Professor Ben-Akiva holds a Ph.D. degree in transportation systems from MIT. He has co-authored two books, including the textbook Discrete Choice Analysis, published by MIT Press, and over 200 papers in refereed journals and conference proceedings. He also co-edited the book Recent Developments in Transport Modeling: Lessons for the Freight Sector published by Emerald. He directs the Intelligent Transportation Systems (ITS) Lab where two traffic simulators have been developed under his supervision: MITSIMLab, an open-source microscopic simulator; and DynaMIT, a mesoscopic simulator Рwhich includes algorithms for dynamic traffic assignment, traffic predictions and route guidance. Professor Ben-Akiva has received honorary degrees from the University of the Aegean, the University of Antwerp, the Universit̩ Lumi̬re Lyon and the Stockholm Royal Institute of Technology (KTH). His awards include the Transportation Science Dissertation prize from the Operations Research Society of America (now INFORMS), the MIT Department of Civil and Environmental Engineering Effective Teaching Award, the MIT Samuel M. Seegal Prize awarded to professors who inspire students to pursue and achieve excellence, the Lifetime Achievement Award of the International Association for Travel Behavior Research, and the Jules Dupuit prize from the World Conference on Transport Research Society. He has worked as a consultant in industries such as transportation, energy, telecommunications, financial services and marketing for a number of private and public organizations, including Hague Consulting Group, RAND Europe, ChoiceStream and Cambridge Systematics, where he is a Senior Principal and a member of the Board of Directors.
Keynote Address:
SMART – Future Urban Mobility
Urban mobility is a field of enormous opportunity due to the confluence of several fundamental developments. These developments include: advances in computing, communications, and sensing technologies; the growing awareness of environmental sustainability issues; the aging of physical infrastructure in developed countries and the need for massive new infrastructure in less developed ones; and the recognition of the vast economic stimulus that can be generated by the modernization and renewal of urban mobility systems worldwide and by the alleviation of the social inequities that severely restrict the mobility of the urban poor.
The goal of the SMART (Singapore – MIT Alliance for Research and Technology) Future Urban Mobility Interdisciplinary Research Group (FM-IRG) is to develop, in and beyond Singapore, a new paradigm for the planning, design and operation of future urban mobility systems. This new research center is based on the premise that the advances in computing, communications and sensing technologies give us powerful capabilities to model, evaluate and optimize urban mobility systems. Organizationally, the project is structured around three pillars:
• Pillar 1: “Networked computing and control” (NCC) will develop enhancements to urban mobility systems using technologies such as mobile mesh networking, real time data fusion and visualization, on-board automation and smart infrastructure.
• Pillar 2: “Integrated modeling of mobility, land-use, environmental, and energy-use impacts” will develop a suite of powerful demand estimation, performance prediction and operation optimization tools, drawing on the availability of NCC-enabled information.
• Pillar 3: “Performance assessment and implementation” will enable more meaningful evaluation of alternative sustainability mobility systems and the development of institutional, regulatory, and pricing mechanisms to support them.
At the heart of the proposed approach will be SimMobility, a simulation platform with an integrated model of human and commercial activities, land use, transportation, environmental impacts, and energy use. This modeling engine will be linked with a range of networked computing and control (NCC) technology-enabled mobility innovations, and with operations research-based decision models, to analyze the impacts of various novel concepts, including real-time information and management systems, and innovative mobility services such as “mobility-on-demand,” and “green logistics”. We will link the behavioral models with state-of-the-art simulators to predict the impacts of mobility demands on transportation networks and services and on vehicular emissions. Similarly, land use models predict the evolution of urban real estate markets. These models also provide inputs to energy/material consumption models. Several challenges are evident: the integration of heterogeneous populations of agents based on highly diverse data sources on households and firms, their activities, trips, real-estate and equipment purchases and energy consumption; and the design and application of methodologies to validate model performance. Integration will allow us to simulate the effects of a portfolio of technology, policy and investment options under alternative future scenarios.
The main axiom for this integrated modeling approach is the premise that urban demands, such as energy and transportation demands are derived from human needs and demand for activities. Hence, a behavioral model of human activities is pivotal to this approach. To test the impact of different policies or investments accurately, we frame a model that microsimulates individual behavior (within the constructs of households and firms) in connection with the associated mobility and energy consumption patterns. The model converts these patterns into their appropriate resource consumption and aggregates these impacts over the entire population to generate the overall effect of the tested policy and/or investment. Once a model run is completed, several indicators can be post-processed from the model output to evaluate the tested policy and/or investment. This methodology allows us to capture the true relationship between transportation and energy in a way that would be abstracted by traditional macro-level models. Furthermore, it permits the identification of the role of each specific variable on the aggregate results, thereby allowing the model to serve as a decision support tool for urban planners and policy makers.
To test transportation system performance, an enhanced multi-modal DynaMIT, a mesoscopic transportation network simulation tool developed by the MIT ITS Lab, will be employed. A state-of-the-art model, designed to support real-time operations of a dynamic traffic management system (including dynamic congestion pricing and incident management), DynaMIT can also be used for offline planning applications, with the capability to account for individual travel choices. At another layer of detail, we expect to use an enhancement of MITSIMLab, also developed by the MIT ITS Lab. A microscopic traffic simulator, MITSIMLab can be used to analyze a range of traffic management system designs, including bus operations and traffic management systems and their interactions. An important challenge we will address in this integrated model will be the explicit consideration of freight movements. This will ultimately permit the analysis and design of integrated multi-modal passenger and freight urban systems.
These modeling capabilities will be employed to derive and apply new evaluation frameworks for sustainable mobility; to evaluate and expand innovative portfolios of future urban mobility options; to develop and apply advanced scenario planning techniques to account for future uncertainties; to design and experiment with alternative institutional configurations and alternative physical urban designs enabled by NCC innovations; and to identify paths for “exporting”/“adapting” the Singapore FM model.
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