Research Article | Open Access
Cheng Cheng, Peng Qi, "Impact Analysis of Parking Price Adjustment on the Quality of Service of Airport Parking Lots for Light Vehicles", Journal of Advanced Transportation, vol. 2019, Article ID 3847837, 9 pages, 2019. https://doi.org/10.1155/2019/3847837
Impact Analysis of Parking Price Adjustment on the Quality of Service of Airport Parking Lots for Light Vehicles
Pricing is a common measure for parking demand management that has been implemented worldwide. However, the impact of parking price on a parking lot’s quality of service is seldom discussed. This study investigated the impacts of a ladder daily maximum fee charging strategy on the quality of service of the Hongqiao International Airport parking lot based on automatic transaction data before and after the strategy was implemented. An evaluation framework considering managers’ and users’ perspectives was designed. The estimation results show that the new price regulation method largely discouraged long-term parking demand and improved the availability of airport parking facilities, especially during long holidays. As a consequence, throughput and income largely increased in the airport, and there were extra time costs during vehicle departures. The price elasticity of parkers with different parking durations was further estimated. The results showed that price sensitivity is relatively inelastic but varies based on parking duration.
Due to travelers’ high reliance on private cars when accessing airports, parking facilities are regarded as essential elements on the land side of airports [1–4]. The operational efficiency of parking facilities has a great impact on airport quality of service (QOS) and is therefore of great concern to airport administrators and authorities.
The operational difficulties faced by airport parking managers largely differ from those that occur in hospital, university, and central business district parking areas due to the distinct parking duration distribution at airport parking lots . Compared with other commercial parking areas, the airport parking duration distribution is relatively long-tailed, with some proportion of vehicles staying for several days or occasionally several weeks. According to statistics, in airport parking lots, approximately 75% of the users (short-term) are served by 10% to 30% of the spaces, but the remaining 25% (long-term) may require up to 90% of the parking spaces . This phenomenon leads to a decrease in the parking space turnover rate and an increase in cruising time, causing dissatisfaction with airport parking management.
To improve the QOS of airport parking lots, possible solutions, such as increasing the public parking supply, reallocating supply among public parking products, building up advanced traveler information system to avoid unnecessary cruising , and adjusting parking rates, were proposed . Among all strategies, parking pricing adjustment was the most popular because it balances demand across facilities by reducing demand for a specific facility, generates revenue, reduces parking demand, and does not require additional investment to expand the parking facilities. The pricing adjustment method can be categorized into several classes: traditional rate adjustment, dynamic rates over time, and pricing based on consumer characteristics . Airport authorities can select specific regulation strategies based on their parking conditions and target users.
In 2014, the Hongqiao International Airport parking lot suffered from spillover caused by an increase in long-term parking demand. To decrease long-term parking demand, the airport authority took an innovative approach by maintaining the parking rates while using ladder maximum daily charging fees based on parking duration for long-term parkers. This novel price strategy was intended to suppress the long-term parking demand and was applied on Feb. 12, 2015.
This new pricing scheme offers a new perspective for managing long-term parking, and a comprehensive evaluation of its effectiveness is needed. In this paper, we propose an evaluation framework to measure the effectiveness of the pricing scheme considering both travelers’ and managers’ demand for parking on workdays and holidays. The key inputs are obtained based on automatic transaction (AT) data and the vehicle arrival/departure rate at Shanghai Hongqiao International Airport before and after the price adjustment (BPA/APA). We further calibrated the price elasticity of travelers and found that price elasticity varied based on parking duration. The proposed evaluation framework has universality and can be applied to the evaluation of various management schemes.
The remainder of this paper is organized as follows. Section 2 presents a comprehensive review of previous studies on the parking pricing method and an evaluation of pricing schemes. The basic information for the Shanghai Hongqiao International Terminal 2 parking lot and the parking price policy before and after rate modifications is introduced in Section 3. The evaluation framework for estimating the pricing scheme effectiveness is presented in Section 4. The results and discussion section is then presented in Section 5. Finally, the conclusion, limitations, and future research directions are discussed in Section 6.
