Research ArticleImpact Analysis of Parking Price Adjustment on the Quality ofService of Airport Parking Lots for Light Vehicles

Cheng Cheng 1,2 and Peng Qi 2,3

1Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China2China Transportation Institute at Tongji, No. 1239, Siping Road, Shanghai 200092, China3College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China

Correspondence should be addressed to Peng Qi; pqi@tongji.edu.cn

Received 24 December 2018; Revised 8 May 2019; Accepted 27 May 2019; Published 10 June 2019

Academic Editor: Rocıo de Ona

Copyright © 2019 Cheng Cheng and Peng Qi. This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properlycited.

Pricing is a common measure for parking demand management that has been implemented worldwide. However, the impact ofparking price on a parking lot’s quality of service is seldomdiscussed.This study investigated the impacts of a ladder daily maximumfee charging strategy on the quality of service of the Hongqiao International Airport parking lot based on automatic transactiondata before and after the strategy was implemented. An evaluation framework considering managers’ and users’ perspectives wasdesigned. The estimation results show that the new price regulation method largely discouraged long-term parking demand andimproved the availability of airport parking facilities, especially during long holidays. As a consequence, throughput and incomelargely increased in the airport, and there were extra time costs during vehicle departures. The price elasticity of parkers withdifferent parking durations was further estimated.The results showed that price sensitivity is relatively inelastic but varies based onparking duration.

1. Introduction

Due to travelers’ high reliance on private cars when accessingairports, parking facilities are regarded as essential elementson the land side of airports [1–4]. The operational efficiencyof parking facilities has a great impact on airport quality ofservice (QOS) and is therefore of great concern to airportadministrators and authorities.

The operational difficulties faced by airport parkingmanagers largely differ from those that occur in hospital,university, and central business district parking areas due tothe distinct parking duration distribution at airport parkinglots [5]. Compared with other commercial parking areas, theairport parking duration distribution is relatively long-tailed,with some proportion of vehicles staying for several days oroccasionally several weeks. According to statistics, in airportparking lots, approximately 75% of the users (short-term) areserved by 10% to 30% of the spaces, but the remaining 25%(long-term) may require up to 90% of the parking spaces[6]. This phenomenon leads to a decrease in the parking

space turnover rate and an increase in cruising time, causingdissatisfaction with airport parking management.

To improve the QOS of airport parking lots, possiblesolutions, such as increasing the public parking supply, real-locating supply among public parking products, building upadvanced traveler information system to avoid unnecessarycruising [7], and adjusting parking rates, were proposed[8]. Among all strategies, parking pricing adjustment wasthe most popular because it balances demand across facil-ities by reducing demand for a specific facility, generatesrevenue, reduces parking demand, and does not requireadditional investment to expand the parking facilities. Thepricing adjustment method can be categorized into severalclasses: traditional rate adjustment, dynamic rates over time,and pricing based on consumer characteristics [8]. Airportauthorities can select specific regulation strategies based ontheir parking conditions and target users.

In 2014, the Hongqiao International Airport parking lotsuffered from spillover caused by an increase in long-termparking demand. To decrease long-term parking demand, the

HindawiJournal of Advanced TransportationVolume 2019, Article ID 3847837, 9 pageshttps://doi.org/10.1155/2019/3847837

2 Journal of Advanced Transportation

airport authority took an innovative approach bymaintainingthe parking rates while using laddermaximumdaily chargingfees based on parking duration for long-term parkers. Thisnovel price strategy was intended to suppress the long-termparking demand and was applied on Feb. 12, 2015.

This new pricing scheme offers a new perspective formanaging long-term parking, and a comprehensive evalua-tion of its effectiveness is needed. In this paper, we proposean evaluation framework to measure the effectiveness of thepricing scheme considering both travelers’ and managers’demand for parking on workdays and holidays. The keyinputs are obtained based on automatic transaction (AT) dataand the vehicle arrival/departure rate at Shanghai HongqiaoInternational Airport before and after the price adjustment(BPA/APA). We further calibrated the price elasticity oftravelers and found that price elasticity varied based onparking duration. The proposed evaluation framework hasuniversality and can be applied to the evaluation of variousmanagement schemes.

