27th Australasian Transport Research Forum, Adelaide, 29 September 1 October 2004

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1 27th Australasan Transport Research Forum, Adelade, 29 September 1 October 2004 Paper ttle: Author(s) name(s): Organsaton(s): Spatal structure effects on ntercty freght demand n developng countres: model and applcaton Amla Aldan and Mchael A P Taylor Transport Systems Centre Unversty of South Australa Contact detals: Transport Systems Centre Unversty of South Australa, Cty East Campus Postal address: GPO Box 4271 Adelade, SA 5001 Telephone: Facsmle: emal: Amyy001@students.unsa.edu.au Abstract (200 words): The obectves of ths paper are to fnd methods that account for spatal structure effects usng aggregate data, and to determne the effects of spatal structure on freght demand. Spatal structure affects travel decsons, f t s not ncluded n the model, then t could lead to a based model. Spatal structure effects are usually studed usng competng destnatons models, whch need dsaggregate data. Ths paper presents the adaptaton of competng destnatons model together wth ntervenng opportuntes models to account for spatal structure effects usng aggregate data. The applcaton of the model shows that competng effects occur at orgn zone, whle agglomeraton effects occur at destnaton zones.

2 2 Spatal structure effects on ntercty freght demand Introducton The obectves of ths paper are to fnd a method to account for spatal structure effects usng aggregate data, and to examne the effects of spatal structure on freght demand. There are three reasons behnd ths research. Frst, spatal structure effects are usually modelled usng dsaggregate data, whch are rarely avalable n developng countres. Second, the nowledge of the effects of spatal structure on transport demand s an mportant nput n transport system and locaton plannng. Thrd, freght demand reflects the extent of spatal nteracton between ctes so that t could be used to examne spatal structural effects. Freght demand s usually consdered as a derved demand. Freght nteracton between two dfferent spatal locatons wll occur when there s a demand for a commodty n one locaton and an oversupply of that commodty n another locaton. Another mportant determnant for freght demand s of course transport mpedance. As ntercty road networs do not usually offer many alternatve routes from one locaton/cty to other locatons/ctes, the cty poston n the road networ could be a ey determnant of freght demand. Applcatons of freght demand models n developng countres tend to gnore ths varable. Fotherngham (1983) stated that when a spatal structure varable s not ncluded n the model, then spatal structure s mplctly reflected n the dstance component, whch s then necessarly based. In Indonesa and probably also n other developng countres, most ctes grew along the man road networ as local road transport networs were usually not well establshed n the ntal urban developments. The trend s stll contnung where new actvty centres emerge along man roads, although other smaller roads have been mproved and some new roads have been bult. These condtons may be due to hgher level of accessblty offered by man roads. The understandng of spatal structure effects on transport could assst planners n selectng locatons of actvtes that have hgh accessblty and could mnmse transport costs to all locatons n a networ. On the other hand, planners also could decde to set prortes n expandng the road networ that wll also produce least transport costs n the whole networ. Followng ths ntroducton, there are fve other sectons to the paper. Secton two descrbes the study area and the data, secton three revews the theores and applcaton of spatal structure models, secton four explans the modellng framewor, secton fve gves the applcaton results and secton sx gves conclusons. Study area and freght demand data Soco-economc condtons The study area of ths research s Central Java provnce n Indonesa. Based on the data from the Central Statstcs Bureau of Central Java (1997, 2002), the soco-economc condtons of the Central Java provnce can be summarsed as follows: 1. Central Java provnce s one of sx provnces on Java Island. The Central Java provnce s dvded nto smaller admnstratve boundares called abupaten (sub-provnce) and ota madya (muncpalty). There are 29 sub-provnces and 6 muncpaltes. Overall, the total

