Reducing the energy intensity of transport with integrated models

Transportation is one of the most energy-intensive human activities. Over the past few decades, planners have paid increasing attention to the externalities associated with transport; and public discourse on climate change has further reinforced this trend. Reducing transport-related emissions, whether through more energy-efficient vehicle propulsion systems or innovative forms of mobility, has become one of the main objectives of transport planners worldwide. It motivates much of today's research into electric vehicles, driverless cars, mobility as a service (MaaS - Mobility as a Service) and many other innovative technologies and concepts. Despite all this attention, methodologies for gaining insight into the impact of these innovations on energy and emissions still have significant limitations.

Firstly, technology-driven models - notably Life Cycle Assessment (LCA) - generally analyze each technology in isolation and therefore fail to capture the interaction of its characteristics (range, durability, charging cycles, etc.) with local conditions (climate, traffic patterns, other established transport technology combinations, etc.) to determine its overall effect on a system's energy and emissions efficiency. Conversely, transportation models represent these usage dynamics well for a typical day's operation, but struggle to link these dynamics to the other activities that determine overall energy consumption and associated emissions, notably electricity generation (electricity generation mix, peak demand, etc.), fossil fuel extraction and refining, and materials and vehicle manufacturing and recycling.

Secondly, LCA and transport models generally compare different prospective "snapshot" scenarios, without assessing the transition to reach these states. Given the urgency of decarbonization and the transition to renewable energies, the speed and cost (monetary, but even more so in terms of energy consumption, emissions, resources, etc.) at which an improved system state can be achieved will determine its desirability and overall performance.

Thirdly, transport models are almost exclusively applied to cities or urban regions, but innovative transport must also offer solutions for long-distance travel, otherwise it is unlikely to be adopted by the public. Consequently, we cannot focus solely on urban solutions to understand the energy implications.

To address these issues, the project, whose full title is "Reducing the energy intensity of transport: combining transport and technology models to inform the transition", will combine the strengths of a state-of-the-art agent-based transport simulation (MATSim) with LCA models of the direct and indirect energy and environmental impacts of various transport technologies. This work will build on previous work (IET Modeling Projects - 2nd edition), but will greatly refine the integration of these models to ensure precise contextualization of each technology and its use (shared mobility, etc.), thus addressing the first issue. The second issue will be addressed by hypothesizing potential transition paths towards future mobility scenarios, verifying their plausibility in terms of energy and resource availability, as well as cumulative energy consumption and greenhouse gas emissions. These scenarios will take into account the dynamic renewal of the vehicle fleet, the evolution over time of production and distribution capacities for renewable electricity and alternative fuels, as well as the availability of critical resources and potential manufacturing bottlenecks. To address the third issue, it will be necessary to examine both regional and inter-regional travel, and how this demand for mobility can be met by innovative mobility solutions to minimize energy consumption. As inter-regional travel is the subject of far less research than regional travel, data on the former is less common and not generally covered by national mobility surveys. Therefore, a first task will be to identify the data that would be appropriate to gain insight into these trips, which will include data mining of smartphones or social media, but may also include specific surveys. Next, the simulation will be adapted to run inter-regional scenarios and determine the characteristics of future transport systems that would be able to satisfy such demand. This research tackles a very common question by taking an unusual route to answering it. Which future mobility scenarios minimize energy consumption and emissions?

The work aims to create and test a tool that provides plausible answers with a combination of methodologies (Agent-Based Modeling and LCA) that is rarely used in transportation planning, but allows for consideration of largely neglected but potentially extremely important issues.

By examining the potential balance points between future transport supply and demand, it provides insight into two key issues: how to develop development paths that will lead us to energy systems with reduced carbon footprints, and how to reduce the energy intensity of transport and bring about positive behavioral change. It will be a powerful tool in the hands of politicians and industry decision-makers to avoid investing in solutions that look promising but may be less effective than expected, or even counterproductive, because they have been evaluated without taking the kind of holistic view adopted in this project.