The remainder of this paper is organized as follows.Section 2 presents a comprehensive review of previous studieson the parking pricing method and an evaluation of pricingschemes. The basic information for the Shanghai HongqiaoInternational Terminal 2 parking lot and the parking pricepolicy before and after rate modifications is introduced inSection 3. The evaluation framework for estimating the pric-ing scheme effectiveness is presented in Section 4.The resultsand discussion section is then presented in Section 5. Finally,the conclusion, limitations, and future research directions arediscussed in Section 6.

2. Literature Review

Parking price has been proven to have a major effect onthe level of service of parking areas. Low parking ratesmay encourage parking demand, leading to the absence ofavailable spaces, while high parking prices may cause excessresources and parking market loss [9, 10]. The optimal park-ing price should offer a trade-off between the available spacesfor potential users and the high utilization of the facilities. Toensure the serviceability of the parking areas, traffic engineersalways choose a management scheme that leads to 15% ofthe parking spaces remaining vacant. This criterion ensuressufficient parking spaces and avoids a dramatic increasein cruising time to find spaces when the occupancy levelbecomes high [11, 12]. Based on this principle, Martens andvan Luipen provided a conceptual model for finding the“right price” for parking based on the calculated economicprice, current parking tariffs, and occupancy level [13]. Fil-ipovitch and Boamah also determined the optimum parkingpolicy for Minnesota State University based on occupancylevel constraints [14]. Jakob et al. designed a dynamic parkingpricing decision model for city-level parking management.The proposed model simultaneously minimizes the totalcruising time and maximizes total revenue [15]. Pu et al.evaluated the on-street parking price sensitivity at the spa-tial level based on San Francisco Municipal TransportationAgency data and estimated potential influencing factors.The results indicated the need for various pricing schemes

for parking areas to balance parking demand and supply[16].

The optimum price also varies due to people’s attitudesin response to price change. To quantify price sensitivity,the concept of elasticity is applied in the parking pricingarea. Twomajor methods, stated preference surveys (SP) [17–21] and revealed preference surveys (RP) [22–25], have beenapplied to estimate this parameter [26].These studies providea quantitative level of price sensitivity for optimum priceestimation as well as reveal some of the potential factors,including trip purpose, parking region, parking time andduration, and income level that affect the response to theparking price.

Along with determining a reasonable price for parkingmanagement, evaluating the impacts of pricing is importantfor organizations and authorities. Cats et al. suggested thatfactors including parking duration, turnover, and park-ing occupancy should be evaluated during the assessmentprocess [25]. Alemi et al. further proposed mixed effectdifference-in-difference models to estimate the effect of pricechanges due to a San Francisco parking pricing programand found that the average parking search time and distancedecline by approximately 15% and 12%, respectively [27].

According to the literature review, methods for designingthe parking pricing rate are well researched. However, fewstudies have focused on evaluating the effectiveness of pricingadjustments, and the methodology for quantifying these isnot clear. The limitations in this area still need further studyand extensions.

3. Introduction of the Hongqiao InternationalAirport Parking Lot and Its Parking Policy

Hongqiao Airport lies in the west of Shanghai and is 13kilometers away from Shanghai’s city center. It is one ofthe busiest airports in China, serving approximately 253,300flights and 37.96 million passengers in 2015. There are twoterminals in the Hongqiao Airport. Terminal 1 is mainly forinternational use, and Terminal 2 serves domestic flights.Theparking lots of Terminal 1 and 2 are operated independently.The data provided are AT data from the Terminal 2 parkinglot.

The Terminal 2 parking lot is open to private vehicles,buses and coaches and separated into 4 different parkingareas. Among them, P6 and P7 are designated for lightprivate vehicles, and 3316 parking spaces in total are providedin these two sections. P5 and P8 are designated for busesand coaches. Vehicles are charged based on their parkingduration. Vehicles do not have to pay the parking fee if theduration is less than 20 minutes, and the maximum chargeper day is regulated.The price for buses and coaches is slightlyhigher than that for private vehicles. Because the behavior ofdrivers of private vehicles is different from that of drivers ofbuses and coaches, our study focused on private vehicles.

