1
Measuring uncertainty in long term travel demand =
forecasting from demographic modelling: Cas=
e study
of the Paris and Montreal metropolitan areas
=
Jimmy
Armoogum (1), Jean-Loup Madre (1) and Yves Bussière (2)
=
2
Table of contents
Tr=
ansport
demand forecasting with an Age-Cohort approach &#=
13;
Mobil=
ity is
measured by two variables:
•<=
span
lang=3DEN-US style=3D'font-size:83%;color:#3333CC;mso-color-index:5'>global
mobility or frequency of trips (average number of trips per =
perso=
n for a
typical week day).
•and t=
he
distance traveled (number of kilometers traveled per person =
for a=
typical
week day).
Ad=
equation
of the model
Comparaisons des estimations issues des enquêtes et de la
modélisation
Uncertainty in transport demand with an age - cohort approach
Conclusion
3
Transport demand forecasting with an age-<=
/span>cohort
approach
•
•
•pi.t :
measures a characteristic or behaviour observed at the date t=3Da+k (year=
of the
survey). when the age of the individual who belongs to the <=
span
style=3D'position:absolute;top:50.0%;left:12.17%;width:79.4%;height:4.25%'=
>gener=
ation k
(defined by his date of birth) is equal to a;
•aa : measure=
s the
behaviour of the generation of reference at the age a. This =
allows
us to define a « Standard Profile » during the life
cycle;
•gk :
measures the gap between the cohort g and the generation of =
refer=
ence gk0 ;
•ei.t: the residual of the model.
•
•
4
Calibration of the model
Model analysis of variance:
–<=
span
lang=3DFR style=3D'mso-fareast-font-family:"MS PGothic";mso-hansi-font-fam=
ily:
"Times New Roman";font-size:86%;color:#3333CC;mso-color-index:5;mso-fareas=
t-language:
JA'>Time unit is 5 years (age and cohorts)
•
The model is run by:
–<=
span
lang=3DFR style=3D'mso-fareast-font-family:"MS PGothic";mso-hansi-font-fam=
ily:
"Times New Roman";font-size:86%;color:#3333CC;mso-color-index:5;mso-fareas=
t-language:
JA'>zone of residence (3 zones)
–<=
span
lang=3DFR style=3D'mso-fareast-font-family:"MS PGothic";mso-hansi-font-fam=
ily:
"Times New Roman";font-size:86%;color:#3333CC;mso-color-index:5;mso-fareas=
t-language:
JA'>individual motorisation (0, 1 and 2 or +)
–<=
span
lang=3DFR style=3D'mso-fareast-font-family:"MS PGothic";mso-hansi-font-fam=
ily:
"Times New Roman";font-size:86%;color:#3333CC;mso-color-index:5;mso-fareas=
t-language:
JA'>gender
•
5
The data
Paris=
Region
:
4 Glo=
bal
surveys (1977, 1984, 1992, 1998)
Sample of 10 000 households =
(3 500
in 1998)
Montr=
eal
region :
6 O-D=
surveys
(1974, 1978, 1982, 1987, 1993, 1998)
Samples of 50 000 to 60 000
households
6
Adequation of the model
To co=
mpare
the results between observations and model in a global way, we carri=
ed out a
regression between the estimates of the surveys and the estim=
ates of
the model at the finest level. i.e. the crossing of the variables:
–<=
span
lang=3DEN-US style=3D'font-size:71%;color:#3333CC;mso-color-index:5'>zone =
of
residence (3 zones);
–<=
span
lang=3DEN-US style=3D'font-size:71%;color:#3333CC;mso-color-index:5'>motor=
ization
(0 car, only one car, and several cars in the household);
–<=
span
lang=3DEN-US style=3D'font-size:71%;color:#3333CC;mso-color-index:5'>gende=
r (2);
–<=
span
lang=3DEN-US style=3D'font-size:71%;color:#3333CC;mso-color-index:5'>age g=
roups
(05-09, 10-14. … 85 or plus);
–<=
span
lang=3DEN-US style=3D'font-size:71%;color:#3333CC;mso-color-index:5'>years=
of the
data collections (4 Global surveys in the Paris region and 6 O-D s=
urveys
in the Montreal region).
7
![](3D"slide0008_image006.gif")
Adequation of the model
10.6
0.31
433.3
0.99
0.97
23.2
0.22
211.5
0.91
0.88
Montreal region
1.75
0.21
141.5
0.99
0.94
1.71
0.09
63.2
0.98
0.77
Paris region
Parameter estimate
Parameter estimate
Intercept
Slope
R²
Sour=
ces:
Calculations from Households transports surveys in Paris (1977, 1984, 1992=
and
1998).
Calculations from Montreal Metropolitan Area O-D surveys (1978, 198=
2,
1987, 1993 and 1998).
