Gdansk Meeting June Scenarios and the Integration of Models Cascading models

Gdansk Meeting June An overview

Gdansk Meeting June Model Cascading Traffic loads Emission rates (TREM) Emissions due to sectors: transport Residential industry Pollution Models: VADIS & OFIS & ISC3 Energy use Technology ranking Energy:MARKAL Energy prices Technology availability Pollutant concentrations Journey travel time Congestion Transport Demand Modal choices Traffic:VISUM OD matrices Land use Uncertainty Demography Econ. growth Scenario description

Gdansk Meeting June Steps 1.Design O/D matrices based on scenarios 2.Run traffic assignments (VISUM : TRAFFIC) 3.Input transport demands in Energy model (MARKAL- Lite : Energy) 4.Input emission rates for technologies (TREM) 5.Obtain technology mix and total emissions of precursors 6.Run global O3 model with total emissions (OFIS : Regional O3 model) 7.Run local pollution models with transport technology mix (VADIS : local pollution model) 8.Compute indicators 9.City comparisons and benchmarking 10.Policy analysis

Gdansk Meeting June Steps 1.Design O/D matrices based on scenarios 2.Run traffic assignments (VISUM : TRAFFIC) 3.Input transport demands in Energy model (MARKAL- Lite : Energy model) 4.Input emission rates for technologies (TREM) 5.Obtain technology mix and total emissions of precursors 6.Run global O3 model with total emissions (OFIS) 7.Run local pollution models with transport technology mix (VADIS) 8.Compute indicators 9.City comparisons and benchmarking 10.Policy analysis

Gdansk Meeting June Drivers for the traffic model (VISUM) O/D matrices Modal choices PTV method –Land use  OD matrix Alternative methods…

Gdansk Meeting June Transport vehicle link results

Gdansk Meeting June Steps 1.Design O/D matrices based on scenarios 2.Run traffic assignments (VISUM) 3.Input transport demands in Energy model (MARKAL- Lite) 4.Input emission rates for technologies (TREM) 5.Obtain technology mix and total emissions of precursors 6.Run global O3 model with total emissions (OFIS) 7.Run local pollution models with transport technology mix (VADIS) 8.Compute indicators 9.City comparisons and benchmarking 10.Policy analysis

Gdansk Meeting June Drivers of the emissions model (TREM) ItemProvided by Traffic volumeTRAFFIC Vehicle speedTRAFFIC Cold start emissions Information needed is provided by TRAFFIC Distribution of vehicles by categories passenger cars, LDV, HDV, etc.) ENERGY Model Distribution of vehicles by classes (based on age and technology) ENERGY Model

Gdansk Meeting June Steps 1.Design O/D matrices based on scenarios 2.Run traffic assignments (VISUM) 3.Input transport demands in Energy model (MARKAL- Lite) 4.Input emission rates for technologies (TREM) 5.Obtain technology mix and total emissions of precursors (MARKAL-Lite) 6.Run global O3 model with total emissions (OFIS) 7.Run local pollution models with transport technology mix (VADIS) 8.Compute indicators 9.City comparisons and benchmarking 10.Policy analysis

Gdansk Meeting June Drivers for the energy model (MARKAL) Energy prices –(constant values used for all periods) Useful demands Available technology –(using the needed energy characteristics parameters)

Gdansk Meeting June Coupling Energy and Traffic Public transport - base case

Gdansk Meeting June Steps 1.Design O/D matrices based on scenarios 2.Run for traffic assignments (VISUM) 3.Input transport demands in Energy model (MARKAL-Lite) 4.Input emission rates for technologies (TREM) 5.Obtain technology mix and total emissions of precursors 6.Run global O3 model with total emissions (REGIONAL O3 model) 7.Run local pollution models with transport technology mix (VADIS) 8.Compute indicators 9.City comparisons and benchmarking 10.Policy analysis

Gdansk Meeting June Emission rates for Geneva Moles/sec/km 2

Gdansk Meeting June Drivers of the ozone model (OFIS) Geneva example Emissions: The emissions are based on the SEDE emissions inventory produced in Meteorology: Wind direction and speed … Boundary conditions: The boundary conditions data comes from the measurements station of Chaumont, located on the "Plateau Suisse"