Based on AT data in 2014, long-term (more than 8hours) parking represents nearly 73.5% of the total occupanttime of the parking lot, although long-term parking demandaccounts for only 8% of the total parking demand. As a result,the airport is faced with a shortage of parking spaces. During

Journal of Advanced Transportation 3

Table 1: Comparison of the T2 parking lot charging rate before and after price modification.

Parking duration Previous rate (RMB) Current rate (RMB)First day More than 1 day First day Second day More than 2 days

< 20 minutes 0 10 0 10 1020 minutes∼ 1 hour 10 101∼2 hours 20 20 20 20 202∼3 hours 25 25 25 25 253∼4 hours 30 30 30 30 304∼5 hours 35 35 35 35 355∼6 hours 40 40 40 40 406∼7 hours 45 45 45 45 457∼8 hours 50 50 50 50 508∼9 hours 50 50 55 55 559∼10 hours 50 50 60 60 6010∼11 hours 50 50 60 65 6511∼12 hours 50 50 60 70 7012∼13 hours 50 50 60 75 7513∼14 hours 50 50 60 80 8014∼15 hours 50 50 60 80 8515∼16 hours 50 50 60 80 9016∼17 hours 50 50 60 80 9517∼18 hours 50 50 60 80 10018∼19 hours 50 50 60 80 10519∼24 hours 50 50 60 80 110

peak hours, drivers are challenged to find vacant parkingspaces.

The airport authority recognized that the parking prob-lem could be alleviated if the long-term parking demand wassuppressed. On Feb. 12, 2015, the airport authority increasedthe parking rate for long-term parking. The original andmodified parking rates are presented in Table 1. For example,the hourly parking rates remain at 10 RMB/h for the first2 hours and 5 RMB/h for each additional hour. However,the maximum parking rates are increased. The previousmaximum parking rate was 50 RMB per day, and after theprice adjustment, this increased to 60 RMB/day and 80RMB/day for the first day and the second day, respectively.For vehicles staying for more than two days, the maximumparking fee increases to 110 RMB/day. The rate for long-termparking increased dramatically in an attempt to reduce thelong-term parking demand.

TheAT data before and after the price adjustment providea good opportunity to estimate parking price elasticity andevaluate the impacts of price adjustment. The AT data fromJan 1st, 2014, to May 30th, 2015, were provided for thisstudy. The data contain the entrance time, parking duration,and parking expense of each vehicle entering the parkinglot within the above time period. The data also containthe employee parking data, which are excluded from theanalysis. The number of flights arriving at and departing fromthis terminal in the same period was also collected for thesubsequent analysis.

4. Evaluation Framework

When estimating the impacts of the new price strategy, itis appropriate to consider the impacts on both customers

and managers. For travelers, the indexes chosen reflect theQOS of the parking lot, while the index for managers mainlyconsiders the profitability and utilization of the parkingfacilities. The evaluation indexes are summarized in Table 2.

4.1. Indexes for Evaluating the QOS of Parking Facilities.QOS describes how well a transportation facility or serviceoperates from the traveler’s perspective [28]. Two differentcategories are considered in this paper to evaluate the QOS ofthe Hongqiao Airport Terminal 2 parking lot: the availabilityof parking facilities and the delay in the parking process.

The Availability of Parking Facilities. The availability of park-ing facilities can be measured by the occupancy level. Thisindex reflects the difference between parking demand andcapacity for a given price. If the demand does not exceedthe capacity, this parameter is below 1, and when capacity isexceeded, it is greater than 1. The occupancy level (OL) canbe estimated based on (1):

𝑂�푡 =𝑃�푡𝑆

(1)

where 𝑂�푡 is the OL in the analysis period at given time pointt, 𝑃�푡 is the number of vehicles parked in the parking lot at thegiven time point t, and 𝑆 is the total parking spaces providedby the parking lot.

