Soyoung Ahn "A stochastic modeling of traffic breakdown for freeway merge bottlenecks"

ABSTRACT

This talk will present a novel breakdown probability model based on microscopic driver behavior for a freeway merge bottleneck. Extending Newell’s car following model to describe the transition from free-flow to congested regimes, two elements of breakdown, trigger and propagation, are derived in terms of vehicle headway. Combining these elements, a general breakdown probability is derived in terms of various parameters related to driver behavior and traffic conditions – other than flow – that can be treated as constants or stochastic with probability distributions. The proposed model is validated with real data. Based on the model properties, a proactive traffic control method is developed considering low penetration rates of connected automated vehicle technologies.

 

BIOSKETCH

Dr. Soyoung Ahn is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin - Madison. She received her Ph.D. in Civil and Environmental Engineering from the University of California, Berkeley in 2005. Before joining UW-Madison in 2013, she was on the faculty in the School of Sustainable Engineering and the Built Environment at Arizona State University from 2006 to 2013. She is an expert in traffic flow analysis and modeling, (numerical) simulations, and traffic control using emerging technologies. Her recent research involves (i) evaluation of performance of connected autonomous vehicles (CAVs), particularly cooperative adaptive cruise control (CACC) vehicles, (ii) development of CACC platoon control strategies, and (iii) development of system control strategies using CAVs. She is the current Chair of TRB’s Traffic Flow Theory and Characteristics Committee, an Associate Editor for Transportation Research Part C, and an editorial board member for Transportation Research Part B. She is also a member of the International Advisory Committee for the International Symposium on Traffic and Transportation Theory.

 

Xuegang (Jeff) Ban "Transportation Big Data: Promises and Issues in the Era of Connectivity, Automation, and Sharing"

ABSTRACT

Big data and related data analytics methods have received much attention recently in transportation for various planning and operational applications. This talk summarizes the promises of big data and illustrates potential issues of some commonly used big data sources in transportation. We then briefly discuss the implications of such issues and suggest a possible pathway that may help address those issues.

BIOSKETCH

Dr. Xuegang (Jeff) Ban is an Associate Professor of the Department of Civil and Environmental Engineering at the University of Washington. He received his B.S. and M.S. in Automobile Engineering from Tsinghua University, and his M.S. in Computer Sciences and Ph.D. in Civil and Environmental Engineering (Transportation) from the University of Wisconsin at Madison. His recent research focuses on using data to reveal behavior/interactions of key components of transportation systems, and developing mathematical/simulations models to capture the interactions and evaluate their system impact. He has published more than 120 papers in refereed journals, as book chapters, or in refereed conference proceedings. He is a member of several committees of Transportation Research Board. He is an Associate Editor of Transportation Research Part C, IEEE Transactions on Intelligent Transportation Systems, and Journal of Intelligent Transportation Systems. He received the CAREER Award from the National Science Foundation in 2011.

 

Marta C González "Data Science to tackle Urban Challenges"

ABSTRACT

I present a review on research related to the applications of big data and information technologies in urban systems. Data sources of interest include: Probe/GPS data, Credit Card Transactions, Traffic and Mobile Phone Data. Key applications are modeling adoption of new technologies and traffic performance measurements. I propose a novel individual mobility modeling framework, TimeGeo, that extracts all required features to model daily mobility from ubiquitous and sparse digital traces. Based on that framework, I present a multi-city study to unravel traffic under various conditions of demand and translate it to the travel time of the individual drivers. First, we start with the current conditions, showing that there is a characteristic time that takes to a representative group of commuters to arrive to their destinations once their maximum density has reached. While this time differs from city to city, it can be explained by the ratio of the vehicle miles traveled to their available street capacity. We identify three states of urban traffic, separated by two distinctive transitions. The first describing the appearance of the first bottle necks, and the second the transition to a complete collapse of the system. The transition to the second state measures the resilience of the various cities and is characterized by a non-equilibrium phase transition.

BIOSKETCH

Marta C. Gonzalez is Associate Professor of City and Regional Planning at the University of California, Berkeley, and a Physics Research faculty in the Energy Technology Area (ETA) at the Lawrence Berkeley National Laboratory (Berkeley Lab). With the support of several companies, cities and foundations, her research team develops computer models to analyze digital traces of information mediated by devices. They process this information to manage the demand in urban infrastructures in relation to energy and mobility. Her recent research uses billions of mobile phone records to understand the appearance of traffic jams and the integration of electric vehicles into the grid, smart meter data records to compare the policy of solar energy adoption and card transactions. Credit to identify habits in spending behavior. Prior to joining Berkeley, Marta worked as an Associate Professor of Civil and Environmental Engineering at MIT, a member of the Operations Research Center and the Center for Advanced Urbanism. She is a member of the scientific council of technology companies such as Gran Data, PTV and the Pecan Street Project consortium.