Gdansk Meeting June OFIS simulation Four city examples

Gdansk Meeting June Steps 1.Design O/D matrices based on scenarios 2.Run for traffic assignments (VISUM) 3.Input transport demands in Energy model (MARKAL-Lite) 4.Input emission rates for technologies (TREM) 5.Obtain technology mix and total emissions of precursors 6.Run global O3 model with total emissions (OFIS) 7.Run local pollution models with transport technology mix (LOCAL POLLUTION MODEL) 8.Compute indicators 9.City comparisons and benchmarking 10.Policy analysis

Gdansk Meeting June TREM  VADIS

Gdansk Meeting June MARKAL  VADIS

Gdansk Meeting June Drivers of the canyon model (VADIS : LOCAL Pollution Model) The building situation Emission sources coordinates definition The meteorological conditions Description and the CO emissions characterization

Gdansk Meeting June Steps 1.Design O/D matrices based on scenarios 2.Run for traffic assignments (VISUM) 3.Input transport demands in Energy model (MARKAL-Lite) 4.Input emission rates for technologies (TREM) 5.Obtain technology mix and total emissions of precursors 6.Run global O3 model with total emissions (OFIS) 7.Run local pollution models with transport technology mix (VADIS) 8.Compute indicators 9.City comparisons and benchmarking (See ESS site : Benchmarking) 10.Policy analysis

Gdansk Meeting June Publications Caratti P., Haurie A., Pinelli D., Zachary D.S. Exploring the fuel cell car future: an integrated energy model at the city level. The Ninth International Conference on Urban Transport and the Environment 2003, Crete, Greece. Borrego C., Miranda A.I., Valente J., Lopes M., Couto J.M., Haurie A. & Drouet L. Studying the impact of urban sustainable transportation on Lisbon air quality. To appear in proceedings of AIR POLLUTION 2003, Eleventh International Conference on Modelling, Monitoring and Management of Air Pollution September 2003 Catania, Italy

Gdansk Meeting June Model Cascading Traffic loads Emission rates (TREM) Pollution Models Emissions due to sectors: transport Residential industry Energy use Technology ranking Energy Energy prices Technology availability Pollutant concentrations Journey travel time Congestion Transport Demand Modal choices Traffic OD matrices Land use Uncertainty Demography Econ. growth Scenario description

Gdansk Meeting June

Gdansk Meeting June MARKAL  VADIS Lisbon Data

Gdansk Meeting June VISUM  MARKAL

Gdansk Meeting June Coupling MARKAL and Traffic Private transport – base case

Gdansk Meeting June

Gdansk Meeting June OFIS scenario results for Geneva

Gdansk Meeting June Coupling MARKAL and Traffic Public Transport – 4 scenarios Public Transport decomposition Market

Gdansk Meeting June Coupling MARKAL and Traffic Private Transport – 4 scenarios

Gdansk Meeting June Traffic Scenarios results for Geneva

Gdansk Meeting June Indicators : TREM

Gdansk Meeting June Indicator : VADIS State indicators: NO x peak concentration (mg/m3): 138,8 CO peak concentration (mg/m3): 1582,4

Gdansk Meeting June Emission rates… example European standards for 9 MARKAL periods TEE1CD 0.1 TEE1CG 0.23 TEE2CD 0.1 TEE2CG TEE3CD TEE3CG TEE4CD TEE4CG

Gdansk Meeting June Import energy prices for MARKAL Fuel/Pe riod BIG26 COA3.74 DSL8.63 DST9.643 ETH7.022 GSL GSW HDG24 LPG MET8.152 MSW0.1 NGA NGFCC7.63 NGI WOR26

Gdansk Meeting June

Gdansk Meeting June Building situation in VADIS domain

Gdansk Meeting June Distribution of vehicles by Categories

Gdansk Meeting June Coupling TREM with the traffic model

Gdansk Meeting June Coupling Pollution models with Traffic and Energy model

Gdansk Meeting June Indicators : from traffic model –Pressure indicators: private transport: Passenger transport demand (pkm per year) public transport: –State indicators: Crowding (hours in an overcrowded public transport: ????? Traffic jams (hours spent in traffic jams): ?????

Gdansk Meeting June VADIS domain

Gdansk Meeting June Scenario comparison with OFIS

Gdansk Meeting June OFIS scenarios for Geneva Max ave sub townE120 Dom Scen ref Scen Scen Scen Scen AOT

Gdansk Meeting June Coupling VISUM and MARKAL Public transport (Train) Private transport (Tramway) Private transport BAUScenario 1 Scenario 2 Scenario 3 Scenario 4 Density-highLowHighLow Demography-highHighLow Population in Active population in