3 Aldan and Taylor 3 area of the Central Java provnce s 3.25 mllon hectares, or around per cent of the total area of the Java Island, or 1.7 per cent of the total area of Indonesa. 2. Central Java provnce was the thrd most populated provnce n Indonesa wth mllon populatons n The populaton was dstrbuted such that the muncpaltes had more populaton than the sub-provnces, and the captal ctes of the sub-provnces had more populaton than the rest of the area of the sub-provnces. The populaton growth was one per cent per year based on the 1996 natonal soco-economc survey. 3. The economc growth, whch s ndcated by gross regonal domestc product (GRDP), was relatvely hgh n 1996 (7.3 per cent). Ths growth declned sgnfcantly n 2001, where t was only 3.33 per cent per year. Ths declne was beleved to be the mpact of the economc crss started n 1998, from whch Indonesa s stll strugglng to recover. The GRDP was manly determned by manufacturng ndustres and agrculture sectors n both 1996 and There was a gap of ncome between cty and rural area and also between sub-provnces n Central Java provnce. For nstance, n 1995 (before economc crss n 1998) the ncome per capta of the cty of Semarang (the captal cty of Central Java provnce) was 4,398,776 Indonesan Rupahs, whle the average of ncome per capta of other subprovnces was only 1,469,524 Indonesan Rupahs. Road networ condtons All captal ctes of the sub-provnces are connected wthn the Central Java road networ. Overall, there were 1,215 m of natonal roads and 2,590 m of provncal roads. The wdth of arteral roads was varyng from 5 m to 15 m, and the wdth of collector road was varyng from 4 m to 14 m. By the year 2000 only 41 per cent of all the roads were n good condton (the Central Statstcs Bureau of Central Java, 2002). From the demand pont of vew, many ntercty roads n the Central Java networ had mxed traffc from non-motorsed vehcles to heavy trucs and buses, whch could also ndcate that the roads had mxed functons from local to arteral. One output of the natonal orgn destnaton study n Indonesa n 1996 was that the percentage of local traffc on ntercty roads n Central Java provnce was relatvely hgh. The percentage of motorcycle and nonmotorsed vehcles from total traffc on ntercty roads reached a number of 12.4 per cent and 30.8 per cent, meanwhle the share of freght transport was relatvely hgh namely 17.1 per cent (Mnstry of Communcatons of Indonesa, 1997). Freght demand data The freght demand data used to calbrate the models are the result of natonal orgn destnaton survey of Indonesa undertaen by the Mnstry of Communcatons of Indonesa n The survey provdes nter sub-provnces aggregate freght demand n tonnes/year, where the data are not dvded by commodty. The study area of ths study s dvded nto 35 zones; so that there are 1190 nter sub-provnces freght flows (Mnstry of Communcatons of Indonesa, 2002).

4 4 Spatal structure effects on ntercty freght demand Modellng spatal structure effects: a revew The effects of spatal structure n spatal choce are commonly represented by a varable called accessblty. Accessblty value ndcates the ease (benefts or costs assocated wth travel) of people or commodtes to travel from specfc locatons to other locatons. Accessblty could also fgure spatal structure and transport networ characterstcs. Thus, we could examne the connecton between transport networ and spatal structure n order to determne the effects of one upon the other usng accessblty (Prmerano, 2001). Spatal structures effects can be examned usng a dsaggregate approach and an aggregate approach. In a dsaggregate approach, several models have been appled such as logt models (Pellegrn and Fotherngham, 2002). In an aggregate approach, researchers usually employed gravty-type models (Guldmann, 1999). Pellegrn and Fotherngham (2002) stated that spatal and aspatal choces dffer n processng nformaton. The number of alternatves s commonly much larger n spatal choces, so that a tradtonal multnomal logt (MNL), whch requres ndvduals to smultaneously evaluate all alternatves, s napproprate. It could happen n the MNL applcatons that a destnaton wth maxmum utlty s not selected because t s never evaluated. Therefore, a weght of the utlty of an alternatve s needed. The weght measures the probablty of the alternatve s actually evaluated. The MNL model then become: exp( V n ) Ln ( G) exp( V n ) c α Pn( ) = = m m exp( V n ) Ln ( G) exp( V n ) c α = 1 = 1 (1) where P n () s the probablty of ndvdual n (from orgn ) selectng destnaton, V n s the utlty of destnaton vewed by ndvdual n n orgn, and L n ( G) s the lelhood that alternatve s n ndvdual n s (from ) chosen cluster G. Ths general model s nown as the competng destnaton model, where c denotes the competng measure and α s an ndex to measure the level of herarchcal nformaton processng, whch needs to be estmated. Competng effects are present f α<0, whch means that alternatves n close proxmty to others are less lely to be selected. Agglomeraton effects are present f α>0, when the attracton of a cluster ncreases as the number of alternatves n t ncreases. If α=0 then there are no competng or agglomeraton effects. If we assume people use a herarchcal nformaton processng strategy by selectng clusters of alternatves frst before selectng a destnaton from wthn a selected cluster, then potental accessblty measures, whch descrbe the accessblty of a destnaton to all other destnatons, can be used. Pellegrn and Fotherngham (2002) suggested usng a Hansen type potental accessblty: α 1 W Ln ( G) = (2) M 1 d