Parking Delay. During the parking process, vehicles sufferextra delay during the access process and the space searchingprocess. The delay during parking can reflect the parkingconditions and parking lot service level. The parking delaycan be summarized by the following equation:

4 Journal of Advanced Transportation

Table 2: Indexes for evaluating the impacts of the new parking price strategy.

Evaluation Index Perspective DetailOccupancy level

TravelersReflects the availability of parking facilities

Access Delay Reflects satisfaction during the access processSearching Delay Reflects the efficiency of the space searching processThroughput

ManagersReflects the utilization and the circulation of parking facilities

Daily Transaction Amount Reflects the parking demand that has been satisfiedIncome Reflects the profitability of the parking facilities

𝑑 = 𝑑�푎 + 𝑑�푑 + 𝑑�푠 (2)

where 𝑑�푎 and 𝑑�푑 are the delays that occur during the arrivaland departure processes, respectively. 𝑑�푠 is the time cost ofsearching for available parking spaces.

Because the access delay (𝑑�푎 and 𝑑�푑) is mainly caused bytime in queues and time cost during tolling, the arrival anddeparture delay can be estimated by the famous Pollaczek-Khintchine formula (see (3)) based on queueing theory [29]:

𝑑 =𝜌 + 𝜆𝜇𝑉

2 (𝜇 − 𝜆)+1

𝜇(3)

where 𝑑 is the average delay of vehicles during the exitingprocess with price 𝑝 and at time period t; 𝜆 is the meanvehicle arrival rate at time 𝑡 when the price equals p; 𝜌 is thesaturation level, which equals 𝜆/𝜇; 𝜇 is the serving frequencyand 1/𝜇 equals the average service time; V represents thevariance in the service distribution.

However, the delay that occurs during the space searchingprocess varies due to the number of parking spaces, the layoutof the parking lot, the efficiency of the parking guidancesystem, and the OL. Some research also indicated that thecruising time would vary according to land use [30]. Whenthe parking facilities are chosen, the searching delay canbe estimated based on a function with the OL as the onlyvariable. Previous studies indicate that when the OL islower than 80∼90%, the cruising time is relatively low, andmotorists can find a parking spot quickly [31, 32]. To simplifythe estimation process, in this paper, we use the OL to reflectthe space searching delay.

4.2. Efficiency of Parking Facilities: A Manager’s Perspective

Throughput. Throughput is the utilization rate for a parkingfacility in a given period of time. This parameter reflects thenumber of vehicles each parking space served during a timeinterval, and a high throughput indicates that the parkingfacilities serve a large demand. This index can be estimatedby (4).

𝑇�푖 =𝐷�푖𝑆

(4)

where𝑇�푖 is the throughput of the analyzed parking lot duringperiod i and 𝐷�푖 is the total number of vehicles parked in theparking lot during period i.

Income and satisfied demand are considered in this paperas well. Income mainly reflects the earning power of the

parking lots. When a new price regulation is established,income should first guarantee the sustainability of the parkinglot. The demand that has been satisfied by the parking lot issimilar to the throughput, as it reflects the utilization of theparking spaces.

4.3. Methodology for Price Elasticity Estimation. In eco-nomics, elasticity is defined as the ratio of the percentagechange in one variable in response to a percentage changein another variable, holding all else constant. This parameteris widely used to quantify how parking demand changesin response to a price adjustment. Because airport parkingdemand may be affected by factors other than price, such asflight volume and holidays, when price elasticity is estimatedbased on its original concept, it is inaccurate.Therefore, a log-based regression method is used to model the relationshipbetween parking demand and potential factors. This methodprovides quantitative results for parking demand caused byother variables and provides the point price elasticity ofcustomers with various parking durations. The log-basedregression model in this paper is set up as follows:

ln𝐷�푖,�푡 = 𝛽0,�푡 + 𝛽1,�푡 ln (𝑓�푖) + 𝛽2,�푡 ln (𝑃�푖,�푡) + 𝛽3,�푡𝑖 + 𝐻

+ 𝜀�푖,�푡

𝐻 = 𝛽4,�푡𝑙𝑡�푖 ⋅ 𝑐 ⋅ ln (𝑃�푖,�푡) + 𝛽5,�푡𝑏𝑙𝑡�푖 ⋅ 𝑐 ⋅ ln (𝑃�푖,�푡)

