EVALUATING THE ROLE OF LAND USE AND TRANSPORT POLICY
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Climate Change Targets and Urban Transport Policy WCTR SIG G3 Conference, April, 13-14 2015 – Valletta, Malta EVALUATING THE ROLE OF LAND USE AND TRANSPORT POLICY IN REDUCING TRANSPORT ENERGY DEPENDENCE OF A CITY Matteo Ignaccolo, Giuseppe Inturri, Michela Le Pira*, Salvatore Caprì, Valentina Mancuso University of Catania, Department of Civil Engineering and Architecture *mlepira@dica.unict.it Research objectives • Highlight the impact of transport on energy sustainability of urban areas • Set up a methodology to calculate a transport energy indicator to support the delivery of sustainable land use and transport urban plans • Test the methodology in a case study Research Question Methodology Case Study Results Conclusions 2 Transport Energy impacts • • • • 1/3 of energy 70% of oil 25% of CO2 emissions 2.5% average rate growth EU Energy and Transport in Figures St atistical Pocket book 2013 Research Question Methodology Case Study Results Conclusions 3 Transport Energy Efficiency 100% Fuel Accessories 2% Engine Engine losses 63% 18% EU Energy and Transport in Figures St atistical Pocket book 2010 Standby 17 % Aero 3% 13% Driveline losses 5% Rolling 4% Braking 6% INTRINSIC ENERGY INEFFICIENCY OF CARS less than 2% of consumed energy is used by the payload Research Question Methodology Case Study Results Conclusions 4 How to reduce urban transport energy demand Land Use Efficiency Vehicle Efficiency Land Use Et kWh Persons pax Dis tan cekm Vehicle _ unit _ consumption kWh / km Vehicle _ capacity pax Load _ factor % Spatial Interactions Transport System Efficiency Transportation Network Research Question Methodology Case Study Results Conclusions 5 Urban density and transport energy Hong Kong Houston Research Question Methodology Case Study Results Conclusions 6 Average Density vs Spatial Dynamics Research Question Methodology Radial monocentric Random polycentric Urban village polycentric Random and radial mono-polycentric Case Study Results Conclusions 7 Literature approach output Statistical (data) Actual energy consumed Modelling (simulations) Ideal energy consumed topic Transport energy indicator indicator output reference CEP Commute-Energy Performance index Actual energy Boussauw and Witlox (2009) IPE Energy Performance Index Actual energy Reiter and Marique (2012) TES Transport Energy Specification Ideal energy Saunders et al. (2008) TED Transport Energy Dependence Ideal energy Inturri et al. (2014) Research Question Methodology Case Study Results Conclusions 8 Land Use- Transport – Energy models PLANNING SCENARIO -Zoning -Residents by zone -Activities by zone -Demand flows -Road network -Pedestrian netw. -Cycling Network -Transit network LAND USE MODEL TRANSPORT MODEL -Vehicle by fuel type -Vehicle Energy Eff. ENERGY MODEL Land Use -Min distance by mode -Transit network density by zone -Travel behaviour criteria NO IMPLEMENT SCENARIO TED<TED* Spatial Interactions YES MODE CHOICE MODEL OPTIMAL DISTRIBUTION ASSIGNMENT Transportation Network Research Question Methodology Case Study TRANSPORT ENERGY DEPENDENCE (TED) Results Conclusions 9 Transport mode choice model dod=shortest path dod < dw yes walking distance TRANSIT DENSITY THRESHOLD no dod < dc yes cycling distance BUS 6.67 km/km2 LRT 3.30 km/km2 METRO 2.50 km/km2 no transit network density > threshold yes car distance no Research Question transit distance Methodology Choice Walking Cycling Regular Bus Transit Bus Rapid Transit Metro Transit Case Study <500m <1000m <300+300m <600+600m <800+800m Results Distance dod dod Stop access/egress Stop access/egress Stop access/egress Conclusions 10 Optimal demand flows assignment m n min Z cij xij i 1 j 1 n x j 1 ij si m x i 1 ij dj xij 0 Hillier and Lieberman, 2001 Research Question Methodology Case Study Results Conclusions 11 Transport Energy Dependence c ij X ijopt p p tpod number of trips assigned from zone o to zone d for the travel purpose p to minimize Z (passengers) lod shortest distance between zone o and zone d (km) ev unit energy consumption of the chosen transport mode (kWh/km) cv capacity of the vehicle (spaces) LFv load factor (passengers/spaces) Mode of transport Unit energy consumption kWh/pax-km Private Car Regular Bus Transit Bus Rapid Transit Metro Transit 0.917 0.325 0.192 0.133 Kenworthy (2003) Research Question Methodology Case Study Results Conclusions 12 Model description Mode choice Travel distances Distance matrix (nxn) Flow matrix (nxn) Optimal demand flow assignment Energy matrix (nxn) Research Question Methodology Case Study Results Conclusions 13 Optimal demand flows assignment (n zones) Travel distances Mode choice dod=shortest path yes dod < dw nxn matrices walking distance Distance matrix Energy matrix [km] [kWh/pax/t] no yes dod < dc Flow matrix [pax/t] ? cycling distance no yes transit network density > threshold no Research Question transit distance Optimal demand flow assignment min(Total energy) Total energy [kWh]= flow matrix*energy matrix [pax/t*kWh/pax/t] car distance Methodology Case Study Results Conclusions 14 Case Study - Catania 300,000 inh. municipality 750,000 inh. greater metropolitan area 7,000 inh/km2 in the urban area 45 km2 in the urban area Research Question Methodology Case Study Results Conclusions 15 public services population Research Question Methodology Case Study jobs Results Conclusions Private transport demand Research Question Public transport demand Methodology Case Study Results Conclusions Mode share Worker mode share in Sicily (ISTAT, 2014) Student mode share in Sicily (ISTAT, 2014) walking 31% 41% 8% walking 14% 7% public transport private car (driver) private car (driver) 23% public transport private car (passenger) private car (passenger) 71% 5% 50.0 40.0 30.0 20.0 10.0 0.0 walking public transport private car (driver) 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 % mode share Evolution of student mode share in Sicily (1994-2014) year private car (passenger) other private vehicle 18 Car ownership rate (cars per 100 inh.) 45,00 Bari Italia Media Roma Catania Cagliari Torino Palermo Milano Napoli Europa 50,00 Genova 55,00 Bologna Trieste 60,00 Firenze 65,00 Venezia 100 inh. Car per autovetture per 100 abitanti 70,00 Reggio Calabria 75,00 Messina Car ownership rate of Italian metropolitan areas tasso di motorizzazione aree metropolitane di Italia ISTAT, 2012 40,00 Research Question Methodology Case Study Results Conclusions Ingoing flows 20.000 veh/h 50% of internal flow Peak hour traffic flow Research Question Road capacity saturation Methodology Case Study Results Conclusions Catania Transport Model • Transport demand: study flows (5 home-to-school trips/week) • Transport supply: - the road network, composed of 516 nodes and 1122 links; - the transit network considers 49 bus lines, 4 BRT lines and 1 metro line. • PTV VISUM software package: shortest paths by mode between all origin and destination pairs by all modes of transport option of transit intermodality Research Question Methodology Shortest path by car Shortest path by transit Case Study Results Conclusions 21 Catania Land use Model Zoning Schools (blue) and university sites (black) kindergarten primary school lower secondary school upper secondary school Student population Research Question Methodology Residents aged 3-18 82,000 University students 44,000 N of schools 148 University sites 16 Case Study Results Conclusions 22 Transport mode choice model dod=shortest path no dod < dw Kindergarten and primary school Lower and upper secondary school, university yes walking distance no dod < dc yes cycling distance Choice Walking Cycling Regular Bus Transit Bus Rapid Transit Metro Transit no transit network density > threshold Transit network yes car distance no Research Question transit distance Methodology Regular Bus Transit Bus Rapid Transit Metro Transit Case Study <500m <1000m <300+300m <600+600m <800+800m Distance dod dod Stop access/egress Stop access/egress Stop access/egress Maximum walking distance (m) Transit density threshold (km/km2) 300 6.67 600 3.30 800 2.50 Results Conclusions 23 Scenarios Scenario 2: Scenario 0 Transit network Introducing 4 BRT lines and 1 Metro line Road network Scenario 1: improving PT accessibility Density threshold: 1026 PT-covered-zones Scenario 3: Comprehensive schools 5km/km2 comprehensive schools (red) and university sites (black) (scenario 3) Research Question Methodology Case Study Results Conclusions 24 Results (1/2) Research Question Methodology Case Study Results Conclusions 25 Results (2/2) Car Bus BRT Metro Sc.0 Sc.1 tCO2eq/stud/year 0.191 0.158 Research Question Methodology Case Study Results Sc.2 Sc.3 0.155 0.143 Conclusions 26 Transport Energy Dependence for different purposes TEDstudy (kWh/student/year) Research Question Methodology TEDwork(kWh/worker/year) Case Study Results Conclusions 28 Conclusions (1/2) • Method that integrates land use, transport and energy models to evaluate the Transport Energy Dependence (TED) of a city • Case study of the urban area of Catania to evaluate the transport energy required for home-to-school/university trips and to assess the impacts of different planning scenarios • Results show that land use and transport improvements cause a reduction in the transport energy dependence and in the GHG emissions • Methodology suitable to evaluate the potential impact of land use, transport and energy policies Research Question Methodology Case Study Results Conclusions 29 29 Conclusions (2/2) Research Question Methodology Case Study Results Conclusions 30 ACKNOWLEDGMENTS http://www.special-eu.org/ Contacts: Giuseppe Inturri (ginturri@dica.unict.it); Matteo Ignaccolo (matig@dica.unict.it) 31 Thank you for your attention! Michela Le Pira mlepira@dica.unict.it 32
