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Systems Analysis of Personal Transportation Needs and Environmental Implications in the
People’s Republic of China
Karolin Kokaz, Dr. Bingjiang Liu, Prof. Peter RogersHarvard University
Presentation Outline
• Spreadsheet Vehicular Air Pollution Information System
• Motivation for the Optimization Model• The Optimization Model - Urban
Transportation Planning for Air Quality Management
• Automobile emissions are the most rapidly growing source of urban air pollution in most Chinese cities.
• Evaluate strategies and policies for guiding the development of Beijing’s transportation sector.
• Determine the impact of economic policies and environmental regulations on future technology choices.
• Analyze the technological and traffic demand and supply options available to China (Beijing) to reduce vehicular pollution.
• Develop a mathematical model that will give the optimal transportation mix to meet the turnover, environmental goals, and other constraints through a variety of policy options at the minimum cost.
Purpose
Transportation Sector in China• Rapid rate of motorization (both number of
passenger trips and distance of passenger trips increased)
• Outdated vehicle technologies and high emissions characteristics
• Inadequate road infrastructure • Land use structure• Mix of transportation modes• Policies regarding transportation modes
China Beijing• 2500 NGV (1995), SEPA 1997:
4600 CNG and 1300 LPG• Projects for 15,000 LPG and
3000-5000 electric vehicles by 2000
• US and China joint venture for electric bicycle, three wheel bicycle, scooters, and motorcycles
• Ban lead: July 1, 2000• New cars will have electric fuel
injection and catalytic converters
• Euro I: January 1, 2000
• Ban lead: July 1, 1997• Euro I since January 1, 1999• Euro II: July 1, 2000• Beijing government plans to
meet Class 2 air quality standards by 2002
• Reduce sulfur level of gasoline and diesel
• Retirement age of vehicles• Convert 6000 taxis and 60,000
cars to LPG and CNG
Beijing
New housing
Beijing Subway & Light Rail
year NV growth rates Cars growth rates1990 7.70% 10.02%1991 8.34% 6.96%1992 5.68% 9.42%1993 8.10% 21.68%1994 11.49% 7.30%1995 16.73% 32.32%1996 13.30% 29.50%1997 13.74% 29.50%
GDP vs Total # of Vehicles and # of Cars in Beijing
0
200,000
400,000
600,000
800,000
1,000,000
1,200,00019
90
1991
1992
1993
1994
1995
1996
1997
year
tota
l # o
f veh
icle
s an
d #
of c
ars
0
5
10
15
20
25
billi
on $ Cars
NV
GDP
Vehicle Emissions Growth vs GDP Growth from 1990 to 1997 in Beijing
0.75
1.00
1.25
1.50
1.75
2.00
1.00 1.09 1.22 1.37 1.72 2.21 2.57 2.87frac tio nal inc re as e in GDP
COCO2HCNOxSO2TSP
Base Year: 1990 = 1
Beijing is developing its suburbanization and decentralization, which will increase the passenger kilometers traveled
0
2
4
6
8
10
12
14
1990 1995 2000 2005 2010 2015
Population in suburban area
Population in rural area
Population in core city
Popu
latio
n, m
illio
n
Source: Beijing’s Master Plan, 1993.
Primarily due to past settlement patterns, the relatively short trips, and government policy to promote bicycle use, Beijing’s transportation heavily relies on buses and bicycles. However, Beijing is beginning to experience a rapid rate of motorization, and most of the recently increased trips come from automobiles.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1981 1986 1990 1995
Percentage of Passenger Trips by Various Transportation Modes
Private carTaxiSubwayBusBicycle
7.2
9.310.2
1.4
3.9
0
2
4
6
8
10
12
London New York Paris Tokyo Beijing0
20
40
60
80
100
120
140V
ehic
le o
wne
rshi
p, m
illio
n
NO
xco
ncen
trat
ion,
µg/
m3
Beijing is characterized by its low vehicle ownership and high pollution: Comparison of Beijing with four big cities
Data for Beijing is in 1998. Others are in 1990.