8
![](3D"slide0010_image007.gif")
Comparison of estimates data vs. Models: IDF
<=
/span>
-6.7%
3.48
± 1.6%
± 0.06
3.73
1998
0.6%
3.47
± 0.9%
± 0.03
3.45
1992
0.9%
3.46
± 0.9%
± 0.03
3.43
1984
-0.3%
3.45
± 1.0%
± 0.03
3.46
1977
Number of trips
-1.2%
16.5
± 3.0%
± 0.5
16.7
1998
-2.5%
15.7
± 1.6%
± 0.2
16.1
1992
1.4%
14.3
± 2.1%
± 0.3
14.1
1984
0.0%
13.0
± 1.9%
± 0.3
13.0
1977
Distance traveled per day (km)
Relative <=
/span>accuracy
(95 %)
Accuracy
(95%)
Relative gap : Survey – Model
Estimations from
Models
Confidence intervals fro=
m surveys
Estimations from surveys<=
span
lang=3DFR style=3D'font-family:"MS PGothic";mso-ascii-font-family:"Times N=
ew Roman";
mso-fareast-font-family:"MS PGothic";mso-hansi-font-family:"Times New Roma=
n";
font-size:64%;mso-fareast-language:JA;mso-special-format:lastCR;display:no=
ne'>
Years
Sources:
Calculations from Households transports surveys in Paris (1977, 1984, 1992=
and
1998).
9
Comparison of estimates data vs. Models: RMM
<=
/span>
![](3D"slide0011_image008.gif")
5.8%
2.56
± 1.0%
± 0.02
2.42
1998
-9.1%
2.39
± 1.1%
± 0.03
2.63
1993
1.9%
2.19
± 1.4%
± 0.03
2.15
1987
1.0%
2.01
± 1.5%
± 0.03
1.99
1982
5.1%
1.87
± 1.4%
± 0.03
1.78
1978
Number of trips
5.8%
14.5
± 3.3%
± 0.5
<=
/div>
13.7
1998
-7.0%
13.2
± 3.9%
± 0.6
<=
/div>
14.2
1993
0.9%
11.2
± 4.1%
± 0.5
<=
/div>
11.1
1987
2.1%
9=
.9
<=
/div>
± 3.6%
± 0.4
<=
/div>
9.7
1982
4.8%
8.=
8
<=
/div>
± 3.6%
± 0.3
<=
/div>
8.4
1978
Distance traveled per day (km)
Relative <=
/span>accuracy
(95 %)
Accuracy
(95%)
Relative gap : Survey – Model
Estimations from
Models
Confidence intervals from surveys
Estimations from surveys<=
span
lang=3DFR style=3D'font-family:"MS PGothic";mso-ascii-font-family:"Times N=
ew Roman";
mso-fareast-font-family:"MS PGothic";mso-hansi-font-family:"Times New Roma=
n";
font-size:64%;mso-fareast-language:JA;mso-special-format:lastCR;display:no=
ne'>
Years
Sources: Calculati=
ons
from Montreal Metropolitan Area O-D surveys (1978, 1982, 1987, 1993 and 19=
98).
10
Comparison of estimates data vs. Models:
average distance of trips in IDF
average distance of trips in IDF
11
Confidence Intervals =
for
projection:
IDF = RMM
IDF = RMM
![](3D"slide0013_image013.gif")
![](3D"slide0013_image016.gif")
![](3D"slide0013_image019.gif")
=
![](3D"slide0013_image022.gif")
12
![](3D"slide0014_image023.gif")
Uncertainty on the projections: case of the IDF
± 29.2%
± 1.00
3.42
± 13.3%
± 2.2
16.2
2 030
Female
± 21.3%
± 0.73
3.42
± 8.6%
± 1.3
15.1
2 015
Female
± 10.5%
± 0.36
3.44
± 4.5%
± 0.6
13.4
2 000
Female
± 24.6%
± 0.86
3.49
± 5.8%
± 1.3
21.7
2 030
Male
± 19.2%
± 0.67
3.49
± 4.7%
± 1.0
21.1
2 015
Male
± 10.8%
± 0.38
3.52
± 3.5%
± 0.7
20.2
2 000
Male
Gende=
r
± 26.8%
± 0.93
3.45
± 5.6%
± 1.1
18.8
2 030
± 20.0%
± 0.69
3.45
± 3.9%
± 0.7
18.0
2 015
± 10.5%
± 0.37
3.48
± 3.0%
± 0.5
16.7
2 000
Relative
Accuracy
(95%)
Accuracy
(95%)
Mean
Relative
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Measuring uncertainty in long term travel demand forecasting from
demographic modelling: Case study of the Paris and Montreal metropolitan ar=
eas
![](3D"")
![](3D"slide0015_image024.gif")
![](3D"slide0015_image025.gif")
![](3D"slide0015_image026.gif")
Accuracy
(95%)
Accuracy
(95%)
Mean
Number of trips
(trips/pers./day)=
Distance traveled
<=
/span>
(km/pers./day)
