in the parking area is shown in the gray part of the above table. The number of high quality berths in the parking area is s=13.

3. Set the unit billing time to 20 minutes, and the part that stops for less than 20 minutes will be charged for 20 minutes. Determine the parking feature data in the parking area. Through the statistics of the smart zone system of the parking lot, the historical experience value of the number of parking vehicles in the parking area is 60, and the parking time distribution is known. At the same time, it is known that there will be an event in the parking area on this day. The estimated number of participants is 30. People, the activity time is from 9:00 to 11:00, and the proportion of people who participate in the event before driving is 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 = 60 + 30 X20% = 66 vehicles with parking demand in the area are grouped according to their parking time t, and the parking demand statistics table is as follows : Parking time average arrival amount q _oi required parking resources accumulation required parking space and time resources 辆 number of vehicles

t; (20min) (vehicle /20min) (one hour) (one hour)

1 3 0.0588 0.0196 0.0196

2 1 0.0196 0.0131 0.0327

3 2 0.0392 0.0392 0.0719

4 6 0.1176 0.1568 0.2287

5 4 0.0784 0.1307 0.3594

6 3 0.0588 0.1176 0.477

7 2 0.0392 0.0915 0.5685

8 2 0.0392 0.1045 0.673

9 3 0.0588 0.1764 0.8494

10 2 0.0392 0.1307 0.9801

11 4 0.0784 0.2875 1.2676

12 2 0.0392 0.1568 1.4244

13 3 0.0588 0.2548 1.6792

14 5 0.098 0.4573 2.1365

15 3 0.0588 0.294 2.4305

16 1 0.0196 0.1045 2.535

17 0 0 0 2.535

18 1 0.0196 0.1176 2.6526

19 2 0.0392 0.2483 2.9009

20 0 0 0 2.9009

21 0 0 0 2.9009

22 3 0.0588 0.4312 3.3321

23 1 0.0196 0.1503 3.4824

24 2 0.0392 0.3136 3.796

25 0 0 0 3.796

26 0 0 0 3.796

27 1 0.0196 0.1764 3.9724

28 0 0 0 3.9724

29 1 0.0196 0.1895 4.1619

30 0 0 0 4.1619

31 1 0.0196 0.2025 4.3644

32 0 0 0 4.3644

33 1 0.0196 0.2156 4.58

34 0 0 0 4.58

35 0 0 0 4.58 36 1 0.0196 0.2352 4.8152

 37 0 0 0 4.8152

 38 1 0.0196 0.2483 5.0635

 39 0 0 0 5.0635

 40 1 0.0196 0.2613 5.3248

 41 0 0 0 5.3248

 42 1 0.0196 0.2744 5.5992

 43 0 0 0 5.5992

 44 0 0 0 5.5992

 45 1 0.0196 0.294 5.8932

 46 1 0.0196 0.3005 6.1937

 47 0 0 0 6.1937

 48 0 0 0 6.1937

 49 1 0.0196 0.3201 6.5138

 50 0 0 0 6.5138

51 0 0 0 6.5138 At the same time, because the number of high quality berths is s=13, the parking space and time resources that can be provided are S _p = 0.85 X5X t ₀ =

 0.85x l3 x (3⁄4 = 3.6833 (hours). By using the parking demand statistics table with the accumulated parking time and space resources of each group

Compared with ∑S, it is found that in the 23rd group of data, that is, when the parking time is 23 X20 = 460min, the accumulated parking space and time resources ∑Si = 3.4824·hours is the closest and does not exceed S _p = 3.6833 · The group of hours. Therefore, the parking time control threshold t _m = 460 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 = 460min, the parking fee for parking in the off-street parking lot is P = 8hx5 yuan / h = 40 yuan. The free parking time for setting high quality berths is to = 0, that is, the vehicle starts to charge as soon as the vehicle stops at the high quality berth. Taking the high quality berth numbered as 02 as an example, the berth grade coefficient γ = 1.21, according to formula (5), when the parking time is t _m , the parking fee of the vehicle parked at the high quality berth 02

P _t = Ρ χ γ = 40x1.21 = 48.4 yuan, when the parking time is the parking time control threshold t _m = 460min, the unit billing time length t _Q = 20min

_{Ν = 21} - _{££ =} 460-0 _{= 23 ο}

To 20 At the same time, according to the cost pricing method, the price limit of the on-street parking space at P1 is 3 yuan/h, that is, the charge for the first unit billing time to = IQmin? 3⁄4 = 1 yuan. 2(P _t -JV- _Pl ) (48.4

 Therefore, Δρ = = 0.10 is obtained by the equation (4).