Share of Air Pollutant Emissions from the Mobile Sector
CO HC NOx SO2 PMMexico, 1994 100% 53.3% 70% 26.5% 4.3%Santiago, 1992 94.2% 82.7% 84.6% 24% 11.5%São Paulo, 1995 96.4% 90.9% 97.3% 85.5% 42.7%Rio de Janeiro, 1978 96.4% 73.2% 69.6% 9.5% 3.5%Beijing, 1992 63.4% 73.5% 21.7%Beijing, 1995 86.2% 49.1% 10%
Source: World Bank Report. Vehicular Air Pollution: Experience from Seven Latin American Urban Cities, 1997. Data in Beijing comes from the report titled China’s Strategies for Controlling Motor Vehicle Emissions, 1997.
0
100000
200000
300000
400000
500000
600000
700000
800000
900000N
o. o
f Veh
icle
s
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
Historical Vehicle Growth in Beijing
MCsTaxisBusesHDDVHDGVLDVCars
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
no. o
f veh
icle
s
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
Vehicle Growth in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0%
20%
40%
60%
80%
100%
no. o
f veh
icle
s
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
Trend of % of Vehicles by Type in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0 2 4 6 8
10 12 14
1980
1986
1992
1998
2004
2010
2016
0
100000
200000
300000
400000
500000
600000
700000
800000
no. o
f veh
icle
s
age
year
Age Distribution of All Vehicles in Beijing (1980-2020)
1 98 0 1 98 1
1 98 2 1 98 3
1 98 4 1 98 5
1 98 6 1 98 7
1 98 8 1 98 9
1 99 0 1 99 1
1 99 2 1 99 3
1 99 4 1 99 5
1 99 6 1 99 7
1 99 8 1 99 9
2 00 0 2 00 1
2 00 2 2 00 3
2 00 4 2 00 5
2 00 6 2 00 7
2 00 8 2 00 9
2 01 0 2 01 1
2 01 2 2 01 3
2 01 4 2 01 5
2 01 6 2 01 7
2 01 8 2 01 9
2 02 0
Vehicle Fleet Average HC Emission Factors Trend
0
10
20
30
40
50
60
70
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019av
g. e
mis
sion
s fa
ctor
s (g
/km
)
CarsTaxisBusesLDVHDGVHDDVMC
Vehicle Fleet Average NOx Emission Factors Trend
020406080
100120140160180
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
avg.
em
issi
ons
fact
ors
(g/k
m)
CarsTaxisBusesLDVHDGVHDDVMC
Vehicle Growth, Speed, Fuel Efficiency and
Emission Factors for Cars
Vehicle Growth and Reduced Speeds
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
1980
1984
1988
1992
1996
2000
2004
2008
2012
2016
2020
year
no o
f veh
icle
s
0
5
10
15
20
25
30
35
40
velo
city
(km
/hr)
NVSpeed
Fuel Efficiency and Emission Factors vs Speed
0
20
40
60
80
100
120
140
160
10 15 20 25 30 35 40 45 50
velocity (km/hr)
EF (g
/km
)
0
1
2
3
4
5
6
7
8
9
10
FE (k
m/lt
) COHCNOxFE
Speed Effect on Fuel Efficiencies and Emission Factors
RA(t) = RA95+SUM(t,AM(“ro”,t)×width)Speed(t) = [-10.635×ln(NV(t))+168.3]×[RA(t)/RA95]×(1+Σfri¹)×(1±fhb2)FEold(v,f,t) = FE95(v,f)×[(1+imp3(v,f)/100)(ORD(t)-1)]
FEnew(v,t) = A(v)×Speed(t)0.2501
FE(v,f,t) = FEold(v,f,t)×FEnew(t)/FEnew(“1995”)EFs(p,v,f,t) = A(v,p,f)×Speed(t)B(v,p,f)
EF(p,v,f,”avg”,t) =E= [(SUM(f,EF(p,v,f,”0”,t)×NV(v,”0”,f,t))+SUM((a,f),EF(p,v,f,”avg”,t-1)×NV(v,a,f,t)))/(SUM((f,a),NV(v,a,f,t)))]×EFs(p,v,f,t)/EFs(p,v,f,“1995”);
EF(“SO2”,v,f,”avg”,t) =E= 2×scontent4×density(f)×1000/FE(v,f,t)EF(“TSP”,v,f,”avg”,t) =E= pcontent4×density(f)/FE(v,f,t)
1 fri: fractional increase in speed from the use of control options2 fhb: fractional increase/decrease in speed from the use of HOV and bus lanes3 imp(v,f) annual fuel efficiency improvement rate4 scontent is the % sulfur content of fuel f and pcontent is the g/kg particulates content of fuel f
UNREGULATED REGULATED
02000400060008000
100001200014000160001800020000
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
CO Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
05000
100001500020000250003000035000400004500050000
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
CO Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
05000
100001500020000250003000035000400004500050000
