Methodology for incorporating modal choice behaviour in bottom-up energy system models

Transcription

1 Methodology for incorporating modal choice behaviour in bottom-up energy system models ETSAP Meeting Madrid, 17-18/11/ Jacopo Tattini, DTU Management Kalai Ramea, UCDavis Maurizio Gargiulo, E4SMA Sonia Yeh, Chalmers University Kenneth Karlsson, DTU Management

2 Modal choice in energy and transport models Model Model type Modeling approach Reference LANDSTRAFIK MODELLEN (LTM) 4-steps traffic simulation model: trip generation, trip distribution, modal choice, route assignment Multinomial logit model (MNL) based on many attributes: level of service and socio-ecoomic description of households Rich, 2015 MIT-EPPA Top-down, General equilibrium Constant elasticities of substitution (CES) to choose between purchased and own-supplied transport Karplus et al., 2013 REMIND-G Hybrid, General equilibrium Three-level nested CES Pietzcker et al., 2010 IMACLIM-R Hybrid, General equilibrium CES complemented by cost budget and time budget constraints Waisman et al., 2013 CIMS Hybrid, General equilibrium MNL model based on travel time, travel cost and LoS (pick-up/drop-off time, walking/waiting time, number of transfers and bike route access) Horne et al., 2005 GCAM Bottom-up, Partial equilibrium simulation Logit model based on the cost of the alternative transport services, on the wage rates and speeds Kyle & Kim, DTU Management Engineering, Technical University of Denmark

3 Modal choice in energy and transport models Model Model type Modeling approach Reference PRIMES-TREMOVE Bottom-up optimization + Partial equilibrium simulation TRAVEL-TIMER Bottom-up simulation + Partial equilibrium simulation UKTCM-MARKAL Bottom-up optimization + Partial equilibrium simulation MESSAGE-MACRO Bottom-up optimization + Top-down simulation TIMES-Ireland & California-TIMES Bottom-up optimization PRIMES linked to an external transport model that determines modal shares via CES TIMES linked to an external transport model that determines modal shares via NMNL MARKAL linked to an external transport model that determines modal shares via elasticities MESSAGE linked to an external transport model that determines modal shares via MNL TIMES model striucture changed to include travel time budget (TTB) and travel time investments (TTI) E3MLab, 2014 Girod et al., 2012 Brand et al., 2012 McCollum et al., Daly et al, 2014 ESME Bottom-up optimization Travel time budget, modal shift potential and rate incorporated Pye & Daly, 2015 For more info: Venturini et al., Improvements in the representation of behaviour in integrated energy and transport models, DTU Management Engineering, Technical University of Denmark

4 Variables for modal choice in LTM Walk_Specific_Constant Walk_Calibration Bike_Specific_Constant Bike_Calibration Walk_TravelTime WALK Utility_Walk Bike_TravelTime BIKE Utility_Bike Car_Congestion_Time Car_Travel_Cost Dummy_Internal CAR Dummy_Region Utility_Car Dummy_Internal Dummy_Urban PUBLIC Dummy_Region Important having a transport model with modal choice that supports the Car_Specific_Constant Car_Calibration Public_Calibration parameterization of modal choice in the BU energy system model LTM Car_Free_Time LTM determines modal shares with MNL Public_In-vehicle_Time model comparing utility functions of modes. Public_Change_Time Public_Wait_Time Public_Walk_Time Public_Travel_Cost Utility_Public Dummy_Internal Dummy_Region Dummy_Midage Dummy_Parking_Cost Dummy_Car_Ownership Dummy_Destination_CPH Dummy_Internal Dummy_Region Dummy_Gender Dummy_Access_Time Dummy_Connector_Time Dummy_Waiting_Time Dummy_Destination_CPH Dummy_Children 4 DTU Management Engineering, Technical University of Denmark

5 Methodology description Purpose: improving behavioural realism of modal shift in BU optimization models Two steps: -Divide transport users into sets of heterogeneous agents -Incorporate intangible costs Methodology overcomes mean-representative decision agent Approach insipired from MESSAGE-TRANSPORT (McCollum et al., ) and COCHIN-TIMES (Bunch et al., 2015) Simulation model LTM required for correct parametrization 5 DTU Management Engineering, Technical University of Denmark

6 Introducing transport users heterogeneity Heterogeneity differentiates intangible costs among subgroups of transport users Dimensions for split determined by empirical evidence based on previous work by LTM transport model. Two dimensions: -Type of urbanization: DKW/DKE, Rural/Suburban/Urban -Income class: 4 levels In the LTM population synthetizer (from TU survey), the split by income crosses with the split by residential area. 6 DTU Management Engineering, Technical University of Denmark

7 Heterogeneity by type of urbanization Based on Origin-Destination (OD) matrix, from the LTM In LTM 907 areas, each one labelled as: Urban, Rural, Suburban (U/R/S) From OD matrix we know the total amount of pkm originated and destined to each of the 907 areas Thanks to U/R/S label, we know how the total travel demand is distributed across the types of urbanization Such a split allows considering spatial differences and differentiate w.r.t access to modes and level of service 7 DTU Management Engineering, Technical University of Denmark

8 Heterogeneity by income The travel demand is split by income classes in order to differentiate the Value of Time (VoT) 8 DTU Management Engineering, Technical University of Denmark

9 Demand segmentation Only some modes available and have different levels of service. Different evaluations of levels of service. 9 DTU Management Engineering, Technical University of Denmark

10 Introducing intangible costs Behavioural preferences are caught through monetization Different propensity towards mode adoption across heterogeneous transport users is captured through intangible costs Intangible costs vary over consumer group, mode and year Intangible costs shall be the same as in LTM: need correspondance between groups in LTM and in TIMES 10 DTU Management Engineering, Technical University of Denmark

11 Generalized price per mode The generalized price per mode Pm,cg,y consists of fuel price FPm,cg,y, non-fuel price NFPm,cg,y and value of time component [DKK/pkm]. Varies across income classes Varies across types of urbanisation 11 DTU Management Engineering, Technical University of Denmark

12 Travel Time Budget (TTB) To ensure consistency with historically observed travel time per-capita, a constraint on the total Travel Time Budget in the system is imposed Rationale: empirical observations (Schäfer and Victor, 2000) In Denmark: 55 minutes/day per-capita (TU survey) 12 DTU Management Engineering, Technical University of Denmark

13 Overall new model structure TIME Fuels Attribute Demands CG1 CG2CG3 CG4 CG5CG6 CG7CG8 CG9. Fossil... Flow Cost CG1 Travel Time Budget CAR PUBLIC BUS COACH REGIONAL TRAIN MOTO MOPED LIGHT TRAIN METRO Flow Cost CG2 Flow Cost CGn Flow Cost CG1 Flow Cost CG2 Flow Cost CGn Flow Cost CG1 BIKE WALK Flow Cost CG2 Flow Cost CGn 13 DTU Management Engineering, Technical University of Denmark

14 Conclusions Methodology allows incorporating modal choice in bottom-up linear optimization models New attributes introduced: TTB, geographical/income split of the demand, modal accessibility, level of service Through heterogeneity of transport users each consumer group has specific preferences. Winner-takes-all behaviour of the model avoided Many data and assumptions are required Simulation model required for calibration of parameters LTM 14 DTU Management Engineering, Technical University of Denmark

