Mobility of the Future

Examining future changes in the transportation sector

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Transportation today accounts for close to 19% of the world’s energy consumption. Energy demand for transportation is expected to rise substantially as a growing middle class in emerging economies demands greater access to transportation. But how will such demand be addressed in the years ahead? The MIT Energy Initiative has organized a multi-disciplinary team from across MIT to examine how the complex interactions between advanced drivetrain options, alternative fuels, refueling infrastructure, consumer choice, vehicle automation, public transit options, mobility-as-a-service business models and government policy will shape the future for mobility.

This study is currently underway with the support of industry stakeholders interested in these evolving dynamics. We are considering many drivetrains, conventional and alternatives fuels, new mobility business models, various policy types, and how people will behave in response to these options. To support this analysis, we are modeling the system at various levels of granularity. At the highest level, we have a global model to analyze economics and policy across all regions of the world. At the most detailed level, we have an agent-based model for urban areas to examine how millions of people will decide to travel in response to price signals and other government policies.

This integrated analysis covers the following major subtopics which are described in greater detail by the project’s principal investigators.

The Vehicles & Fuels team is led by William H. Green, the Hoyt C. Hottel Professor of Chemical Engineering. Green is the Faculty Chair of the Mobility of the Future project and a world leader in computer modeling fuel chemistry. He has received the American Chemical Society’s Glenn Award in Fuel Chemistry (their “best paper” award) three times. He has published several papers evaluating proposed new fuels, including a detailed techno-economic evaluation of the societal benefit associated with changing the octane rating system in North America, with Professor John Heywood.

While petroleum-fueled Otto (spark-ignited) and diesel vehicles currently meet most of the demand for ground transportation, many other fuels (including electricity) and vehicle types have been suggested as alternatives. The fundamental question we are addressing is how to quantify the societal cost/benefit for these proposed alternatives.

The Vehicles & Fuels team is focused on collecting and computing techno-economic engineering parameters that underlie the Consortium’s modeling work (Figure 1). These parameters include fuel economy and pollutant emissions from a wide range of possible future fuel and engine combinations, including costs and emissions associated with producing and distributing each fuel. We are also estimating uncertainties on all these parameters, so we will be able to identify which parameters are known “accurately enough,” and which parameter values need to be refined.

Figure 1. Overview of Vehicles & Fuels team’s focus

We are collaborating with other members of the Mobility of the Future team to include these parameters in both a top-down economic model ( MIT’s Economic Projection and Policy Analysis (EPPA) model – see model description in the Global Economic and Policy Modeling Section and Figure 2 for the key transport-related elements in the model), and a bottom-up fleet model (Figure 3) which consider the implications of vehicle purchases in more detail. We are working to quantitatively determine the societal cost/benefit of a variety of policy options, e.g. regulations and taxes which push consumers to purchase certain types of vehicles which use certain fuels. While many of our parameter values are derived from the experience of the developed countries in North America and Europe, we are particularly interested in the much larger changes that will happen in China over the next three decades. We are currently enhancing the EPPA model so that it uses higher-fidelity representations for fuels and personal vehicles, and for different regions of China. We are particularly interested in quantifying the techno-economic-environmental merits of electric vehicles relative to improved-efficiency internal combustion engines (including hybrids). Among other options, we are evaluating proposed uses of natural gas in powering future transportation, e.g. burned to provide electricity for electric vehicles, used to make gaseous fuels such as H2 or CNG, or converted into a liquid fuel. We are also collaborating with Professor Heywood’s team to quantify the “chicken and egg” problem associated with introducing any new fuel, i.e. how much investment is needed in new fueling infrastructure to make customers comfortable in buying a vehicle that relies on a new fuel?

Figure 2. Key transport-related elements in the EPPA model

Figure 3. Diagram of bottom-up fleet model and its connection with the economic model

The team for the Global Economic and Policy Modeling task is led by Sergey Paltsev, senior research scientist at the MIT Energy Initiative and deputy director of the MIT Joint Program on the Science and Policy of Global Change. The team (which includes Joint Program researchers Henry Chen, Abbas Ghandi, and Paul Kishimoto) has an extensive experience in analyzing diverse aspects of the future mobility pathways, including impacts of fuel economy standards, low-carbon options for transport and electricity, hydrogen fuel cell cars, natural gas vehicles, and hybrid cars, which are published in the leading peer-reviewed journals like Journal of Transport Economics and Policy, Climate Policy, Transportation, Energy Economics, Transportation Research Part A, Economic Modeling, and others. The team’s work on the U.S. fuel economy standards was recognized with the 2012 Pyke Johnson Award from the Transportation Research Board.

