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Sustainable Transport Indicators:
-from Raw Data to Indicators Vehicle Activity Study, Shanghai
ChinaChanghong CHEN
Jim Lents, Matt Barth, Nick NikkilaLee Schipper, Nancy Kete
Qiguo JING, Cheng HUANG, Haikun WANG(Shanghai Academy of Environmental Sciences)
BAQ 2004Agra, India
6-8 December, 2004
BackgroundBackground Shanghai is one of the largest megacities in the world with Shanghai is one of the largest megacities in the world with
some population of 17 million, close to Mexico Citysome population of 17 million, close to Mexico City
It is very active in economic development with more than It is very active in economic development with more than per capita GDP of 5000 USD, which is 4 time higher than per capita GDP of 5000 USD, which is 4 time higher than national averagenational average
Economic development drives rapid growth of vehicle Economic development drives rapid growth of vehicle population population
To avoid vehicle pollution, lots of studies have been done To avoid vehicle pollution, lots of studies have been done since early 1990’ssince early 1990’s
BackgroundBackground The studies provided lots of policy The studies provided lots of policy
recommendation to local government in recommendation to local government in vehicle emission reduction for air quality vehicle emission reduction for air quality management during 1990’smanagement during 1990’s
However, due to rapid growth of vehicle However, due to rapid growth of vehicle population in recent years, new plan for population in recent years, new plan for vehicle emission control is requested in a vehicle emission control is requested in a very urgent wayvery urgent way
BackgroundBackground To meet policy requirement, an To meet policy requirement, an
international cooperation was launched in international cooperation was launched in early 2004. The project is financially early 2004. The project is financially supported by US Energy Foundation, Shell supported by US Energy Foundation, Shell Foundation, and technically supported by Foundation, and technically supported by EMBARQ, WRI, UCR, USEPA, and SensorsEMBARQ, WRI, UCR, USEPA, and Sensors
Modal Splits in Shanghai, Modal Splits in Shanghai, 1986-20001986-2000
38 31
7
19 33
27
12
5
15
3625
39
65
2
0%
20%
40%
60%
80%
100%
1986 1995 2000
步行 自行车+助动车 摩托 汽车 公交WalkBicycle+Light Duty Motorcycle
Motorcycle Car
Public Transit
Growth of Vehicle Population in Shanghai, Growth of Vehicle Population in Shanghai, 1988-20021988-2002
E:\Changhong CHEN\ 对外合作 \ 能源基金会 \ 交通项目 \ 基础数据 \ 机动车统计报表 .xls
Objective of this StudyObjective of this Study To get better understanding transportation modal split, To get better understanding transportation modal split,
vehicle behavior, vehicle emission status in Shanghaivehicle behavior, vehicle emission status in Shanghai
To build a bottom-up and air quality associated sustainable To build a bottom-up and air quality associated sustainable transport indicator systemtransport indicator system
To build a vehicle emission model for policy scenario To build a vehicle emission model for policy scenario analysis and health benefit study, and for evaluation of the analysis and health benefit study, and for evaluation of the transportation sustainability in Shanghaitransportation sustainability in Shanghai
To provide policy recommendation to local government in To provide policy recommendation to local government in building up a sustainable transportbuilding up a sustainable transport
Characteristics of transport Characteristics of transport & environment system& environment system
Pre-Co-constrain, element A is constrain of Pre-Co-constrain, element A is constrain of element B, element B will be the constrain element B, element B will be the constrain of element Aof element A
