Transcript 1-3_Timo_Elolahde

Traffic Model System and Emission Calculations of
the Helsinki Metropolitan Area Council
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General information about the area
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Location of Helsinki in Europe
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Definitions of areal divisions
YTV area includes the cities of Helsinki,
Espoo, Vantaa and Kauniainen.
Surrounding areas include eight
municipalities around the YTV area.
Helsinki region = YTV area + surrounding
area = 12 municipalities
Metropolitan area is used to describe an area
contained within approximately a 100
kilometre radius from Helsinki. It consists of
72 municipalities.
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Commuting in the Helsinki Metropolitan Area 1980–2002
PKS
YTV
Hämeenlinna
Lahti
47,000 commuters
Mikkeli
in 1980
Kotka
Tammisaari
Proportion of
commuters in the
municipality’s work
force
Hämeenlinna
YTV
88,000 commuters
in 1990
Over 35 %
10 - 35 %
2 - 10 %
Tampere
Lahti
Kotka
Turku
Tammisaari
YTV
108,000 commuters
in 2002
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Population and the number of jobs in the YTV Area
980 300
185 400
95 000
800000
600000
570 700
Vantaa
227 400
104 000
400000
559 000
Espoo
Helsinki
200000
369 000
0
8 500 2 700
Population Jobs
31.12.2004 31.12.2003
in YTV area
Kauniainen
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YTV area target network in 2030
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Journeys made daily by public transport and by car
within the YTV area
Journeys
(1000/day)
Share taken by
public transport (%)
100
1 500
Private car
75
1 000
66
53
50
42
Public transport
500
39
39
(38)
25
0
1966
0
1976
1988 1995 2000 2005
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1966 1976 1988 1995 2000 2005
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Traffic model system
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Traffic model system
Traffic is divided into three parts
• internal trips made by the
inhabitants of the region
• trips generated by HelsinkiVantaa airport (air passengers
and employees)
• external trips (cars only)
• freight transport (vans and
lorries)
•Modes
• walk, bicycle
• public transit
• car (as driver or passenger)
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Trip categories
• home-based work trips
• home-based school trips
• other home-based trips
• non-home-based trips
•Time periods
• morning peak hour
• average hour of the day
• evening peak hour
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Traffic model system
Tools
• Emme/2 macros (contain Unix file handling commands)
• SAS programs (preparation of input, writing some macros)
• FORTRAN programs (summary of results)
• Unix scripts (renaming output files)
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Feedback in the four-step model system
internal trips
airport trips
trip generation
trip generation
destination choice
destination choice
mode choice
mode choice
external trips
commercial trips
total demand and route choice
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Model types
trip category
trip generation
mode choice
destination
choice
home-based
work trips
trips / person
working
logit model
logit model
home-based
school trips
trips / person of
school age
distance table
(distribution)
logit model
other homebased trips
trips / inhabitant
logit model
logit model
non-home-based
trips
trips / inhabitant
logit model
logit model
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Logit model and logsum
probability of alternative i
Pi 
eVi
J
e
.
Vj
j 1
J
logsum = ln (  e j )
V
j 1
where
Vi   1 x1i   2 x 2i   n x ni , where
Vi
j
x jk


=
benefit function of alternative i
coefficient of variable x j (j = 1,2, , n)
value of variable x j in alternative k.
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Variables used in models
•Mode choice models
• nr of transfers, transit
• travel time, transit or car
• travel cost, transit or car
• parking place availability
(arriving trips / parking place)
• parking cost
• cars/household
• ln(distance), walk or bicycle
• distance 0-5 km, walk or bicycle
• distance 5-10 km, walk or
bicycle
• dummy variables
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•Destination choice models
• logsum of mode choice
• scale factor (inhabitants, jobs)
• ln(jobs)
• dummy variables
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Mode combinations possible
Influence of the number of modes (ms149, ms199, ms249, ms299)
on text registers and description fields of matrices
(e.g. ”morning peak %t2% work trips”)
text register
3
4
-4
5
t1
Walk+bicycle
Walk+bicycle
Walk
Walk
t2
transit
Bus+tram
transit
Bus+tram
t3
Car
Car
Car
Car
t4
NO BIKE
NO BIKE
bicycle
bicycle
t5
NO RAIL
Heavy rail
NO RAIL
Heavy rail
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Principles applied in coding macros
• The same selection of possible variables in all models (except
school trips)
• No constants in the model formulas but the coefficients of the
models are in scalars
• Systematics in matrix numbers
• If a variable is not in the model, its coefficient is zero
• Only the number of the first input matrix is given as a macro
parameter, other consecutive numbers are calculated (e.g. nr of
transfers in matrix %2%, transit time in matrix r2=%2%+1)
• Logical scalars (school trip models in macro
school_%ms250%.mac, where ms250=96 or ms250=2001)
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Scalars containing the coefficients
model
destination
destination
destination
mode
mode
mode
mode
mode
mode
mode
mode
variables
logsum
scale factor
ln(jobs)
dummy, walk
dummy, bus+tram
dummy, car
travel cost, heavy rail
travel cost, bus+tram
travel cost, car
parking ratio
parking cost
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coefficients
home-based other homework trips
based trips
ms106
ms156
ms107
ms157
ms108
ms158
ms110
ms160
ms111
ms161
ms112
ms162
ms115
ms165
ms116
ms166
ms117
ms167
ms118
ms168
ms119
ms169
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non-homebased trips
ms206
ms207
ms208
ms210
ms211
ms212
ms215
ms216
ms217
ms218
ms219
Writing an Emme/2 macro with a SAS program
Why?
