Report

Traffic Model System and Emission Calculations of the Helsinki Metropolitan Area Council Timo Elolähde 1 General information about the area Timo Elolähde 2 Location of Helsinki in Europe Timo Elolähde 3 Definitions of areal divisions YTV area includes the cities of Helsinki, Espoo, Vantaa and Kauniainen. Surrounding areas include eight municipalities around the YTV area. Helsinki region = YTV area + surrounding area = 12 municipalities Metropolitan area is used to describe an area contained within approximately a 100 kilometre radius from Helsinki. It consists of 72 municipalities. Timo Elolähde 4 Commuting in the Helsinki Metropolitan Area 1980–2002 PKS YTV Hämeenlinna Lahti 47,000 commuters Mikkeli in 1980 Kotka Tammisaari Proportion of commuters in the municipality’s work force Hämeenlinna YTV 88,000 commuters in 1990 Over 35 % 10 - 35 % 2 - 10 % Tampere Lahti Kotka Turku Tammisaari YTV 108,000 commuters in 2002 Timo Elolähde 5 Population and the number of jobs in the YTV Area 980 300 185 400 95 000 800000 600000 570 700 Vantaa 227 400 104 000 400000 559 000 Espoo Helsinki 200000 369 000 0 8 500 2 700 Population Jobs 31.12.2004 31.12.2003 in YTV area Kauniainen Timo Elolähde 6 YTV area target network in 2030 Timo Elolähde 7 Journeys made daily by public transport and by car within the YTV area Journeys (1000/day) Share taken by public transport (%) 100 1 500 Private car 75 1 000 66 53 50 42 Public transport 500 39 39 (38) 25 0 1966 0 1976 1988 1995 2000 2005 Timo Elolähde 1966 1976 1988 1995 2000 2005 8 Traffic model system Timo Elolähde 9 Traffic model system Traffic is divided into three parts • internal trips made by the inhabitants of the region • trips generated by HelsinkiVantaa airport (air passengers and employees) • external trips (cars only) • freight transport (vans and lorries) •Modes • walk, bicycle • public transit • car (as driver or passenger) Timo Elolähde Trip categories • home-based work trips • home-based school trips • other home-based trips • non-home-based trips •Time periods • morning peak hour • average hour of the day • evening peak hour 10 Traffic model system Tools • Emme/2 macros (contain Unix file handling commands) • SAS programs (preparation of input, writing some macros) • FORTRAN programs (summary of results) • Unix scripts (renaming output files) Timo Elolähde 11 Feedback in the four-step model system internal trips airport trips trip generation trip generation destination choice destination choice mode choice mode choice external trips commercial trips total demand and route choice Timo Elolähde 12 Model types trip category trip generation mode choice destination choice home-based work trips trips / person working logit model logit model home-based school trips trips / person of school age distance table (distribution) logit model other homebased trips trips / inhabitant logit model logit model non-home-based trips trips / inhabitant logit model logit model Timo Elolähde 13 Logit model and logsum probability of alternative i Pi eVi J e . Vj j 1 J logsum = ln ( e j ) V j 1 where Vi 1 x1i 2 x 2i n x ni , where Vi j x jk = benefit function of alternative i coefficient of variable x j (j = 1,2, , n) value of variable x j in alternative k. Timo Elolähde 14 Variables used in models •Mode choice models • nr of transfers, transit • travel time, transit or car • travel cost, transit or car • parking place availability (arriving trips / parking place) • parking cost • cars/household • ln(distance), walk or bicycle • distance 0-5 km, walk or bicycle • distance 5-10 km, walk or bicycle • dummy variables Timo Elolähde •Destination choice models • logsum of mode choice • scale factor (inhabitants, jobs) • ln(jobs) • dummy variables 15 Mode combinations possible Influence of the number of modes (ms149, ms199, ms249, ms299) on text registers and description fields of matrices (e.g. ”morning peak %t2% work trips”) text register 3 4 -4 5 t1 Walk+bicycle Walk+bicycle Walk Walk t2 transit Bus+tram transit Bus+tram t3 Car Car Car Car t4 NO BIKE NO BIKE bicycle bicycle t5 NO RAIL Heavy rail NO RAIL Heavy rail Timo Elolähde 16 Principles applied in coding macros • The same selection of possible variables in all models (except school trips) • No constants in the model formulas but the coefficients of the models are in scalars • Systematics in matrix numbers • If a variable is not in the model, its coefficient is zero • Only the number of the first input matrix is given as a macro parameter, other consecutive numbers are calculated (e.g. nr of transfers in matrix %2%, transit time in matrix