Pre-Co-condition, element A is condition of Pre-Co-condition, element A is condition of element B, element B will be the condition element B, element B will be the condition of element Aof element A
““Egg-Chicken” relatedEgg-Chicken” related
Indicator Pyramid Indicator Pyramid StructureStructure
Indicator Pyramids: Hierarchy
Summary
Indicators
Detailed Data
Detailed Indicators
Source: Lee, 2004
Collection of Detailed Data:GDP, Population, Income, Land use, road length, vehicle numbers, type of vehicles, transport modal split, vehicle mileage travelled, vehicle fuel use, vehicle emissions, transportation volume, traffic
safety, congestion, average speed, air quality, etc
Identification of Requested Data:Social Economic Data, Transportation Data, Air Quality Data
Integrated Indicators
Create Group Indicators:Social Economic Indicator,
Transportation Indicator, Air Quality Indicator
Express of transport sustainability Express of transport sustainability –Differentiation from traditional studies–Differentiation from traditional studies
Historical situationHistorical situation Current statusCurrent status Trend of the future in BAU Trend of the future in BAU
scenarioscenario Policy and sustainabilityPolicy and sustainability
Economic development
GDP per capita
Income per capita
Others
Transportsystem
Vehiclepopulation increase
Roadconstruction
Transportation modal split
Transportdemand
VehiclePopulation
increase
Environmental issues
Air pollutant emission and
air quality degradation
0
50
100
150
200
250
300
350
400
450
1995 2000 2005 2010 2015 2020 2025 2030 2035
Inco
me
per c
apita
0
50
100
150
200
250
300
350
1995 2000 2005 2010 2015 2020 2025 2030 2035
Inco
me
per c
apita
Transport saturation
0
50
100
150
200
250
300
350
1995 2000 2005 2010 2015 2020 2025 2030 2035
Inco
me
per c
apita
Air quality degadation
Air quality improvement
Inte
gra
ted A
ssessm
ent
of su
stain
ability
of
transp
ort
Interaction of elementsInteraction of elements
Data Resource Data Resource of Shanghai Transport Indicator Systemof Shanghai Transport Indicator System
Statistic data directly from Statistics BureauStatistic data directly from Statistics Bureau
Vehicle population, safety, congestion data from Public Security Vehicle population, safety, congestion data from Public Security BureauBureau
Transportation system data, e.g. road length, parking lot, access to Transportation system data, e.g. road length, parking lot, access to transport, fuel use, travel mileage, etc, from Construction transport, fuel use, travel mileage, etc, from Construction Committee, Urban Transport Management Bureau, Bus Company, Committee, Urban Transport Management Bureau, Bus Company, Truck CompanyTruck Company
Air quality data from Environmental Protection BureauAir quality data from Environmental Protection Bureau
Emission trends from Shanghai Academy of Environmental Sciences Emission trends from Shanghai Academy of Environmental Sciences (SAES)(SAES)
International Cooperation International Cooperation of Shanghai Transport Indicator Systemof Shanghai Transport Indicator SystemC-1 Shanghai Construction CommitteeC-1 Shanghai Construction CommitteeC-2 Shanghai Environmental Protection BureauC-2 Shanghai Environmental Protection BureauC-3 Shanghai Development and Reform CommitteeC-3 Shanghai Development and Reform CommitteeC-4 Shanghai Public Security BureauC-4 Shanghai Public Security BureauC-5 Shanghai Urban Transport Management BureauC-5 Shanghai Urban Transport Management BureauC-6 Shanghai Urban Planning Bureau, and etc.C-6 Shanghai Urban Planning Bureau, and etc.
I-1 US Energy FoundationI-1 US Energy FoundationI-2 Shell FoundationI-2 Shell FoundationI-3 US Environmental Protection AgencyI-3 US Environmental Protection AgencyI-4 University California, Riverside, U.S.AI-4 University California, Riverside, U.S.AI-5 World Resource Institute (WRI), U.S.AI-5 World Resource Institute (WRI), U.S.AI-6 Sensors Co.I-6 Sensors Co.
