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
Reducing transport activity
- Understanding transportation data
- Modeling demand
- Shared mobility
- Freight routing and consolidation
- Alternatives to transport
Improving vehicle efficiency
- Designing for efficiency
- Autonomous vehicles
Alternative fuels and electrification
- Electric vehicles
- Alternative fuels
- Passenger preferences
- Enabling low-carbon options
- Chapter 8: "Transport" in the IPCC Fifth Assessment Report (2014): An overview of climate change mitigation from freight and passenger transport. Available here.
- Leveraging digitalization for sustainability in urban transport (2019): A perspective on opportunities, risks, and policy actions for digitalization in urban transport systems.
- Decarbonizing intraregional freight systems with a focus on modal shift (2018): An overview of "strategies for decarbonizing freight transportation...and literature and data relevant to estimating the global decarbonization potential through modal shift."
- The Future of Trucks (2017): A report from the International Energy Agency on road freight, and its implications for energy and the environment.
Online Courses and Course Materials
- Transportation Research Board: "As part of the National Academies of Sciences, Engineering, and Medicine, the Transportation Research Board (TRB) provides leadership in transportation improvements and innovation through trusted, timely, impartial, and evidence-based information exchange, research, and advice regarding all modes of transportation." Website here.
- International Transport Forum: An inter-governmental organization "promot[ing] carbon-neutral mobility to help stop climate change" and "provid[ing] decision makers with tools to select CO2 mitigation measures that deliver on their climate commitment." Website here.
- International Transport Energy Modeling (iTEM): "[A]n open group of people and organizations interested in the role of energy in the world's transport system." Website here.
Libraries and Tools
- "Tackling Climate Change with Machine Learning". Cite journal requires
- Sims R., R. Schaeffer, F. Creutzig, X. Cruz-Núñez, M. D’Agosto, D. Dimitriu, M.J. Figueroa Meza, L. Fulton, S. Kobayashi, O. Lah, A. McKinnon, P. Newman, M. Ouyang, J.J. Schauer, D. Sperling, and G. Tiwari, 2014: Transport. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
- Creutzig, Felix; Franzen, Martina; Moeckel, Rolf; Heinrichs, Dirk; Nagel, Kai; Nieland, Simon; Weisz, Helga (2019). "Leveraging digitalization for sustainability in urban transport". Global Sustainability. 2. doi:10.1017/sus.2019.11. ISSN 2059-4798.
- Kaack, Lynn H; Vaishnav, Parth; Morgan, M Granger; Azevedo, Inês L; Rai, Srijana (2018-08-15). "Decarbonizing intraregional freight systems with a focus on modal shift". Environmental Research Letters. 13 (8): 083001. doi:10.1088/1748-9326/aad56c. ISSN 1748-9326.
- Teter, Jacob, Pierpaolo Cazzola, Timur Gul, Eamonn Mulholland, Pharoah Le Feuvre, Simon Bennett, Paul Hugues et al. "The future of trucks: implications for energy and the environment." (2017): 1-167.