2. Literature Review
Parking price has been proven to have a major effect on the level of service of parking areas. Low parking rates may encourage parking demand, leading to the absence of available spaces, while high parking prices may cause excess resources and parking market loss [9, 10]. The optimal parking price should offer a trade-off between the available spaces for potential users and the high utilization of the facilities. To ensure the serviceability of the parking areas, traffic engineers always choose a management scheme that leads to 15% of the parking spaces remaining vacant. This criterion ensures sufficient parking spaces and avoids a dramatic increase in cruising time to find spaces when the occupancy level becomes high [11, 12]. Based on this principle, Martens and van Luipen provided a conceptual model for finding the “right price” for parking based on the calculated economic price, current parking tariffs, and occupancy level . Filipovitch and Boamah also determined the optimum parking policy for Minnesota State University based on occupancy level constraints . Jakob et al. designed a dynamic parking pricing decision model for city-level parking management. The proposed model simultaneously minimizes the total cruising time and maximizes total revenue . Pu et al. evaluated the on-street parking price sensitivity at the spatial level based on San Francisco Municipal Transportation Agency data and estimated potential influencing factors. The results indicated the need for various pricing schemes for parking areas to balance parking demand and supply .
The optimum price also varies due to people’s attitudes in response to price change. To quantify price sensitivity, the concept of elasticity is applied in the parking pricing area. Two major methods, stated preference surveys (SP) [17–21] and revealed preference surveys (RP) [22–25], have been applied to estimate this parameter . These studies provide a quantitative level of price sensitivity for optimum price estimation as well as reveal some of the potential factors, including trip purpose, parking region, parking time and duration, and income level that affect the response to the parking price.
Along with determining a reasonable price for parking management, evaluating the impacts of pricing is important for organizations and authorities. Cats et al. suggested that factors including parking duration, turnover, and parking occupancy should be evaluated during the assessment process . Alemi et al. further proposed mixed effect difference-in-difference models to estimate the effect of price changes due to a San Francisco parking pricing program and found that the average parking search time and distance decline by approximately 15% and 12%, respectively .
According to the literature review, methods for designing the parking pricing rate are well researched. However, few studies have focused on evaluating the effectiveness of pricing adjustments, and the methodology for quantifying these is not clear. The limitations in this area still need further study and extensions.
3. Introduction of the Hongqiao International Airport Parking Lot and Its Parking Policy
Hongqiao Airport lies in the west of Shanghai and is 13 kilometers away from Shanghai’s city center. It is one of the busiest airports in China, serving approximately 253,300 flights and 37.96 million passengers in 2015. There are two terminals in the Hongqiao Airport. Terminal 1 is mainly for international use, and Terminal 2 serves domestic flights. The parking lots of Terminal 1 and 2 are operated independently. The data provided are AT data from the Terminal 2 parking lot.
The Terminal 2 parking lot is open to private vehicles, buses and coaches and separated into 4 different parking areas. Among them, P6 and P7 are designated for light private vehicles, and 3316 parking spaces in total are provided in these two sections. P5 and P8 are designated for buses and coaches. Vehicles are charged based on their parking duration. Vehicles do not have to pay the parking fee if the duration is less than 20 minutes, and the maximum charge per day is regulated. The price for buses and coaches is slightly higher than that for private vehicles. Because the behavior of drivers of private vehicles is different from that of drivers of buses and coaches, our study focused on private vehicles.
Based on AT data in 2014, long-term (more than 8 hours) parking represents nearly 73.5% of the total occupant time of the parking lot, although long-term parking demand accounts for only 8% of the total parking demand. As a result, the airport is faced with a shortage of parking spaces. During peak hours, drivers are challenged to find vacant parking spaces.
The airport authority recognized that the parking problem could be alleviated if the long-term parking demand was suppressed. On Feb. 12, 2015, the airport authority increased the parking rate for long-term parking. The original and modified parking rates are presented in Table 1. For example, the hourly parking rates remain at 10 RMB/h for the first 2 hours and 5 RMB/h for each additional hour. However, the maximum parking rates are increased. The previous maximum parking rate was 50 RMB per day, and after the price adjustment, this increased to 60 RMB/day and 80 RMB/day for the first day and the second day, respectively. For vehicles staying for more than two days, the maximum parking fee increases to 110 RMB/day. The rate for long-term parking increased dramatically in an attempt to reduce the long-term parking demand.
