Event Type: Seminar

Two analytical models are introduced for predicting the allocation to ports and transportation channels of containerized goods imported from Asia to North America. Assuming fixed distributions for container flow-times, the Long-Run Model heuristically solves a mixed integer non-linear program to determine the least-cost supply-chain strategies for importers. The Short-Run Model uses estimates of the flow times as a function of traffic volumes on fixed infrastructure to iteratively develop the best near-term strategies.

The models allocate imports to alternative ports and logistics channels so as to minimize total transportation and inventory costs for each importer. Alternative logistics channels include direct shipment of marine containers via truck or rail, and trans-loading cargoes in the hinterlands of the ports of entry from marine containers into domestic trailers or containers.

The models are calibrated on industrial data. Minimum volume requirements and capacities for ports and landside channels are considered. The results are analyzed, and are used to describe the mix of supply-chain strategies utilized by various types of importers and the consequent trends in import flows by port and landside channel. Policy recommendations for governments, transportation and logistics service providers, and importers are also provided. (Joint work with Rob Leachman at U.C. Berkeley)

Payman Jula is an Associate Professor at Beedie School of Business, Simon Fraser University (SFU), Vancouver, Canada, where he teaches courses related to operations management, and decision making under uncertainty. Payman has a PhD in Industrial Engineering and Operations Research from University of California at Berkeley. His research interests are in transportation and logistics, and applications of operations management in the manufacturing and service industries. Payman has studied the economics of Asia – North America supply chains, and today will present his work in this area.

Transportation involves human, infrastructure, vehicle, and environmental interactions and is therefore a very complicated system. Traditionally, transportation has been studied through classical methods, typically with ideal assumptions, limited data support, and poor computing resources. While the theories (such as traffic flow and driver behavior models) developed through these efforts provide valuable insights in understanding transportation-related issues, they are often ineffective in large-scale transportation system analysis with massive amount of data. Also, transportation activities have been found affecting public health, air quality, environmental sustainability, etc., but our understanding in these relationships has been trivial and far from complete.

With recent advances in sensing, networking, and computing technologies, more and more transportation-related data and computational resources become available. These new assets are likely to bring in new opportunities to understand transportation systems better and address those critical transportation issues in a faster, more accountable, and more cost-effective way. From big data to big discoveries and big decisions: what is the gap and what needs to be done? Clearly, a new theoretical framework is needed to integrate the quickly growing massive amount of data, typically from numerous sources of varying spatial and temporal characteristics, into the large-scale transportation problem solving and decision making processes. Efforts along this line are likely to form up a new subject area, namely e-science of transportation, in the years to come. The speaker will share his vision and pilot research in linking big data to big discoveries and big decisions through his talk.

Dr. Yinhai Wang is a professor in transportation engineering at the University of Washington (UW). He has a Ph.D. in transportation engineering from the University of Tokyo (1998), a master’s degree in computer science from the UW, and another master’s degree in construction management and a bachelor degree in civil engineering from Tsinghua University, China. Dr. Wang is the founding director of the Smart Transportation Applications and Research Laboratory (STAR Lab) at the UW. He also serves as the director for Pacific Northwest Transportation Consortium (PacTrans), USDOT University Transportation Center for Federal Region 10.

Dr. Wang’s active research fields include traffic sensing, transportation safety, e-science of transportation, traffic operations, traffic simulation, and intelligent transportation systems (ITS). He has over 170 academic publications and delivered more than 240 academic talks. He was the winner of the ASCE Journal of Transportation Engineering Best Paper Award for 2003. Dr. Wang serves as members of three Transportation Research Board committees: Transportation Information Systems and Technology Committee (ABJ50), Freeway Operations Committee (AHB20), and Highway Capacity and Quality of Services (AHB40). He is currently on the Board of Governors for the ASCE Transportation & Development Institute. He was an elected member of the Board of Governors for the IEEE ITS Society from 2010 to 2013. Additionally, Dr. Wang is associate editor for three journals: Journal of ITS, Journal of Computing in Civil Engineering, and Journal of Transportation Engineering.

