Parking Demand Forecasting in Airport Ground Transportation System: Case Study in Hongqiao Airport

Transcription

1 Internatonal Symposum on Computers & Informatcs (ISCI 25) Parkng Demand Forecastng n Arport Ground Transportaton System: Case Study n Hongqao Arport Ln Chang, a, L Wefeng, b*, Huanh Yan 2, c, Yang Ge, d 48 Cao an Road, Jadng Dstrct, Shangha, Chna 2 9 North Zhongshan Road(2nd), Yangpu Dstrct, Shangha, Chna a Lnch@tongj.edu.cn, b lwf3@gmal.com, c yanggel@63.com, d smed _hq@63.com Abstract Ths paper addressed the parkng demand forecastng n arport ground transportaton system, and demonstrated here n Hongqao Arport. The movng-average model (MA model) s used to model and estmate the parkng demand wth the scheduled arrval passengers and the departure passengers. The results ndcated that all the coeffcents are sgnfcant whch confrmed the valdaton of the proposed approach. Keywords: Hongqao Arport; Ground transportaton; Parkng demand forecastng; ARIMA model Introducton Hongqao Internatonal Arport s stuated n the western outskrts of Shangha, Chna, about 3 klometers (about 8 mles) from downtown area and about 6 klometers (about 37 mles) from Pudong Internatonal Arport. Beng the frst cvlan arport n Shangha, Hongqao Internatonal Arport s more than eghty years old. After a seres of renovatons, t has become one of the three nternatonal ar transt centers n Chna. 9 arlnes currently fly from here to both domestc and nternatonal ctes. 25. The authors - Publshed by Atlants Press 2242

2 Hongqao Arport Termnal 2 s bult-up and come nto usage on March 6, 2, whch s a support to 2 Shangha Expo. After the extenson, t shown to the publc a new termnal wth a new runway of 33 meters and a new set of assstant facltes, especally a hgh-effcent arport ground transportaton system. The new arport ground transportaton system ncludes two metro lnes, several bus lnes and arport express, and taxs system, and also parkng-lot for passengers who arrved by prvate cars. Metro Tax Bus Termnal 2 Fg. Illustraton of the parkng lots n Termnal 2 However, wth the rapd development of motorzaton n Chna, more and more people have shfted to rely on prvate cars as ther prmary travel modes. It not only caused the severe congeston on urban road network and more gas emsson, but also lead to the lack of suffcent parkng-lots, especally n central busness dstrcts (CBD) or external transport hubs such as Hongqao Arport and Pudong Arport. Takng Hongqao Arport Termnal 2 as an example, there are 4 parkng lots now n servce for Termnal 2. The parkng lots are located on the two sdes of the eastern part of the transportaton center, whch s connected wth the termnal, shown n Fg.. P6 and P7 parkng lots are for lght-duty vehcles only (only cars lower than.9 meters are allowed n), whle P5 and P8 parkng lots are for overszed vehcles. Here we refer to the prvate-car parkng lots (P6 and P7) whch are wth a total of 3,4 parkng spaces avalable. Nevertheless, after the 4 years development, the problem of nsuffcent parkng spaces durng the peak-hours of flghts has been 2243

3 more and more severe n Termnal 2. As shown n Fg. 2, the average parkng saturablty especally n P7 has nearly reached the., whle the worst-case parkng saturablty durng peak hours n both parkng lots have been over-saturaton. A new round of parkng lots plannng and constructon should get started and new operaton management measures should be deployed. Fg. 2(a) Illustraton of parkng saturablty changes accordng to hour of day n P6 Fg. 2(b) Illustraton of parkng saturablty changes accordng to hour of day n P7 2244

4 Thus, n terms of the next-step arport parkng lots plannng and effcent comprehensve arport ground transportaton system manegement, ths paper addressed the parkng demand forecastng of the parkng lots n Hongqao Arport, based on the avalable data of parkng occupancy and the numbers of flght passengers. Lterature Revew In the recent decades, a number of works have been focusng on the area of parkng demand forecastng. A SPGR model based on land use and LC Model based on prvate car trps were establshed n []; Chen establshed another SPGR Model and then gave a precse algorthm to analyze the nfluence factors of parkng demand [2]; Guan establshed a PSDF Model [3]. Accordng to constructon ndces from the planned land n all traffc areas, Zhang set up an mproved SPGR Model to forecast parkng demand [4]. Under the lmtaton of network capacty and network servce level, Ba, et al modfed an OD forecast and parkng demand forecast method [5]. Model of Parkng Demand Forecastng Model Constructon. The movng-average model (MA model) s used to model the unvarate tme seres models n the tme seres analyss. Wth the scheduled arrval passengers nput, the parkng demand A and the departure passengers notaton of the proposed model s wrtten D of the -th tme perod as X can be estmated based on the MA model. The q m n. X = m+ ε + X ε + A q + D φ t t t t t+ t+ = = = () 2245