Works have been done up to Works have been done up to datedate
Historical data collectedHistorical data collected Social economic, transportation, air quality data
Vehicle emission model introducedVehicle emission model introduced International Vehicle Emission Model (IVEM) from UCR Local policy and vehicle emission scenario analysis model from SAES
Measurement of input data for vehicle emission modelsMeasurement of input data for vehicle emission models Vehicle driving habit Frequency of engine start-up Vehicle technology Traffic volume Vehicle emission factors, particularly the heavy duty vehicle emissions
Scenarios analysis by SHA_VEMScenarios analysis by SHA_VEM
New emission standards implementedNew emission standards implemented HDVHDV LDVLDV MCMC
IM ProgramIM Program
Ole vehicle Ole vehicle scrappingscrapping
NOx emission from different type of vehicles NOx emission from different type of vehicles under medium growth of vehicle populationunder medium growth of vehicle population
0
5
10
15
20
25
3020
02
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
NO
x E
mis
sion
, 10
k to
n
HDB HDT Taxi LDB LDT MCs
NOx emission from different type of vehicles NOx emission from different type of vehicles under medium growth of vehicle populationunder medium growth of vehicle population
0.0
5.0
10.0
15.0
20.0
25.0
2000 2005 2010 2015 2020 2025 2030 2035
BAU IM HDB-E4 HDT-E4 Taxi E4
LDB E4 LDT E4 MC IM+
Field Survey of Input DataField Survey of Input Data
for Vehicle Emission Modelfor Vehicle Emission Model
Four Parts of the StudyFour Parts of the Study
Driving behavior in Shanghai (CGPS)Driving behavior in Shanghai (CGPS) Start-patterns of vehicles (VOCE)Start-patterns of vehicles (VOCE) General vehicle distribution (Video)General vehicle distribution (Video) Specific technology distribution Specific technology distribution
(Surveys)(Surveys)
Driving behavior – passenger carsDriving behavior – passenger cars
A routesA routes
C routesC routes
B routesB routes
Driving behavior – passenger carsDriving behavior – passenger cars
Driving behavior – passenger Driving behavior – passenger carscars
Hour Car One Car Two Car Three0700-0800 A-1 B-1 C-10800-0900 A-2 B-2 C-20900-1000 A-3 B-3 C-31000-1100 A-1 B-1 C-11100-1200 A-2 B-2 C-21200-1300 A-3 B-3 C-31300-1400 A-1 B-1 C-1
Day One (June 9, 2004)
Hour Car One Car Two Car Three0700-0800 C-2 A-2 B-20800-0900 C-3 A-3 B-30900-1000 C-1 A-1 B-11000-1100 C-2 A-2 B-21100-1200 C-3 A-3 B-31200-1300 C-1 A-1 B-11300-1400 C-2 A-2 B-2
Day Two (June 10, 2004)
Hour Car One Car Two Car Three1400-1500 A-1 B-1 C-11500-1600 A-2 B-2 C-21600-1700 A-3 B-3 C-31000-1100 A-1 B-1 C-11100-1200 A-2 B-2 C-21200-1300 A-3 B-3 C-31300-1400 A-1 B-1 C-1
Day Three (June 11, 2004)
A: Residential area 1B: Commercial areaC: Residential area 21: Highway2: Arterial3: Residential
Hour Car One Car Two Car Three0700-0800 B-3 C-3 A-30800-0900 B-1 C-1 A-10900-1000 B-2 C-2 A-21000-1100 B-3 C-3 A-31100-1200 B-1 C-1 A-11200-1300 B-2 C-2 A-21300-1400 B-3 C-3 A-3
Day Four (June 14, 2004)
Hour Car One Car Two Car Three1400-1500 C-2 A-2 B-21500-1600 C-3 A-3 B-31600-1700 C-1 A-1 B-11700-1800 C-2 A-2 B-21800-1900 C-3 A-3 B-31900-2000 C-1 A-1 B-12000-2100 C-2 A-2 B-2
Day Five (June 15, 2004)
Hour Car One Car Two Car Three1400-1500 B-3 C-3 A-31500-1600 B-1 C-1 A-11600-1700 B-2 C-2 A-21700-1800 B-3 C-3 A-31800-1900 B-1 C-1 A-11900-2000 B-2 C-2 A-22000-2100 B-3 C-3 A-3