Do you want to copy and paste
this section 24 times and edit
the parts which are underlined?
1
y
ms311
y
wt24h
home-based work trips
~?q=1
y
mf301
Solution:
Give the changing part as data
cards and write the rest of the
macro with a SAS program (or
with some programming
language).
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y
gn01,gn04
o
+
+
~?b=1
2
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Essential parts of the SAS program
filename outfi2 'K:\Emme2\summary_matr_demo2.mac';
data matr;
length mxnro msnro $ 5 name $ 6 descr $ 40;
input mxnro $ 4-8 msnro $ 10-14 name $ 16-21 descr $ 23-62 ;
cards;
mo09 ms301 nrinha total nr of inhabitants
mf301 ms311 wt24h home-based work trips 24h
ms999
last line
;
data _null_;
set matr;
file outfi2;
if _N_ = 1 then do;
put "~#" / "~#** calculate sums of vectors" / " 3.21" ;
end;
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Essential parts of the SAS program
nro = substr(msnro,3,3);
if (nro ne '999') then do;
put "~# *** matrix " _N_ " *** " ;
put " 1" / " y" / msnro $ 2-6 / " y" / name $ 2-7 / descr $ 2-41
/ "~?q=1" / " y" // mxnro $ 2-6 /// " y" ;
if (substr(mxnro,1,2) = 'mo') then put " gn01,gn04" // " +" ;
else if nro in ('311')
then put " gn01,gn04" // " o" // " +" / " +" ;
put "~?b=1" / " 2" ;
end;
if nro = '999' then do;
put " q" / "~#** output the list of scalars" /
" reports=summary_matr_demo.txt" /
" 3.14" / " 2" / " ms" / "~?b=1" / " 2" / " q" /
" reports=%1%" / "~/ *** summary_matr_demo.mac ***" ;
end;
run;
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Estimation of models
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Traffic surveys
Internal trips
• trips made by the inhabitants of the YTV area (four cities)
during one day (24 h) in autumn 2000
• personal trip diary interview, 8,666 persons and 28,553 trips
Trips generated by Helsinki-Vantaa airport
• 875 air passengers and 801 employees (flying and non-flying)
• survey made in autumn 2001
External trips and freight transport
• origin-destination study made in autumn 1988
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Model estimation
Internal trips
• estimation made by Ms Nina Karasmaa (Helsinki University of
Technology, Transportation Engineering)
• Alogit program
• More than 50 model sets were estimated and tested
• Differences e.g. in number of modes and model hierarchy
(mode choice after destination choice or vice versa)
• Three modes in the model set selected.
Trips generated by Helsinki-Vantaa airport
• estimation made by Mr Jyrki Rinta-Piirto (Strafica Ltd)
External trips and freight transport
• models estimated in 1990 are based in changes in land use.
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Emission calculations
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”Minor” problem in emission calculations
• Traffic models produce demand matrices for three weekday hours.
• Finnish Meteorological Institute needs emissions for every hour of
the year for dispersion calculations.
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Principle of emission calculations
auto demand matrices (car+van, lorry)
* morning peak
* average hour of the day
* evening peak
transit demand matrices
* morning peak
* average hour of the day
transit assignment (speed, @voltr)
* two hours
regression models
emissions of bus links
(fuel, CO2, SO2, CO, NOx, PM, HC)
and rail links (@energy, CO2, NOx, PM)
* morning peak
* average hour of the day
auto demand matrices
* 10 weekday hours
* 7 Saturday hours
* 7 Sunday hours
auto assignment (speed, volau, volad)
* 10+7+7 hours
regression models
emissions of auto links and centroids
(fuel, CO2, SO2, CO, NOx, PM, HC)
* 10 weekday hours
* 7 Saturday hours
* 7 Sunday hours
emissions of bus and rail links
(CO2, SO2, CO, NOx, PM, HC)
* 24 weekday hours
* 24 Saturday hours
* 24 Sunday hours
copying or interpolation
emissions of other hours
* 14 weekday hours
* 17 Saturday hours
* 17 Sunday hours
total emissions
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Emission calculations
Tools
• Emme/2 macros (contain Unix file handling commands)
• FORTRAN programs (copying or interpolation from link data
of 10+7+7 hours to 14+17+17 hours and summary of results)
• Unix scripts (dialog of FORTRAN run, renaming output files)
Emission factors
• fuel consumption, CO2, SO2, NOx, particles (PM), CO, HC
• polynomial functions of average speed (from assignment)
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Examples of emission factors:
NOx emissions of cars and vans
1,4
emission (g/km/veh)
1,2
1
car kat 2005
car diesel 2005
0,8
van diesel 2000
0,6
car kat 2030