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
CO Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
UNREGULATED REGULATED
0
500
1000
1500
2000
2500
3000
3500
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
HC Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0
1000
2000
3000
4000
5000
6000
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
HC Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0
1000
2000
3000
4000
5000
6000
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
HC Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
Percentage of Emissions by Vehicles in 1995
CO Emissions by Vehicle Type (tons/day)
Cars14%
Taxis19%
LDV47%
Buses2%
HDDV1%HDGV
15%
MC2% Cars
TaxisBusesLDVHDGVHDDVMC
HC Emissions by Vehicle Type (tons/day)
LDV59%
Taxis13%
Cars10%
Buses1%
MC6%HDGV
8%
HDDV3% Cars
TaxisBusesLDVHDGVHDDVMC
Total = 1,627,111 tons/yr
Total = 235,024 tons/yr
Percentage of Emissions by Vehicles in 2020
Total = 1,132,125 tons/yr
Total = 7,149,756 tons/yr
CO Emissions by Vehicle Type (tons/day)
LDV40%
Taxis8%
Cars40%
Buses1%
HDDV1%
HDGV8%
MC2% Cars
TaxisBusesLDVHDGVHDDVMC
HC Emissions by Vehicle Type (tons/day)
LDV51%
Taxis6%
Cars34%
Buses1%
MC2%
HDGV5%
HDDV1% Cars
TaxisBusesLDVHDGVHDDVMC
UNREGULATED REGULATED
0
200
400
600
800
1000
1200
1400
1600
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
NOx Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0
200
400
600
800
1000
1200
1400
1600
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
NOx Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0
100
200
300
400
500
600
700
800
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
NOx Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
UNREGULATED REGULATED
0
1
2
3
4
5
6
7
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
TSP Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0123456789
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
TSP Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0123456789
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
TSP Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
Percentage of Emissions by Vehicles in 1995
NOx Emissions by Vehicle Type (tons/day)
LDV39%
Taxis7%
Cars5% Buses
7%
MC0%
HDGV6%
HDDV36%
CarsTaxisBusesLDVHDGVHDDVMC
TSP Emissions by Vehicle Type (tons/day)
LDV15%
Taxis6%
Cars4% Buses
9%HDDV61%
HDGV4%
MC1% Cars
TaxisBusesLDVHDGVHDDVMC
Total = 120,846 tons/yr
Total = 818 tons/yr
Percentage of Emissions by Vehicles in 2020
Total = 2,342 tons/yr
Total = 290,540 tons/yr
TSP Emissions by Vehicle Type (tons/day)
LDV24%
Taxis2%
Cars38%
Buses5%
HDDV28%
HDGV3%
MC0% Cars
TaxisBusesLDVHDGVHDDVMC
NOx Emissions by Vehicle Type (tons/day)
LDV48%
Taxis8%
Cars24%
Buses6%
MC0%HDGV
3%
HDDV11% Cars
TaxisBusesLDVHDGVHDDVMC
UNREGULATED REGULATED
0
10
20
30
40
50
60
70
80
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
SO2 Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0
20
40
60
80
100
120
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
SO2 Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0
20
40
60
80
100
120
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
SO2 Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
UNREGULATED REGULATED
020000400006000080000
100000120000140000160000180000
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
CO2 Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0
50000
100000
150000
200000
250000
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
CO2 Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
0
50000
100000
150000
200000
250000
tons
/day
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
CO2 Emissions from Vehicles in Beijing
MCHDDVHDGVLDVBusesTaxisCars
Percentage of Emissions by Vehicles in 1995 Percentage of Emissions by Vehicles in 2020
CO2 Emissions by Vehicle Type (tons/day)