+ 𝛽6,�푡𝑙𝑡�푖 + 𝛽7,�푡𝑏𝑙𝑡�푖

(5)

where 𝐷�푖,�푡 is the parking demand during the ith weekfor those parking with a duration within parking durationcategory t; 𝑃�푖,�푡 is the parking price in week 𝑖 for parkingduration category t; 𝑓�푖 is the total volume of arriving anddeparting flights inHongqiao Terminal 2 inweek i; parameter𝑖 is proposed in this equation to consider the average growthrate of parking demand; H is the holiday effect group. Thisgroup contains several parameters to reflect the holidayeffects on parking demand and elasticity during holidays. Indetail, lt is the dummy variable parameter considering theexistence of long-term holidays. blt is the dummy variablefor weeks before long-term holidays. c depicts whether theparking price is new within the ith week. It would be 1 ifthe parking price is changed. 𝛽0,t to 𝛽7,t are the estimationparameters for duration category t, and 𝜀i,t is the residual forparking duration 𝑡 in the ith week.

As presented in (5), the dependent variable is the loga-rithm of the weekly parking data. To differentiate users withdifferent parking durations, we divide the data into 5 different

Journal of Advanced Transportation 5

Table 3: Parking demand variation before and after price adjustment.

Duration (hour) Demand(veh/day)< 20 min 1∼3 4∼8 9∼24 25∼48 49∼72 73∼120 >120

BPA 2630 5298 277 312 415 254 152 30 9369APA 3230 5516 284 311 378 207 114 20 10061Percentage Change (%) 22.8 4.1 2.3 -0.5 -8.8 -18.2 -25.3 -34 7.4

Table 4: Statistical results of the QOS parameters before and after price adjustment.

BPA APAOrdinary Days Long-term Holidays Ordinary Days Long-term Holidays

Occupancy Level (OL) 0.927 1.08 0.806 0.795Entrance Delay (s) 10.72 10.27 12.05 10.43Exit Delay (s) 33.74 30.08 41.88 28.16

categories and run the regression for each category separately.The total parking demand, mean parking price, and totalarrival and departure flight volumes are calculated to calibratethe model in (5).

In addition, to consider the difference in parking demandbetween ordinary weeks and holidays, dummy variables areused to represent the type of day. Weeks within long-termholidays or before long-term holidays are coded as 1 in thecorresponding parameters.

In this model, the parking price elasticity for ordinaryweekdays equals the amount of 𝛽2,�푡. In addition, 𝛽2,�푡 + 𝛽5,�푡,𝛽2,�푡 + 𝛽6,�푡 would be the parking price elasticity for theweeks before long-term holidays and the weeks of long-termholidays, respectively.

5. Evaluation and Discussion

5.1. Basic Statistics of the Parking Characteristics in HongqiaoTerminal 2 Parking Lot

Parking Duration Variation. The increase in the maximumparking fee led to variation in the parking duration distri-bution (Table 3). Parking demand with 8∼24 hours durationdid not significantly decrease. However, with the increase inthe parking fee, overall parking demand decreased by 9∼34%based on the duration length.

Although the long-term parking demand was dramat-ically reduced, the new price regulation strategy largelyencourages short-term parking demand, driving 7.4% ingrowth (from 9369 to 10,061 veh/d).This rise is largely drivenby free parking demand,which saw anearly 23% increase.Theparking demand for 1∼3 hours and 4∼8 hours also increasedby 4.1% and 2.3%, respectively.