car&van diesel 2025
0,4
0,2
0
10
20
30
40
50
60
70
80
90
100 110 120
average speed (km/h)
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Examples of emission factors:
NOx emissions of trucks and buses
16
emission (g/km/veh)
14
12
trailer truck 2000 (EU 2)
10
single-unit truck 2000 (EU 2)
bus 2000 (EU 2)
8
trailer truck 2025 (EU 5)
single-unit truck 2025 (EU 5)
bus 2025 (EU 5)
6
4
2
0
10
20
30
40
50
60
70
80
90
100
average speed (km/h)
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Examples of emission factors:
CO2 emissions of cars and vans
350
emission (g/km/veh)
300
250
car kat 2000
200
car diesel 2005
van diesel 2000
car kat 2025
150
car diesel 2030
van diesel 2025
100
50
0
10
20
30
40
50
60
70
80
90
100 110 120
average speed (km/h)
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Examples of emission factors:
CO2 emissions of trucks and buses
emission (g/km/veh)
2500
2000
1500
trailer truck 2000&2025
single-unit truck 2000&2025
bus 2000&2025
1000
500
0
10
20
30
40
50
60
70
80
90
100
average speed (km/h)
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Proportions of vehicle types in
emission calculations (volau)
2005
cars and vans
cars, non-kat
cars, kat-1995
cars, kat-2020
cars, diesel 1995
cars, diesel 2020
vans, diesel 1995
vans, diesel 2020
total
0
80 4)
10
10
100
2030
0
0
85 5)
0
5
0
10
100
percentage
in scalar
ms80
ms81
ms82
ms83
ms84
ms69
ms85
4) emission factors for average petrol car in 2000 (43 % non-kat, 52 % EU0-2, 5 % EU3, 0 % EU4-5)
5) emission factors for average petrol car in 2025 ( 0 % non-kat, 0 % EU0-2, 25 % EU3, 75 % EU4-5)
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Proportions of vehicle types in
emission calculations (volad and bus)
2005
percentage
in scalar
2030
trucks
single-unit trucks EU 0-2
trailer combination trucks EU 0-2
single-unit trucks EU 4-5
trailer combination trucks EU 4-5
total
70
30
100
5
2
65
28
100
ms86
ms87
ms88
ms89
buses
LPG or CNG buses
buses in Helsinki EU 0-2
regional buses EU 0-2
buses in Helsinki EU 4-5
regional buses EU 4-5
100
100
-
0
0
100
100
ms81
ms83
ms82
ms84
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Regression models in emission calculations
• The regression models have been estimated using volume counts
on four cordon lines.
• For auto assignment, the volumes (car+van and truck) for each
hour of the day (10+7+7) are used as regressands and three
forecasted hours (morning peak, evening peak and an average
hour of the day) as regressors of the model. The models are used
for calculating the demand matrices for each hour.
• For transit assignment, the bus volumes for each hour of the day
(3*24) are used as regressands and two forecasted hours
(morning peak and an average hour of the day) as regressors of
the model. The models are used for calculating the link volumes
and emissions for each hour.
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Emission calculations
• emission on regular link [kg/h] = volume [veh/h] * length [km] *
emission [g/km/veh] / 1000
• cold starts (three classes of motor temperature) and emissions of
connector links handled as emissions of the area (in the centroid)
• example of copying and interpolation of the emission (from
10+7+7 hours to 14+17+17 hours)
hour
4am- 5am
5am- 6am
6am- 7am
7am- 8am
8am- 9am
9am-10am
10am-11am
hour
4- 5
5- 6
6- 7
7- 8
8- 9
9-10
10-11
weekday emission
EMIS_WD_23_5
EMIS_WD_23_5
(EMIS_WD_23_5 + EMIS_WD_7)/2.
EMIS_WD_7
EMIS_WD_8
EMIS_WD_9_13
EMIS_WD_9_13
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Principle of emission calculations (repeated)
auto demand matrices (car+van, lorry)
* morning peak
* average hour of the day
* evening peak
transit demand matrices
* morning peak
* average hour of the day
transit assignment (speed, @voltr)
* two hours
regression models
emissions of bus links
(fuel, CO2, SO2, CO, NOx, PM, HC)
and rail links (@energy, CO2, NOx, PM)
* morning peak
* average hour of the day
auto demand matrices
* 10 weekday hours
* 7 Saturday hours
* 7 Sunday hours
auto assignment (speed, volau, volad)
* 10+7+7 hours
regression models
emissions of auto links and centroids
(fuel, CO2, SO2, CO, NOx, PM, HC)
* 10 weekday hours
* 7 Saturday hours
* 7 Sunday hours
emissions of bus and rail links
(CO2, SO2, CO, NOx, PM, HC)
* 24 weekday hours
* 24 Saturday hours
* 24 Sunday hours
copying or interpolation
emissions of other hours
* 14 weekday hours
* 17 Saturday hours
* 17 Sunday hours
total emissions
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Thank you for your patience and interest!
Any questions?
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