LDV45%
Cars13%
Taxis18%
Buses3%
MC4%
HDGV5%
HDDV12% Cars
TaxisBusesLDVHDGVHDDVMC
CO2 Emissions by Vehicle Type (tons/day)
LDV41%
Cars50%
Taxis4%
Buses1%
MC1%
HDGV1%
HDDV2%
CarsTaxisBusesLDVHDGVHDDVMC
Total = 9,922,926 tons/yr Total = 60,153,968 tons/yr
SO2 Emissions by Vehicle Type (tons/day)
LDV33%
Taxis12%
Cars9%
Buses5%
HDDV30%
HDGV9%
MC2% Cars
TaxisBusesLDVHDGVHDDVMC
SO2 Emissions by Vehicle Type (tons/day)
LDV33%
Taxis3%
Cars48%
Buses2%
HDDV9%HDGV
4%
MC1% Cars
TaxisBusesLDVHDGVHDDVMC
Total = 5,759 tons/yr Total = 26,232 tons/yr
VKT, No. of Vehicles, and Emissions Trend
0
2
4
6
8
10
12
14
16
18
20
22
24
26
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
CO NOx SO2 HC TSP CO2 NV VKT
Inde
x: 1
980
= 1
UNREGULATED REGULATEDCO Concentration in Beijing City
0
5000
10000
15000
20000
25000
30000
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
mic
rogr
ams/
m3
CO conc.CO std
CO Concentration in Beijing City
0
5000
10000
15000
20000
25000
30000
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
mic
rogr
ams/
m3
CO conc.CO std
NOx Concentration in Beijing City
0
50
100
150
200
250
300
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
mic
rogr
ams/
m3
NOx conc.NOx std
NOx Concentration in Beijing City
0
50
100
150
200
250
300
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
mic
rogr
ams/
m3
NOx conc.NOx std
UNREGULATED REGULATEDTSP Concentration in Beijing City
0
100
200
300
400
500
600
700
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
mic
rogr
ams/
m3
TSP conc.TSP std
TSP Concentration in Beijing City
0
100
200
300
400
500
600
700
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
mic
rogr
ams/
m3
TSP conc.TSP std
SO2 Concentration in Beijing City
0
20
40
60
80
100
120
140
160
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
mic
rogr
ams/
m3
SO2 conc.SO2 std
SO2 Concentration in Beijing City
0
20
40
60
80
100
120
140
160
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
mic
rogr
ams/
m3
SO2 conc.SO2 std
Health Impacts of Mobile Sources Emissions in Beijing
DRC Cases Cost/Case Total Cost Cases Cost/Case Total Cost Cases Cost/Case Total CostUS $ US $ million US $ US $ million US $ US $ million
1995 2000 2020Premature mortality(/1million) 16.6 10152 36804.03 373.65 14714 49183.57 723.67 43940 130487.03 5733.55
1.2 7339 855.91 6.28 10636 1143.80 12.17 31764 3034.58 96.3923.5 143723 21.40 3.08 208295 28.60 5.96 622037 75.86 47.19
0.0575 29363785 5.14 150.80 42556497 6.86 292.06 127087369 18.21 2313.940.00169 170541 85.59 14.60 247162 114.38 28.27 738106 303.46 223.98
Asthma attacks(/asthmatic) 0.0326 2392525 6.85 16.38 3467451 9.15 31.73 10354924 24.28 251.380.183 93453437 0.86 79.99 135440676 1.14 154.92 404469365 3.03 1227.40
Chronic bronchitis(/100,000) 6.12 37429 8559.08 320.36 54245 11438.04 620.46 161994 30345.82 4915.85965.12 1869.23 14809.68
Premature mortality(/1million) 2.4 253 36804.03 9.31 553 49183.57 27.22 2183 130487.03 284.89RS in children(/1000children) 0.018 313 0.86 0.00 685 1.14 0.00 2702 3.03 0.01Chest discomfort in adults(/adult) 0.01 879631 0.86 0.75 1925356 1.14 2.20 7595949 3.03 23.05
10.06 29.42 307.95
975.18 1898.65 15117.63
PM10
RHA (/100,000)ERV (/100,000)RAD in adults (/adult)LRI in children(/child)
RS in adults(/adult)
TOTAL
Subtotal
SO2
Subtotal
RHA: respiratory hospital visits, ERV: emergency room visits, RAD: restricted activity days, LRI: lower respiratory illnesses, RS: respiratory symptoms.
Value of Time and Fuel Costs of Commuting & Health Costs of Mobile Sources Emissions in Beijing
million $ 1995 2000 2020Value of Time 182.33 743.63 19,514.94Fuel Costs 641.00 1,569.22 19,509.85Health Costs 975.18 1,898.65 15,117.62
Also should include accidents costs, and agricultural and materials damages from air pollution.
