Transportation systems form a complex web that is fundamental to an active and prosperous society. Globally, the transportation sector accounts for about a quarter of energy-related CO2 emissions. In contrast to the electricity sector, however, transportation has not made significant progress to lower its CO2 emissions and much of the sector is regarded as hard to decarbonize. This is because of the high energy density of fuels required for many types of vehicles, which constrains low-carbon alternatives, and because transport policies directly impact end-users and are thus more likely to be controversial.
Passenger and freight transportation are each responsible for about half of transport GHG emissions. Both freight and passengers can travel by road, by rail, by water, or by air (referred to as transport modes). Different modes carry vastly different carbon emission intensities. At present, more than two-thirds of transportation emissions are from road travel, but air travel has the highest emission intensity and is responsible for an increasingly large share. Strategies to reduce GHG emissions from transportation include:
- Reducing transport activity,
- Improving vehicle efficiency,
- Alternative fuels and electrification,
- Modal shift (shifting to lower-carbon options, like rail).
Each of these mitigation strategies offers opportunities for ML. While many of us probably think of autonomous vehicles and ride-sharing when we think of transport and ML, these technologies have uncertain impacts on GHG emissions, potentially even increasing them. In reality, ML can play a role for decarbonizing transportation that goes much further. ML can improve vehicle engineering, enable intelligent infrastructure, and provide policy-relevant information. Many interventions that reduce GHG emissions in the transportation sector require changes in planning, maintenance, and operations of transportation systems, even though the GHG reduction potential of those measures might not be immediately apparent. ML can help in implementing such interventions, for example by providing better demand forecasts. Typically, ML strategies are most effective in tandem with strong public policies.
Machine Learning Application Areas
- IPCC AR5 Chapter on Transportation
- Creutzig, F. et al. Leveraging digitalization for sustainability in urban transport. (2019)
- Kaack L. et al. Decarbonizing intraregional freight systems with a focus on modal shift. 2018.
- Teter, J. et al. The Future of Trucks (2017)
Journals and conferences
- Transportation Research Board (conference and journal)
Societies and organizations
- International Transport Forum (inter-governmental organization)
- International Transport Energy Modeling (iTEM) (convenes members from the transport-energy modeling community)
Past and upcoming events
Libraries and tools
- "Tackling Climate Change with Machine Learning". Cite journal requires