1

Sustainable Transport Indicators: -from Raw Data to Indicators

Vehicle Activity Study, Shanghai China

Changhong CHENJim Lents, Matt Barth, Nick Nikkila

Lee Schipper, Nancy KeteQiguo JING, Cheng HUANG, Haikun WANG

(Shanghai Academy of Environmental Sciences)saeschen@pm25.org

BAQ 2004Agra, India

6-8 December, 2004

BackgroundBackgroundShanghai is one of the largest Shanghai is one of the largest megacitiesmegacities in the in the world with some population of 17 million, close world with some population of 17 million, close to Mexico Cityto Mexico City

It is very active in economic development with It is very active in economic development with more than per capita GDP of 5000 USD, which is more than per capita GDP of 5000 USD, which is 4 time higher than national average4 time higher than national average

Economic development drives rapid growth of Economic development drives rapid growth of vehicle population vehicle population

To avoid vehicle pollution, lots of studies have To avoid vehicle pollution, lots of studies have been done since early 1990been done since early 1990’’ss

BackgroundBackgroundThe studies provided lots of policy The studies provided lots of policy recommendation to local government in vehicle recommendation to local government in vehicle emission reduction for air quality management emission reduction for air quality management during 1990during 1990’’ss

However, due to rapid growth of vehicle However, due to rapid growth of vehicle population in recent years, new plan for vehicle population in recent years, new plan for vehicle emission control is requested in a very urgent emission control is requested in a very urgent wayway

BackgroundBackgroundTo meet policy requirement, an international To meet policy requirement, an international cooperation was launched in early 2004. The cooperation was launched in early 2004. The project is financially supported by US Energy project is financially supported by US Energy Foundation, Shell Foundation, and technically Foundation, Shell Foundation, and technically supported by EMBARQ, WRI, UCR, USEPA, and supported by EMBARQ, WRI, UCR, USEPA, and SensorsSensors

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Modal Splits in Shanghai, Modal Splits in Shanghai, 19861986--20002000

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, 19881988--20022002

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

Growth of Vehicle Population in Shanghai, Growth of Vehicle Population in Shanghai, 19881988--20022002

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 StudyTo get better understanding transportation modal To get better understanding transportation modal split, vehicle behavior, vehicle emission status in split, vehicle behavior, vehicle emission status in ShanghaiShanghai

To build a bottomTo build a bottom--up and air quality associated up and air quality associated sustainable transport indicator systemsustainable transport 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 analysis and health benefit study, and for evaluation of the transportation sustainability in Shanghaiof the transportation sustainability in Shanghai

To provide policy recommendation to local To provide policy recommendation to local government in building up a sustainable transportgovernment in building up a sustainable transport

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Characteristics of transport & Characteristics of transport & environment systemenvironment system

PrePre--CoCo--constrain, element A is constrain of constrain, element A is constrain of element B, element B will be the constrain of element B, element B will be the constrain of element Aelement A

PrePre--CoCo--condition, element A is condition of condition, element A is condition of element B, element B will be the condition of element B, element B will be the condition of element Aelement A

““EggEgg--ChickenChicken”” relatedrelated

Indicator Pyramid Indicator Pyramid StructureStructure

Indicator Pyramids:Hierarchy

SummaryIndicators

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

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Express of transport sustainability Express of transport sustainability ––Differentiation from traditional studiesDifferentiation from traditional studies

Historical situationHistorical situationCurrent statusCurrent statusTrend of the future in BAU scenarioTrend of the future in BAU scenarioPolicy 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 emissionand

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

Integrated Assessment of

sustainability of transport

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 Vehicle population, safety, congestion data from Public Security BureauSecurity Bureau

Transportation system data, e.g. road length, parking lot, Transportation system data, e.g. road length, parking lot, access to transport, fuel use, travel mileage, etc, from access to transport, fuel use, travel mileage, etc, from Construction Committee, Urban Transport Management Construction Committee, Urban Transport Management Bureau, Bus Company, Truck CompanyBureau, Bus Company, Truck Company

Air quality data from Environmental Protection BureauAir quality data from Environmental Protection Bureau

Emission trends from Shanghai Academy of Emission trends from Shanghai Academy of Environmental Sciences (SAES)Environmental Sciences (SAES)

International Cooperation International Cooperation of Shanghai Transport Indicator Systemof Shanghai Transport Indicator System

CC--1 Shanghai Construction Committee1 Shanghai Construction CommitteeCC--2 Shanghai Environmental Protection Bureau2 Shanghai Environmental Protection BureauCC--3 Shanghai Development and Reform Committee3 Shanghai Development and Reform CommitteeCC--4 Shanghai Public Security Bureau4 Shanghai Public Security BureauCC--5 Shanghai Urban Transport Management Bureau5 Shanghai Urban Transport Management BureauCC--6 Shanghai Urban Planning Bureau, and etc.6 Shanghai Urban Planning Bureau, and etc.