Temporal Access Demand Variation. To analyze the impactsof the price adjustment, the fluctuations in daily averagedemand accessing the Hongqiao Terminal 2 parking lotbefore and after the price adjustment and the coefficient ofthe demand variance are presented in Figure 1. The increasein parking demand mainly occurs between 12 pm and 0 am,especially after 17 pm. The CV estimation indicates that the

variation in the parking demand remains stable before andafter the price adjustment.

Change in Temporal Parking Demand. With the reduction inovernight and long-term parking, the OL of the HongqiaoTerminal 2 parking lot also decreased considerably after theprice adjustment (Figure 2). During the long-term SpringFestival holiday in 2014, 3709 vehicles parked in the parkinglots during the peak period, which exceeded the designedcapacity. Nearly 44% of the time, the parking lot was oper-ating under a high level of occupancy (OL >0.85). However,after the price adjustment, the maximum occupancy droppedto 2735, and the OL was less than 0.85 during the 2015Spring Festival. For parking lot performance on ordinarydays, the maximum occupancy was 4004 between 2014/11/17and 2014/11/23, which exceeded the lot capacity by approxi-mately 20.7%. With the current price policy, the highest OLdecreased to 3447, and the parking lot OL was over 0.85 only26% of the time between 2015/3/16 and 2015/3/22. All of theseresults indicate the effectiveness of this policy in improvingairport parking lot performance.

5.2. Performance Estimation

QOS Analysis. The average occupancy levels during the peakperiod are estimated based on the AT data and presentedin Table 4 along with the entrance and exit delay. Theanalysis results indicate that, with the application of theladder maximum charging fee, the availability of parkingspaces increased, especially during long-term holidays. Thepeak hour OL during the long-term holiday drops to 0.795,which corresponds to 1.08 in 2014. The peak hour OL duringordinary days also experienced a 13% decrease due to thenew price strategy. The original OL during the peak periodsuggests difficulty in searching available spaces. After priceregulation, because the OL level is approximately 0.8, vehiclescan easily find available spaces and reduce searching delay.

We also investigated the entrance and exit service ratesto further estimate delays. According to the survey results,the average service time at the entrance and exit gate is8 and 17 seconds, respectively, with standard deviations of3.1 seconds and 7.8 seconds. Based on these results, the

6 Journal of Advanced Transportation

1 2 4 6 8 10 12 14 16 18 20 22 240

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(d)

Figure 1: Comparison of parking demand before and after parking price adjustment: (a) parking demand; (b) exiting demand; (c) coefficientof variation of arrivals; (d) coefficient of variation of departures.

average entrance and exit delay during the peak period areestimated. It is clear that the new price strategy encouragedshort-term parking demand during the peak period, causingpressure during the access process. The exit delay increasedto 41.88 seconds/vehicle. The entrance delay also increasedby approximately 1.3 seconds. Due to the low access demandduring long-term holidays, the access delay is relatively lowcompared with that on ordinary days. The access pressureis not as serious as it is during ordinary days; therefore, theaccess delay difference is not significant during long-termholidays.

Profitability and Facility Utilization Analysis. The averagethroughput, transaction amount, and income per day arecalculated based on the AT data and presented in Table 5.The throughput results for both ordinary days and long-term holidays increased after the price adjustment. Theseparameters indicate that greater demand was served by theparking facilities in the Hongqiao Terminal 2 parking lotafter the price adjustment. The increase in the maximumdaily charging fee also made the parking lot more profitable,partially due to the increase in short-term parkers. Mean-while, income growth also saw a contribution from long-termparkers, because the prices are inelastic for long-termparkers.This is discussed in the next section.

5.3. Elasticity Analysis. The elasticities of the terminal 2parking lot users with different durations are calibrated, andparameters with p-values less than 0.05 are presented inTable 6.The price elasticity of long-term parkers is within therange of -0.228 and -0.689.These figures indicate that parkingdemand is relatively inelastic (𝛽2 > −1). The sensitivity toprice changes also varies due to the parking time length.Those parking for 2∼3 days are much more sensitive toprice changes than those in other duration categories. Extraelasticity before long-term holidays is also identified. Thisshows that increasing the maximum charging fees for long-term parkers has further helped decrease parking demandduring holidays even if the elasticity is relatively low for usersstaying between 9 and 72 hours. The extra elasticity for long-term holidays is nonsignificant and thus is not provided inthis paper.

