This document proposes a methodology for incorporating modal choice behavior into bottom-up energy system models. The methodology introduces transport user heterogeneity by splitting users into groups based on urbanization type and income class. It also incorporates intangible costs to capture differences in preferences across groups. Demand is segmented and a generalized price is calculated for each mode, consumer group, and year. A travel time budget constraint is also included to ensure consistency with observed travel times. The methodology aims to improve behavioral realism over models that use a single representative decision-maker. It is inspired by other hybrid models and requires parameters from a transportation simulation model for calibration.
RES-T-NEXT, IEA RETD workshop in London, 26th August 2015IEA_RETD
IEA-RETD Report: Next Generation Policy Instruments for Renewable Transport (RES-T-NEXT)
David de Jager, Operating Agent IEA-RETD
The RES-T-Next project aims at providing an analysis of next generation RES-T policy instruments and recommendations regarding private and urban transport in order to increase the level of energy used from renewable sources and to decrease GHG emissions.
Shiyu Yan delivered this presentation at a joint ESRI-UCD conference tilted 'Energy research to enable climate change mitigation' on 17 September 2019.
Photos from the conference are available to view on the ESRI website here: https://www.esri.ie/events/esri-ucd-conference-energy-research-to-enable-climate-change-mitigation
SATIMGE-2020
Bruno Merven, Faaiqa Hartley, Andrew Marquard, Fadiel Ahjum, Bryce McCall, Alison Hughes,
Gregory Ireland, and Jesse Burton, Energy Systems Research Group, University of Cape Town
RES-T-NEXT, IEA RETD workshop in London, 26th August 2015IEA_RETD
IEA-RETD Report: Next Generation Policy Instruments for Renewable Transport (RES-T-NEXT)
David de Jager, Operating Agent IEA-RETD
The RES-T-Next project aims at providing an analysis of next generation RES-T policy instruments and recommendations regarding private and urban transport in order to increase the level of energy used from renewable sources and to decrease GHG emissions.
Shiyu Yan delivered this presentation at a joint ESRI-UCD conference tilted 'Energy research to enable climate change mitigation' on 17 September 2019.
Photos from the conference are available to view on the ESRI website here: https://www.esri.ie/events/esri-ucd-conference-energy-research-to-enable-climate-change-mitigation
SATIMGE-2020
Bruno Merven, Faaiqa Hartley, Andrew Marquard, Fadiel Ahjum, Bryce McCall, Alison Hughes,
Gregory Ireland, and Jesse Burton, Energy Systems Research Group, University of Cape Town
DYNAMIC RESOURCE ALLOCATION IN ROAD TRANSPORT SECTOR USING MOBILE CLOUD COMPU...IAEME Publication
Literature review revealed application of various techniques for efficient use of existing resources in road transport sector vehicles, operators and related facilities. This issue assumes bigger dimensions in situations where there are multiple routes and the demand in the routes is highly fluctuating over the day. The application of the existing techniques as reported in literature addresses above issues to a considerable extent. However the main draw back in existing techniques is lack of
proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation.
Accessibility Analysis and Modeling in Public Transport Networks - A Raster b...Beniamino Murgante
Accessibility Analysis and Modeling in Public Transport Networks - A
Raster based Approach
Morten Fuglsang, - National Environmental Research Institute, Aarhus
University and Aalborg University Copenhagen
Henning Sten Hansen - Aalborg University Copenhagen
Bernd Münier - National Environmental Research Institute, Aarhus University
Jillian Anable, The Centre for Transport Research, University of Aberdeen
Christian Brand, The Environmental Change Institute, University of Oxford
Nick Eyre, The Environmental Change Institute, University of Oxford
Deriving on-trip route choices of truck drivers by utilizing Bluetooth data,...SalilSharma26
This paper models on-trip route choices of the truck drivers. Second, we assess the inefficiencies of those routing decisions. This paper utilizes Bluetooth data, loop detector data, and variable message sign data to model the route choices of truck drivers. The trucks are inferred from Bluetooth data by applying a Gaussian mixture model-based clustering technique. We apply both a binary logit model and a mixed logit model to derive the route choices of truck drivers on a case study between the port of Rotterdam and hinterland in the Netherlands. The model results indicate truck drivers significantly value travel distance, instantaneous travel time and lane closure information en-route. The estimate of travel distance varies significantly among truck drivers. While 38 percent of truck drivers do not take the shortest time path, 48 percent of truck drivers do not choose the system-optimal path.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Inaugural Professorial lecture by Simon Shepherd, Professor of Choice Modelling & Policy Design. Institute for Transport Studies, University of Leeds, 9th September 2014.
