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

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. 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. 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. 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. 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. 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. 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. 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)
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2016

9. DTU Management Engineering, Technical University of Denmark
Demand segmentation
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2016
Only some modes
available and have
different levels of
service.
Different
evaluations of
levels of service.

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. DTU Management Engineering, Technical University of Denmark
Generalized price per mode
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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. 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. 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. 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. DTU Management Engineering, Technical University of Denmark15
Jacopo Tattini
jactat@dtu.dk
…QUESTIONS, SUGGESTIONS?!?!

16. DTU Management Engineering, Technical University of Denmark16 5 December
2016

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. 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. 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
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Modal choice model in LTM

20. DTU Management Engineering, Technical University of Denmark20 5 December 2016
Variables for modal choice in LTM

21. DTU Management Engineering, Technical University of Denmark21 5 December 2016
Variables for modal choice in LTM

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)
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2016

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
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2016