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Changhong CHEN Jim Lents, Matt Barth, Nick Nikkila Lee Schipper, Nancy Kete

Sustainable Transport Indicators: -from Raw Data to Indicators Vehicle Activity Study, Shanghai China. Changhong CHEN Jim Lents, Matt Barth, Nick Nikkila Lee Schipper, Nancy Kete Qiguo JING, Cheng HUANG, Haikun WANG (Shanghai Academy of Environmental Sciences) saeschen@pm25.org. BAQ 2004

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Changhong CHEN Jim Lents, Matt Barth, Nick Nikkila Lee Schipper, Nancy Kete

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  1. Sustainable Transport Indicators: -from Raw Data to IndicatorsVehicle Activity Study, Shanghai China Changhong CHEN Jim Lents, Matt Barth, Nick Nikkila Lee Schipper, Nancy Kete Qiguo JING, Cheng HUANG, Haikun WANG (Shanghai Academy of Environmental Sciences) saeschen@pm25.org BAQ 2004 Agra, India 6-8 December, 2004

  2. Background • Shanghai is one of the largest megacities in the world with some population of 17 million, close to Mexico City • It is very active in economic development with more than per capita GDP of 5000 USD, which is 4 time higher than national average • Economic development drives rapid growth of vehicle population • To avoid vehicle pollution, lots of studies have been done since early 1990’s

  3. Background • The studies provided lots of policy recommendation to local government in vehicle emission reduction for air quality management during 1990’s • However, due to rapid growth of vehicle population in recent years, new plan for vehicle emission control is requested in a very urgent way

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

  5. Modal Splits in Shanghai, 1986-2000 Bicycle+Light Duty Motorcycle Motorcycle Public Transit Walk Car

  6. Growth of Vehicle Population in Shanghai, 1988-2002 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

  7. Objective of this Study • To get better understanding transportation modal split, vehicle behavior, vehicle emission status in Shanghai • To build a bottom-up and air quality associated sustainable transport indicator system • To build a vehicle emission model for policy scenario analysis and health benefit study, and for evaluation of the transportation sustainability in Shanghai • To provide policy recommendation to local government in building up a sustainable transport

  8. Characteristics of transport & environment system • Pre-Co-constrain, element A is constrain of element B, element B will be the constrain of element A • Pre-Co-condition, element A is condition of element B, element B will be the condition of element A • “Egg-Chicken” related

  9. Indicator Pyramid Structure

  10. Indicator Pyramids:Hierarchy Summary Indicators Detailed Indicators Detailed Data Source: Lee, 2004

  11. Integrated Indicators Create Group Indicators: Social Economic Indicator, Transportation Indicator, Air Quality Indicator 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

  12. Express of transport sustainability –Differentiation from traditional studies • Historical situation • Current status • Trend of the future in BAU scenario • Policy and sustainability

  13. Interaction of elements GDP per capita Economic development Income per capita Others Transport demand Integrated Assessment of sustainability of transport Vehicle population increase Transport system Road construction Transportation modal split Vehicle Population increase Air pollutant emission and air quality degradation Environmental issues

  14. Data Resource of Shanghai Transport Indicator System • Statistic data directly from Statistics Bureau • Vehicle population, safety, congestion data from Public Security Bureau • Transportation system data, e.g. road length, parking lot, access to transport, fuel use, travel mileage, etc, from Construction Committee, Urban Transport Management Bureau, Bus Company, Truck Company • Air quality data from Environmental Protection Bureau • Emission trends from Shanghai Academy of Environmental Sciences (SAES)

  15. International Cooperation of Shanghai Transport Indicator System C-1 Shanghai Construction Committee C-2 Shanghai Environmental Protection Bureau C-3 Shanghai Development and Reform Committee C-4 Shanghai Public Security Bureau C-5 Shanghai Urban Transport Management Bureau C-6 Shanghai Urban Planning Bureau, and etc. I-1 US Energy Foundation I-2 Shell Foundation I-3 US Environmental Protection Agency I-4 University California, Riverside, U.S.A I-5 World Resource Institute (WRI), U.S.A I-6 Sensors Co.