II--1 US Energy Foundation1 US Energy FoundationII--2 Shell Foundation2 Shell FoundationII--3 US Environmental Protection Agency3 US Environmental Protection AgencyII--4 University California, Riverside, U.S.A4 University California, Riverside, U.S.AII--5 World Resource Institute (WRI), U.S.A5 World Resource Institute (WRI), U.S.AII--6 Sensors Co.6 Sensors Co.

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Works have been done up to dateWorks have been done up to date

Historical data collectedHistorical data collectedSocial economic, transportation, air quality data

Vehicle emission model introducedVehicle emission model introducedInternational Vehicle Emission Model (IVEM) from UCRLocal policy and vehicle emission scenario analysis model from SAES

Measurement of input data for vehicle Measurement of input data for vehicle emission modelsemission models

Vehicle driving habitFrequency of engine start-upVehicle technologyTraffic volumeVehicle emission factors, particularly the heavy duty vehicle emissions

Scenarios analysis by SHA_VEMScenarios analysis by SHA_VEM

New emission standards implementedNew emission standards implementedHDVHDVLDVLDVMCMC

IM ProgramIM Program

Ole vehicle Ole vehicle scrappingscrapping

NOxNOx emission from different type of vehicles emission from different type of vehicles under medium growth of vehicle populationunder medium growth of vehicle population

0

5

10

15

20

25

30

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

2024

2026

2028

2030

NO

x E

mis

sion

, 10k

ton

HDB HDT Taxi LDB LDT MCs

6

NOxNOx emission from different type of vehicles 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)StartStart--patterns of vehicles (VOCE)patterns of vehicles (VOCE)General vehicle distribution (Video)General vehicle distribution (Video)Specific technology distribution (Surveys)Specific technology distribution (Surveys)

Driving behavior Driving behavior –– passenger carspassenger cars

A routesA routes

C routesC routes

B routesB routes

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Driving behavior Driving behavior –– passenger carspassenger carsDriving behavior Driving behavior –– passenger carspassenger cars

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 Example Driving Runs –– passenger carspassenger cars Driving Behavior Driving Behavior -- Buses, Buses, Trucks and TaxiTrucks and Taxi’’ss

Riders With GPS On BusesRiders With GPS On BusesGPS Placed In Working TrucksGPS Placed In Working TrucksGPS Placed In Working Taxis GPS Placed In Working Taxis Days One, Two, Four = MorningDays One, Two, Four = MorningDays Three, Five, Six = AfternoonDays Three, Five, Six = AfternoonVehicles Must Operate In Metro AreaVehicles Must Operate In Metro Area

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Example Driving Runs Example Driving Runs –– truckstrucks Driving Runs Driving Runs –– truckstrucks•• 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 average maximum speed: 66 kphkph•• average moving time: 63%average moving time: 63%•• average idle time: 37%average idle time: 37%

Example Driving Runs Example Driving Runs –– busesbuses Driving Runs Driving Runs –– busesbuses•• 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 average maximum speed: 67 kphkph•• average moving time: 81%average moving time: 81%•• average idle time: 19%average idle time: 19%

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Example Driving Runs Example Driving Runs –– taxistaxis Driving Runs Driving Runs –– taxistaxis•• 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 average maximum speed: 107 kphkph•• 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 Driving Runs –– motorcyclemotorcycle•• total motorcycle data collected: ~84,000 seconds (23 hrs)total motorcycle data collected: ~84,000 seconds (23 hrs)•• average distance traveled for 7 hours: 33 kmaverage distance traveled for 7 hours: 33 km•• average maximum speed: 62 average maximum speed: 62 kphkph•• average moving time: 70%average moving time: 70%•• average idle time: 30%average idle time: 30%

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Start Patterns of VehiclesStart Patterns of Vehicles