Year
13
Uncertainty on the projections: case of the IDF
± 31.3%
± 1.16
3.69
± 10.3%
± 2.5
23.7
2 030
2 cars or +
± 23.1%
± 0.86
3.73
± 8.0%
± 1.9
23.2
2 015
2 cars or +
± 11.8%
± 0.45
3.78
± 5.0%
± 1.1
22.1
2 000
2 cars or +
± 20.2%
± 0.69
3.42
± 8.6%
± 1.4
16.2
2 030
1 car
± 16.5%
± 0.56
3.40
± 7.9%
± 1.3
15.9
2 015
1 car
± 9.2%
± 0.32
3.46
± 6.1%
± 0.9
15.7
2 000
1 car
± 30.3%
± 0.86
2.84
± 16.2%
± 1.8
10.8
2 030
0 car
± 22.3%
± 0.65
2.89
± 10.7%
± 1.1
10.3
2 015
0 car
± 11.7%
± 0.35
2.98
± 4.2%
± 0.4
9.6
2 000
0 car
Motorisation
± 25.5%
± 0.88
3.43
± 8.3%
± 1.9
22.9
2 030
Outer suburbs
± 18.9%
± 0.65
3.44
± 6.2%
± 1.4
22.5
2 015
Outer suburbs
± 8.8%
± 0.31
3.45
± 4.5%
± 1.0
21.3
2 000
Outer suburbs
± 32.5%
± 1.16
3.57
± 5.3%
± 0.9
16.0
2 030
Inner suburbs
± 24.4%
± 0.86
3.51
± 2.6%
± 0.4
15.2
2 015
Inner suburbs
± 14.0%
± 0.49
3.47
± 1.1%
± 0.1
14.0
2 000
Inner suburbs
± 25.6%
± 0.85
3.30
± 10.4%
± 1.3
12.0
2 030
Center
± 20.1%
± 0.68
3.39
± 6.4%
± 0.8
11.7
2 015
Center
± 11.0%
± 0.39
3.55
± 3.1%
± 0.4
11.3
2 000
Center
Zone of résidence=
Relative
Accuracy
(95%)
Accuracy
(95%)
Mean
Relative
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Measuring uncertainty in long term travel demand forecasting from
demographic modelling: Case study of the Paris and Montreal metropolitan ar=
eas
![](3D"slide0016_image027.gif")
Accuracy
(95%)
Accuracy
(95%)
Mean
Number of trips
(trips/pers./day)=
Distance traveled
<=
/span>
(km/pers./day)
Year
14
Uncertainty on the projections: case of the RMM
± 16.6%
± 0.52
3.13
± 15.4%
± 2.8
18.2
2 021
Female
± 16.3%
± 0.47
2.88
± 14.5%
± 2.4
16.5
2 011
Female
± 14.7%
± 0.38
2.58
± 13.1%
± 1.9
14.5
2 001
Female
± 17.9%
± 0.57
3.19
± 15.5%
± 2.9
18.7
2 021
Male
± 17.3%
± 0.52
3.00
± 14.6%
± 2.6
17.5
2 011
Male
± 15.6%
± 0.43
2.75
± 13.5%
± 2.2
15.9
2 001
Male
Gender
± 17.1%
± 0.54
3.16
± 15.2%
± 2.8
18.4
2 021
± 16.9%
± 0.50
2.93
± 14.4%
± 2.5
17.0
2 011
± 15.2%
± 0.41
2.66
± 13.2%
± 2.0
15.2
2 001
Relative
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------=_NextPart_01C65728.507F82C0
Content-Location: file:///C:/6E4646F9/Uncertainty.files/slide0018.htm
Content-Transfer-Encoding: quoted-printable
Content-Type: text/html; charset="us-ascii"
Measuring uncertainty in long term travel demand forecasting from
demographic modelling: Case study of the Paris and Montreal metropolitan ar=
eas
•
------=_NextPart_01C65728.507F82C0
Content-Location: file:///C:/6E4646F9/Uncertainty.files/master01.htm
Content-Transfer-Encoding: quoted-printable
Content-Type: text/html; charset="us-ascii"
numSlds )
gSldJumpIdx =3D numSlds;
if ( gSldJumpIdx >=3D 0 ) {
if ( gSldJumpIdx =3D=3D 0 )
gSldJumpIdx =3D 1;
var jumpTo =3D parseInt(gSldJumpIdx);
gSldJump =3D 0; gSldJumpIdx =3D "";
win.GoToSld( parent.GetSldList().mList[jumpTo-1].mSldHref )
}
}
}
function _KDH()
{
if( event.keyCode =3D=3D 8 ) {
event.returnValue =3D 0;
parent.GoToPrevSld();
}
}
function DocumentOnClick()
{
if( IsNts() || parent.HideMenu() ) return;
if( ( g_allowAdvOnClick && !parent.IsFramesMode() ) ||
(event && (event.keyCode=3D=3D32) ) )
parent.GoToNextSld();
}
var g_supportsPPTHTML =3D SupportsPPTHTML(), g_scaleInFrame =3D 1, gId=3D""=
, g_bgSound=3D"",
g_scaleHyperlinks =3D false, g_allowAdvOnClick =3D 1, g_showInBrowser =
=3D 0, gLoopCont =3D 0, gUseSldTimings =3D 1;