5 Aldan and Taylor 5 where M s the total number of alternatves, W s the mass of destnaton zone, and d s the dstance from to (all other alternatves avalable to person n and orgn ). Large values mean alternatves are n close proxmty and low values mean alternatves are spatally solated. Guldman (1999) accounted for the effects of spatal structure on the nter-cty telecommuncaton flows. The effects were measured usng competng destnaton (CD) factors and ntervenng opportuntes (IO) factors. IO factors are based on the dea of Stouffer (1940) who argued the number of persons gong a gven dstance s drectly proportonal to the number of opportunty at that dstance and nversely proportonal to the number of ntervenng opportuntes. Guldman s (1999) model was a gravty-type model. Hs basc model s as follow: F = f ( F, D, P, XO, XD, A ) (3) where F s measure of the flow from locaton to locaton, D s the dstance from to, P s telephone prce per unt of flow from to, and XO and XD varables charactersng the flow-orgnatng maret at and the flow-recevng maret at, whle A represents CD/IO factors. He concluded that spatal structure has sgnfcant effects on telecommuncaton flow patterns and that all destnatons compete. Model framewor The development of freght demand models mostly are at an aggregate level n whch the classc four-stage model s modfed to sut the characterstcs of freght (Ortuzar and Wllumsen, 1994). We use another nd of aggregate demand model namely the drect or smultaneous demand model to account for the spatal structure effects on freght demand. The drect demand models are closely related to the general econometrc models of demand. The applcaton of the models clamed to avod some of the weanesses of the conventonal four-step model of travel demand. The attractveness of drect demand models s that they calbrate smultaneously trp generaton, dstrbuton and mode choce, ncludng attrbutes of competng modes and a wde range of level of servce and actvty varables. There has been an applcaton of drect demand model for estmatng regonal road freght movement n Java Island, Indonesa (Safrudn et al, 1999). They concluded that the applcaton of the model s nconclusve, where the model cannot reach best ft between observed and estmated data. The applcaton of drect demand models has been manly n the nter-urban context, wth very few applcatons n urban areas as these models are clamed to be useful for demand analyss where the zones are large. There are some models wth dfferent forms that have been appled n ntercty studes (see Ortuzar and Wllumsen, 1994). Another model was developed and appled by Smth (1977) to predct rural round trps by passengers per month as a functon of level of transt servce and total populaton able to access the servce. The form of the drect demand model s essentally lnear or quas-lnear statstcal regresson:

6 6 Spatal structure effects on ntercty freght demand T mr mr mr = α X β (4) mr where α mr and β mr are parameters to be calbrated. The X mr represent varous attrbutes of demand zones, destnaton, modes and routes (Oppenhem, 1996). The drect demand model s also one of gravty-type models. An example of common form of the model s le the one developed by Kraft (n Ortuzar and Wllumsen, 1994) as follows: T m α m α ) m ( ) m c m β ( ) β =θ ( ) 2 P P I I [( t ] (5) where P s populaton, I s ncome, t and c are travel tme and cost of travel between and by mode, and θ, β, and α are parameters to calbrate. The problem wth ths form s that as long as travel costs from to are equal to travel costs from to, then T should be equal to T, whch rarely happens wth real data. To overcome ths lmtaton, Wrasnghe and Kumarage (1998) put restrctons on ther model so that the model s for one drecton only where P P. Another method to overcome that lmtaton s by reformng the model structure nto the followng form (Manhem, as cted n Ortuzar and Wllumsen, 1994): T β1 β1 β2 β 2 m α m α = θ P P I I [( t ) m ( c ) m ] (6) m 1 2 The problem wth equaton 6 s that P, P, I, and I must all be statstcally sgnfcant. If that condton cannot be fulflled, then t s possble to have only P and I, or P and I n the equaton. In the dsaggregate approach explaned n the prevous secton, spatal structure effects are ncluded by gvng weghts to the utlty of alternatves. In ths paper we try to apply the concepts of dsaggregate approach usng aggregate data. As a utlty value s not avalable n the aggregate approach, the weght s then attached to varables consdered to affect freght demand. The concepts of competng destnatons and ntervenng opportuntes are also appled by usng an accessblty measure. The varables of destnaton zones are weghted usng a competng destnaton factor and the varables of orgn zones are weghted usng an ntervenng opportuntes factor. In ths paper the competng destnatons factor (C ) s defned as total dstance from other destnatons to destnaton, and ntervenng opportuntes factor (O ) s defned as total dstance from other destnatons (except ) to orgn. λ C = D ; (7) and

7 Aldan and Taylor 7 λ O = D (8) Fgure 1 and fgure 2 llustrate the basc dea of the competng destnaton model and the ntervenng opportuntes model appled n ths paper. D D Fgure 1 Competng destnaton model D D Fgure 2 Intervenng opportuntes model By weghtng varables wth C and O, then P P s not equal to P P, as the varables charactersng a zone wll have dfferent values dependng on ts poston, whether as orgn or destnaton. Ths approach then reduces the lmtaton of equaton 5. Equaton 5 then become: T β (( )( )) 1 β (( )( )) 2 m α m α = θ P O P C I O I C [( t ) m ( c ) m ] (9) m 1 2 The dstance exponents (λ and λ ) may be determned by tral and error process to fnd the best ft of the model.

8 8 Spatal structure effects on ntercty freght demand For both orgn and destnaton zones, competng effects are present f λ >0 and λ >0. For orgn zones, competng effects mean that orgn zones n close proxmty to others tend to generate fewer trps. For destnaton zones, competng effects mean that alternatves n close proxmty to others tend to attract fewer trps. For both orgn and destnaton zones, agglomeraton effects are present f λ <0 and λ <0. For orgn zones, agglomeraton effects mean that orgn zones n close proxmty to others tend to generate more trps. For destnaton zones, agglomeraton effects mean that alternatves n close proxmty to others tend to attract more trps. If λ =0 and λ =0 then there are no competng or agglomeraton effects. Emprcal analyss results Equaton 9 s a model form that accounts for the effects of spatal structure on travel demand. As well as the dstances between ctes, the avalable soco economc varables consdered to affect freght demand are populaton, number of households, gross regonal domestc product (GRDP), GRDP of agrcultural sector, GRDP of manufacturng ndustres, and GRDP of trade actvtes. We frst develop a model wthout ncorporatng the competng destnaton factor and the ntervenng opportuntes factor (λ = 0 and λ = 0) as a comparatve benchmar. In order to have a model n whch all varables are sgnfcant, the bacward elmnaton technque s appled (Taylor et al, 1996). The followng are the name of varables: DIS for dstance, GDP for GRDP product, POP for populaton product, HOU for household product, AGR for GRDP of agrcultural sector product, IND for GRDP of manufacturng ndustres product, and TRA for GRDP of tradng actvtes product. The benchmar model s as follow (T denotes freght demand from to n tonnes/year): LnT = LnDIS LnGDP LnHOU 0.266LnAGR (10) ( 2.115) ( ) (7.844) (2.031) ( 3.389) Although all t values (n parentheses) are sgnfcant, the R 2 of for ths model s qute low. It mght due to the data qualty where of the 1190 orgn destnaton pars, there are 329 orgn and destnaton pars that have no freght flows at all,.e. the freght demand s 0. Ths mght also be due to aggregaton bas as freght demand data are aggregated wthout specfyng commodty groups. Accordng to Nam (1997), the freght transport s hghly dverse, whch affects the choce of mode and also destnaton. As each commodty could have dfferent characterstcs so that each commodty should have dfferent form of model. Another lely factor responsble for the poor performance of equaton 10 s that travel mpedance needs to be defned more precsely usng detaled forms such as generalsed cost nstead of usng travel dstance. The tral and error process step n fndng the dstance exponents (λ and λ ) values and most mportantly the spatal structure effects on freght demand s done by combnng values from