5 where θ, φ are the parameter of the model, µ s the expectatons of t t t q X t, and ε, ε,, ε are the whte nose error terms. The movng-average model s essentally a fnte mpulse response flter wth some addtonal nterpretaton placed on t. The proposed algorthm n ths paper s capable of the parkng demand estmaton and adaptaton to sut the changng stuaton of passenger arrvals, passenger departure, flght delays and parkng demand n pror perods. The output can be appled as the reference of early-warnng and the relevant countermeasures. Parameter Determnaton. The parameters n the Equaton () are determned based on the basc assumptons lsted as follows. Accordng to the statstcal results of the parkng tme n Hongqao Arport, the vehcles whose parkng tme s more than three hours represents just twelve percent of all the parkng vehcles. The assumpton s made that the current occupancy of parkng space s nfluenced by the occupancy of the past two hours. Therefore, the parameter q s determned to be 2. 2 The occupancy of parkng spaces s nfluenced by the departure passengers. Hongqao Arport s the man domestc arport servng Shangha. For the passengers of domestc flghts, the recommended arrval tme at the arport pror to the departure of flghts s usually two hours. Thus, the parameter n s determned to be 3. 3 The occupancy of parkng spaces s also nfluenced by the arrval passengers. Takng the results of passenger trace observaton n Hongqao Arport and the delay of flghts nto consderaton, the parameter m s determned to be 2. Results and Dscusson. The parkng lots of Hongqao Arport are dvded nto two separated parts: the northern part and the southern part. The parkng demand of the two parts of the parkng lots are estmated respectvely. 2246

6 Table Results of the Parkng Demand Forecastng Model. a. Parkng Demand Forecastng Model of the Northern Part Estmate SE t Sg. Occupancy of parkng space Constant MA Lag Lag Arrval passengers Numerator Arrval passengers ( hour later) Arrval passengers (2 hour later) Departure passengers ( Departure passengers (2 Departure passengers (3 Numerator Numerator Numerator Numerator Numerator

7 b. Parkng Demand Forecastng Model of the Southern Part Estmate SE t Sg. Occupancy of parkng Constant space 5 8 MA Lag Lag Arrval passengers Numerator Arrval passengers ( hour later) Arrval passengers (2 hour later) Departure passengers (2 Departure passengers (3 Numerator Numerator Numerator Numerator The demand forecastng model of the northern part s X = X.773X +.5A +.46A +.32A +.7D +.2D +.22D. (2) t t t 2 t t+ t+ 2 t+ t+ 2 t+ 3 All the coeffcents are sgnfcant and the R-squared s.88. The obtaned model well fts the data. 2248

8 Accordng to Equaton (2) and Table -a, the current occupancy of parkng space s negatvely related to the occupancy of the past two hours. Moreover, the current arrval passengers and the departure passengers n the comng 2~3 hours nfluence obvously on the parkng demand. The demand forecastng model of the southern part s X = X.73X +.74A +.52A +.4A +.25D +.8D. (3) t t t 2 t t+ t+ 2 t+ 2 t+ 3 All the coeffcents are sgnfcant and the R-squared s.87. Accordng to Equaton (3) and Table -b, the current occupancy of parkng space s also negatvely related to the occupancy of the past two hours. However, compared wth the model of the northern part, the departure passengers n the next hour does not have sgnfcant nfluence on the parkng demand. It mght result from more extra tme set asde by the passengers from the south n case the unexpectedly bad traffc condton. References [] Yan K., Parkng demand forecastng model, Shenzhen Internatonal Conferences on Cty Statc Transportaton and Traffc Management. (994) 25~28. [2] Chen J., Wang W., Yan K., Forecastng research of urban parkng facltes demand, Journal of Southeast Unversty. 29 (999) [3] Guan H., Wang X., Wang X., The research on forecastng method for parkng demandng, Journal of Bejng Unversty of Technology. 7 (26) [4] Zhang J., A study of the plannng method and ts applcaton for the urban parkng, Urban Transport of Chna. (23) [5] Ba Y., Xue K., Yang X., Forecastng method of parkng-demand based on capacty-of-network, Journal of Traffc and Transport Engneerng. 2(24)

9 225