var g_showAnimation =3D g_supportsPPTHTML && SupportsPPTAnimation() && ( (w=
indow.name=3D=3D"PPTSld" && !parent.IsFramesMode()) || g_showInBrowser );va=
r g_animManager =3D null;
var g_animUseRuntime =3D false;
var g_animItemsToHide, g_animInteractiveItems, g_animSlideTime;
var g_animMainSequence =3D null;
var ENDSHOW_MESG=3D"スライド ショӦ=
0;の最後です。 クリッ=
クすると終了します=
2290;", SCREEN_MODE=3D"Frames", gIsEndShow=3D0, NUM_VIS_SLDS=3D16, SCRIPT_H=
REF=3D"script.js", FULLSCR_HREF=3D"fullscreen.htm";
var gCurSld =3D gPrevSld =3D 1, g_offset =3D 0, gNtsOpen =3D gHasNts =3D gO=
tlTxtExp =3D 0, gHasNarration =3D 0, gOtlOpen =3D true
window.gPPTHTML=3DSupportsPPTHTML()
var g_fBaseHyperlink =3D false;
var gMainDoc=3Dnew Array(new hrefList("slide0001.htm",1,-1,1),new hrefList(=
"slide0002.htm",1,-1,1),new hrefList("slide0004.htm",1,-1,1),new hrefList("=
slide0005.htm",1,-1,1),new hrefList("slide0006.htm",1,-1,1),new hrefList("s=
lide0007.htm",1,-1,1),new hrefList("slide0008.htm",1,-1,1),new hrefList("sl=
ide0010.htm",1,-1,1),new hrefList("slide0011.htm",1,-1,1),new hrefList("sli=
de0012.htm",1,-1,1),new hrefList("slide0013.htm",1,-1,1),new hrefList("slid=
e0014.htm",1,-1,1),new hrefList("slide0015.htm",1,-1,1),new hrefList("slide=
0016.htm",1,-1,1),new hrefList("slide0017.htm",1,-1,1),new hrefList("slide0=
018.htm",1,-1,1));
/*********************************************
Frameset functions
These functions control slide navigation
and state of the frameset.
**********************************************/
function RemoveFilePrefixFromHref(href)
{
var regExp =3D /^file:\/\/\//i;
return href.replace(regExp, "")
}
function FullScrInit()
{
g_allowAdvOnClick =3D GetCurSld().mAdvOnClk
document.body.style.backgroundColor=3D"black"
document.oncontextmenu=3Dparent._CM;
document.onkeydown =3D _KDH;
document.ondragstart=3DCancel
document.onselectstart=3DCancel
self.focus()
}
function Redirect( frmId )
{=09
var str=3Ddocument.location.hash,idx=3Dstr.indexOf('#'), sId=3DGetSldId()
if(idx>=3D0) str=3Dstr.substr(1);
if( window.name !=3D frmId && ( sId !=3D str) ) {
obj =3D GetObj("Main-File")
window.location.href=3Dobj.href+"#"+sId
return 1
}
return 0
}
var MHTMLPrefix =3D CalculateMHTMLPrefix();=20
function CalculateMHTMLPrefix()
{
if ( document.location.protocol =3D=3D 'mhtml:') {=20
href=3Dnew String(document.location.href)=20
Start=3Dhref.indexOf('!')+1=20
End=3Dhref.lastIndexOf('/')+1=20
if (End < Start)=20
return href.substring(0, Start)=20
else=20
return href.substring(0, End)=20
}
return '';
}
function GetTags(base,tag)
{
if(g_supportsPPTHTML) return base.all.tags(tag);
else return base.getElementsByTagName(tag);
}
function UpdNtsPane(){ if(frames["PPTNts"]) PPTNts.location.replace( MHTMLP=
refix+GetHrefObj( gCurSld ).mNtsHref ) }
function UpdNavPane( sldIndex ){ if(gNavLoaded) PPTNav.UpdNav() }
function UpdOtNavPane(){ if(gOtlNavLoaded) PPTOtlNav.UpdOtlNav() }
function UpdOtlPane(){ if(gOtlLoaded) PPTOtl.UpdOtl() }
function SetHasNts( fVal )
{
if( gHasNts !=3D fVal ) {
gHasNts=3DfVal
UpdNavPane()
}
}
function ToggleOtlText()
{
gOtlTxtExp=3D!gOtlTxtExp
UpdOtlPane()
}
function ClearMedia()
{
// Clear any sounds playing before launching another browser window. Other=
wise,
// in fullscreen mode, you'll continue to hear the sound in the frames mod=
e.