For the Mobility of the Future study, the team will enhance the MIT Economic Projection and Policy Analysis (EPPA) model to investigate the interactions between different transportation technologies (e.g., ICE, PHEV, BEV), fuel prices, transportation policies, energy policies, and climate policies in an economy-wide setting at a global level (disaggregated by 18 regions).

Figure 1. Regions of the EPPA model

The analysis will assess implications for GHG emissions, air pollutants, fuel consumption, fleet composition, economic growth, and macroeconomic cost of avoided CO2 emissions. The main target questions for the team are: What are the impacts of energy, transportation, and climate policies on energy demand, on the economy, on the environment including global climate change? How will the vehicle fleet and fuel mix evolve in response to various policy scenarios? What are the macroeconomic costs of different policy options?

The EPPA model is a dynamic multi-sector, multi-region, computable general equilibrium (CGE) model of the world economy with detailed representation of energy technologies, GHG emissions, air pollutants, and land use change, and transportation. For this study, the EPPA model will be updated for the latest projections of costs of electric, plug-in hybrid and fuel cell vehicles, fuel standards, fleet dynamics, regional vehicle demand dynamics, and economic development. Additional information about the EPPA model is available here.

Figure 2. Schematic overview of the household transportation details and the circular flow of goods and resources in the EPPA model

The PI for this project is Moshe Ben-Akiva, Edmund K. Turner Professor of Civil and Environmental Engineering and Director of the Intelligent Transportation Systems (ITS) Lab at MIT. Carlos Lima Azevedo, Research Scientist, and Jimi Oke, Postdoctoral Associate, are the key researchers on the project and work for Professor Ben-Akiva in the ITS Lab.

Since its inception, the ITS Lab has conducted numerous studies of transportation systems and developed network modeling and simulation tools. The lab’s areas of research include discrete choice and demand modeling techniques, activity-based models, freight transport modeling, and data mining and analytics for behavioral modeling. More recently, we have focused on designing effective “smart mobility” solutions that are real-time and app-based with personalization, optimization and prediction capabilities. Our objective is to develop and enhance intelligent algorithms that make smart mobility truly smart. We use optimization and behavioral models and test alternative solutions using mobile sensing and simulation platforms developed at the ITS Lab. Examples of our smart mobility solutions include Flexible Mobility on Demand (FMOD), Autonomous Mobility on Demand (AMOD), and Sustainable Travel Incentives with Prediction, Optimization and Personalization (TRIPOD). FMOD provides a personalized menu of paratransit options; menu optimization increases user benefits and vehicle fleet utilization and Bayesian algorithms learn about travelers’ preferences from repeated choices. AMOD provides taxi service with an optimized fleet size and predictive rebalancing. In TRIPOD, we are developing a real-time simulation-based system with an optimization framework to predict traffic, energy consumption and to apply effective incentive strategies.

The goal of the Urban Mobility project within the Mobility of the Future study is to develop a viable framework for the analysis and prediction of traveler and transportation system responses to future decarbonization policies and technologies at the urban level. This framework incorporates four interconnected research streams: (1) modeling of behavioral preferences with regard to emerging vehicle and fuel technologies and innovative mobility services, such as on-demand, shared and autonomous solutions; (2) development of extended simulation capabilities to predict such behaviors and their impact on the performance of future urban networks using an activity- and agent-based framework developed at the ITS Lab called SimMobility; (3) identification of prevailing urban attributes and typologies that describe mobility, energy and emissions characteristics for cities worldwide; and (4) development of prototype cities in an enhanced simulation environment and the performance of multi-objective robust scenario discovery to predict outcomes of potential policy and technological alternatives on urban mobility and environmental performance.

Figure 1: Simulation of Future Mobility in Prototypical Cities: Research Framework

Ultimately, we are applying the scenario discovery approach in SimMobility to find robust mobility cases through 2050. Strategies to be included in this approach are fleet/mode substitutions, road pricing, emissions regulations, and infrastructure interventions. The key result will be the quantitative measure of performance for an ensemble of future strategy-states in terms of mobility patterns, transportation levels-of-service, emission levels, and energy consumption.

The Recharging and Refueling Infrastructure project of the Mobility of the Future study is led by Professor John Heywood and Senior Lecturer Dr. Chintan Vaishnav. Heywood and his team have worked extensively on promising options for reducing the energy consumption and greenhouse gas emissions from transportation through changes in technology, fuels, and vehicle use. His group’s most recent report on this topic is On the Road toward 2050, MITEI Report, November, 2015. Vaishnav has extensive experience in analyzing disruptive technology and is a leader in the field of System Dynamics modeling.

Two of the more promising alternatives to internal combustion engine land transportation vehicles are electrified vehicles and fuel cell-powered vehicles. The former brings in electricity as an energy carrier, the latter hydrogen. Such vehicles are now being sold in increasing numbers. The expansion of the electricity recharging infrastructure and the need to build a hydrogen refueling system, respectively, are constraining the growth in sales and use of these two alternatives. The project is investigating both of these types of infrastructure needs.