Costs under Different 2020 Scenarios for BeijingAt 6 km/hr At 16km/hr With Tokyo's pass-trip mix
million $ 2020 2020 2020Value of Time 19,514.94 9,348.51 8,631.77Fuel Cost 19,509.85 15,281.42 17,879.25Health Costs 15,117.62 11,794.00 8,635.45Total 54,142.41 36,423.93 35,146.47
MATHEMATICAL MODEL
• Include all modes of transportation• Include different types of fuels and
technologies for each mode• Include investment opportunities in
infrastructure for all transportation modes• Include different control options
TRANSPORTATION MODES FOR BEIJING
methanolHDV
Motor Vehicles
Cars
Taxis
LDV
Buses
HDGV - gasoline
HDDV - diesel
MC
Tricycles
hybrid
fuel cells
methanol
electric
natural gas
gasoline
methanol
natural gas
electric
gasoline
Light Rail - electric
Subway - electric
Walk
Bicycle
ethanol
electric
diesel
gasoline
ethanol
LPG
electric
natural gas
ethanol
LPG
diesel
gasoline
hybrid
hybrid
diesel
Energy Use per Km and per Passenger-Km for Different Transportation Modes in Beijing (1995)
4.206
4.043
8.529
4.849
1.132
13.140
20.232
1.682
1.123
0.171
1.616
0.943
0.060
0.169
Cars
Taxis
Buses
LDV
MC
Subway
Light-Rail
MJ/pass-kmMJ/km
Emissions/Passenger-km for Different Modes of Transportation in Beijing
year CO NOx SO2 HC TSP CO2
cars 1995 24.44 0.7134 0.0509 2.528 0.0034 120.6taxis 1995 16.46 0.5029 0.0349 1.664 0.0023 88.4
buses 1995 1.90 0.4692 0.0154 0.193 0.0037 13.9LDV 1995 24.68 1.5786 0.0607 4.560 0.0041 145.4MC 1995 12.81 0.0745 0.0299 4.434 0.0020 122.3All 1995 15.04 1.3582 0.0629 2.405 0.0104 99.0
emissions/pass-km (g/pass-km)
CO Emissions Per Passenger Km (1995)
24.44
16.46
1.90
24.68
12.81
0 10 20 30
cars
taxis
buses
LDV
MC
g/pass-km
Pollution Control Options for The Transportation Sector• Technology options (such as new vehicle emission standards, fuelreformulation, alternative fuels) alone are not enough - standards will still be exceeded• Infrastructure investments (build roads and develop infrastructure to sustain the growth in transportation) - road area in Beijing is 6.1% whereas in other developed cities goes up to 30%• Traffic management options to reduce congestion and increase speeds (a set of transportation system improvements such as arranging the traffic flow direction, and installation and better coordination of traffic signals)• Employer based controls such as giving transit passes, arranging telecommuting programs, providing ride-matching information and services, and modified work schedules• Enhanced I/M and accelerated vehicle retirement programs• Improve public transit as a good alternative for the commuters and also by options such as parking management and road fees discourage extensive use of cars• Environmental education and awareness programs• Land use management
Control Options To Be Considered in The Model
• Incentive related and educational policy options:– education and driver behavior– ride sharing– telecommuting
• TDM measures– I/M programs– traffic management– parking management– provide HOV and bus lanes
• Technical policy options– engine designs– improve fuel quality– catalytic converters– fuel switching– decrease scrappage rate– infrastructure investments– increase transit services
• Pricing Measures:– tax measures– subsidize transit services– subsidize clean fuels
MAX NET BENEFITS =Value of Time, Healthand Materials Damages - Costsfrom Vehicular Air Pollution
Change Constraints
MIN TOTAL COSTS
Look at results of $, Health, Time, and Other Damages.
Agree on Policy
air quality standardstotal emission limitsdemand constraint (pass-km)budget constraintfuel capacity limitslogical constraints
stair quality standardstotal emission limitsdemand constraint (pass-km)budget constraintfuel capacity limitslogical constraintsAccounting on Value of Time, Healthand Materials Damages
st
Total Costs = Costs of Implementing a Package of Policy Options
= Fuel Costs + Cost of Vehicle + Infrastructure Investments
+ Other Operations and Maintenance Costs
= ∑ present value of all annualized costs
• Construction costs• Land costs
• Technology options• Fuel options• Management options• Legislative options• Incentive related and
educational options• Pricing measures
• repairs, maintenance, tires,oil,…
• parking costs• ownership costs (insurance,
license, registration, taxes,depreciation, finance charge)
For example: lifetime forhighways may be assumedto be 35 years and forrailroads 50 years.