if (PPTSld.pptSound) PPTSld.pptSound.loop =3D 0;
}
function FullScreen()
{=20
if ( PPTSld.g_animUseRuntime )
PPTSld.document.body.pause();
ClearMedia();
var href =3D ( document.location.protocol =3D=3D 'mhtml:') ? FULLSCR_HREF =
: FULLSCR_HREF+"#"+GetHrefObj(gCurSld).mSldHref;
if (MHTMLPrefix !=3D "")
href =3D RemoveFilePrefixFromHref(href)
if(PPTNav.event.ctrlKey) {
var w =3D (window.screen.availWidth * 1.0) / 2.0
var h =3D w * (PPTSld.g_origH * 1.0) / PPTSld.g_origW
win =3D window.open( MHTMLPrefix+href,null,"toolbar=3D0,resizable=3D1,top=
=3D0,left=3D0," + "width=3D"+ w + ",height=3D" + h );
if( win.document.body && PPTSld.g_animUseRuntime )
win.document.body.PPTSldFrameset=3Dwindow;
}
else
{
win =3D window.open( MHTMLPrefix+href,null,"fullscreen=3Dyes" );
if( win.document.body && PPTSld.g_animUseRuntime )
win.document.body.PPTSldFrameset=3Dwindow;
}
}
function ToggleVNarration()
{
rObj=3DPPTSld.document.all("NSPlay")
if( rObj && !PPTSld.g_animUseRuntime ) {
if( (rObj.playState =3D=3D 1)||(rObj.playState =3D=3D 0) )
rObj.Play()
else if( rObj.playState =3D=3D 2 )
rObj.Pause()
else
return;
}
else if( PPTSld.g_animUseRuntime )
{
narObj =3D PPTSld.document.all("narrationID")
if( narObj )
narObj.togglePause()
}
}
function GetCurSldNum()
{ =20
obj=3DGetHrefObj(gCurSld)
if( obj.mOrigVis =3D=3D 1 )
return obj.mSldIdx
else =20
return gCurSld
}
function GetNumSlds()
{ =20
if( GetHrefObj(gCurSld).mOrigVis =3D=3D 1 )
return GetSldList().mNumVisSlds;
else
return GetSldList().mList.length
}
function GetSldNum( href )
{
for(ii=3D0; ii 1 )
PopSldList();
else if( !IsFramesMode() ) {
if( gLoopCont )
GoToFirst()
else
EndShow()
}
}
function GoToPrevSld()
{
ii=3DgCurSld-1
if( ii > 0 ) {
obj=3DGetHrefObj(ii)
while ( obj && ( obj.mVis =3D=3D 0 ) && ( ii>0 ) )
obj=3DGetHrefObj(--ii)
if( ii =3D=3D 0 ) ii=3D1
GoToSldNum(ii)
}
}
function GoToFirst(){ GoToSld( GetHrefObj(1).mSldHref ) }
function GoToLast()
{
ii=3DGetSldList().mList.length
if( ii !=3D gCurSld )
GoToSld( GetHrefObj(ii).mSldHref )
}
function GoToSldNum( num )
{
if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue
obj =3D GetHrefObj( num )
obj.mVis=3D1
gPrevSld=3DgCurSld
gCurSld =3D num;
=09
if (MHTMLPrefix !=3D "")
PPTSld.location.replace(MHTMLPrefix+RemoveFilePrefixFromHref(obj.mSldHref=
))
else
PPTSld.location.replace(obj.mSldHref)
=09
if( IsFramesMode() ) {
UpdNavPane(); UpdOtlPane(); UpdNtsPane()
}
}
function GoToSld( href )
{
if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue
GetHrefObj( GetSldNum(href) ).mVis=3D1
if (MHTMLPrefix !=3D "")
PPTSld.location.replace(MHTMLPrefix+RemoveFilePrefixFromHref(href))
else
PPTSld.location.replace(href)
}
function SldUpdated( id )
{
if( id =3D=3D GetHrefObj(gCurSld).mSldHref ) return
gPrevSld=3DgCurSld
gCurSld=3DGetSldNum(id)
if( IsFramesMode() ) {
UpdNavPane(); UpdOtlPane(); UpdNtsPane()
}
}
function PrevSldViewed(){ GoToSld( GetHrefObj(gPrevSld).mSldHref ) }
function HasPrevSld() { return ( gIsEndShow || ( gCurSld !=3D 1 && GetHrefO=
bj( gCurSld-1 ).mVis =3D=3D 1 )||( GetCurSldNum() > 1 ) ) }
function HasNextSld() { return (GetCurSldNum() !=3D GetNumSlds()) }
function CloseWindow() {
if( HideMenu() ) return;
=09
var event =3D PPTSld.event;
if( !IsFramesMode() && event && (event.keyCode=3D=3D27 || event.keyCode=3D=
=3D32 || event.type=3D=3D"click" ) )
window.close( self );
CatchNumKeys( self, event );
}
function Unload() { gIsEndShow=3D0; }
function SetupEndShow() {
gIsEndShow=3D1;
PPTSld.document.body.scroll=3D"no";
PPTSld.document.onkeypress=3DCloseWindow;
PPTSld.document.onclick=3DCloseWindow;
PPTSld.document.oncontextmenu=3D_CM;
}
function EndShow()
{
if( IsFramesMode() ) return
if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue
doc=3DPPTSld.document
var dir =3D doc.body.dir
if( dir !=3D "rtl" ) dir =3D "ltr";
doc.open()
doc.writeln(' 0) {
PushSldList(sldList,fEnd);
gCurSld =3D 1;
}
else
if( PPTSld.event ) PPTSld.event.cancelBubble=3Dtrue
}
function PushSldList(arr,fEnd) {
var ii =3D gSldStack.length;
gSldStack[ii] =3D new SldList(arr,gCurSld,fEnd);
GoToSld( gSldStack[ii].mList[0].mSldHref );
}
function PopSldList() {
if (gSldStack[gSldStack.length-1].fEndShow)
EndShow()
else {
gCurSld =3D gSldStack[gSldStack.length-1].mCurSld;
gSldStack[gSldStack.length-1] =3D null;
gSldStack.length--;
var sldList =3D gSldStack[gSldStack.length-1];
GoToSld( sldList.mList[gCurSld - 1].mSldHref );
}
}
var custShowList=3Dnew Array();
/*********************************************
Navigation button implementation
There are 2 types of buttons: ImgBtn, TxtBtn
implemented as function objects. They share
a similiar interface so the event handlers
can call SetActive, for example, on a button=20
object without needing to know exactly=20
what type of button it is.