The project is addressing two basic questions. The first: What are the more effective strategies for building up the electricity recharging infrastructure in ways that would enhance growth in EV sales and use, in the various world regions? The parallel question is: What are the more effective ways to build up a hydrogen-refueling infrastructure that encourages the sale and use of fuel-cell vehicles?

As part of this project, a Systems Dynamics model developed by Professor David Keith (MIT Sloan School), focused on alternative vehicle deployment, will be enhanced and extended by the project team to examine these questions. A highly simplified representation of the current system dynamics model is shown in Figure 1. The extended model will be used to assess the sensitivity of EV sales growth to critical EV recharging infrastructure system parameters and variables. A parallel study will investigate the more promising approaches for building up a hydrogen fuel infrastructure, essentially from scratch.

Figure 1. Simplified view of the logic contained in the System Dynamics model for alternative fuel vehicles (AFVs). Some of the variables that influence the purchase of alternative fuel vehicles include awareness, range anxiety and infrastructure

These studies will provide data on anticipated vehicle sales volumes for a set of policy scenarios, and on the characteristics of these infrastructure evolutions. These results will be used in existing models of the in–use vehicle stock which aggregate the energy and environmental impacts of the many anticipated changes in vehicle design and use that are starting to occur, and are components of our overall Mobility of the Future study. This project will assess and compare the likely success of various government policies and corporate strategies aimed at encouraging EV and fuel cell vehicle adoption in the U.S. and abroad, and the expected impacts.

Figure 2. Electric vehicles recharging their batteries at a dual-port charging station. Consumer purchases of electric or hydrogen fuel-cell vehicles are dependent of the recharging/refueling infrastructure network

The System Dynamics project of the Mobility of the Future study is led by Dr. Chintan Vaishnav, a senior lecturer at MIT’s Sloan School of Management. Vaishnav is a member of the MIT Sloan System Dynamics Group, which has a sustained history of modeling the diffusion of alternative fuel vehicles, refueling and recharging infrastructures, and analyzing its implications for the environment. Their most recent version, Driving the Future, is an alternative fuel vehicle market simulator.

The dynamics of two evolving trends in the present environment raise important questions about the future of automotive industry. First, how will the evolution of recharging and refueling infrastructures impact the deployment of alternative fuel vehicles, especially electric and hydrogen vehicles? Second, how will the evolution of shared mobility (ride-hailing, carsharing, ridesharing, bikesharing, etc.) impact the conventional mobility paradigm of owned vehicles? The system dynamics modeling project is contributing to the investigation of these questions. To learn more about the first question, read about the Recharging and Refueling Infrastructure project being developed in collaboration with Professor John Heywood.

The rapid rise of the shared mobility paradigm, more evident with the diffusion of ride-hailing services such as Uber, raises one basic question: under what conditions will the shared mobility paradigm disrupt, co-exist with, or fail to disrupt the present paradigm of owned mobility? The answer to this question has implications for end customers, automobile manufacturers, refueling and recharging infrastructure providers, transportation and city planners, and beyond.

Vaishnav’s work in industries such as telecommunications has led to a general model of disruption. This model integrates key insights from innovation and strategy literature such as resources, capabilities, make vs. buy decisions, and other questions pertinent to firm behavior. Similarly, it incorporates theories of consumer behavior when choosing between price, quality, innovation, compatibility, under the influence of network externalities and switching costs. By doing so, the model allows for understanding conditions under which technology and industry disruptions do and do not occur. We are working to adapt this model to study shared vs. owned mobility competition. This is the first time the model will be adapted to study competition between forms of services. Figure 1 shows the overview for this System Dynamics model.

Figure 1. General structure for the Disruption Model for representing competition between owned mobility and shared mobility

These studies will provide insights into the structure of competition between shared and owned mobility paradigms that are important for managerial and regulatory decisions. As consumers react to increased transportation options and begin to alter the size of automotive markets, the model can be used to explore how incumbents decide between exploiting current competencies vs. exploring new, innovative trends.

Jinhua Zhao is the Edward H. and Joyce Linde Assistant Professor of City and Transportation Planning in the Department of Urban Studies and Planning at MIT. He brings behavioral science and transportation technology together to shape travel behavior, design mobility systems, and improve transportation policies. Zhao directs the JTL Urban Mobility Lab and co-directs the Transit Lab at MIT.

He leads two research projects as part of the MIT Energy Initiative’s Mobility of the Future study: (1) Global Comparison of Mobility Culture, and (2) Mobility Management Instruments in China: Scenario Setting 2050.

