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

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

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

  1. 1. Methodology for incorporating modal choice behaviour in bottom-up energy system models ETSAP Meeting Madrid, 17-18/11/2016 Jacopo Tattini, DTU Management Kalai Ramea, UCDavis Maurizio Gargiulo, E4SMA Sonia Yeh, Chalmers University Kenneth Karlsson, DTU Management
  2. 2. DTU Management Engineering, Technical University of Denmark2 5 December 2016 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, 2011 Modal choice in energy and transport models
  3. 3. DTU Management Engineering, Technical University of Denmark3 5 December 2016 Model Model type Modeling approach Reference PRIMES-TREMOVE Bottom-up optimization + Partial equilibrium simulation PRIMES linked to an external transport model that determines modal shares via CES E3MLab, 2014 TRAVEL-TIMER Bottom-up simulation + Partial equilibrium simulation TIMES linked to an external transport model that determines modal shares via NMNL Girod et al., 2012 UKTCM-MARKAL Bottom-up optimization + Partial equilibrium simulation MARKAL linked to an external transport model that determines modal shares via elasticities Brand et al., 2012 MESSAGE-MACRO Bottom-up optimization + Top-down simulation MESSAGE linked to an external transport model that determines modal shares via MNL McCollum et al., 2016 TIMES-Ireland & California-TIMES Bottom-up optimization TIMES model striucture changed to include travel time budget (TTB) and travel time investments (TTI) Daly et al, 2014 ESME Bottom-up optimization Travel time budget, modal shift potential and rate incorporated Pye & Daly, 2015 Modal choice in energy and transport models For more info: Venturini et al., Improvements in the representation of behaviour in integrated energy and transport models, 2017
  4. 4. DTU Management Engineering, Technical University of Denmark4 5 December 2016 Variables for modal choice in LTM WALK Dummy_Internal Dummy_Region Utility_WalkWalk_TravelTime Walk_Specific_Constant Walk_Calibration BIKE Dummy_Internal Dummy_Region Utility_BikeBike_TravelTime Bike_Specific_Constant Bike_Calibration Dummy_Urban CAR Dummy_Internal Dummy_Region Utility_Car Car_Free_Time Car_Specific_Constant Car_Calibration Car_Travel_Cost Dummy_Parking_Cost Dummy_Car_Ownership Dummy_Destination_CPHDummy_Midage Car_Congestion_Time PUBLIC Utility_Public Public_Calibration Public_In-vehicle_Time Public_Travel_Cost Dummy_Internal Dummy_Region Dummy_Connector_Time Dummy_Waiting_Time Dummy_Destination_CPHDummy_Gender Dummy_Access_Time Dummy_Children Public_Change_Time Public_Wait_Time Public_Walk_Time Important having a transport model with modal choice that supports the parameterization of modal choice in the BU energy system model  LTM LTM determines modal shares with MNL model comparing utility functions of modes.
  5. 5. DTU Management Engineering, Technical University of Denmark5 5 December 2016 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., 2016) and COCHIN-TIMES (Bunch et al., 2015) • Simulation model LTM required for correct parametrization
  6. 6. DTU Management Engineering, Technical University of Denmark 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 6 5 December 2016 In the LTM population synthetizer (from TU survey), the split by income crosses with the split by residential area.
  7. 7. DTU Management Engineering, Technical University of Denmark 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 7 5 December 2016 • Such a split allows considering spatial differences and differentiate w.r.t access to modes and level of service
  8. 8. DTU Management Engineering, Technical University of Denmark Heterogeneity by income The travel demand is split by income classes in order to differentiate the Value of Time (VoT) 8 5 December 2016
  9. 9. DTU Management Engineering, Technical University of Denmark Demand segmentation 9 5 December 2016 Only some modes available and have different levels of service. Different evaluations of levels of service.
  10. 10. DTU Management Engineering, Technical University of Denmark 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 5 December 2016
  11. 11. DTU Management Engineering, Technical University of Denmark Generalized price per mode 11 5 December 2016 Varies across income classes Varies across types of urbanisation 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].
  12. 12. DTU Management Engineering, Technical University of Denmark 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 5 December 2016
  13. 13. DTU Management Engineering, Technical University of Denmark13 5 December 2016 Overall new model structure CAR MOTO PUBLIC BUS COACH MOPED LIGHT TRAIN METRO BIKE WALK REGIONAL TRAIN Fossil Fuels Demands TIME Travel Time Budget ... CG1 CG2CG3CG4CG5CG6CG7CG8CG9 ……. Flow Cost CG1 Flow Cost CG1 Flow Cost CG2 Flow Cost CG2 Flow Cost CGn Flow Cost CGn … .. Flow Cost CG1 Flow Cost CG1 Flow Cost CG2 Flow Cost CG2 Flow Cost CGn Flow Cost CGn … .. Flow Cost CG1 Flow Cost CG1 Flow Cost CG2 Flow Cost CG2 Flow Cost CGn Flow Cost CGn … .. Attribute
  14. 14. DTU Management Engineering, Technical University of Denmark 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 5 December 2016
  15. 15. DTU Management Engineering, Technical University of Denmark15 Jacopo Tattini jactat@dtu.dk …QUESTIONS, SUGGESTIONS?!?!
  16. 16. DTU Management Engineering, Technical University of Denmark16 5 December 2016
  17. 17. DTU Management Engineering, Technical University of Denmark17 5 December 2016 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. 107-124. • 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. 429-439. • 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. 152- 166. • 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. 59-77. • 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. 295-305. • 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. 3012-3024. • McCollum, D. L., Wilson, C., Pettifor, H., Ramea, K., Krey, V., Riahi, K., Bertram, C., Lin, Z., Edelenbosch, O. Y., Fujisawa, S. (2016). Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices. Transportation Research Part D: Transport and Environment, 1–10. • 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. 97-107. • 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. 171-205. • 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. 106-129.
  18. 18. DTU Management Engineering, Technical University of Denmark 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 • 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 5 December 2016 The trips to and back (tour) are all allocated to same group to ensure that same mode is used.
  19. 19. DTU Management Engineering, Technical University of Denmark • 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 5 December 2016 Modal choice model in LTM
  20. 20. DTU Management Engineering, Technical University of Denmark20 5 December 2016 Variables for modal choice in LTM
  21. 21. DTU Management Engineering, Technical University of Denmark21 5 December 2016 Variables for modal choice in LTM
  22. 22. DTU Management Engineering, Technical University of Denmark 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 5 December 2016
  23. 23. DTU Management Engineering, Technical University of Denmark 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 5 December 2016

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