Day Six (June 16, 2004)
Example Driving Runs – passenger Example Driving Runs – passenger carscars
Driving Behavior - Buses, Driving Behavior - Buses, Trucks and Taxi’sTrucks and Taxi’s
Riders With GPS On BusesRiders With GPS On Buses GPS Placed In Working TrucksGPS Placed In Working Trucks GPS Placed In Working Taxis GPS Placed In Working Taxis Days One, Two, Four = MorningDays One, Two, Four = Morning Days Three, Five, Six = AfternoonDays Three, Five, Six = Afternoon Vehicles Must Operate In Metro AreaVehicles Must Operate In Metro Area
Example Driving Runs – trucksExample Driving Runs – trucks
Driving Runs – trucksDriving Runs – trucks• total truck data collected: ~268,640 seconds (75 hrs)total truck data collected: ~268,640 seconds (75 hrs)• average distance traveled for 7 hours: 120 kmaverage distance traveled for 7 hours: 120 km• average maximum speed: 66 kphaverage maximum speed: 66 kph• average moving time: 63%average moving time: 63%• average idle time: 37%average idle time: 37%
Example Driving Runs – busesExample Driving Runs – buses
Driving Runs – busesDriving Runs – buses• total bus data collected: ~201,600 seconds (56 hrs)total bus data collected: ~201,600 seconds (56 hrs)• average distance traveled for 7 hours: 67 kmaverage distance traveled for 7 hours: 67 km• average maximum speed: 67 kphaverage maximum speed: 67 kph• average moving time: 81%average moving time: 81%• average idle time: 19%average idle time: 19%
Example Driving Runs – taxisExample Driving Runs – taxis
Driving Runs – taxisDriving Runs – taxis• total taxi data collected: ~305,964 seconds (85 hrs)total taxi data collected: ~305,964 seconds (85 hrs)• average distance traveled for 7 hours: 131 kmaverage distance traveled for 7 hours: 131 km• average maximum speed: 107 kphaverage maximum speed: 107 kph• average moving time: 66%average moving time: 66%• average idle time: 34%average idle time: 34%
Example Driving Runs – Example Driving Runs – motorcyclemotorcycle
Driving Runs – motorcycleDriving Runs – motorcycle• total motorcycle data collected: ~84,000 seconds (23 total motorcycle data collected: ~84,000 seconds (23
hrs)hrs)• average distance traveled for 7 hours: 33 kmaverage distance traveled for 7 hours: 33 km• average maximum speed: 62 kphaverage maximum speed: 62 kph• average moving time: 70%average moving time: 70%• average idle time: 30%average idle time: 30%
Start Patterns of VehiclesStart Patterns of Vehicles
VVehicle ehicle OOperating perating CCharacteristics haracteristics EEnunciators (VOCE) Units Installed nunciators (VOCE) Units Installed On 76 Passenger Vehicles and Taxis.On 76 Passenger Vehicles and Taxis.
Install on Tuesday, June 6Install on Tuesday, June 6thth and and remove on June 17remove on June 17thth..
Maintain Log Of Vehicles Using VOCEMaintain Log Of Vehicles Using VOCE
A videoA video
C videoC video
B videoB video
General technology General technology distributiondistribution
Video TapingVideo Taping
General technology General technology distributiondistribution
Video tape recording: 20 minutes, 7 times/day, 6 days = 14 hours 42 hours
Specific technology Specific technology distributiondistribution
Parking lot survey: 1600 passenger cars, YY taxis
Fuel typeEngine sizeModel year
ManufacturerModel
MileageA/C
TransmissionCatalytic
F/A systemMaintenance
