Air Transport Demand. Ta-Hui Yang Associate Professor Department of Logistics Management National Kaohsiung First Univ. of Sci. & Tech.

Transcription

1 Ar Transport Demand Ta-Hu Yang Assocate Professor Department of Logstcs Management Natonal Kaohsung Frst Unv. of Sc. & Tech. 1

2 Ar Transport Demand Demand for ar transport between two ctes or two regons depends on Soco-economc characterstcs of the regons The characterstcs of the transportaton system that lns them 2

3 Ar Transport Demand Models to evaluate ar transportaton demand most often evaluate The number of potental passengers The number of passenger lometers that can be acheved The expected number of operatons (tae offs and landngs) A percentage share of the number of ar passengers out of the total number of passengers 3

4 Ar Transport Demand Estmaton The process of forecastng transportaton demand most often comprses the followng steps: Trp generaton Trp dstrbuton Modal splt Trp assgnment 4

5 Classfcaton: Compettve Mode Whether or not the model ncludes compettve modes of transportaton Models that are ndependent of the characterstcs of alternatve modes of transportaton Multmode models 5

6 Independent of Other Modes The arplane s the predomnant mode of transportaton on many long-dstance traffc routes. Therefore, demand for ar transportaton on long-haul routes should be estmated ndependently of other modes of transportaton. 6

7 Multmode Multmode models are prmarly used to estmate demand for ar transportaton on short-haul routes. Ar transportaton demand on shorter routes s usually estmated smultaneously wth the estmaton of demand on other modes of transportaton. 7

8 Classfcaton: Macro vs. Mcro Classfcaton of ar transportaton demand model Macroscopc models Mcroscopc models 8

9 Classfcaton: Macro vs. Mcro Macroscopc models are used to estmate the development level of ar transportaton n a certan country or regon Estmate The number of passengers The number of arplane operatons The number of passenger lometers 9

10 Classfcaton: Macro vs. Mcro Mcroscopc models estmate Demand between two ctes The passenger traffc at an arport The number of passengers along a specfc route The number of passengers n each class 10

11 Macroscopc Models Macroscopc Models : Demand s a functon of tme Factors that affect the number of passengers are not taen nto consderaton 11

12 Macroscopc Models t : tme y : the number of ar passengers that changes over tme Model 1, m, ; parameters y t + m Model calbraton : can be the least squares method 12

13 Macroscopc Models Model 2 y a b t logarthmc form log y log a + t log b Advantage : a, b can be estmated usng the least square method 13

14 Macroscopc Models Model 3 : modfed exponental curve y + a b When a<0, b<1 t y t : fxed saturaton level 14

15 Macroscopc Models Model 4 : Gompertz curve y a b t Logarthmc form log y log + b t log a 15

16 Macroscopc Models When log a<0,b<1 y t : saturaton level 16

17 Macroscopc Models Model 5: Logstc curve Logstc curve, or called Pearl-Reed curve y b e at Has a shape smlar to the Gompertz curve 1 + y t 17

18 Macroscopc Models The lease squares method cannot be appled to estmate the parameters of : Modfed exponental carves Pearl-Reed curve Gompertz curve The three-pont methods have proven very successful n estmaton the parameters of these curves 18

19 Macroscopc Models Macroscopc models : Demand s a functon of soco-economc characterstcs Dependent varables The number of passengers The number of operatons The number of passenger lometers Independent varables Chosen from soco-economc characterstcs and characterstcs of the transportaton system 19

20 Macroscopc Models Most often soco-economc Populaton Natonal ncome Personal consumpton Volume of trade Number of tourst Most often transportaton system The cost of transportaton Speed / travel tme 20

21 Macroscopc Models Model : m n yt : the total number of soco-economc characterstcs : the total number of transportaton system characterstcs : the number of ar passengers n tme t S t : the value of the -th soco-economc characterstcs n tme t T t : the value of the -th transportaton system characterstcs n tme t a, b, c : parameter y t a m 1 S b t n 1 T c t 21

22 Macroscopc Models Logarthmc form log y t m n log a + b log St a, b, c parameters estmaton : Multple regresson technque C log T t Maxmum lelhood functon 22

23 Trp Dstrbuton Trp dstrbuton models When the total number of trps that a regon can generate has been establshed, the trps are then dstrbuted. Trp dstrbuton : establshes the number of trps between ndvdual zones. Commonly used models Entropy model Gravty model 23

24 Trp Dstrbuton The Gravty model an analogy to Newton s Law of Gravty f Α 2 d Β f Α Β d : the number of trps between cty and cty : constant : the sze of cty : the sze of cty : the dstance between cty and cty 24

25 Trp Dstrbuton Α, Β s most often taen as the number of emtted or attracted trps,.e. Α a, Β b Problems n the orgnal gravty model : not satsfed by the followng flow conservaton equatons n m f a, f 1 1 b 25

26 Trp Dstrbuton Modfed Gravty model f a b f ( d ) : coeffcents assocated wth the number of trps emtted or attracted by the ctes, d : dstance functon, can be dstance, travel tme etc., or a combnaton of dfferent varables f ( ) 26

27 27 Trp Dstrbuton Snce Smlarly ( ) ( ) n n n d f b a d f b a a f ( ) m m d f a b f 1 1 1

28 Multmode Models Multmode models Aggregated models Aggregated models tae certan soco-economc characterstcs nto consderaton. Dsaggregated models Dsaggregated models start wth the ndvdual as the one mang the decson to travel and therefore operate wth certan soco-economc characterstcs related to the ndvdual, obtaned by surveyng passengers. Dsaggregated models can also quantfy the effect of comfort or the feelng of safety. 28

29 Multmode Models Aggregated models : abstract mode model Dsaggregated models : choce models 29