Intelligent Transportation Systems (ITS) encompass a broad range of wireless and wireline communications-based information, control and electronics technologies. When integrated into the transportation system infrastructure, and in vehicles themselves, these technologies help to monitor and manage traffic flow and capacity, reduce congestion, provide alternate routes to travelers, enhance productivity, and save lives, time and money.

Mr. Quon will be providing an overview of traditional infrastructure projects to develop a framework of how ITS technologies can be used to effectively manage traffic. Case studies of ITS projects in the Los Angeles region will be introduced to show the process of identifying traffic problems and responding with the appropriate ITS strategies that bridges the gap between existing infrastructure and technology through Intermodal Integration.

This will lead into a discussion of ITS strategies including: adaptive ramp metering, Connected Corridors, Bike Detection, Bus Signal Priority, Smart Park and Ride, Express Park, Smart Arterials, Smarter Highways, Express Lanes and Traffic Signal Synchronization.

Frank Quon is currently the Executive Officer in the Highway Program with the Los Angeles County Metropolitan Transportation Authority (Metro). He holds responsibilities for the delivery of Highway Projects funded through Metro and the Countywide Signal Synchronization/Bus Speed Priority Program. He manages the delivery of $12-$15 billion highway projects throughout Los Angeles County. Previously, Frank was the Deputy District Director for Operations in District 7. He had the responsibility for the safety and operation of the state freeway and highway system in Los Angeles and Ventura Counties. Frank earned his Bachelor of Science degree in Civil Engineering from Loyola Marymount University.

Ever since the term “SCM”first emerged in 1982 by Keith Oliver, one of strategic consultants at BAH, it has been widely acknowledged that supply chain management could be used as a strategic differentiator in a global business environment. As the network of supply gets more complicated and dispersed, this trend becomes a normal phenomenon among many of the leading corporates. Under this environment, SCM is no more a simple and tactical principle that governs the operation of a corporate: rather, it has to be highlighted as somewhat novel concept which is directly related to the core value of an enterprise. Starting with a basic question about the true identity of the SCM, this presentation will redefine the meaning of SCM from corporate’s core value system. To this end, various business cases will be discussed to draw some significant implications that can be observed only when we look SCM from value perspectives.

Dr. Jung Ung Min received his Ph.D degree in Civil and Environmental Engineering from Stanford University. He is currently an Associate Professor with Asia-Pacific School of Logistics and the Graduate School of Logistics in Inha University, Korea. His current research interests include Supply Chain Management Strategy, Supply Chain Solutions, and Logistics Security. Using his past experience in Samsung SDS as a senior consultant, he is actively participating in industry consultancy and advisory boards of many leading Korean companies such as Samsung Electronics, LG Electronics, Hyundai Motors, and etc. He has authored for 14 peer-reviewed journal papers and 2 books.

Driving behavior caused by vehicle interactions, such as acceleration, deceleration, and stop-and-go, is highly associated with traffic safety and the environment. The purpose of this study is to investigate whether traffic safety can be linked to environmental conditions, more specifically crash potential and on-road vehicle emissions. Individual vehicle trajectory data obtained from the US-101 freeway, as a part of the Next Generation Simulation (NGSIM) project, was used to investigate the relationship. A probabilistic rear-end crash potential model and a motor vehicle emission simulator (MOVES) were adopted to characterize traffic safety and environmental conditions, respectively. Both the crash potential index (CPI) and the vehicle emission index (VEI) were derived, and then investigated through correlation, regression, and clustering analyses. The findings revealed that the relationship is positively correlated and statistically significant. In addition, the results showed that severely congested traffic conditions, which include frequent stop-and-go situations and the formation of shockwaves, lead to greater crash potential as well as vehicle emissions. In summary, traffic safety and environmental conditions are positively associated. The outcomes of this study are expected to be used as useful fundamentals in developing effective vehicle safety and emission control programs and policies.