Robert Hampshire "Smart Cities: Data and Decision science for parking management "

ABSTRACT

Parking management has been a vexing problem for cities since the invention of the automobile. Among the concerns are traffic congestion, air pollution, and greenhouse gas emissions caused by drivers searching for available parking—an activity colloquially known as cruising. Recently, there has been a wave of interest in effective curb parking management, particularly through performance-based pricing, has arisen in cities as diverse as Seoul, Mexico City, New York, Seattle, Los Angeles, and Budapest. The movement is exemplified by San Francisco, which introduced variable priced parking to improve space availability and reduce cruising.  I will present some empirical observations, stochastic models, simulation models, statistical methods and pricing optimization results for the management of parking. This work has inspired the deployment of several parking management programs around the work.

BIOSKETCH

Robert C. Hampshire is research assistant professor in UMTRI's Human Factors group.  He received a PhD in Operations Research and Financial Engineering from Princeton University in 2007. His research focuses on management,  policy, modeling, and optimization of mobility services such as smart parking, connected vehicles, autonomous vehicles, ride sharing, bike sharing, car sharing and person-2-person car sharing. This work is supported by the National Science Foundation, U.S. Department of Transportation, industry partners and several nonprofit foundations. He uses statistics, stochastic modeling, simulation and dynamic optimization to develop design and management strategies.  

 

Rainald Löhner "Crowd Management Via Multisensory Input, Fast Computing, Data Bases and Deep Learning"

ABSTRACT

Megatrends worldwide, such as rural to city migration, increase in the number of megacities, increase in mobility and wealth, increase in freedom of expression, the rise of social media, and the loss of interpersonal contact and experience due to internet addiction have led to a rapid increase in mass gatherings. Sport events, concerts, demonstrations, large political events, congresses, city fetes and fairs, amusements parks, large transportation hubs and pilgrimage sites routinely gather from tens of thousands to millions of pedestrians in a small area. The safety and comfort, as well as the user experience in these events is of prime importance to organizers and authorities. Once densities exceed a certain threshold (typically 4p/sqm), dangerous situations can occur. Some of these have led to considerable loss of life and injuries in the past. Thus far, many of these gatherings have been managed via a combination of historial data, CCTV camera coverage and ‘feet on the ground’. Recent advances in automated multisensory input (from CCTV, cell-phones, RFID tags, or any other means), fast and accurate agent-based simulation tools, artificial neural nets and deep learning lead us propose a new management concept: the Digital Twin Enhanced Command and Control Center for Crowds (DTEC4). DTEC4 should allow to move from managing ‘what is’ to managing ‘what will be’, thus greatly improving safety and comfort for mass gatherings.

The talk will focus on the DTEC4 concept and recent progress in achieving it.

BIOSKETCH

He received a MSc in Mechanical Engineering from the Technische Universit¨at Braunschweig, Germany, as well as a PhD and DSc in Civil Engineering from the University College of Swansea, Wales, where he studied under Profs. Ken Morgan and Olgierd Zienkiewicz. His areas of interest include numerical methods, solvers, grid generation, parallel computing, visualization, pre-processing, fluid-structure interaction as well as shape and process optimization. His codes and methods have been applied in many fields, including aerodynamics or airplanes, cars and trains, hydrodynamics of ships, submarines and UAVs, shock-structure interaction, dispersion analysis in urban areas, haemodynamics of vascular diseases, fundamental studies on chaotic, turbulent flows, as well as evacuation and management of mass events.


He is the author of more than 750 scientific publications covering the fields enumerated above, as well as a textbook on Applied CFD Techniques. He has given numerous invited lectures at international conferences, universities, research institutes and private companies worldwide, received many awards, and co-organized several international Conferences.

Benjamin Seibold "Traffic Waves, Autonomous Vehicles, and the Future of Traffic Modeling"

ABSTRACT

Via analysis and simulations of traffic flow models, we demonstrate that stop-and-go waves in vehicular traffic flow can arise from instabilities, caused by the collective driving dynamics of the humans on the road. Moreover, these nonlinear waves are mathematical analogs of detonation waves. We then take a leap into the near future, in which a few connected and automated vehicles (CAVs) will be immersed in the traffic stream. We present theoretical as well as experimental results that show how a small number of CAVs can be employed for future traffic flow control to dissipate, and even prevent, traffic waves. We close with an outlook on how traffic flow on our roadways is about to change fundamentally, and how this will greatly affect traffic modeling at the interface of applied mathematics and transportation science and engineering.

BIOSKETCH

Dr. Benjamin Seibold is an Associate Professor in the Department of Mathematics at Temple University (Philadelphia, PA). He is the Director of the Center for Computational Mathematics and Modeling. Before joining Temple, he received his PhD from the University of Kaiserslautern, Germany, and was an Instructor at MIT. He works in applied and computational mathematics with specific applications in traffic flow, fluid dynamics, radiation transport, materials, and bio-medical phenomena. His traffic flow research is centered around non-equilibrium phenomena (such as stop-and-go waves), and the nonlinear interplay of microscopic and macroscopic structures. One of his current projects, supported by NSF Cyber-Physical-Systems, is devoted to studying how a small fraction of automated vehicles on the road can dissipate and prevent stop-and-go traffic waves.

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