VVehicle ehicle OOperating perating CCharacteristics haracteristics EEnunciators (VOCE) Units Installed On 76 nunciators (VOCE) Units Installed On 76 Passenger Vehicles and Taxis.Passenger Vehicles and Taxis.Install on Tuesday, June 6Install on Tuesday, June 6thth and remove and remove on June 17on June 17thth..Maintain Log Of Vehicles Using VOCEMaintain Log Of Vehicles Using VOCE

A videoA video

C videoC video

B videoB video

General technology distributionGeneral technology distribution

Video TapingVideo TapingGeneral technology distributionGeneral technology distribution

Video tape recording: 20 minutes, 7 times/day, 6 days = 14 hours ⇔ 42 hours

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Specific technology distributionSpecific technology distribution

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

tribu

tion,

车辆数

分布

，%

General technology distributionGeneral technology distributionShanghaiShanghai

05

101520253035404550

<=4 8 12 16 20 24 28 32 36 40 44 48 >48

Vehicle mileage, 车公里，万公里

Dis

tribu

tion

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

30V

ehic

le A

ge车

龄分布

，%

总车公里 Vehicle Mileage 车龄分布 Vehicle Age Distribution

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General technology distributionGeneral technology distributionShanghaiShanghai

y = 0.0462x2 + 1.438xR2 = 0.8209

-

5

10

15

20

25

30

0 2 4 6 8 10 12

Vehicle Age

Vehi

cle

Mile

age,

km

/yr

General Technology DistributionGeneral Technology Distribution

100%

100%

100%

100%

100%

100%

100%

100%

100%

Total

109720.8%3.5%0.01%6.8%1.8%1.8%5.7%3.5%19.2%36.9%All Day

Grand Total

101617.9%4.6%0.00%7.6%1.9%1.7%6.1%3.3%18.4%38.6%13:00-20:00Total

117823.4%2.6%0.01%6.1%1.8%1.8%5.3%3.7%20.0%35.4%06:00-13:00Total

13565.4%0.4%0.36%4.1%2.1%0.0%1.4%1.3%27.3%57.6%13:00-20:00C

155019.0%1.3%0.30%4.5%2.1%0.2%1.1%1.6%21.8%48.0%06:00-13:00C

77919.4%2.0%0.00%10.4%0.8%0.0%0.8%1.3%27.6%37.7%13:00-20:00B

83018.7%2.4%0.00%10.9%1.6%0.1%1.3%2.0%29.7%33.3%06:00-13:00B

110416.6%6.9%0.01%5.0%2.9%3.3%10.8%5.0%10.0%39.4%13:00-20:00A

138726.9%2.8%0.01%2.5%1.9%3.1%8.4%4.9%12.5%36.9%06:00-13:00A

Veh/HrMopedMotor-cycles

Large Bus

Med Bus

Small Bus

Large Truck

Med Truck

Small TruckTaxi

PassCar

TimeRoute

Technical Support from Technical Support from USEPAUSEPA--through WRIthrough WRI

USEPA provides us a great USEPA provides us a great

opportunities to get better opportunities to get better

understanding of emission from understanding of emission from

heavy duty vehicles in Chinaheavy duty vehicles in China

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Many Different Kinds of Trucks, BusesMany Different Kinds of Trucks, Buses

14

15

Vehicle testedVehicle tested

Testing road: Free way, arterial, and residential Testing road: Free way, arterial, and residential roadroad13 type of vehicles were tested, among of wh13 type of vehicles were tested, among of whicich h 11 vehicles were medium11 vehicles were medium--duty diesel trucks, 2 duty diesel trucks, 2 lightlight--duty diesel passenger cars, and one heavyduty diesel passenger cars, and one heavy--duty diesel busduty diesel bus25 type of runs conducted, including loaded and 25 type of runs conducted, including loaded and unloaded, accelerated, decelerated, idle, and unloaded, accelerated, decelerated, idle, and cruise speedcruise speed

Data obtainedData obtained

TTimes of effective data: more than imes of effective data: more than 600,000 data600,000 dataRaw dRaw data obtained: longitude & latitude & ata obtained: longitude & latitude & altitude, vehicle speed, air/fuel ratio, fuel altitude, vehicle speed, air/fuel ratio, fuel consumption (g/s), pollutant exhausted consumption (g/s), pollutant exhausted concentration (concentration (ppmppm, %), pollutant , %), pollutant exhausted ratio (g/s), vehicle travel exhausted ratio (g/s), vehicle travel mileage (mile), fuel economy, etc.mileage (mile), fuel economy, etc.