The Global Comparison of Mobility Culture project seeks to measure car pride and car dependence in cities across different countries, explore the sociodemographic, policy, and other influences that contribute to the formation of car pride, and model how car pride and car dependence influence travel behavior (such as car usage and ownership). Implementing a pre-tested survey in select cities across the globe, we will collect primary data on respondent’s travel behavior and attitudes towards mobility using psychometric tools including the Implicit Association Test.

Figure 1. Distribution of the Implicit Association Test (IAT): measure of ‘car pride’ in and around New York City A negative “D-score” (in blue) indicates car-pride/bus-shame, while a positive “D-score” (in yellow) indicates bus-pride/car-shame. The size of the point represents the number of respondents at each location.

The Mobility Management Instruments in China project surveys the landscape of municipal transportation policies and constructs future policy scenarios for China. We begin by characterizing current municipal transportation policies along four dimensions: policy instruments, policy objectives, stakeholders, and local contexts. We examine the variations common patterns in the process of transportation policy-making across Chinese cities. We aim to identify trends in urban transportation systems, mobility patterns, and transportation policy-making over time and use this understanding of the dynamics of transportation policy to develop a set of scenarios. These scenarios will explore three key dimensions—(i) technology development, (ii) mobility policy assertiveness, and (iii) urban land use regulations—to illustrate and benchmark a broad range of plausible mobility futures.

The Impact of Automated Vehicles on the Future of Mobility is being studied by Dr. Bryan Reimer and his team at the MIT AgeLab. Reimer is an author of over 200 papers on automotive safety. He founded and leads two academic-industry partnerships that examine the evolving automotive ecosystem. The Advanced Human Factors Evaluator for Attentional Demand (AHEAD) consortium is developing the next generation of driver attention measurement tools for both traditionally manually controlled and emerging automated systems. The Advanced Vehicle Technology (AVT) consortium is developing an understanding of driver use of emerging vehicle technologies of production level automated driving systems as well as future automated vehicle system characteristics.

As part of MIT Energy Initiative’s Mobility of the Future study, Reimer and MIT research scientist Ashley Nunes are exploring a range of factors envisioned to affect the development, deployment, procurement and operation of automated vehicles within the global transportation ecosystem. While technical advances dominate the news, successful transformation of the mobility ecosystem will need to also consider alignment of political, economic, ethical, legal, environmental, social, and human factors (Figure 1). A range of identified factors (Figure 2) will be used to construct a comprehensive ontology specifying their relevant contribution to automated vehicle deployment. The relative importance of factors is expected to vary across the globe (e.g. policy changes are easy to envision in some global markets but difficult in others). To enhance our understanding of this research gap, efforts are focused on:

  1. Documenting what is known about the factors and how they may influence automated vehicle deployment.
  2. Surveying stakeholders’ perceptions regarding the importance of factors in the global marketplace.
  3. Characterizing the potential for automated vehicles to reshape mobility of the future in select global economies based upon a set of scenarios selected for factor alignment.

Figure 1: Types of factors that may affect the development and deployment of autonomous vehicles

Figure 2: Examples of factors that may affect the development and deployment of autonomous vehicles

Professor Christopher Knittel, the George P. Shultz Professor of Applied Economics and Director for the Center for Energy and Environmental Policy Research, leads the Mobility of the Future research on Mobility Policy and Analytics. Chris has extensive experience in economic analysis of energy and transportation systems, including an evaluation of the unintended consequences of transportation carbon policies, the effects of the ‘Cash for Clunkers’ rebate system, and the impact of falling natural gas prices on coal usage. As part of the Mobility of the Future study, Chris is seeking to understand future personal vehicle ownership trends—namely, are Millennials going to purchase vehicles at rates consistent with previous generations?Car sales from 2012 showed low sales among Millennials, but in recent years sales have been rising. Projecting to the future, there is great uncertainty in vehicle ownership trends.  An accurate prediction relies on answering the question: Are the observed trends in low vehicle ownership a result of emerging ridesharing and other technologies that present alternatives to tradition vehicle ownership, a lingering effect of the Great Recession, or a consequence of broader social and demographic trends such as delayed marriage and child birth? The resolution to this question has important implications for prospective vehicle demand and for projecting infrastructure needs, gasoline tax revenues, and energy demand.

To address these unknowns, an econometric analysis of vehicle ownership trends may reveal whether millennial purchasing habits are significantly different from previous generations. The National Household Transportation Survey provides information for car ownership in the United States, as well as demographic information for the household. Tracking generational habits through different iterations of the surveys can reveal vehicle-purchasing trends for generations as they age. Looking globally, a similar approach can be invoked to better understand ownership trends in other countries, where different economic and social factors may be driving car ownership. A full understanding of how vehicle ownership may change in the next decades relies on an analysis of how emerging economies are affected by technological and social changes. This combination of analyses can provide better forecasts for anticipated vehicle demand and ownership.

Current Members

The current consortium members include: