DateSunday September 20, 2020 from 5:00 PM to 7:00 PM
LocationIEEE Intelligent Transportation Systems Conference (Online)
OrganizerControl Systems Technology
Public debate about the future of mobility and transportation is increasingly informed by predictions about the impact of Autonomous Vehicles (AVs). As AVs are approaching market-readiness, it becomes more critical that we answer questions about them:
- How can we design profitable and sustainable mobility systems that leverage AVs?
- What will these new forms of mobility and transportation mean for society?
- How can we ensure that such technologies benefit all members of society, improving equity rather than undermining it?
This workshop will gather experts from transportation, operations research, robotics, and urban
planning in order to:
- identify challenges and opportunities for the future of transportation that are triggered by the advent of AVs,
- identify modeling and control methodologies to address them,
- share insights from early deployments and turn such insights into an actionable research roadmap.
Topics of interest
- Theoretical modeling methods and analysis tools
- Real-time control algorithms
- Programmatic tools
- Simulation tools
- Technology infusion
- Real-world case studies
This workshop is geared towards mobility and robotics researchers, industry practitioners, and public officials whose work involves the design, deployment, operation, or regulation of autonomous mobility systems. The workshop will present a variety of tools and studies that underpin future transportation systems, and will hopefully prompt fruitful conversations from the variety of stakeholders in such a monumental transformation of our transportation infrastructure.
|17:00 - 17:15||Samitha Samaranayake, Cornell University||Sustainable and equitable mobility via transit centric autonomous transportation system: Affordable, equitable and efficient access to personal mobility is a fundamental societal need---with broad implications for personal well-being, economic mobility, education, and public health. Emerging mobility services have disrupted the urban transportation ecosystem and instilled hope that new data-driven mobility solutions can improve personal mobility for all. While these apps provide a valuable service, as evident by their popularity, there are many questions regarding their scalability, efficiency, impact on equity, and negative externalities (e.g. congestion) --- issues that can not be resolved by AV fleets. On the other hand, traditional public transit systems provide affordable and community-oriented access to personal mobility, but have their own operational limitations. This talk will focus on integrating public transit operations with agile, demand-responsive services to enable personal mobility for all and the role of AVs in this context.|
|17:15 - 17:30||Milos Balac, ETH Zürich|| |
Modelling Shared Autonomous On-Demand (Transit) Services: In this talk, I will share my findings and view of the shared mobility in Zurich's autonomous future. By utilizing an agent-based modeling approach, discrete-choice models, cost structures for potential AV services, and optimization algorithms, I will analyze various shared mobility solutions and discuss how different modeling approaches shape our findings. Together we will see how our view and understanding of the shared services changes with the increasing complexity of the modeling method. I will touch upon two potential shared mobility solutions: single-occupancy shared mobility and on-demand transit service. By looking at the potential benefits and limitations of the proposed shared mobility solutions, I will discuss possible ways to move forward.
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17:30 - 17:45
Carlos Lima, Technical University of Denmark
What about demand? Evaluating automated mobility-on-demand in different urban typologies via integrated demand-supply simulation: In a series of recent studies, we have developed and showcased a framework for analysis of systemic impacts of future automated mobility on-demand (AMoD). We build on prior work in classifying the world’s cities into 12 urban typologies that represent distinct land-use and behavior characteristics and in building a simulation prototype city representative of a given typology. Prototypes were then modelled in a state-of-the-art integrated demand microsimulation and supply mesoscopic and fleet management simulation. We showcase the impacts on demand, congestion, energy and emissions across the entire transportation system for different AMoD implementation policies. The urban form and demand characteristics of the different typologies will ultimately affect the efficiency of different AMoD implementations.
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|17:45 - 18:00||Christos Cassandras, Boston University|| |
A decentralized optimization framework for the "internet of cars": The “Internet of Cars” refers to a cyber-physical multi-agent system consisting of Connected Automated Vehicles (CAVs) whose ultimate goal includes automating all aspects of mobility, from interconnected self-driving vehicles to on-demand sharing of transportation resources. The availability of large amounts of data, ubiquitous wireless connectivity, and the critical need for scalability open the door for new control and optimization methods that will enable the creation and effective operation of such a system. A decentralized optimal control framework will be presented to show how CAVs can operate to achieve the goals of reducing congestion and energy consumption while ensuring passenger comfort and guaranteeing safety requirements. This framework can be applied to any part of a transportation system where resource contention arises, such as signal-free intersections, merging points, and lane-change maneuvers. When including noise or more complex vehicle dynamics and objective functions, we will introduce an approach based on Control Barrier Functions (CBFs) which allows the problem to be solved in real time and closely track the optimal control solution. Simulation examples will be included to show that this approach applied to CAVs can significantly improve system performance under human-driven vehicles.
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|18:00 - 18:15||Kara Kockelman, University of Texas||Large-Scale Shared AV Simulations with Geofences, Stop Aggregation, & Parking Restrictions: |
With autonomous vehicles (AVs) still in the testing phase, researchers and planners rely on simulation to explore shared AV (SAV) fleet operations and system-design strategies. SAV operations with and without dynamic ride-sharing (DRS) or “pooling” options across five Chicago-area geofences are compared here, along with pickup and drop-off stop (PUDO) aggregation (to help merge riders) and curb-use restrictions on busy streets across the Bloomington, Illinois and Minneapolis-St Paul (MSP) regions. The geofences include the central business district of Chicago, the city’s formal boundaries, the region’s suburban core, its exurban core, and the entire 20-county region. Results demonstrate how service area limitations lower SAV response times, lower system-wide VMT across all modes, and ensure rather uniform response times over space. As expected, SAVs perform best in areas with high demand and with DRS active. Inside Bloomington, various PUDO spacings and trip-demand densities were studied, also using the agent-based simulation model POLARIS. Results suggest that greater PUDO spacings and higher SAV-use levels increase SAV occupancies marginally, while lowering VMT notably, as compared to door-to-door SAV fleet operations without DRS or PUDOs. A quarter-mile PUDO spacing is recommended in downtown regions to keep walking trips short and demand relatively high. At 0.25 mi PUDO spacings (thoughtfully placed, using origin and destination clusters), travelers walked less than 2 min at either trip end, on average, while 0.5 mi spacings lead to about 3.5 min of walking. It is also important to prepare for queuing areas at PUDOs in settings of high trip densities, to limit curbside congestion. The MSP study used the MATSim model to track 2% and 5% of the 7-county region’s 9.5 million daily person-trips and 20% of the Twin Cities’ trips. Results suggest the average SAV in this region can serve at most 30 person-trips per day with less than 5-minute average wait time, thereby replacing about 10 household vehicles but generating 13% more vehicle-miles traveled (VMT). With dynamic ride-sharing (DRS), SAV fleetwide VMT fell almost 20%, on average, while empty VMT (eVMT) fell by 26%. While eVMT and wait times are relatively high (averaging 22.5% and 11.5 min) during peak times of day, they fall significantly in the PM peak if DRS is offered and actively used. Compared to idling-at-curb scenarios, parking-restricted scenarios (not allowing parked SAVs on the busiest streets) generated 8% more VMT across all four companion scenarios. Relying on 52 mi/gallon hybrid electric SAVs was estimated to lower travelers’ energy use by 21% and reduce tailpipe emissions by 30%, assuming no new or longer trips. A 106 mi/gallon equivalent battery-electric fleet does much better by lowering energy use by 64%.
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|18:15 - 18:30||Stephen Zoepf, Lacuna Technologies|| |
Sustainable Transportation and the Digital Public Realm: The traditional transportation industry is rapidly evolving into a personal mobility industry as shared access begins to replace private sales, electric vehicles begin to replace gasoline, and automation begins to replace human driving ability. In this talk we discuss ways to define sustainability in transportation, and methods we can use measure and improve it. As demands increase on our transportation systems, and as private actors are able to exploit inefficient policy mechanisms, externalities are starting to emerge, and we need new policy tools to capture these social costs. We explore examples of early policy tools and applications to new mobility systems.
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Mauro Salazar is an Assistant Professor in the Control Systems Technology group at Eindhoven University of Technology (TU/e). He received the Ph.D. degree in Mechanical Engineering from ETH Zürich in 2019. Before joining TU/e he was a Postdoctoral Scholar in the Autonomous Systems Lab at Stanford University. Dr. Salazar’s research is at the interface of control theory and optimization, and is aimed at the development of a comprehensive set of tools for the design, the deployment and the operation of sustainable mobility systems. Dr. Salazar received the Outstanding Bachelor Award and the Excellence Scholarship and Opportunity Award from ETH Zürich. Both his Master thesis and PhD thesis were recognized with the ETH Medal, and he was granted the Best Student Paper award at the 2018 Intelligent Transportation Systems Conference.
Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he is the Director of the Autonomous Systems Laboratory and Co-Director of the Center for Automotive Research at Stanford. Before joining Stanford, he was a Research Technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He is a recipient of a number of awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an Office of Naval Research Young Investigator Award, a National Science Foundation Early Career (CAREER) Award, a NASA Early Career Faculty Award, and an Early-Career Spotlight Award from the Robotics Science and Systems Foundation. He was identified by the American Society for Engineering Education (ASEE) as one of America's 20 most highly promising investigators under the age of 40. His work has been recognized with best paper nominations or awards at the European Control Conference, at the IEEE International Conference on Intelligent Transportation Systems, at the Field and Service Robotics Conference, at the Robotics: Science and Systems Conference, at the ROBOCOMM Conference, and at NASA symposia.
Samitha Samaranayake is an Assistant Professor in the School of Civil and Environmental Engineering at Cornell University with graduate field faculty affiliations in Operations Research and Information Engineering, the Center for Applied Mathematics, and Systems Engineering. His research interests center on the modeling, analysis and control of networked infrastructure systems with a focus on transportation systems. He is particularly interested in developing computationally efficient solution techniques and algorithms that enable practical applications. He has worked in the server technologies group at Oracle, the design for test (DFT) group at Synopsys, the transit algorithms team at Google, and the French National Institute for Research in Computer Science and Control (INRIA).
Miloš Balać is a Postdoctoral Research Scientist at the Institute for Transport Planning at ETH Zurich. He received his Ph.D. from ETH Zurich in 2019. Currently, he leads MATSim related developments at the group of Prof. Axhausen. His primary research focuses on modeling innovative mobility solutions and their inevitable impacts on the transportation system and people, and developing the framework and methods for agent-based population synthesis for various regions around the world. He is a co-developer of the eqasim framework for agent-based population synthesis and a co-founder of the Odyssée start-up based in France.
Carlos is an Associate Professor at DTU Management, Technical University of Denmark, in the Machine Learning for Smart Mobility Group. His main research interests are the mathematical modeling and simulation of human mobility, smart mobility services and the modeling, development and assessment of new technologies in transportation systems. He is also a Research Affiliate at the ITSLab at the Massachusetts Institute of Technology (MIT). Before joining DTU, he was a Research Scientist at the ITSLab and the Executive Director of the Transportation Education Committee at MIT. His early career started as a research scholar at LNEC (Portugal) and then as a Senior PostDoctoral Associate at SMART (Singapore).
Christos G. Cassandras is Distinguished Professor of Engineering at Boston University. He is Head of the Division of Systems Engineering, Professor of Electrical and Computer Engineering, and co-founder of Boston University’s Center for Information and Systems Engineering. He received a B.S. degree from Yale University, M.S.E.E from Stanford University, and S.M. and Ph.D. degrees from Harvard University. In 1982-84 he was with ITP Boston, Inc. where he worked on the design of automated manufacturing systems. In 1984-1996 he was a faculty member at the Department of Electrical and Computer Engineering, University of Massachusetts/Amherst. He specializes in the areas of discrete event and hybrid systems, cooperative control, stochastic optimization, and computer simulation, with applications to computer and sensor networks, manufacturing systems, and transportation systems. He has published over 400 refereed papers in these areas, and six books. Dr. Cassandras was Editor-in-Chief of the IEEE Transactions on Automatic Control from 1998 through 2009 and is currently an Editor of Automatica. He was the 2012 President of the IEEE Control Systems Society. He is the recipient of several awards, including the 2011 IEEE Control Systems Technology Award, the Distinguished Member Award of the IEEE Control Systems Society (2006), the 1999 Harold Chestnut Prize (IFAC Best Control Engineering Textbook) for Discrete Event Systems: Modeling and Performance Analysis, a 2011 prize and a 2014 prize for the IBM/IEEE Smarter Planet Challenge competition, the 2014 Engineering Distinguished Scholar Award at Boston University, several honorary professorships, a 1991 Lilly Fellowship and a 2012 Kern Fellowship. He is a member of Phi Beta Kappa and Tau Beta Pi. He is also a Fellow of the IEEE and a Fellow of the IFAC.
Dewitt Greer Professor of Civil, Architectural & Environmental Engineering at the University of Texas at Austin, Kara Kockelman is a registered professional engineer and holds a PhD, MS, and BS in civil engineering, a master’s of city planning, and a minor in economics from the University of California at Berkeley. Dr. Kockelman has been a professor of transportation engineering at the University of Texas at Austin for the past 22 years. She is primary and co-author of over 170 journal articles (and two books) across a variety of subjects, nearly all of which involve transportation-related data analysis. Her primary research interests include planning for shared and autonomous vehicle systems, the statistical modeling of urban systems (including models of travel behavior, trade, and location choice), energy and climate issues (vis-à-vis transport and land use decisions), the economic impacts of transport policy, and crash occurrence and consequences. She is recipient of the UC Berkeley Medal, MIT Tech Review’s Top 100 (young) Innovators Award, an NSF CAREER Award, ASCE’s Bechtel Energy Award, and many others. Her CV and paper pre-prints can be found at www.caee.utexas.edu/prof/kockelman.
Dr. Stephen Zoepf is the Chief of Policy Development for Ellis & Associates, where he helps guide the development of open-source software products for cities to manage modern transportation systems. He holds a Ph.D., M.Sc. and B.Sc. from MIT and has two decades of experience in transportation and mobility. Stephen previously led the Center for Automotive Research at Stanford as Executive Director, helped the U.S. Department of Transportation efforts to integrate confidential data into national vehicle energy policy modeling, and worked as an engineer and product manager at BMW and Ford. He was an ENI Energy Initiative Fellow, a Martin Energy Fellow, and a recipient of the Barry McNutt award from the Transportation Research Board and the Infinite Mile award from MIT. His research has been covered in numerous popular press articles, initiated a Congressional probe, and has been lampooned in The Onion.