**********************************************/
//----------------------------------
function ImgBtn( oId,bId,w,action )
//----------------------------------
{
var t=3Dthis
t.Perform =3D _IBP
t.SetActive =3D _IBSetA
t.SetInactive=3D _IBSetI
t.SetPressed =3D _IBSetP
t.SetDisabled=3D _IBSetD
t.Enabled =3D _IBSetE
t.ChangeIcon =3D null
t.UserAction =3D action
t.ChgState =3D _IBUI
t.mObjId =3D oId
t.mBorderId=3D bId
t.mWidth =3D w
t.mIsOn =3D t.mCurState =3D 0
}
function _IBSetA()
{
if( this.mIsOn ) {
obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 )
obj.style.posTop=3D0
}
}
function _IBSetI()
{
if( this.mIsOn ) {
obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 )
obj.style.posTop=3D0=20
}
}
function _IBSetP()
{
if( this.mIsOn ) {
obj=3Dthis.ChgState( gShadowClr,gHiliteClr,2 )
obj.style.posLeft+=3D1; obj.style.posTop+=3D1
}
}
function _IBSetD()
{ =20
obj=3Dthis.ChgState( gFaceClr,gFaceClr,0 )
obj.style.posTop=3D0=20
}
function _IBSetE( state )
{
var t=3Dthis
GetObj( t.mBorderId ).style.visibility=3D"visible"
if( state !=3D t.mIsOn ) {
t.mIsOn=3Dstate
if( state )
t.SetInactive()
else
t.SetDisabled()
}
}
function _IBP()
{
var t=3Dthis
if( t.mIsOn ) {
if( t.UserAction !=3D null )
t.UserAction()
if( t.ChangeIcon ) {
obj=3DGetObj(t.mObjId)
if( t.ChangeIcon() )
obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-4)*t.mWidth
else
obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-0)*t.mWidth
}
t.SetActive()
} =20
}
function _IBUI( clr1,clr2,nextState )
{
var t=3Dthis
SetBorder( GetObj( t.mBorderId ),clr1,clr2 )
obj=3DGetObj( t.mObjId )
obj.style.posLeft=3Dobj.style.posLeft+(t.mCurState-nextState)*t.mWidth-obj=
.style.posTop
t.mCurState=3DnextState
return obj
}
//-----------------------------------------
function TxtBtn( oId,oeId,action,chkState )
//-----------------------------------------
{
var t=3Dthis
t.Perform =3D _TBP
t.SetActive =3D _TBSetA
t.SetInactive=3D _TBSetI
t.SetPressed =3D _TBSetP
t.SetDisabled=3D _TBSetD
t.SetEnabled =3D _TBSetE
t.GetState =3D chkState
t.UserAction =3D action
t.ChgState =3D _TBUI
t.mObjId =3D oId
t.m_elementsId=3D oeId
t.mIsOn =3D 1
}
function _TBSetA()
{
var t=3Dthis
if( t.mIsOn && !t.GetState() )
t.ChgState( gHiliteClr,gShadowClr,0,0 )
}
function _TBSetI()
{
var t=3Dthis
if( t.mIsOn && !t.GetState() )
t.ChgState( gFaceClr,gFaceClr,0,0 )
}
function _TBSetP()
{
if( this.mIsOn )
this.ChgState( gShadowClr,gHiliteClr,1,1 )
}
function _TBSetD()
{ =20
this.ChgState( gFaceClr,gFaceClr,0,0 )
this.mIsOn =3D 0
}
function _TBSetE()
{
var t=3Dthis
if( !t.GetState() )
t.ChgState( gFaceClr,gFaceClr,0,0 )
else
t.ChgState( gShadowClr,gHiliteClr,1,1 )
t.mIsOn =3D 1
}
function _TBP()
{
var t=3Dthis
if( t.mIsOn ) {=20
if( t.UserAction !=3D null )
t.UserAction()
if( !t.GetState )
return
if( t.GetState() )
t.SetPressed()
else
t.SetActive()
} =20
}
function _TBUI( clr1,clr2,lOffset,tOffset )
{
SetBorder( GetObj( this.mObjId ),clr1,clr2 )
Offset( GetObj( this.m_elementsId ),lOffset,tOffset )
}
function Offset( obj, top, left ){ obj.style.top=3Dtop; obj.style.left=3Dle=
ft }
function SetBorder( obj, upperLeft, lowerRight )
{
s=3Dobj.style;
s.borderStyle =3D "solid"
s.borderWidth =3D 1=20
s.borderLeftColor =3D s.borderTopColor =3D upperLeft
s.borderBottomColor=3D s.borderRightColor =3D lowerRight
}
function GetBtnObj(){ return gBtnArr[window.event.srcElement.id] }