General technology distributionGeneral technology distributionShanghaiShanghai
0
10
20
30
40
50
60
70
80
90
1 2 3 4 5 6 7 8 9 10 11 12 >12
Vehicle Mileage, 年行驶里程,万km/yr
Dis
trib
utio
n, 车
辆数
分布
,%
General technology distributionGeneral technology distributionShanghaiShanghai
0
5
10
15
20
25
30
35
40
45
50
<=4 8 12 16 20 24 28 32 36 40 44 48 >48
Vehicle mileage, 车公里,万公里
Dis
trib
utio
n车
公里
-车
辆数
分布
,%
General technology distributionGeneral technology distributionShanghaiShanghai
0
5
10
15
20
25
30
16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1
( )车龄 年
Veh
icle
Mile
age
总车
公里
,万
km/ 车
0
5
10
15
20
25
30
Veh
icle
Age
车龄
分布
,%
总车公里 Vehicle Mileage 车龄分布 Vehicle Age Distribution
General technology distributionGeneral technology distributionShanghaiShanghai
y = 0.0462x2 + 1.438x
R2 = 0.8209
-
5
10
15
20
25
30
0 2 4 6 8 10 12
Vehicle Age
Veh
icle
Mile
age,
km
/yr
General Technology DistributionGeneral Technology DistributionRoute Time
Pass
CarTaxi
Small Truck
Med Truck
Large Truck
Small Bus
Med Bus
Large Bus
Motor- cycles
Moped Total Veh/Hr
A06:00-13:00
36.9% 12.5% 4.9% 8.4% 3.1% 1.9% 2.5% 0.01% 2.8% 26.9% 100% 1387
A13:00-20:00
39.4% 10.0% 5.0% 10.8% 3.3% 2.9% 5.0% 0.01% 6.9% 16.6% 100% 1104
B06:00-13:00
33.3% 29.7% 2.0% 1.3% 0.1% 1.6% 10.9% 0.00% 2.4% 18.7% 100% 830
B13:00-20:00
37.7% 27.6% 1.3% 0.8% 0.0% 0.8% 10.4% 0.00% 2.0% 19.4% 100% 779
C06:00-13:00
48.0% 21.8% 1.6% 1.1% 0.2% 2.1% 4.5% 0.30% 1.3% 19.0% 100% 1550
C13:00-20:00
57.6% 27.3% 1.3% 1.4% 0.0% 2.1% 4.1% 0.36% 0.4% 5.4% 100% 1356
Total06:00-13:00
35.4% 20.0% 3.7% 5.3% 1.8% 1.8% 6.1% 0.01% 2.6% 23.4% 100% 1178
Total13:00-20:00
38.6% 18.4% 3.3% 6.1% 1.7% 1.9% 7.6% 0.00% 4.6% 17.9% 100% 1016
Grand Total
All Day 36.9% 19.2% 3.5% 5.7% 1.8% 1.8% 6.8% 0.01% 3.5% 20.8% 100% 1097
Technical Support from Technical Support from USEPA-through WRIUSEPA-through WRI
USEPA provides us a great USEPA provides us a great
opportunities to get better opportunities to get better
understanding of emission understanding of emission
from heavy duty vehicles in from heavy duty vehicles in
ChinaChina
Many Different Kinds of Trucks, Many Different Kinds of Trucks, BusesBuses
Vehicle testedVehicle tested
Testing road: Free way, arterial, and Testing road: Free way, arterial, and residential roadresidential road
13 type of vehicles were tested, among of 13 type of vehicles were tested, among of which 11 vehicles were medium-duty which 11 vehicles were medium-duty diesel trucks, 2 light-duty diesel passenger diesel trucks, 2 light-duty diesel passenger cars, and one heavy-duty diesel buscars, and one heavy-duty diesel bus
25 type of runs conducted, including 25 type of runs conducted, including loaded and unloaded, accelerated, loaded and unloaded, accelerated, decelerated, idle, and cruise speeddecelerated, idle, and cruise speed
Data obtainedData obtained
Times of effective data: more than Times of effective data: more than 600,000 data600,000 data
Raw data obtained: longitude & latitude Raw data obtained: longitude & latitude & altitude, vehicle speed, air/fuel ratio, & altitude, vehicle speed, air/fuel ratio, fuel consumption (g/s), pollutant fuel consumption (g/s), pollutant exhausted concentration (ppm, %), exhausted concentration (ppm, %), pollutant exhausted ratio (g/s), vehicle pollutant exhausted ratio (g/s), vehicle travel mileage (mile), fuel economy, etc.travel mileage (mile), fuel economy, etc.