W(W_1) 23x(23-l) Therefore, the parking charge price for the premium berth numbered 02 is 1 yuan for the first 20min, and then the price for each 20min is 0.10 yuan higher than the previous 20min, which is the second lOmin charges 1.10 yuan, the third 20min charges 1.20 yuan, the fourth 20min charges 1.30 yuan... and so on, as shown in the following table:

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.

Claims

Claim

 A berth grading dynamic pricing method with priority short stop, classifying berths in a parking area, and pricing high quality berths, the steps of which include:

1) establishing berth information table, the table comprising a berth number average parking garage away from the region of each berth garage entrance d _e, a berth nearest elevator walk d _w, Nearest berth from a self-service payment machine ^ , the number n of cameras in the area that can monitor the berth and the area A of the berth, all of which are obtained by field measurement;

2) berth grading, determining high number of berths s, of berths factors graded to be considered include the berths dealers convenient level c _e, berths pedestrian convenient level c _w, berths contributory convenient degrees _C, this convenient berth size Degree c _a , safety degree of berth c _{s ;}

3) Determine the unit billing time i _Q; determine the parking characteristic data in the parking area, including the number of vehicles Q with parking demand in the parking area and the parking time t of the vehicle with parking demand; determine the parking time control threshold t _{m ;}

4) Determine the premium berth parking charge price p„ and adjust it. The adjustment method includes multiplying the price sensitivity coefficient μ of the parking user in the parking area where the value should be 1 ≤ μ ≤ 1.5, and Discounted parking users are offered discounts;

 5) Determine the real-time detection interval duration ^, the actual number of vehicles parked at the high quality berth every ^ length (^ and the real-time occupancy rate of the quality berth (^ for real-time statistics and detection;

 6) Compare the real-time detection data with the predicted data to determine the premium berth parking charge price for the subsequent period.

2. The preferential short stop berth grading dynamic pricing method according to claim 1, wherein the berth grading method should consider the following factors:

i. The convenience of the berth is c _e , calculated by ^ = /d _e , where d _e represents the average distance of the berth from the entrance and exit of each parking lot in the parking area, indicating that all berths in the parking area are away from the parking area The average of the average route of the car entrance and exit;

人. The pedestrian convenience c _{w of} the berth is calculated according to c _w = cC/d _w , where ^^ indicates that the berth is within walking distance of the nearest elevator in the parking area, cC indicates that all berths in the parking area are within the parking area The average of the recent walking distance of the elevator;

Ii i. The convenience of the berth payment _C , calculated by = c^/, which indicates the distance of the berth from the nearest self-service payment machine, indicating the average distance of all the berths in the parking area from the nearest self-service payment machine;

Iv. The convenience of the berth size c _a , calculated by =, where ^ 1 indicates the area of the berth, indicating the average of all berth areas in the parking area;

v. The safety level of the berth c _s , when the berth can only be monitored by a surveillance camera, ^ = 1; when the berth can be monitored by two surveillance cameras at the same time, it = 1.25; when the berth can simultaneously When monitored by three or more surveillance cameras, it = 1.5; when the berth is in the blind zone of all cameras and cannot be monitored When it is c _s =

3. The prioritized short stop berth grading dynamic pricing method according to claim 1, wherein said determining the quality berth quantity s needs to be γ = (c _e + c _f + c _s + c _a + c _w / (S x γο) calculates the gradation coefficient γ of the berth, where γο represents the minimum standard of the set high quality berth comprehensive condition, the value range is 1 2; when the berth is convenient, c _e , pedestrian convenience c _w , payment convenience _C , berth size convenience c _a and safety degree one of the values is greater than or equal to 1.5, or the berth grade coefficient γ is greater than 1, the berth is a high quality berth, the rest The berth is an ordinary berth.

The berth hierarchical dynamic pricing method for priority short stop according to claim 1, wherein the unit charging duration i _{Q is} equal to 1 minute ≤ i _Q ≤ 30 minutes.

The prioritized short-stop berth grading dynamic pricing method according to any one of claims 1 to 4, characterized in that: determining the number Q of vehicles having parking demand in the parking area and the parking time of vehicles having parking demand At t, first obtain at least two of the following four related data:

 a) The number of parking vehicles in the parking area mentioned in the same section (? j and historical experience value of parking duration ί j: refers to the data stored in the intelligent parking facility or manual recording, respectively, the high quality berth and the common berth at the same time Number of parking vehicles, summing the two to obtain the number of parking vehicles in the parking area; recording the parking time of each vehicle at the same time; randomly collecting the recorded values of multiple days and taking the average value, that is, the number of parking vehicles in the parking area Historical experience value and historical experience value of parking duration; At the same time, when performing historical data extraction statistics, the selected date shall be separately counted according to the difference between the three types of demand, such as working day, weekend and special holiday;

b) real-time traffic flow ρ _{π 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 _ΠΙ , i _m : on the mobile terminal APP refers to the number and time period in which the premium berths in the parking area are reserved on the relevant mobile terminal application ;;