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17

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Result of measurementResult of measurement

Typical results (1)Typical results (1)

0

1

2

3

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5

6

-3.0 -2.0 -1.0 0.0 1.0 2.0 3.0

Acceleration, m/s2

Fuel use, ml/s

0

10

20

30

40

50

60

70

0 200 400 600 800 1000

Vehicle Speed, km/hr

-3.0

-2.0

-1.0

0.0

1.0

2.0

3.0

0 200 400 600 800 1000

Acceleration, m/s2

0

1

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0 200 400 600 800 1000

Fuel Consumption, ml/s

Typical results (2)Typical results (2)Overall

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

0 10 20 30 40 50 60 70 80 90

速度, km/hr

加速

度, m

/s2

Typical results (3)Typical results (3)HW

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

0 10 20 30 40 50 60 70 80 90

速度, km/hr

加速度

, m/s

2

19

Typical results (4)Typical results (4)AR

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

0 10 20 30 40 50 60 70 80 90

速度, km/hr

加速度

, m/s

2

Typical results (5)Typical results (5)SAR

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

0 10 20 30 40 50 60 70 80 90

速度, km/hr

加速度

, m/s

2

Typical results (6)Typical results (6)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

-5 -3 -1 1 3 5

加速度, m/s2

CO排放量

, g/s

0.00

0.05

0.10

0.15

0.20

0.25

0.30

-5 -3 -1 1 3 5

加速度, m/s2

NO

x排放量

, g/s

0.000

0.005

0.010

0.015

0.020

0.025

0.030

0.035

0.040

-5 -3 -1 1 3 5

加速度, m/s2

THC排

放量

, g/s

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

10

12

14

16

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

THC

, ppm

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

14

16

18

车速

, km

/hr

NOx

车速

20

Vehicle Specification:Truck: 5-ton Diesel TruckTravel mileage: 10700 kmEngine: DI

Overall Test Results:Total Distance Traveled (mi) 20.005Total Distance Traveled (km) 32.195Total Fuel Consumed (gal) 1.469Total Fuel Consumed (l) 5.562Overall Fuel Economy (mpg) 13.614Overall Fuel Economy (l/100 km) 17.278

Overall Emissions (Distance Specific):CO2 (g/km) 451.3CO (g/km) 5.603NOx (g/km) 9.906THC (g/km) 3.116

6.96 7.16 5.78 7.32 g/km综合(Overall)

3.35 3.37 3.53 3.27 g/km减速(Deceleration)

5.41 5.36 5.11 5.63 g/km等速(Cruise)

10.00 10.25 8.23 10.43 g/km加速(Acceleration)NOx排放因子

(NOx emission factor)

1.77 2.01 1.27 1.81 g/km综合(Overall)

1.19 1.34 1.04 1.13 g/km减速(Deceleration)

1.43 1.65 1.13 1.43 g/km等速(Cruise)

1.95 2.13 1.46 1.99 g/km加速(Acceleration)HC排放因子

(HC emission factor)

4.41 4.40 2.65 5.18 g/km综合(Overall)

2.00 1.96 1.82 2.11 g/km减速(Deceleration)

3.01 3.13 2.18 3.42 g/km等速(Cruise)

6.62 6.32 3.70 7.85 g/km加速(Acceleration)CO排放因子

(CO emissionfactor)

17.4 17.5 13.9 18.7 L/100km百公里油耗 (Fuel economy)

186.0 66.7 36.4 83.0 km测试里程 (road length for testing)

28.5 28.7 28.9 28.2 %减速(Deceleration)

31.0 30.3 31.8 31.7 %等速(Cruise)

23.6 22.7 36.2 20.5 %加速(Acceleration)

16.9 18.3 3.1 19.6 %怠速 (Idle)时间比

(share of time)

84.2 64.1 84.2 68.5 km/hr最高速度 (MAX Speed)

23.0 19.4 35.0 23.0 km/hr平均速度(avg)速度-加速度

(Acceleration)

2909012368374312979个工况点

综合道路Overall

城市次干道Res. Road

快速道路Freeway

城市主干道Arterial

单位Unit

项 目Item

Thank YouThank You