function BtnOnOver(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetActive() }
function BtnOnDown(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetPressed() }
function BtnOnOut(){ b=3DGetBtnObj(); if( b !=3D null ) b.SetInactive() }
function BtnOnUp()
{
b=3DGetBtnObj()
if( b !=3D null )
b.Perform()
else
Upd()
}
function GetNtsState(){ return parent.gNtsOpen }
function GetOtlState(){ return parent.gOtlOpen }
function GetOtlTxtState(){ return parent.gOtlTxtExp }
function NtsBtnSetFlag( fVal )
{
s=3Ddocument.all.item( this.m_flagId ).style
s.display=3D"none"
if( fVal )
s.display=3D""
else
s.display=3D"none"
}
function _BSetA_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S=
etActive() }
function _BSetI_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S=
etInactive() }
function _BSetP_Border(){ b =3D gBtnArr[this.mObjId]; if( b !=3D null ) b.S=
etPressed() }
function _BSetA_BorderImg()
{=20
b =3D gBtnArr[this.mBorderId]=20
if( b !=3D null && this.mIsOn && !b.GetState() ) {
obj=3Dthis.ChgState( gHiliteClr,gShadowClr,2 )
obj.style.posTop=3D0
}
}
function _BSetI_BorderImg()
{=20
b =3D gBtnArr[this.mBorderId]
if( b !=3D null && this.mIsOn && !b.GetState() ) {
obj=3Dthis.ChgState( gFaceClr,gFaceClr,1 )
obj.style.posTop=3D0
}
}
var gHiliteClr=3D"THREEDHIGHLIGHT",gShadowClr=3D"THREEDSHADOW",gFaceClr=3D"=
THREEDFACE"
var gBtnArr =3D new Array()
gBtnArr["nb_otl"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOtlPan=
e,GetOtlState )
gBtnArr["nb_otlElem"] =3D new TxtBtn( "nb_otl","nb_otlElem",parent.ToggleOt=
lPane,GetOtlState )
gBtnArr["nb_nts"] =3D new ImgBtn( "nb_nts","nb_ntsBorder",10,parent.ToggleN=
tsPane )
gBtnArr["nb_nts"].SetActive =3D _BSetA_BorderImg;
gBtnArr["nb_nts"].SetInactive =3D _BSetI_BorderImg;
gBtnArr["nb_ntsBorder"] =3D new TxtBtn( "nb_ntsBorder","nb_ntsElem",parent.=
ToggleNtsPane,GetNtsState )
gBtnArr["nb_ntsElem"] =3D new TxtBtn( "nb_ntsBorder","nb_ntsElem",parent.To=
ggleNtsPane,GetNtsState )
gBtnArr["nb_prevBorder"] =3D gBtnArr["nb_prev"]=3D new ImgBtn( "nb_prev","n=
b_prevBorder",30,parent.GoToPrevSld )
gBtnArr["nb_nextBorder"] =3D gBtnArr["nb_next"]=3D new ImgBtn( "nb_next","n=
b_nextBorder",30,parent.GoToNextSld )
gBtnArr["nb_sldshw"]=3D new ImgBtn( "nb_sldshw","nb_sldshwBorder",18,parent=
.FullScreen )
gBtnArr["nb_sldshwBorder"] =3D new TxtBtn( "nb_sldshw","nb_sldshwBorder",pa=
rent.FullScreen,null )
gBtnArr["nb_sldshwBorder"].SetActive =3D _BSetA_Border;
gBtnArr["nb_sldshwBorder"].SetInactive =3D _BSetI_Border;
gBtnArr["nb_sldshwText"] =3D new TxtBtn( "nb_sldshw","nb_sldshwText",parent=
.FullScreen,null )
gBtnArr["nb_sldshwText"].SetActive =3D _BSetA_Border;
gBtnArr["nb_sldshwText"].SetInactive =3D _BSetI_Border;
gBtnArr["nb_voice"] =3D gBtnArr["nb_voiceBorder"] =3D new ImgBtn( "nb_voice=
","nb_voiceBorder",18,parent.ToggleVNarration )
gBtnArr["nb_otlTxtBorder"] =3D gBtnArr["nb_otlTxt"]=3D new ImgBtn( "nb_otlT=
xt","nb_otlTxtBorder",23,parent.ToggleOtlText )
gBtnArr["nb_ntsBorder"].m_flagId=3D "nb_nts"
gBtnArr["nb_ntsBorder"].SetFlag =3D NtsBtnSetFlag
gBtnArr["nb_otlTxt"].ChangeIcon=3D GetOtlTxtState
/*********************************************
Context menu implementation
_CM() is the function that's hooked up to
the oncontextmenu event. Once we're asked to
show the menu, we first build it by creating
DIVs on-the-fly. Then we position it=20
within the screen area so it doesn't get
clipped.