For audio recording see: www.its.leeds.ac.uk/about/events/inaugural-lectures2014
www.its.leeds.ac.uk/people/s.shepherd
www.its.leeds.ac.uk/research/themes/dynamicmodelling
Variable Renewable Energy in China's TransitionIEA-ETSAP
Variable Renewable Energy in China's Transition
Ding Qiuyu, UCL Energy Institute
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
The Nordics as a hub for green electricity and fuelsIEA-ETSAP
The Nordics as a hub for green electricity and fuels
Mr. Till ben Brahim, Energy Modelling Lab, Denmark
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
The role of Norwegian offshore wind in the energy system transitionIEA-ETSAP
The role of Norwegian offshore wind in the energy system transition
Dr. Pernille Seljom, IFE, Norway
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Detail representation of molecule flows and chemical sector in TIMES-BE: prog...IEA-ETSAP
Detail representation of molecule flows and chemical sector in TIMES-BE: progress and challenges
Mr. Juan Correa, VITO, Belgium
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Green hydrogen trade from North Africa to Europe: optional long-term scenario...IEA-ETSAP
Green hydrogen trade from North Africa to Europe: optional long-term scenarios with the JRC-EU-TIMES model
Ms. Maria Cristina Pinto, RSE - Ricerca sul Sistema Energetico, Italy
Ms. Maria Cristina Pinto, RSE - Ricerca sul Sistema Energetico, Italy
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Optimal development of the Canadian forest sector for both climate change mit...IEA-ETSAP
Optimal development of the Canadian forest sector for both climate change mitigation and economic growth: an original application of the North American TIMES Energy Model (NATEM)
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Presentation on IEA Net Zero Pathways/RoadmapIEA-ETSAP
Presentation on IEA Net Zero Pathways/Roadmap
Uwe Remme, IEA
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Flexibility with renewable(low-carbon) hydrogenIEA-ETSAP
Flexibility with renewable hydrogen
Paul Dodds, Jana Fakhreddine & Kari Espegren, IEA ETSAP
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Bioenergy in energy system models with flexibilityIEA-ETSAP
Bioenergy in energy system models with flexibility
Tiina Koljonen & Anna Krook-Riekola, IEA ETSAP
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Reframing flexibility beyond power - IEA Bioenergy TCPIEA-ETSAP
Reframing flexibility beyond power
Mr. Fabian Schipfer, IEA Bioenergy TCP
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Decarbonization of heating in the buildings sector: efficiency first vs low-c...IEA-ETSAP
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16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Mr. Andrea Moglianesi, VITO, Belgium
The Regionalization Tool: spatial representation of TIMES-BE output data in i...IEA-ETSAP
The Regionalization Tool: spatial representation of TIMES-BE output data in industrial clusters for future energy infrastructure analysis
Ms. Enya Lenaerts Vito/EnergyVille, Belgium
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Synthetic methane production prospective modelling up to 2050 in the European...IEA-ETSAP
Synthetic methane production prospective modelling up to 2050 in the European Union
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Ms. Marie Codet, Centre de mathématiques appliquées - Mines ParisTech; France
Energy Transition in global Aviation - ETSAP Workshop TurinIEA-ETSAP
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Mr. Felix Lippkau, IER University of Suttgart, Germany
16–17th november 2023, Turin, Italy, etsap meeting, etsap winter workshop, semi-annual meeting, november 2023, Politecnico di Torino Lingotto, Torino
Integrated Energy and Climate plans: approaches, practices and experiencesIEA-ETSAP
Integrated Energy and Climate plans: approaches, practices and experiences
VO: reduce the distance between modellers and DM,
VO: the work process
- Making modifications collaboratively,
- Running the model,
- Reports and collaborative analysis
VedaOnline
Mr Rocco De Miglio
16–17th november 2023, amit kanudia, etsap meeting, etsap winter workshop, italy, kanors-emr, mr rocco de miglio, mr. amit kanudia kanors-emr, november 2023, politecnico di torino, semi-annual meeting, torino, turin, vedaonline
Updates on Veda provided by Amit Kanudia from KanORS-EMRIEA-ETSAP
Veda online updates - Veda for open-source models
TIMES and OSeMOSYSBrowse, Veda Assistant
VEDA2.0, VEDAONLINE, VEDA
Mr. Amit Kanudia KanORS-EMR
16–17th november 2023, etsap meeting, etsap winter workshop, italy, mr. amit kanudia kanors-emr, november 2023, politecnico di torino lingotto, semi-annual etsap meeting, torino, turin
Energy system modeling activities in the MAHTEP GroupIEA-ETSAP
Energy system modeling activities in the MAHTEP Group
Dr Daniele Lerede, Politecnico di Torino
16–17th november 2023, dr daniele lerede, etsap meeting, etsap winter workshop, italy, mathep group, november 2023, politecnico di torino, semi-annual meeting, turin
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
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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)
8 5 December
2016
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. 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
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. 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?!?!
17. DTU Management Engineering, Technical University of Denmark17 5 December 2016
Bibliography
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carbon futures. Energy Policy 41, pp. 107-124.
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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.
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discrete choice studies of personal transportation decisions. Energy Economics 27(1), pp. 59-77.
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gas emissions and primary energy demands. Energy Policy 39(5), pp. 3012-3024.
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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.
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towards a 2°C climate target. Conference talk at the International Society for Ecological Economics (ISEE) 11th BIENNIAL
CONFERENCE Oldernburg.
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97-107.
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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
19 5 December 2016
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)
22 5 December
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
23 5 December
2016