d) the estimated parking demand for temporary activities in the known parking area ^, i refers to the number of participants in the temporary activities to be held in the parking area and the time of the event, the length of parking and the activity of the induced parking demand The duration of the meeting is consistent; using the acquired data, the number of vehicles Q with parking demand in the parking area and the parking time t of vehicles with parking demand are calculated according to one of the following three methods: i) Number of vehicles with parking demand in the parking area Q = Historical experience value of the number of parking vehicles in the parking area at the same time (?! + Number of participants in temporary parking activities in the parking area (?! VX car travel) The sharing ratio of the car travel ratio is greater than 0.1. Less than 0. 3, obtained by field sampling survey; the parking time in the parking area is the historical experience value of the parking time period ί! and the temporary parking area The parking time distribution of the activity-induced parking demand is superimposed;

 Ii) Number of vehicles with parking demand in the parking area Q = Number of reserved berths in APP ^^! + Historical experience of number of parking vehicles in the parking area at the same time (? J x (l -

APP booking users accounted for all parking users;) + number of participants in temporary parking activities (? _w X car travel sharing ratio;

The proportion of the APP reservation users to all users is obtained through a sample survey. The share ratio of the car travel is greater than 0.1. Less than 0.3, obtained by field sampling survey; the parking time in the parking area is t. The historical experience value ί j and the parking time length of the parking demand determined by the historical data, the APP reservation data ^ and the parking time length t! V of the parking demand induced by the temporary activity in the parking area are superimposed; ii i) Number of vehicles with parking demand in the parking area Q = Real-time traffic flow of surrounding roads (? _π X Historical experience value of the number of parking vehicles in the parking area at the same time / Historical average of real-time traffic flow of surrounding roads

The distribution of the total demand t time t is consistent with the distribution 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 according to the charging standard notified at the time of the reservation. carried out.

The prioritized short-stop berth grading dynamic pricing method according to any one of claims 1 to 4, wherein the parking time control threshold value is determined according to the following steps:

(1) The number of vehicles Q with parking demand in the parking area and the parking time t of vehicles with parking demand, the total vehicle Q with parking demand in the parking area is the parking time of the vehicle with parking demand t group statistics, the group distance is the unit billing time t _Q , the parking time of the i-th data is ti = ixt _Q , the number of vehicles is c, and the range of i is i = 1, 2, 3 T/t ₀ , where T is the total pricing duration; (2) From the number of vehicles in the i-th group, calculate the average arrival amount per hour of the i-th group of vehicles _qi)i = _qi /(r/t ₀ );

(3) long calculated number of parking the vehicle group i s Resources needed = q _oi X t _i reaches the parking duration and the quantity q _Qi of the i-th group of vehicles per time t _Q of the i-th group of _vehicles;

(4) Calculate the amount of parking space-time resources required for the accumulation of vehicles in the former i group by the number of parking space-time resources S ₁ S ₂ required by each group of vehicles ∑ Si = Si + S ₂ +... + Si;

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

(6) The ΣSi, ΣS ₂ Σ Si and berth can provide high spatial and temporal resources parking S _p is compared, to find a V, so that [Sigma nearest to but not more than S _p, in its category i 'corresponding to The parking time length ^ is the parking time control threshold t _m .

The prioritized short-stop berth grading dynamic pricing method according to any one of claims 1 to 4, wherein the high-quality berth parking fee price is determined according to 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 t for high quality berths _;

(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) Calculate the parking charge of the vehicle parked at a high quality berth when the parking time is t _m by the = P _t ' χ γ, where P _t ' is the parking charge price of the ordinary berth when the parking time is „, γ is the quality The grading factor of the berth, when the grade factor γ < 1 of the high quality berth, is calculated as 1;

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

(f) Calculate the free parking time for the quality berth by = _Pl + (n - 1) ■ Δρ ^ The price of the η t _Q duration after the end p „ρ, where _Pl is the quality berth free parking time ^ the first t after the end _The price of the _Q duration, Δρ is the price increase variance of the premium berth.

The prioritized short-stop berth hierarchical dynamic pricing method according to any one of claims 1 to 4, wherein the real-time detection data is compared with the predicted data, and when ≥ 10, the quality of the subsequent time period is determined. The criteria for determining the parking fee for berth parking are:

0.85 Q _p ≤ Q _r ≤ and 0.7 ≤ O _r ≤ 0.9 , where the predicted demand from the start of the pricing period to the current time (? p = <? X from the beginning of the pricing period to the current time / the total length of the pricing period Duration T; if this criterion is met, the original charging plan is unchanged; if not, the steps 4) to 6) described in claim 1 need to be re-executed, the relevant parameters are updated, and new charges are formulated and issued. Program.