The AT data before and after the price adjustment provide a good opportunity to estimate parking price elasticity and evaluate the impacts of price adjustment. The AT data from Jan 1st, 2014, to May 30th, 2015, were provided for this study. The data contain the entrance time, parking duration, and parking expense of each vehicle entering the parking lot within the above time period. The data also contain the employee parking data, which are excluded from the analysis. The number of flights arriving at and departing from this terminal in the same period was also collected for the subsequent analysis.
4. Evaluation Framework
When estimating the impacts of the new price strategy, it is appropriate to consider the impacts on both customers and managers. For travelers, the indexes chosen reflect the QOS of the parking lot, while the index for managers mainly considers the profitability and utilization of the parking facilities. 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 service operates from the traveler’s perspective . Two different categories are considered in this paper to evaluate the QOS of the Hongqiao Airport Terminal 2 parking lot: the availability of parking facilities and the delay in the parking process.
The Availability of Parking Facilities. The availability of parking facilities can be measured by the occupancy level. This index reflects the difference between parking demand and capacity for a given price. If the demand does not exceed the capacity, this parameter is below 1, and when capacity is exceeded, it is greater than 1. The occupancy level (OL) can be estimated based on (1):where is the OL in the analysis period at given time point t, is the number of vehicles parked in the parking lot at the given time point t, and is the total parking spaces provided by the parking lot.
Parking Delay. During the parking process, vehicles suffer extra delay during the access process and the space searching process. The delay during parking can reflect the parking conditions and parking lot service level. The parking delay can be summarized by the following equation:where and are the delays that occur during the arrival and departure processes, respectively. is the time cost of searching for available parking spaces.
Because the access delay ( and ) is mainly caused by time in queues and time cost during tolling, the arrival and departure delay can be estimated by the famous Pollaczek-Khintchine formula (see (3)) based on queueing theory :where is the average delay of vehicles during the exiting process with price and at time period t; is the mean vehicle arrival rate at time when the price equals p; ρ is the saturation level, which equals ; is the serving frequency and equals the average service time; V represents the variance in the service distribution.
However, the delay that occurs during the space searching process varies due to the number of parking spaces, the layout of the parking lot, the efficiency of the parking guidance system, and the OL. Some research also indicated that the cruising time would vary according to land use . When the parking facilities are chosen, the searching delay can be estimated based on a function with the OL as the only variable. Previous studies indicate that when the OL is lower than 80~90%, the cruising time is relatively low, and motorists can find a parking spot quickly [31, 32]. To simplify the estimation process, in this paper, we use the OL to reflect the space searching delay.
4.2. Efficiency of Parking Facilities: A Manager’s Perspective
Throughput. Throughput is the utilization rate for a parking facility in a given period of time. This parameter reflects the number of vehicles each parking space served during a time interval, and a high throughput indicates that the parking facilities serve a large demand. This index can be estimated by (4).where is the throughput of the analyzed parking lot during period i and is the total number of vehicles parked in the parking lot during period i.
Income and satisfied demand are considered in this paper as 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 parking lot. The demand that has been satisfied by the parking lot is similar to the throughput, as it reflects the utilization of the parking spaces.
4.3. Methodology for Price Elasticity Estimation
In economics, elasticity is defined as the ratio of the percentage change in one variable in response to a percentage change in another variable, holding all else constant. This parameter is widely used to quantify how parking demand changes in response to a price adjustment. Because airport parking demand may be affected by factors other than price, such as flight volume and holidays, when price elasticity is estimated based on its original concept, it is inaccurate. Therefore, a log-based regression method is used to model the relationship between parking demand and potential factors. This method provides quantitative results for parking demand caused by other variables and provides the point price elasticity of customers with various parking durations. The log-based regression model in this paper is set up as follows:where is the parking demand during the ith week for those parking with a duration within parking duration category t; is the parking price in week for parking duration category t; is the total volume of arriving and departing flights in Hongqiao Terminal 2 in week i; parameter is proposed in this equation to consider the average growth rate of parking demand; H is the holiday effect group. This group contains several parameters to reflect the holiday effects on parking demand and elasticity during holidays. In detail, lt is the dummy variable parameter considering the existence of long-term holidays. blt is the dummy variable for weeks before long-term holidays. c depicts whether the parking price is new within the ith week. It would be 1 if the parking price is changed. to are the estimation parameters for duration category t, and is the residual for parking duration in the ith week.