Creating the DIVs using createElement() means
we don't have to write out any extra HTML
into the slide HTML files.
**********************************************/
var sNext=3D"次へ",sPrev=3D"前へ",sEnd=3D"ス&=
#12521;イド ショーの終了",s=
Font=3D"ＭＳ 明朝",sArrow=3D"矢印",sFre=
eform=3D"フリーフォーム",sRect=3D=
"四角形",sOval=3D"楕円"
function ShowMenu()
{
BuildMenu();
var doc=3DPPTSld.document.body,x=3DPPTSld.event.clientX+doc.scrollLeft,y=
=3DPPTSld.event.clientY+doc.scrollTop
m =3D PPTSld.document.all.item("ctxtmenu")
m.style.pixelLeft=3Dx
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Measuring uncertainty in long term travel demand forecasting from
demographic modelling: Case study of the Paris and Montreal metropolitan ar=
eas
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Accuracy
(95%)
Accuracy
(95%)
Mean
Relative
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Measuring uncertainty in long term travel demand forecasting from
demographic modelling: Case study of the Paris and Montreal metropolitan ar=
eas
![](3D"slide0017_image028.gif")
![](3D"slide0017_image029.gif")
![](3D"slide0017_image030.gif")
![](3D"slide0017_image031.gif")
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Accuracy
(95%)
Accuracy
(95%)
Mean
Number of trips
(trips/pers./day)=
Distance traveled
<=
/span>
(km/pers./day)
Year
15
Uncertainty on the projections: case of the RMM
± 15.5%
± 0.52
3.33
± 12.3%
± 2.8
22.8
2 021
2 cars or +
± 14.6%
± 0.46
3.11
± 11.6%
± 2.5
21.1
2 011
2 cars or +
± 15.6%
± 0.44
2.82
± 13.2%
± 2.5
18.9
2 001
2 cars or +
± 14.5%
± 0.49
3.37
± 12.6%
± 2.5
19.9
2 021
1 car
± 15.6%
± 0.49
3.11
± 13.5%
± 2.5
18.2
2 011
1 car
± 13.5%
± 0.38
2.81
± 11.4%
± 1.9
16.2
2 001
1 car
± 30.5%
± 0.72
2.36
± 38.3%
± 3.6
9.4
2 021
0 car
± 26.5%
± 0.58
2.17
± 31.3%
± 2.6
8.3
2 011
0 car
± 21.5%
± 0.43
1.98
± 23.6%
± 1.7
7.2
2 001
0 car
Motorisation
± 17.3%
± 0.55
3.18
± 15.3%
± 4.0
26.1
2 021
Outer suburbs
± 16.4%
± 0.49
2.95
± 14.3%
± 3.5
24.1
2 011
Outer suburbs
± 14.7%
± 0.40
2.68
± 13.1%
± 2.9
21.7
2 001
Outer suburbs
± 14.7%
± 0.43
2.90
± 13.9%
± 2.1
14.8
2 021
Inner suburbs
± 14.7%
± 0.43
2.90
± 13.4%
± 1.9
13.8
2 011
Inner suburbs
± 13.2%
± 0.35
2.65
± 12.3%
± 1.6
12.6
2 001
Inner suburbs
± 18.4%
± 0.58
3.15
± 15.7%
± 1.4
8.6
2 021
Center
± 18.9%
± 0.56
2.93
± 16.3%
± 1.3
8.0
2 011
Center
± 16.9%
± 0.45
2.64
± 15.3%
± 1.1
7.2
2 001
Center
Zone of residence=
Relative
Accuracy
(95%)
Accuracy
(95%)
MeanRelative
Accuracy
(95%)
Accuracy
(95%)
MeanNumber of trips
(trips/pers./day)
Distance traveled
(km/pers./day)
Year
16
Conclusion
•The
demographic approach outlines the structural determinants for long term tr=
ends
of mobility
•It gi=
ves
generally good results with errors in the 10-15% range even for long term
forecasting
•The P=
aris -
Montreal comparison shows that for projections at a relatively global leve=
l,
very large samples do not improve significantly the precision of the model=
.
•In
retrospective analysis, the model may help to detect errors due to changes=
in
survey techniques (i.e. survey period extended to spring in Paris in 1997,=
or
two members of the household interviewed in 1993 in Montreal instead of on=
ly
one adult member)
•Estim=
ation of
errors are important to give benchmarks in the predictive capacity of a mo=
del
and thus reduce uncertainty in the planning process. &#=
13;
•
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