  16. Works have been done up to date • Historical data collected • Social economic, transportation, air quality data • Vehicle 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 models • Vehicle driving habit • Frequency of engine start-up • Vehicle technology • Traffic volume • Vehicle emission factors, particularly the heavy duty vehicle emissions

  17. Scenarios analysis by SHA_VEM • New emission standards implemented • HDV • LDV • MC • IM Program • Ole vehicle scrapping

  18. NOx emission from different type of vehicles under medium growth of vehicle population

  19. NOx emission from different type of vehicles under medium growth of vehicle population

  20. Field Survey of Input Datafor Vehicle Emission Model

  21. Four Parts of the Study • Driving behavior in Shanghai (CGPS) • Start-patterns of vehicles (VOCE) • General vehicle distribution (Video) • Specific technology distribution (Surveys)

  22. Driving behavior – passenger cars B routes A routes C routes

  23. Driving behavior – passenger cars

  24. Driving behavior – passenger cars Day One (June 9, 2004) A: Residential area 1 B: Commercial area C: Residential area 2 1: Highway 2: Arterial 3: Residential Hour Car One Car Two Car Three 0700-0800 A-1 B-1 C-1 0800-0900 A-2 B-2 C-2 Day Two (June 10, 2004) 0900-1000 A-3 B-3 C-3 Hour Car One Car Two Car Three 1000-1100 A-1 B-1 C-1 0700-0800 C-2 A-2 B-2 1100-1200 A-2 B-2 C-2 0800-0900 C-3 A-3 B-3 Day Three (June 11, 2004) 1200-1300 A-3 B-3 C-3 0900-1000 C-1 A-1 B-1 Hour Car One Car Two Car Three 1300-1400 A-1 B-1 C-1 1000-1100 C-2 A-2 B-2 1400-1500 A-1 B-1 C-1 1100-1200 C-3 A-3 B-3 1500-1600 A-2 B-2 C-2 1200-1300 C-1 A-1 B-1 B-3 1600-1700 A-3 C-3 1300-1400 C-2 A-2 B-2 1000-1100 A-1 B-1 C-1 1100-1200 A-2 B-2 C-2 1200-1300 A-3 B-3 C-3 1300-1400 A-1 B-1 C-1

  25. Example Driving Runs – passenger cars

  26. Driving Behavior - Buses, Trucks and Taxi’s • Riders With GPS On Buses • GPS Placed In Working Trucks • GPS Placed In Working Taxis • Days One, Two, Four = Morning • Days Three, Five, Six = Afternoon • Vehicles Must Operate In Metro Area

  27. Example Driving Runs – trucks

  28. Driving Runs – trucks • total truck data collected: ~268,640 seconds (75 hrs) • average distance traveled for 7 hours: 120 km • average maximum speed: 66 kph • average moving time: 63% • average idle time: 37%

  29. Example Driving Runs – buses

  30. Driving Runs – buses • total bus data collected: ~201,600 seconds (56 hrs) • average distance traveled for 7 hours: 67 km • average maximum speed: 67 kph • average moving time: 81% • average idle time: 19%

  31. Example Driving Runs – taxis

  32. Driving Runs – taxis • total taxi data collected: ~305,964 seconds (85 hrs) • average distance traveled for 7 hours: 131 km • average maximum speed: 107 kph • average moving time: 66% • average idle time: 34%

  33. Example Driving Runs – motorcycle

  34. Driving Runs – motorcycle • total motorcycle data collected: ~84,000 seconds (23 hrs) • average distance traveled for 7 hours: 33 km • average maximum speed: 62 kph • average moving time: 70% • average idle time: 30%

  35. Start Patterns of Vehicles • Vehicle Operating Characteristics Enunciators (VOCE) Units Installed On 76 Passenger Vehicles and Taxis. • Install on Tuesday, June 6th and remove on June 17th. • Maintain Log Of Vehicles Using VOCE

  36. General technology distribution B video A video C video

  37. Video Taping

  38. General technology distribution Video tape recording: 20 minutes, 7 times/day, 6 days = 14 hours  42 hours

  39. Specific technology distribution Fuel type Engine size Model year Manufacturer Model Mileage A/C Transmission Catalytic F/A system Maintenance Parking lot survey: 1600 passenger cars, YY taxis

  40. General technology distributionShanghai

  41. General technology distributionShanghai

  42. General technology distributionShanghai

  43. General technology distributionShanghai

  44. General Technology Distribution

  45. Technical Support from USEPA-through WRI

  46. USEPA provides us a great opportunities to get better understanding of emission from heavy duty vehicles in China

  47. Many Different Kinds of Trucks, Buses

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