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

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

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) basedon many attributes: level of serviceand 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


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


via NMNL

Girod et al., 2012

UKTCM-MARKAL Bottom-up optimization +

Partial equilibrium

simulation

MARKAL linked to an external transport


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


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_InternalDummy_Region

Utility_Car

Car_Free_Time

Car_Specific_Constant Car_Calibration

Car_Travel_Cost

Dummy_Parking_CostDummy_Car_OwnershipDummy_Destination_CPHDummy_Midage

Car_Congestion_TimePUBLIC

Utility_Public

Public_Calibration

Public_In-vehicle_Time

Public_Travel_Cost

Dummy_InternalDummy_Region

Dummy_Connector_TimeDummy_Waiting_TimeDummy_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.


Methodology description

• Purpose: improving behavioural realism of modal shift in BU optimizationmodels

• 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

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 workby LTM transport model. Two dimensions:

-Type of urbanization: DKW/DKE, Rural/Suburban/Urban

-Income class: 4 levels

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In the LTM population synthetizer (from TU survey), the split by income crosses with the split by residential

area.


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

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• Such a split allows considering spatial differences and differentiate w.r.t access to modes and level of service


Heterogeneity by income

The travel demand is split by income classes in order to differentiate the Value of Time (VoT)

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Demand segmentation

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Only some modes available and have different levels of

service.

Different evaluations of

levels of service.


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 betweengroups in LTM and in TIMES

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Generalized price per mode

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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].


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)

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Overall new model structure

CAR

MOTO

PUBLIC BUS

COACH

MOPED

LIGHT TRAIN

METRO

BIKE

WALK

REGIONAL TRAIN

Fossil

FuelsDemands

TIME

Travel Time Budget

...

CG1 CG2CG3 CG4 CG5CG6 CG7CG8 CG9 …….

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


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

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DTU Management Engineering, Technical University of Denmark15

Jacopo [email protected]

…QUESTIONS, SUGGESTIONS?!?!


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.


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

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The trips to and back (tour) are all allocated to same group to ensure that same mode is used.


• 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


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 bothfuel 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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Further info

• Relative shares of the demand segments (corresponding to consumergroups obtained crossing income and type of urbanization) have to beprojected over time. This means that every year the initial total demandis multiplied by the shares in order to get a set of transport demandprojections per each consumer group. Each demand segment can besatified by the same portfolio of modes.

• Modes can be exactly the same across demand segments or slightlyvariated across groups: for instance, mileage of bike, walk and car canchange 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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Methodology for incorporating modal choice behaviour in bottom-up energy system models

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