Modelling Transport Modal Choice and Its Impacts on Climate Mitigation.

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TY  - CONF
  - Daly, H.; Ramea, K.; Chiodi, A.; Yeh, S.; Gargiulo, M.; Ó Gallachóir, B. P.
  - International Energy Workshop 2012
  - Modelling Transport Modal Choice and Its Impacts on Climate Mitigation.
  - 2012
  - June
  - Published
  - 1
  - ()
  - 19-JUN-12
  - 21-JUN-12
  - Transport accounts for 23% of energy-related CO2 emissions globally and transport CO2 emissions are projected to double by 2050. Climate change mitigation clearly requires a focus on transport that should include improved representation of travel behavior change in addition to increased vehicle efficiency and low-carbon fuels. Most available energy/ economy/ environment modelling tools focus however on technology and fuel switching and tend to poorly incorporate travel behavior. This paper addresses this gap by focusing on a key element of travel behavior, namely modal shifting. We introduce a novel approach to modelling modal choice in TIMES, a bottom-up, technology rich, least cost energy systems model. In typical TIMES models, individual modal travel demand is exogenously defined over the model time horizon and while technologies can compete within modes on the basis of cost (fuel costs, investment costs and O;M costs), there is no competition between modes. Here, we build a simple illustrative TIMES model, in which future overall travel demand is exogenously defined but not specified by individual mode. We allow competition between modes and impose a constraint on overall travel time in the system. This constraint represents the empirically observed travel time budget (TTB) of individuals and enables competition based on time as well as on cost, ensuring that faster and more expensive modes can compete. We further introduce a new variable, called travel time investment (TTI), which acts as a proxy for infrastructure investments (for example, new bus services or rail lines) to reduce the time  associated with travel. We populate the model with data from California, US and generate results to 2020 for a reference scenario, an investments scenario and a CO2 emissions reduction scenario. The results show the significance of modal shifting in the CO2 mitigation scenario.
  - http://iew2012.odandbrown.co.uk/files/2012/06/Daly.pdf
DA  - 2012/06
ER  - 
@inproceedings{V180361001,
   = {Daly, H. and  Ramea, K. and  Chiodi, A. and  Yeh, S. and  Gargiulo, M. and  Ó Gallachóir, B. P.},
   = {International Energy Workshop 2012},
   = {{Modelling Transport Modal Choice and Its Impacts on Climate Mitigation.}},
   = {2012},
   = {June},
   = {Published},
   = {1},
   = {()},
  month = {Jun},
   = {21-JUN-12},
   = {{Transport accounts for 23% of energy-related CO2 emissions globally and transport CO2 emissions are projected to double by 2050. Climate change mitigation clearly requires a focus on transport that should include improved representation of travel behavior change in addition to increased vehicle efficiency and low-carbon fuels. Most available energy/ economy/ environment modelling tools focus however on technology and fuel switching and tend to poorly incorporate travel behavior. This paper addresses this gap by focusing on a key element of travel behavior, namely modal shifting. We introduce a novel approach to modelling modal choice in TIMES, a bottom-up, technology rich, least cost energy systems model. In typical TIMES models, individual modal travel demand is exogenously defined over the model time horizon and while technologies can compete within modes on the basis of cost (fuel costs, investment costs and O;M costs), there is no competition between modes. Here, we build a simple illustrative TIMES model, in which future overall travel demand is exogenously defined but not specified by individual mode. We allow competition between modes and impose a constraint on overall travel time in the system. This constraint represents the empirically observed travel time budget (TTB) of individuals and enables competition based on time as well as on cost, ensuring that faster and more expensive modes can compete. We further introduce a new variable, called travel time investment (TTI), which acts as a proxy for infrastructure investments (for example, new bus services or rail lines) to reduce the time  associated with travel. We populate the model with data from California, US and generate results to 2020 for a reference scenario, an investments scenario and a CO2 emissions reduction scenario. The results show the significance of modal shifting in the CO2 mitigation scenario.}},
   = {http://iew2012.odandbrown.co.uk/files/2012/06/Daly.pdf},
  source = {IRIS}
}
AUTHORSDaly, H.; Ramea, K.; Chiodi, A.; Yeh, S.; Gargiulo, M.; Ó Gallachóir, B. P.
TITLEInternational Energy Workshop 2012
PUBLICATION_NAMEModelling Transport Modal Choice and Its Impacts on Climate Mitigation.
YEAR2012
MONTHJune
STATUSPublished
PEER_REVIEW1
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START_DATE19-JUN-12
END_DATE21-JUN-12
ABSTRACTTransport accounts for 23% of energy-related CO2 emissions globally and transport CO2 emissions are projected to double by 2050. Climate change mitigation clearly requires a focus on transport that should include improved representation of travel behavior change in addition to increased vehicle efficiency and low-carbon fuels. Most available energy/ economy/ environment modelling tools focus however on technology and fuel switching and tend to poorly incorporate travel behavior. This paper addresses this gap by focusing on a key element of travel behavior, namely modal shifting. We introduce a novel approach to modelling modal choice in TIMES, a bottom-up, technology rich, least cost energy systems model. In typical TIMES models, individual modal travel demand is exogenously defined over the model time horizon and while technologies can compete within modes on the basis of cost (fuel costs, investment costs and O;M costs), there is no competition between modes. Here, we build a simple illustrative TIMES model, in which future overall travel demand is exogenously defined but not specified by individual mode. We allow competition between modes and impose a constraint on overall travel time in the system. This constraint represents the empirically observed travel time budget (TTB) of individuals and enables competition based on time as well as on cost, ensuring that faster and more expensive modes can compete. We further introduce a new variable, called travel time investment (TTI), which acts as a proxy for infrastructure investments (for example, new bus services or rail lines) to reduce the time  associated with travel. We populate the model with data from California, US and generate results to 2020 for a reference scenario, an investments scenario and a CO2 emissions reduction scenario. The results show the significance of modal shifting in the CO2 mitigation scenario.
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URLhttp://iew2012.odandbrown.co.uk/files/2012/06/Daly.pdf
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