9 Aldan and Taylor 9 1 to 1 for both competng destnaton factor and ntervenng opportuntes factor. The results are summarsed n table 1. Table 1: The summary of smulaton results (t values are n parentheses) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Varables λ =-1, λ =0 λ =-1, λ =-1 λ =0, λ =-1 λ =1, λ =1 λ =1, λ =0 λ =0, λ =1 λ =-1, λ =1 λ =1, λ =-1 Constant (0.825) (11.637) (3.575) (-4.226) (-2.048) (-4.483) (-2.694) (1.191) LnDIS ( ) ( ) (15.128) ( ) ( ) ( ) ( ) (15.077) LnGDP (7.990) (11.216) (10.894) (7.647) (11.173) (4.690) (8.063) (10.275) LnPOP LnHOU (2.095) LnAGR (-3.179) LnIND (-2.221) (-2.761) (-3.911) (-3.788) (2.853) (-4.547) (2.452) (-3.911) (-3.546) LnTRA (-1.836) R The man concluson from Table 1 s that spatal structure affects freght demand n the study area. Ths concluson s supported by the mprovement made by model 1, model 6, and model 7 compared to the benchmar model (equaton 10). Model 1 shows that agglomeraton effects are present n the destnaton zones as λ <0 and the absence of ntervenng opportuntes factor mproves the benchmar model by 0.05 per cent. On the other hand, model 6 shows that competng effects are present n orgn zones as λ >0 and the absence of competng destnaton factors mproves the benchmar model by 1.3 per cent. Model 1 explans that a zone located n close proxmty to others tends to attract more freght demand. Whle, model 6 explans that a zone located n close proxmty to others tends to produce fewer freght demand. The best model s acheved when competng destnaton factor of 1 and ntervenng opportuntes of 1 are gven (model 7), t mproves the benchmar model by 1.4 per cent. The result of model 1, model 6, and model 7 mght prove that the effects of ntervenng opportuntes factors are stronger than competng destnatons factor. An attempt s made to strengthen ths concluson by ncreasng the value of λ and λ. An ncrease n λ does not gan much mprovement, whle an ncrease n λ does. Ths clearly supports the concluson that the effects of ntervenng opportuntes factors are stronger than competng destnatons factor. These results are shown n Table 2.

10 10 Spatal structure effects on ntercty freght demand Table 2: The summary of smulaton results (t values are n parentheses) Model 9 Model 10 Model 11 Varables λ =-2, λ =1 λ =-2, λ =2 λ =-1, λ =2 Constant (2.930) (-0.152) (-3.714) LnDIS ( ) ( ) ( ) LnGDP (12.231) (12.155) (7.934) LnPOP LnHOU (2.091) LnAGR (-3.302) (-4.305) (-4.121) LnIND LnTRA R GRDP of the agrculture sector appears n all models wth negatve sgn. Ths mght be because the agrcultural products are consumed locally, or transported to external zones, whch are not taen nto account n ths study. In fact, producers n Central Java fll some needs for agrcultural products n the maor ctes n Java Island such as Jaarta. Overall, the method presented n ths paper s vald for use n accountng for spatal structural effects on freght demand and also on any spatal nteractons wth the use of aggregate data. Concluson The effects of spatal structure on freght demand has been analysed usng a modfed competng destnatons model and ntervenng opportuntes. The modfcaton s made to sut those models wth aggregate data. The modfcaton gves a vald result, and can be used to account for spatal structural effects on spatal nteractons. The applcaton of the modfed model has mproved the performance of the tradtonal drect demand model both practcally and theoretcally. Ths s demonstrated by the mprovement made by the modfed models compared to the tradtonal model. The results ndcate that agglomeraton effects are present n destnaton zones and competng effects are present n the orgn zones. We stll need further research to valdate ths model by applyng to other types of spatal nteractons such as ntercty passenger travel and by tang nto account all varables representng mpedance such as travel tme, travel costs, mode relablty, etc. Another mportant research to mprove the model presented n ths paper s by ncorporatng dfferent accessblty measures.

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