As presented in (5), the dependent variable is the logarithm of the weekly parking data. To differentiate users with different parking durations, we divide the data into 5 different categories and run the regression for each category separately. The total parking demand, mean parking price, and total arrival and departure flight volumes are calculated to calibrate the model in (5).
In addition, to consider the difference in parking demand between ordinary weeks and holidays, dummy variables are used to represent the type of day. Weeks within long-term holidays or before long-term holidays are coded as 1 in the corresponding parameters.
In this model, the parking price elasticity for ordinary weekdays equals the amount of . In addition, , would be the parking price elasticity for the weeks before long-term holidays and the weeks of long-term holidays, respectively.
5. Evaluation and Discussion
5.1. Basic Statistics of the Parking Characteristics in Hongqiao Terminal 2 Parking Lot
Parking Duration Variation. The increase in the maximum parking fee led to variation in the parking duration distribution (Table 3). Parking demand with 8~24 hours duration did not significantly decrease. However, with the increase in the parking fee, overall parking demand decreased by 9~34% based on the duration length.
Although the long-term parking demand was dramatically reduced, the new price regulation strategy largely encourages short-term parking demand, driving 7.4% in growth (from 9369 to 10,061 veh/d). This rise is largely driven by free parking demand, which saw a nearly 23% increase. The parking demand for 1~3 hours and 4~8 hours also increased by 4.1% and 2.3%, respectively.
Temporal Access Demand Variation. To analyze the impacts of the price adjustment, the fluctuations in daily average demand accessing the Hongqiao Terminal 2 parking lot before and after the price adjustment and the coefficient of the demand variance are presented in Figure 1. The increase in 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 and after the price adjustment.
Change in Temporal Parking Demand. With the reduction in overnight and long-term parking, the OL of the Hongqiao Terminal 2 parking lot also decreased considerably after the price adjustment (Figure 2). During the long-term Spring Festival holiday in 2014, 3709 vehicles parked in the parking lots during the peak period, which exceeded the designed capacity. Nearly 44% of the time, the parking lot was operating under a high level of occupancy (OL >0.85). However, after the price adjustment, the maximum occupancy dropped to 2735, and the OL was less than 0.85 during the 2015 Spring Festival. For parking lot performance on ordinary days, the maximum occupancy was 4004 between 2014/11/17 and 2014/11/23, which exceeded the lot capacity by approximately 20.7%. With the current price policy, the highest OL decreased to 3447, and the parking lot OL was over 0.85 only 26% of the time between 2015/3/16 and 2015/3/22. All of these results indicate the effectiveness of this policy in improving airport parking lot performance.
5.2. Performance Estimation
QOS Analysis. The average occupancy levels during the peak period are estimated based on the AT data and presented in Table 4 along with the entrance and exit delay. The analysis results indicate that, with the application of the ladder maximum charging fee, the availability of parking spaces increased, especially during long-term holidays. The peak hour OL during the long-term holiday drops to 0.795, which corresponds to 1.08 in 2014. The peak hour OL during ordinary days also experienced a 13% decrease due to the new price strategy. The original OL during the peak period suggests difficulty in searching available spaces. After price regulation, because the OL level is approximately 0.8, vehicles can easily find available spaces and reduce searching delay.
We also investigated the entrance and exit service rates to further estimate delays. According to the survey results, the average service time at the entrance and exit gate is 8 and 17 seconds, respectively, with standard deviations of 3.1 seconds and 7.8 seconds. Based on these results, the average entrance and exit delay during the peak period are estimated. It is clear that the new price strategy encouraged short-term parking demand during the peak period, causing pressure during the access process. The exit delay increased to 41.88 seconds/vehicle. The entrance delay also increased by approximately 1.3 seconds. Due to the low access demand during long-term holidays, the access delay is relatively low compared with that on ordinary days. The access pressure is not as serious as it is during ordinary days; therefore, the access delay difference is not significant during long-term holidays.
Profitability and Facility Utilization Analysis. The average throughput, transaction amount, and income per day are calculated 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. These parameters indicate that greater demand was served by the parking facilities in the Hongqiao Terminal 2 parking lot after the price adjustment. The increase in the maximum daily charging fee also made the parking lot more profitable, partially due to the increase in short-term parkers. Meanwhile, income growth also saw a contribution from long-term parkers, because the prices are inelastic for long-term parkers. This is discussed in the next section.