15 QUESTIONS, SUGGESTIONS?!?! Jacopo Tattini 15 DTU Management Engineering, Technical University of Denmark

16 16 DTU Management Engineering, Technical University of Denmark

17 Bibliography Brand, C., Tran, M., Anable, J. (2012). The UK transport carbon model: An integrated life cycle approach to explore low carbon futures. Energy Policy 41, pp Daly, H. E., Ramea, K., Chiodi, A., Yeh, S., Gargiulo, M., Gallachóir, B. Ó. (2014). Incorporating travel behaviour and travel time into TIMES energy system models. Applied Energy 135, pp E3MLab/ICCS at National Technical University of Athens (2014). PRIMES-TREMOVE Transport Model, Detailed model description. Girod, B., van Vuuren, D. P., Deetman, S. (2012). Global travel within the 2 C climate target. Energy Policy 45, pp Horne, M., Jaccard, M., Tiedemann, K. (2005). Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions. Energy Economics 27(1), pp Karplus, V. J., Paltsev, S., Babiker, M., Reilly, J. M. (2013). Applying engineering and fleet detail to represent passenger vehicle transport in a computable general equilibrium model. Economic Modelling 30(216), pp Kyle, P., & Kim, S. H. (2011). Long-term implications of alternative light-duty vehicle technologies for global greenhouse gas emissions and primary energy demands. Energy Policy 39(5), pp McCollum, D. L., Wilson, C., Pettifor, H., Ramea, K., Krey, V., Riahi, K., Bertram, C., Lin, Z., Edelenbosch, O. Y., Fujisawa, S. (). Improving the behavioral realism of global integrated assessment models: An application to consumers vehicle choices. Transportation Research Part D: Transport and Environment, Pietzcker, R., Moll, R., Bauer, N., Luderer, G. (2010). Vehicle technologies and shifts in modal split as mitigation options towards a 2 C climate target. Conference talk at the International Society for Ecological Economics (ISEE) 11th BIENNIAL CONFERENCE Oldernburg. Pye, S., & Daly, H. (2015). Modelling sustainable urban travel in a whole systems energy model. Applied Energy 159, pp Rich, J., Nielsen O.A., Brems, C., Hansen, C.O. (2010). Overall design of the Danish National transport model, Annual Transport Conference at Aalborg University 2010 Schäfer, A., & Victor, D. G. (2000). The future mobility of the world population. Transportation Research Part A: Policy and Practice 34(3), pp Waisman, H. D., Guivarch, C., Lecocq, F. (2013). The transportation sector and low-carbon growth pathways: modelling urban, infrastructure, and spatial determinants of mobility. Climate Policy 13(sup01), pp DTU Management Engineering, Technical University of Denmark

18 Split by type of urbanization From the OD matrix of the LTM I can divide the total land travel demand in the following groups: (departing-to) DKW R-DKW R DKW R-DKW S DKW R-DKW U DKW S-DKW R DKW S-DKW S DKW S-DKW U 9 demand groups DKW U-DKW R DKW U-DKW S DKW U-DKW U DKE R-DKE R DKE R-DKE S DKE R-DKE U DKE S-DKE R DKE S-DKE S DKE S-DKE U 9 demand groups DKE U-DKE R DKE U-DKE S DKE U-DKE U DKW R-DKE R DKW R-DKE S DKW R-DKE U DKW S-DKE R DKW S-DKE S DKW S-DKE U 9 demand groups DKW U-DKE R DKW U-DKE S DKW U-DKE U The trips to and back (tour) are all allocated to same group to ensure that same mode is used. DKE R-DKW R DKE R-DKW S DKE R-DKW U DKE S-DKW R DKE S-DKW S DKE S-DKW U 9 demand groups DKE U-DKW R DKE U-DKW S DKE U-DKW U Total: 36 demand segments 18 DTU Management Engineering, Technical University of Denmark

19 Modal choice model in LTM The probability of choosing mode m in zone d among j alternatives is calculated with multinomial logit model (NML): Expression of the utility function: The inputs required to the model are: - Alternative specific constant kj - Parameters βk - Exogenous variables xd,j,1 xd,j,k 19 DTU Management Engineering, Technical University of Denmark

20 Variables for modal choice in LTM 20 DTU Management Engineering, Technical University of Denmark

21 Variables for modal choice in LTM 21 DTU Management Engineering, Technical University of Denmark

22 Further info Possible different metrics to segment population in addition to type of urbanization and income class: trip purpose (business/not-business) and car ownership level (else this is set as a constraintper each area, from LTM) Incorporate infrastructure constraints for each zone R/S/U: for both fuel and road/railway (infrastructure availability is usually considered an important variable) -Road: data of flow on roads from StatisticDenmark, km of road can be found and cost of road is divided among U/R/S -Rail: from LTM? -Fuel infrastructure has different densities in different areas Incorporate other main drivers of modal choice (dummy in LTM): number of license holder, income, age Incorporate other discomfort costs grounded on empirical evidence: flexibility, isolation (private space), comfort. They are not included in LTM (difficult to parametrize) 22 DTU Management Engineering, Technical University of Denmark

23 Further info Relative shares of the demand segments (corresponding to consumer groups obtained crossing income and type of urbanization) have to be projected over time. This means that every year the initial total demand is multiplied by the shares in order to get a set of transport demand projections per each consumer group. Each demand segment can be satified by the same portfolio of modes. Modes can be exactly the same across demand segments or slightly variated across groups: for instance, mileage of bike, walk and car can change depending on the income group if a correlation is found Some modes are not available for trips longer than a given threshold 23 DTU Management Engineering, Technical University of Denmark