r2=%2%+1) • Logical scalars (school trip models in macro school_%ms250%.mac, where ms250=96 or ms250=2001) Timo Elolähde 17 Scalars containing the coefficients model destination destination destination mode mode mode mode mode mode mode mode variables logsum scale factor ln(jobs) dummy, walk dummy, bus+tram dummy, car travel cost, heavy rail travel cost, bus+tram travel cost, car parking ratio parking cost Timo Elolähde coefficients home-based other homework trips based trips ms106 ms156 ms107 ms157 ms108 ms158 ms110 ms160 ms111 ms161 ms112 ms162 ms115 ms165 ms116 ms166 ms117 ms167 ms118 ms168 ms119 ms169 18 non-homebased trips ms206 ms207 ms208 ms210 ms211 ms212 ms215 ms216 ms217 ms218 ms219 Writing an Emme/2 macro with a SAS program Why? Do you want to copy and paste this section 24 times and edit the parts which are underlined? 1 y ms311 y wt24h home-based work trips ~?q=1 y mf301 Solution: Give the changing part as data cards and write the rest of the macro with a SAS program (or with some programming language). Timo Elolähde y gn01,gn04 o + + ~?b=1 2 19 Essential parts of the SAS program filename outfi2 'K:\Emme2\summary_matr_demo2.mac'; data matr; length mxnro msnro $ 5 name $ 6 descr $ 40; input mxnro $ 4-8 msnro $ 10-14 name $ 16-21 descr $ 23-62 ; cards; mo09 ms301 nrinha total nr of inhabitants mf301 ms311 wt24h home-based work trips 24h ms999 last line ; data _null_; set matr; file outfi2; if _N_ = 1 then do; put "~#" / "~#** calculate sums of vectors" / " 3.21" ; end; Timo Elolähde 20 Essential parts of the SAS program nro = substr(msnro,3,3); if (nro ne '999') then do; put "~# *** matrix " _N_ " *** " ; put " 1" / " y" / msnro $ 2-6 / " y" / name $ 2-7 / descr $ 2-41 / "~?q=1" / " y" // mxnro $ 2-6 /// " y" ; if (substr(mxnro,1,2) = 'mo') then put " gn01,gn04" // " +" ; else if nro in ('311') then put " gn01,gn04" // " o" // " +" / " +" ; put "~?b=1" / " 2" ; end; if nro = '999' then do; put " q" / "~#** output the list of scalars" / " reports=summary_matr_demo.txt" / " 3.14" / " 2" / " ms" / "~?b=1" / " 2" / " q" / " reports=%1%" / "~/ *** summary_matr_demo.mac ***" ; end; run; Timo Elolähde 21 Estimation of models Timo Elolähde 22 Traffic surveys Internal trips • trips made by the inhabitants of the YTV area (four cities) during one day (24 h) in autumn 2000 • personal trip diary interview, 8,666 persons and 28,553 trips Trips generated by Helsinki-Vantaa airport • 875 air passengers and 801 employees (flying and non-flying) • survey made in autumn 2001 External trips and freight transport • origin-destination study made in autumn 1988 Timo Elolähde 23 Model estimation Internal trips • estimation made by Ms Nina Karasmaa (Helsinki University of Technology, Transportation Engineering) • Alogit program • More than 50 model sets were estimated and tested • Differences e.g. in number of modes and model hierarchy (mode choice after destination choice or vice versa) • Three modes in the model set selected. Trips generated by Helsinki-Vantaa airport • estimation made by Mr Jyrki Rinta-Piirto (Strafica Ltd) External trips and freight transport • models estimated in 1990 are based in changes in land use. Timo Elolähde 24 Emission calculations Timo Elolähde 25 ”Minor” problem in emission calculations • Traffic models produce demand matrices for three weekday hours. • Finnish Meteorological Institute needs emissions for every hour of the year for dispersion calculations. Timo Elolähde 26 Principle of emission calculations auto demand matrices (car+van, lorry) * morning peak * average hour of the day * evening peak transit demand matrices * morning peak * average hour of the day transit assignment (speed, @voltr) * two hours regression models emissions of bus links (fuel, CO2, SO2, CO, NOx, PM, HC) and rail links (@energy, CO2, NOx, PM) * morning peak * average hour of the day auto demand matrices * 10 weekday hours * 7 Saturday hours * 7 Sunday hours auto assignment (speed, volau, volad) * 10+7+7 hours regression models emissions of auto links and centroids (fuel, CO2, SO2, CO, NOx, PM, HC) * 10 weekday hours * 7 Saturday hours * 7 Sunday hours emissions of bus and rail links (CO2, SO2, CO, NOx, PM, HC) * 24 weekday hours * 24 Saturday hours * 24 Sunday hours copying or interpolation emissions of other hours * 14 weekday hours * 17 Saturday hours * 17 Sunday hours total emissions Timo Elolähde 27 Emission calculations Tools • Emme/2 macros (contain Unix file handling commands) • FORTRAN programs (copying or interpolation from link data of 10+7+7 hours to 14+17+17 hours and summary of results) • Unix scripts (dialog of FORTRAN run, renaming output files) Emission factors • fuel consumption, CO2, SO2, NOx, particles (PM), CO, HC • polynomial functions of average speed (from assignment) Timo Elolähde 28 Examples of emission factors: NOx emissions of cars and vans 1,4 emission (g/km/veh) 1,2 1 car kat 2005 car diesel 2005 0,8 van diesel 2000 0,6 car kat 2030 car&van diesel 2025 0,4 0,2 0 10 20 30 40 50 60 70 80 90 100 110 120 average speed (km/h) Timo Elolähde 29 Examples of emission factors: NOx emissions of trucks and buses 16 emission (g/km/veh) 14 12 trailer truck 2000 (EU 2) 10 single-unit truck 2000 (EU 2) bus 2000 (EU 2) 8 trailer truck 2025 (EU 5) single-unit truck 2025 (EU 5) bus 2025 (EU 5) 6 4 2 0 10 20 30 40 50 60 70 80 90 100 average speed (km/h) Timo Elolähde 30 Examples of emission factors: CO2 emissions of cars and vans 350 emission (g/km/veh) 300 250 car kat 2000 200 car diesel 2005 van diesel 2000 car kat 2025 150 car diesel 2030 van diesel 2025 100 50 0 10 20 30 40 50 60 70 80 90 100 110 120 average speed (km/h) Timo Elolähde 31 Examples of emission factors: CO2 emissions of trucks and buses emission (g/km/veh) 2500 2000 1500 trailer truck 2000&2025 single-unit truck 2000&2025 bus 2000&2025 1000 500 0 10 20 30 40 50 60 70 80 90 100 average speed (km/h) Timo Elolähde 32 Proportions of vehicle types in emission calculations (volau) 2005 cars and vans cars, non-kat cars, kat-1995 cars, kat-2020 cars, diesel 1995 cars, diesel 2020 vans, diesel 1995 vans, diesel 2020 total 0 80 4) 10 10 100 2030 0 0 85 5) 0 5 0 10 100 percentage in scalar ms80 ms81 ms82 ms83 ms84 ms69 ms85 4) emission factors for average petrol car in 2000 (43 % non-kat, 52 % EU0-2, 5 % EU3, 0 % EU4-5) 5) emission factors for average petrol car in 2025 ( 0 % non-kat, 0 % EU0-2, 25 % EU3, 75 % EU4-5) Timo Elolähde 33 Proportions of vehicle types in emission calculations (volad and bus) 2005 percentage in scalar 2030 trucks single-unit trucks EU 0-2 trailer combination trucks EU 0-2 single-unit trucks EU 4-5 trailer combination trucks EU 4-5 total 70 30 100 5 2 65 28 100 ms86 ms87 ms88 ms89 buses LPG or CNG buses buses in Helsinki EU 0-2 regional buses EU 0-2 buses in Helsinki EU 4-5 regional buses EU 4-5 100 100 - 0 0 100 100 ms81 ms83 ms82 ms84 Timo Elolähde 34 Regression models in emission calculations • The regression models have been estimated using volume counts on four cordon lines. • For auto assignment, the volumes (car+van and truck) for each hour of the day (10+7+7) are used as regressands and three forecasted hours (morning peak, evening peak and an average hour of the day) as regressors of the model. The models are used for calculating the demand matrices for each hour. • For transit assignment, the bus volumes for each hour of the day (3*24) are used as regressands and two forecasted hours (morning peak and an average hour of the day) as regressors of the model. The models are used for calculating the link volumes and emissions for each hour. Timo Elolähde 35 Emission calculations • emission on regular link [kg/h] = volume [veh/h] * length [km] * emission [g/km/veh] / 1000 • cold starts (three classes of motor temperature) and emissions of connector links handled as emissions of the area (in the centroid) • example of copying and interpolation of the emission (from 10+7+7 hours to 14+17+17 hours) hour 4am- 5am 5am- 6am 6am- 7am 7am- 8am 8am- 9am 9am-10am 10am-11am hour 4- 5 5- 6 6- 7 7- 8 8- 9 9-10 10-11 weekday emission EMIS_WD_23_5 EMIS_WD_23_5 (EMIS_WD_23_5 + EMIS_WD_7)/2. EMIS_WD_7 EMIS_WD_8 EMIS_WD_9_13 EMIS_WD_9_13 Timo Elolähde 36 Principle of emission calculations (repeated) auto demand matrices (car+van, lorry) * morning peak * average hour of the day * evening peak transit demand matrices * morning peak * average hour of the day transit assignment (speed, @voltr) * two hours regression models emissions of bus links (fuel, CO2, SO2, CO, NOx, PM, HC) and rail links (@energy, CO2, NOx, PM) * morning peak * average hour of the day auto demand matrices * 10 weekday hours * 7 Saturday hours * 7 Sunday hours auto assignment (speed, volau, volad) * 10+7+7 hours regression models emissions of auto links and centroids (fuel, CO2, SO2, CO, NOx, PM, HC) * 10 weekday hours * 7 Saturday hours * 7 Sunday hours emissions of bus and rail links (CO2, SO2, CO, NOx, PM, HC) * 24 weekday hours * 24 Saturday hours * 24 Sunday hours copying or interpolation emissions of other hours * 14 weekday hours * 17 Saturday hours * 17 Sunday hours total emissions Timo Elolähde 37 Thank you for your patience and interest! Any questions? Timo Elolähde 38