Sustainable Transport Indicators: -from Raw Data to Indicators Vehicle Activity Study, Shanghai...

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)

[email protected]

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

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10

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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

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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

，%


0

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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

，%


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5

10

15

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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


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)

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-3. 0 -2. 0 -1. 0 0. 0 1. 0 2. 0 3. 0Accel erati on, m/s2

Fuel

use

, ml

/s

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Vehi

cle

Spee

d, k

m/hr

- 3. 0

-2. 0

-1. 0

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1. 0

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3. 0

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Acce

lera

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, m/

s2

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Fuel

Con

sump

tion

, ml

/s


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

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-1.0 -0.5 0.0 0.5 1.0 1.5

加速度, m/s2

油耗

, L/h

r

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车速

, km

/hr

油耗

车速

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加速度, m/s2

CO

, %

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车速

, km

/hr

CO

车速

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加速度, m/s2

TH

C, p

pm

C

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车速

, km

/hr

THC

车速

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加速度, m/s2

NO

x, p

pm

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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)


等速 (Cruise) g/km 1.43 1.13 1.65 1.43


综合 (Overall) g/km 1.81 1.27 2.01 1.77

NOx 排放因子

(NOx emission factor)


等速 (Cruise) g/km 5.63 5.11 5.36 5.41


综合 (Overall) g/km 7.32 5.78 7.16 6.96

Thank YouThank You

Sustainable Transport Indicators: -from Raw Data to Indicators Vehicle Activity Study, Shanghai...

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