For example for rail:• Capital expenditure for electrification• Signals and train control facilities• Per mile road bed trackage costs• Terminal costs• Operations and maintenance costs
+ cost from switching fuel & cost of control options utilized – cost
of fuel savings
CONSTRAINTS
• Sum of demand (pass-km) by each mode (t) ≤ Turnover projections (t)
• Annualized Infrastructure Investment Costs + Public Transport Vehicle Costs and their O&M costs + Costs of
Control Options + Subsidies – Taxes ≤ Budget for each year allocated to the transportation sector
• Total Emissions (taking into consideration the reductions resulting from the use of different policy options) ≤ Air
Quality Limits
• Concentration of Each Pollutant (as a function of emissions) ≤ Air Quality Standards
• Logical Constraints (example: sum of fraction of vehicle v using option oo equals 1)
• Age distribution, emission factors calculation, fuel efficiencies, calculation of electricity use by electric vehicle v,
speeds, fuel consumption, # of vehicles in each year, infrastructure construction, and utilization of control options
• Bounds on vehicle numbers for different types & fuel switching option for vehicles
• Calculation of fractional reduction of commute time from investments into infrastructure (keeping in mind the
increase in the total # of vehicles)
• Fuel Capacity Limits (example: Total use of NG in transportation sector in year i ≤ Total available NG supply for
the transportation sector for that year)
• Social Cost Equation ($) = Value of Time + Health Impacts of Air Pollution + Materials Damages from Air
Pollution In traffic time each hour may beassumed to be worth 50% of yourwage
Increase in concentration of pollutantsdue to mobile sources emissions result inhealth and materials damages.
Types of Results from The Model∗ Optimization of urban transportation systems for minimum overall cost and least
environmental damage meeting all economic, technical, and policy constraints will yield the following information:
• Obtain trade-off curves for cost, emissions, and pass-km demand • Average vehicle emission factors, fuel efficiencies, vehicle population (type, age, fuel),
land use patterns, fuel consumption• Breakdown of turnover (% of pass-km demand and VKT being satisfied by each mode)• Cost breakdown (%) of the optimal system over the model time horizon: vehicle costs,
O&M costs, fuel costs, infrastructure investments, fuel switching costs, costs of control options utilized
• Investment into different control options each year• % of fuel switching of vehicle v from fuel type f to f1 and extent of control options
utilized each year and over the total model time horizon• Total emissions of pollutant p from mobile sources exhaust emissions from each vehicle
type v• Resulting concentrations from these emissions and health impacts• Social costs: health and materials damages from air pollution caused by vehicle
emissions and value of time spent in traffic• Energy consumption by each mode • Shadow prices of constraints• Vehicle growth rates, mode choice, road area, number of vehicles per km of road,
average road speeds
Passenger-kilometer travel (PKT)
Non-Motorized Vehicles
Motorized Vehicles
wal
k
bicy
cle
Roadway Railway
othe
rs
mot
orcy
cle
taxi car
bus
light
rail
subw
ay
Turnover demand
Transport mode
Fuel availability
Air amelioration
Socialbenefits
GasDieselNGLPGElectricMethanol
others
Electric
ity
Diesel
Local
air
ameli
orat
ion
Reduced health and materials damage, and time savings
Capital cost
Vehicle costDepreciationO&M costOwnership costsPollution controlOthers
Fuel cost
Local air
amelioration
Emission capsEm
ission
caps
Other requirements
Constraints
Traff
ic co
nges
tion
Budget constraints
Logic
al lim
its
Other constraints
Other costsLD
V
Max
imiz
e be
nefit
s →M
inim
ize
tota
l ann
ualiz
ed c
osts
The transportation system should emphasize the movement of people, not vehicles.
ADVANTAGES• Simulation vs Optimization• Extensive list of control options• Passenger-km demand as the driving force• Time frame (annual) evaluation - not one step future• Valuation of future costs and accounting on social
costs• User friendly design at the fingertips of the decision
maker• Visual representation of final optimal set of options
(GIS)