5.3. Elasticity Analysis
The elasticities of the terminal 2 parking lot users with different durations are calibrated, and parameters with p-values less than 0.05 are presented in Table 6. The price elasticity of long-term parkers is within the range of -0.228 and -0.689. These figures indicate that parking demand is relatively inelastic (). The sensitivity to price changes also varies due to the parking time length. Those parking for 2~3 days are much more sensitive to price changes than those in other duration categories. Extra elasticity before long-term holidays is also identified. This shows that increasing the maximum charging fees for long-term parkers has further helped decrease parking demand during holidays even if the elasticity is relatively low for users staying between 9 and 72 hours. The extra elasticity for long-term holidays is nonsignificant and thus is not provided in this paper.
Meanwhile, the calibration results also indicate that demand for parking with a duration of 25~72 hours is elastic to the number of flights. This phenomenon does not exist in the categories with other durations. The results also indicated that before and during long-term holidays, parking demand of less than 3 days would decrease, and parking areas would be occupied by vehicles with longer parking durations. This may be caused by the demand for long-term travel during holidays.
Due to the high level of long-term parking demand, the Shanghai Hongqiao airport implemented a ladder maximum daily charging fee to suppress the increase in long-term parkers. This study estimated the impacts of this innovative price strategy on the QOS of this parking lot based on AT data from before and after the strategy was implemented.
An evaluation framework was proposed, and the evaluated indexes were carefully selected according to the user and manager perspectives. The estimated results indicated that the OL during the peak period dropped approximately 13% 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 further estimated the price elasticity of customers with various parking durations and other factors that affect parking demand. The results indicated that parking price was relatively inelastic and varied with parking duration.
In conclusion, this paper presented a framework for assessing the effectiveness of parking price adjustment considering various aspects, including OL, access day via parking, throughput, income, etc. The proposed framework can be further implemented to evaluate the effectiveness of other parking management strategies as well. The elasticity calibration results indicated that the price elasticity would vary due to the parking duration; this result could provide insights to airport parking management. For future studies, in order to improve the accuracy of QOS evaluation at airport parking lot, the impact of price adjustment on various type of vehicles should be considered. Moreover, the cruising time of vehicles at airport parking lot should be further discussed and considered in such studies.
The service time data used to support the findings of this study are included within the article.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
- M. Coogan, “Ground access to major airports by public transportation,” ACRP (Airport Cooperative Research Programme) Report 4, Transportation Research Board of the National Academies, Washington, DC, USA, 2008.
- A. J. Reynolds-Feighan and K. J. Button, “An assessment of the of the capacity and congestion levels at European airports,” Journal of Air Transport Management, vol. 5, no. 3, pp. 113–134, 1999.
- I. Humphreys and S. Ison, “Planning for sustainability: the role of airport surface access strategies as a means of reducing the dependency on the private car for airport access trips?” Association for European Transport, 2009.
- T. Budd, S. Ison, and T. J. Ryley, “Airport ground access: issues and policies,” in Proceedings of the Transportation Research Board 90th Annual Meeting (No. 11-0780), 2011.
- J. Consultancy, “Walker parking consultants, mannix group and dmr consulting (2010)‘Guidebook for evaluating airport parking strategies and supporting technologies’, airport cooperative research programme (ACRP) Report 24,” Transportation Research Board of the National Academies, Washington, DC, USA, 2010.
- V. Psaraki, A. Stathopoulos, and C. Abakoumkin, “Parking capacity requirements for relocated airports: The new Athens international airport,” Transportation Research Record, no. 1788, pp. 19–25, 2002.
- F. Zong, Y. Tian, Y. He, J. Tang, and J. Lv, “Trip destination prediction based on multi-day GPS data,” Physica A: Statistical Mechanics and its Applications, vol. 515, pp. 258–269, 2019.
- Ricondo & Associates, DMR Consulting, Resource Systems Group, Inc, Airport Cooperative Research Program, and United States. Federal Aviation Administration. (2010). Handbook to Assess the Impacts of Constrained Parking at Airports (Vol. 34). Transportation Research Board.
- J. Simićević, S. Vukanović, and N. Milosavljević, “The effect of parking charges and time limit to car usage and parking behaviour,” Transport Policy, vol. 30, pp. 125–131, 2013.
- D. Shoup, “The high cost of free parking, american planning association,” Journal of Planning Education & Research, vol. 17, no. 1, pp. 3–20, 2005.