Result of measurementResult of measurement
Typical results (1)Typical results (1)
0
1
2
3
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6
-3. 0 -2. 0 -1. 0 0. 0 1. 0 2. 0 3. 0Accel erati on, m/s2
Fuel
use
, ml
/s
0
10
20
30
40
50
60
70
0 200 400 600 800 1000
Vehi
cle
Spee
d, k
m/hr
- 3. 0
-2. 0
-1. 0
0. 0
1. 0
2. 0
3. 0
0 200 400 600 800 1000
Acce
lera
tion
, m/
s2
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Fuel
Con
sump
tion
, ml
/s
Typical results (2)Typical results (2)
Typical results (3)Typical results (3)
Typical results (4)Typical results (4)
Typical results (5)Typical results (5)
Typical results (6)Typical results (6)
Typical results (7)Typical results (7)
油耗13
油耗 1油耗 4
油耗 20
油耗 7
油耗 6
油耗 5
油耗 8
油耗 9
油耗10
油耗11
油耗12
油耗 21
车速 1-6
车速 7
车速 8
车速 9
车速10
车速 11
车速 13车速 15
车速 17
车速 19
车速 21车速 23
0
2
4
6
8
10
12
-1.0 -0.5 0.0 0.5 1.0 1.5
加速度, m/s2
油耗
, L/h
r
0
2
4
6
8
10
12
14
16
18
车速
, km
/hr
油耗
车速
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
-1.0 -0.5 0.0 0.5 1.0 1.5
加速度, m/s2
CO
, %
0
2
4
6
8
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18
车速
, km
/hr
CO
车速
200
300
400
500
600
700
-1.0 -0.5 0.0 0.5 1.0 1.5
加速度, m/s2
TH
C, p
pm
C
0
2
4
6
8
10
12
14
16
18
车速
, km
/hr
THC
车速
0
200
400
600
800
1000
1200
-1.0 -0.5 0.0 0.5 1.0 1.5
加速度, m/s2
NO
x, p
pm
0
2
4
6
8
10
12
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16
18
车速
, km
/hr
NOx
车速
Vehicle Specification:
Truck: 5-ton Diesel Truck
Travel mileage: 10700 km
Engine: DI
Overall Test Results:
Total Distance Traveled (mi) 20.005
Total Distance Traveled (km) 32.195
Total Fuel Consumed (gal) 1.469
Total Fuel Consumed (l) 5.562
Overall Fuel Economy (mpg) 13.614
Overall Fuel Economy (l/100 km) 17.278
Overall Emissions (Distance Specific):
CO2 (g/km) 451.3
CO (g/km) 5.603
NOx (g/km) 9.906
THC (g/km) 3.116
项 目Item
单位Unit
城市主干道Arterial
快速道路Freeway
城市次干道Res. Road
综合道路Overall
工况点 个 12979 3743 12368 29090
速度 - 加速度
(Acceleration)
平均速度 (avg) km/hr 23.0 35.0 19.4 23.0
最高速度 (MAX Speed) km/hr 68.5 84.2 64.1 84.2
时间比
(share of time)
怠速 (Idle) % 19.6 3.1 18.3 16.9
加速 (Acceleration) % 20.5 36.2 22.7 23.6
等速 (Cruise) % 31.7 31.8 30.3 31.0
减速 (Deceleration) % 28.2 28.9 28.7 28.5
测试里程 (road length for testing) km 83.0 36.4 66.7 186.0
百公里油耗 (Fuel economy) L/100km 18.7 13.9 17.5 17.4
CO 排放因子
(CO emissionfactor)
加速 (Acceleration) g/km 7.85 3.70 6.32 6.62
等速 (Cruise) g/km 3.42 2.18 3.13 3.01
减速 (Deceleration) g/km 2.11 1.82 1.96 2.00
综合 (Overall) g/km 5.18 2.65 4.40 4.41
HC 排放因子
(HC emission factor)
加速 (Acceleration) g/km 1.99 1.46 2.13 1.95
等速 (Cruise) g/km 1.43 1.13 1.65 1.43
减速 (Deceleration) g/km 1.13 1.04 1.34 1.19
综合 (Overall) g/km 1.81 1.27 2.01 1.77
NOx 排放因子
(NOx emission factor)
加速 (Acceleration) g/km 10.43 8.23 10.25 10.00
等速 (Cruise) g/km 5.63 5.11 5.36 5.41
减速 (Deceleration) g/km 3.27 3.53 3.37 3.35
综合 (Overall) g/km 7.32 5.78 7.16 6.96
Thank YouThank You