Cheol Oh received the Ph.D. degree in civil engineering-transportation from the University of California, Irvine. He is currently an Associate Professor with the Department of Transportation and Logistics Engineering, Hanyang University at Ansan, Korea. His research interests include traffic operations and control, traffic safety, and intelligent transportation systems (ITS). He is primarily focused on the development and application of information technologies toward safer and more efficient transportation systems. He has authored 94 peer-reviewed journal papers (Korean:66, International:28).Network and Spatial Economics. He is also on the editorial board of Transportation Research Part B, Journal of Intelligent Transportation Systems, and IET Intelligent Transportation Systems Journal.

Understanding the traffic evolution process after an unexpected network disruption is of great significance to traffic engineers who are responsible for traffic restoration. In this talk, we will discuss our recent findings on the day-to-day traffic equilibration process following the unexpected collapse and eventual reopening of the I-35W Bridge over the Mississippi River in Minneapolis. Following the I-35W Bridge collapse, drivers were observed to drastically avoid areas near the disruption site until the perceived congestion in that area gradually diminished. After the reopening of the disrupted link, despite a complete restoration of network topology, it was found that total demand restoration on that link did not occur, implying that a different traffic equilibrium was reached. Due to the rare occurrence of the network disruption event, such behaviour has not been reported in the literature and none of the existing day-to-day traffic assignment models are capable of explaining the empirical evidences. To fill in this gap, we have developed a nonlinear dynamic system that is capable of describing the transient states of a disrupted network, answering questions related to the traffic evolution trajectory from a disequilibrium (due to a network disruption) toward an equilibrium. Our models are calibrated and validated using the data collected from the Twin Cities network after the bridge collapse and reopening. To the best of our knowledge, this is the first time that day-to-day traffic equilibration models have been constructed and compared against real world observations.

Dr. Henry Liu is currently an associate professor of Civil Engineering at the University of Minnesota – Twin Cities. Before joining the University of Minnesota, he was an assistant professor at Utah State University and a post-doctoral researcher at the California PATH Program of UC-Berkeley. He received his Ph.D. degree in Civil and Environmental Engineering from the University of Wisconsin at Madison in 2000 and his B.E. degree in Automotive Engineering from Tsinghua University (China) in 1993. Dr. Liu’s research interests are in the area of traffic network monitoring, modeling, and control, which includes traffic flow modeling and simulation, traffic signal operations, and equilibrium traffic assignment. On these topics, he has published more than 50 articles in peer-reviewed journals. Dr. Liu is currently the overview paper editor of Transportation Research Part C and an associate editor of Network and Spatial Economics. He is also on the editorial board of Transportation Research Part B, Journal of Intelligent Transportation Systems, and IET Intelligent Transportation Systems Journal.

This talk describes a new framework for solving control and estimation problems in systems modeled by scalar conservation laws with convex flux, with applications to highway traffic flow estimation and control. Using an equivalent Hamilton-Jacobi formulation, we show that the solution to the original PDE can be written semi-analytically. Using the properties of the solutions to HJ PDEs, we prove that when the data of the problem is prescribed in piecewise affine form, the constraints of the model are mixed integer linear. This property enables us to identify a class of transportation engineering problems (control, estimation, fault detection, user privacy analysis) that can be solved exactly using MILPs.

Christian Claudel is an assistant professor of Electrical Engineering and Mechanical engineering at KAUST. He received the PhD degree in EECS from UC Berkeley in 2010, and the Ms degree in Plasma Physics from Ecole Normale Superieure de Lyon in 2004. He received the Leon Chua Award from UC Berkeley in 2010 for his work on Mobile Millennium. His research interests include control and estimation of distributed parameter systems, wireless sensor networks and environmental sensing systems.