- D. Shoup, “he politics and economics of parking on campus,” in Transport Demand Management Measures: An International Perspective, StephenIson, Rye, and Tom, Eds., pp. 121–149, 2008.
- G. Pierce, H. Willson, and D. Shoup, “Optimizing the use of public garages: pricing parking by demand,” Transport Policy, vol. 44, pp. 89–95, 2015.
- M. Martens and B. van Luipen, “To a better future with a motivated parking tariff: practical applications for an economic based parking tariff,” in Proceedings of World Parking Symposium, Breda, Netherlands, 2009.
- A. Filipovitch and E. Frimpong Boamah, “A systems model for achieving optimum parking efficiency on campus: the case of Minnesota State University,” Transport Policy, vol. 45, pp. 86–98, 2016.
- M. Jakob, M. Menendez, and J. Cao, “A dynamic macroscopic parking pricing and decision model,” Transportmetrica B, pp. 1–25, 2018.
- Z. Pu, Z. Li, J. Ash, W. Zhu, and Y. Wang, “Evaluation of spatial heterogeneity in the sensitivity of on-street parking occupancy to price change,” Transportation Research Part C: Emerging Technologies, vol. 77, pp. 67–79, 2017.
- D. W. Gillen, “Estimation and specification of the effects of parking costs on urban transport mode choice,” Journal of Urban Economics, vol. 4, no. 2, pp. 186–199, 1977.
- K. J. Dueker, J. G. Strathman, and M. J. Bianco, “Strategies to attract auto users to public transportation,” TCRP Report 40, Transportation Research Board National Research Council, Washington, DC, USA, 1998.
- D. A. Hensher and J. King, “Parking demand and responsiveness to supply, pricing and location in the Sydney central business district,” Transportation Research Part A: Policy and Practice, vol. 35, no. 3, pp. 177–196, 2001.
- N. Abboud, Solana Beach Joint Development Project: Parking Cost Elasticity Study, Wilson & Company, Inc., San Diego, Calif, USA, 2006.
- J. Simicevic, N. Milosavljevic, G. Maletic, and S. Kaplanovic, “Defining parking price based on users' attitudes,” Transport Policy, vol. 23, 2012.
- A. K. Kanafani and L. H. Lan, “Development of pricing strategies for airport parking: a case study at San Francisco airport,” International Journal of Transport Economic, vol. 15, 1988.
- J. A. Kelly and J. P. Clinch, “Temporal variance of revealed preference on-street parking price elasticity,” Transport Policy, vol. 16, no. 4, pp. 193–199, 2009.
- D. B. Ottosson, H. Lin, and C. Chen, “Price elasticity of on-street parking demand - a case study from seattle,” in Proceedings of the Transportation Research Board 91st Annual Meeting, 2012.
- O. Cats, C. Zhang, and A. Nissan, “Empirical evaluation of an on-street parking pricing scheme in the city center,” in Proceedings of the Transportation Research Board 94th Annual Meeting, 2015.
- S. Concas and N. Nayak, “A Meta-analysis of parking pricing elasticity,” in Proceedings of the Transportation Research Board 91st Annual Meeting (No. 12-4207), 2012.
- F. Alemi, C. Rodier, and C. Drake, “Cruising and on-street parking pricing: A difference-in-difference analysis of measured parking search time and distance in San Francisco,” Transportation Research Part A: Policy and Practice, vol. 111, pp. 187–198, 2018.
- H. C. Manual, Highway Capacity Manual, DC, Washington, DC, USA, 2010.
- S. I. Gass, Encyclopedia of Operations Research and Management Science, C. M. Harris, Ed., Kluwer, Boston, MA, USA, 2012.
- F. Zong, T. Wu, and H. Jia, “Taxi drivers' cruising patterns-insights from taxi GPS traces,” IEEE Transactions on Intelligent Transportation Systems, vol. 99, pp. 1–12, 2018.
- R. Weinberger, J. Kaehny, and M. Rufo, US Parking Policies: An Overview of Management Strategies, Institute for Transportation and Development Policy, New York, NY, USA, 2010.
- D. B. Ottosson, C. Chen, T. Wang, and H. Lin, “The sensitivity of on-street parking demand in response to price changes: A case study in Seattle, WA,” Transport Policy, vol. 25, pp. 222–232, 2013.
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