Meanwhile, the calibration results also indicate thatdemand for parking with a duration of 25∼72 hours is elasticto the number of flights. This phenomenon does not exist inthe categories with other durations. The results also indicatedthat before and during long-term holidays, parking demandof less than 3 days would decrease, and parking areas wouldbe occupied by vehicles with longer parking durations. Thismay be caused by the demand for long-term travel duringholidays.

Journal of Advanced Transportation 7

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Figure 2: The occupancy of the Hongqiao Terminal 2 parking lot: (a) the occupancy during the Spring Festival before price adjustment; (b)the occupancy during the Spring Festival after price adjustment; (c) the occupancy during an ordinary week before price adjustment; (d) theoccupancy during an ordinary week after price adjustment.

6. Conclusion

Due to the high level of long-term parking demand, theShanghai Hongqiao airport implemented a ladder maximumdaily charging fee to suppress the increase in long-termparkers. This study estimated the impacts of this innovativeprice strategy on the QOS of this parking lot based on ATdata from before and after the strategy was implemented.

An evaluation framework was proposed, and the eval-uated indexes were carefully selected according to the userand manager perspectives. The estimated results indicatedthat the OL during the peak period dropped approximately13% and 26% on ordinary days and long-term holidays,respectively, and throughput and daily income increased with

the implementation of this novel price strategy. We furtherestimated the price elasticity of customers with various park-ing durations and other factors that affect parking demand.The results indicated that parking pricewas relatively inelasticand varied with parking duration.

In conclusion, this paper presented a framework forassessing the effectiveness of parking price adjustment con-sidering various aspects, including OL, access day via park-ing, throughput, income, etc. The proposed framework canbe further implemented to evaluate the effectiveness ofother parking management strategies as well. The elasticitycalibration results indicated that the price elasticity wouldvary due to the parking duration; this result could provideinsights to airport parking management. For future studies,

8 Journal of Advanced Transportation

Table 5: Statistical results for profitability and facility utilization before and after price adjustment.

BPA APAOrdinary Days Long-term Holidays Ordinary Days Long-term Holidays

Throughput 2.834 1.707 3.034 1.879Daily TransactionAmount (Veh/d)

9396.7 5660.4 10061.1 6230.8

Daily income(million RMB/d)

0.193 0.214

Table 6: Elasticity results for parking lot users with different duration levels.

Parking Duration9∼24 hours 25∼48 hours 49∼72 hours 73∼120 hours Over 120 hours

𝛽0(t-Statistic)

8.6075(6.976) - - 9.155

(17.075) -

𝛽1(t-Statistic) - 1.203

(7.313)1.283

(11.786) - -

𝛽2(t-Statistic)

-0.228(-1.639)

-0.514(-1.600)

-0.689(-2.929)

-0.394(-3.124)

-0.358(-3.176)

𝛽3(t-Statistic)

-0.001(-2.237) - -0.002

(-2.025) - -0.004(-3.748)

𝛽4(t-Statistic)

-0.067(-2.037) - - - -

𝛽5(t-Statistic)

-0.047(-3.296)

-0.117(-2.906)

-0.079(-1.917) - -

𝛽6(t-Statistic)

-1.074(-15.778)

-1.498(-12.830)

-0.540(-5.307)

0.530(6.315)

0.760(6.297)

𝛽7(t-Statistic) - -0.445

(-3.807)-0.848(-8.328)

-0.340(-4.055)

0.777(6.444)

R2 0.818 0.775 0.665 0.545 0.599

in order to improve the accuracy of QOS evaluation at airportparking lot, the impact of price adjustment on various type ofvehicles should be considered. Moreover, the cruising time ofvehicles at airport parking lot should be further discussed andconsidered in such studies.

Data Availability

The service time data used to support the findings of thisstudy are included within the article.

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper.

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