Although a normative approach was developed to address the issue of capturing spatial-temporal constraints in a utility maximization framework for activity-based modeling, two key issues linger. The first is a need for a parameter estimation approach for each household’s set of parameters given multiple objectives such that observed arrival times and sequences can both be replicated. An approach was recently proposed and tested using an inverse optimization method. It has been partially applied to calibrate truck activity patterns. The second key issue is overcoming the complexity of the underlying NP-hard problem, particularly to apply the model to analyze different scenarios with numerous repeated runs. The concept of reoptimization — altering a prior instance solution — has been shown by researchers to generally be NP-hard as well, but proven that reoptimization heuristics result in tighter worst-case bounds. Two reoptimization heuristics are proposed for a selective extension of the HAPP model (which cannot be solved without heuristics) and tested in a computational experiment involving 100 zones and 500 simulated households. Results suggest that reoptimization algorithms are effective for selective vehicle routing problems, and solutions appear to be within tighter bounds than using a genetic algorithm without prior instance information. These new developments encourage further research to incorporate normative routing models to address routing/scheduling choices in existing activity-based models.

Dr. Chow obtained his Ph.D. at UC Irvine in 2010 under Dr. Amelia Regan. He has been a postdoc at UCI from 2010-2012, working
with Dr. Stephen Ritchie on freight forecast modeling and with Dr. Will Recker on the household activity pattern problem. Dr. Chow
will be leaving UCI on May 29th and starting as an Assistant Professor at Ryerson University in Toronto, Canada, on June 1st.

This presentation talks about a series of recent applications using high-resolution vehicle-detector actuation and signal phase change event data. High-resolution event data provides detailed detector occupied times and time gaps between two consecutive vehicles. Such information is of great importance to help understand traffic and driving behaviors under congested conditions. High-resolution data are first used to investigate queuing dynamics at congested signalized intersections. A model, combined with the Lighthill-Whitham-Richards (LWR) theory, is developed to estimate intersection queue length under congested situation, a situation in which the traditional input-output method cannot work. This research then investigates the arterial fundamental diagram (AFD), in which the highly scattered cloud is lack of explanations so far. Using high-resolution data, we found that the scatter in the AFD is mainly caused by signal operations instead of traffic congestion. The final part of the talk presents an investigation of driving behaviors using a whole year’s high-resolution data. A model is developed to describe the stochasticity of drivers’ gap selection. This model is also used to investigate the potential impact on the fundamental diagram caused by the stochasticity of drivers’ gap selection.

Dr. Xinkai Wu is an Assistant Professor of Civil Engineering at California State Polytechnic University Pomona. His research interests include urban traffic operations, network traffic flow modeling and simulation, driving behavior study, and applications of Intelligent Transportation Systems (ITS). Dr. Wu graduated from the University of Minnesota with a Ph.D. in Civil Engineering in 2010. He is the co-inventor of a pending patent: SMART-SIGNAL (Systematic Monitoring of Arterial Road Traffic Signals) technology, which collects and archives real-time high-resolution event data on signalized arterials. 

This presentation will describe ongoing research to analyze the impacts of extreme weather (and other) events on transportation systems and to develop decision support tools for system operators. The Desert Road in New Zealand, prone to snowy and icy conditions, volcanic eruptions, lahars, and seismic events, will be used as a case study. The recent Christchurch earthquakes and their impact on transport will also be discussed. The presentation will also examine opportunities for providing decision support to pilots, airlines, and air traffic controllers during periods of convective weather. One of the themes of the presentation will be the motivation for, as well as the setup and solution of, multicriteria transportation system management problems. Often overlooked objectives of such a problem include minimizing risk, inequity, and environmental impacts. There will also be some discussion of the importance of simulation for managing road networks during extreme weather events and the danger of taking simulation results at face value, particularly in aviation systems research.

Kenneth Kuhn is a Lecturer in the Department of Civil and Natural Resources Engineering at the University of Canterbury in Christchurch, New Zealand. His research interests include infrastructure management, logistics and supply chain management, and aviation systems. Prior to moving to New Zealand, Kenneth worked for two years for the National Aeronautics and Space Administration in California. He graduated from UC Berkeley with a Ph.D. in Civil Engineering in 2006, having written a dissertation
under the supervision of Professor Samer Madanat.