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This page is about the intersection of transportation and machine learning in the context of climate change mitigation and adaptation. For an overview of transportation as a whole, please see the Wikipedia page on this topic.

A schematic of selected opportunities to reduce greenhouse emissions from transportation using machine learning. From "Tackling Climate Change with Machine Learning."[1]

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[edit | edit source]

Reducing transportation activity[edit | edit source]

  • Understanding mobility patterns: Large amounts of geolocated traces are being collected that enable to analyze mobility patterns, which can be useful for example for planning transportation networks. ML can help find relevant patterns, such as transportation modes.
  • Modeling demand for passenger and freight transportation: Designing efficient transportation systems requires to know well the transportation demand in order to be well adapted to it. ML can improve standard demand modelling tools such as discrete choice models.
  • Enabling low-carbon shared mobility: For shared mobility to be a low-carbon option, it needs to effectively enable to reduce the number of kilometers travelled by pooling users. ML can help real-time decision for example for ride-hailing services.
  • Routing for freight and passenger vehicles: By taking the best possible route, aircrafts, cars and other vehicles can get to their destination with less energy compared to the choices that are most often made. ML can help navigate through large number of possible choices to find optimal pathways.
  • Freight consolidation: Bundling shipments together through freight consolidation can dramatically reduce the number of trips and associated GHG emissions. ML can optimize complex relationship between the various dimensions involved in shipping decisions, such as shipment mode and origin-destination pairs.

Improving vehicle efficiency[edit | edit source]

  • Designing efficient vehicles: Margin emission reduction can be achieved by designing more energy-efficient vehicles, for example by improving engines or brake systems. ML can help engine design, aerodynamic research or additive manufacturing.
  • Improving driving efficiency: Margin emission reduction can be achieved by a more efficient driving, which includes a better speed management or routing decisions. ML has been extensively applied to autonomous driving, and some applications enable energy gains.
  • Optimizing public transportation services: So that many commuters use public transportation services, more energy-efficient that private vehicles, this services must propose time-efficient and reliable options. ML can be used in various ways, for example by predicting bus arrival time and their uncertainty.

Alternative fuels and electrification[edit | edit source]

  • Electric vehicles driving cycle optimization: Optimizing the driving efficiency of electric vehicle has specific challenges related to batteries. ML can help predict various relevant metrics such as battery state based on driving cycles.
  • Electric vehicle charging infrastructure: Deploying electric vehicles at scale requires an adequate charging infrastructure, with various planning, scheduling and management issues. ML can help for example with predicting usage of the infrastructure and load prediction.
  • Accelerated science for alternative fuels: Alternative fuels have the potential to provide low-carbon solutions while retaining the properties of fossils, but most of them remain at an early stage of development. Machine learning can help accelerate this development by learning patterns in experimental or operational data in order to guide future experiments/operations.
  • Electric vehicle demand response: Battery electric vehicles are typically not used for more than a fraction of the day, allowing them to act as energy storage for the grid at other times, where charging and discharging is controlled for example by price signals. ML can help enable demand response application by forecasting or controlling signals.

Modal shift[edit | edit source]

  • Understanding passenger modal preferences: In order for large fractions of trips to be shifted from unsustainable transportation modes (e.g. private vehicles) to more sustainable modes (e.g. public transportation), it is key to understand why passengers currently use a mode rather than another one, and under which conditions would they be willing to change their habits. ML can help understand data and model behaviors related to trips and their motivations.
  • Fostering urban cycling: Cycling is a low-carbon, healthy way to commute in cities, but inhabitants often use this mode less than they would like to due to inappropriate biking infrastructure, which makes cycling more complicated and dangerous. ML can be used in the planning and management of new biking infrastructure, in particular in the context of shared bike services.
  • Supporting public transportation network expansion: In the absence of a well-ramified public transportation network (which can include trains, subways, buses and other modes), the populations in the areas poorly covered have little access to low-carbon transportation options. ML can be used in the planning and design process of new public transit lines.
  • Enabling low-carbon multi-modal mobility solutions: In cities with modern transportation systems, the fastest way to get a one's destination can be to use a combination of several modes, for example a shared bike combined with a subway. ML can be used to develop apps and services to generate such itineraries using several low-carbon options.

Background Readings[edit | edit source]

  • Chapter 8: "Transport" in the IPCC Fifth Assessment Report (2014)[2]: An overview of climate change mitigation from freight and passenger transport. Available here.
  • Leveraging digitalization for sustainability in urban transport (2019)[3]: 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)[4]: 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)[5]: A report from the International Energy Agency on road freight, and its implications for energy and the environment.

Online Courses and Course Materials[edit | edit source]

  • Transforming Urban Mobility: Components of Transport Planning for Sustainable Cities, by UCL, TUMI and GIZ on Future Learn. In this course, you will examine how different types of public transport can be employed to make urban transport more sustainable. Course available here.

Conferences, Journals, and Professional Organizations[edit | edit source]

Major conferences[edit | edit source]

Major journals[edit | edit source]

  • Transportation Research parts A-E: A set of transportation journals focusing on policy and practice (A), methodology (B), emerging technologies (C), links with the environment (D), and logistics (E).
  • Transport reviews: A journal providing authoritative and up to date research-based reviews of transport related topics that are informative to those that are knowledgeable in the subject area. Website here.

Major organizations[edit | edit source]

  • 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[edit | edit source]

🌎 This section is currently a stub. You can help by adding resources, as well as 1-2 sentences of context for each resource.

Data[edit | edit source]

  • US Commodity Flow Survey: Commodity flows as surveyed by the US Census Bureau every 5 years.
  • Traffic Count Data: National and sub-national agencies report traffic counts at different spatial and temporal resolutions, e.g. Germany or California
  • Alternative Fuels Data Center: This website provides information, data, and tools to help fleets and other transportation decision makers find ways to reach their energy and economic goals through the use of alternative and renewable fuels, advanced vehicles, and other fuel-saving measures.

References[edit | edit source]

  1. "Tackling Climate Change with Machine Learning". Cite journal requires |journal= (help)
  2. 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.
  3. 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.
  4. 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.
  5. 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.