Transportation Seminars
Tuesday, October 27, 3:-4:30 PM, Traffic and Transportation Lab, H353
Dr. Jie Lu, Clemson University
Sustainable Transportation System Infrastructure System Planning Under Uncertainty
Abstract: Many highway bridges in the United States, especially aging bridges, can be seriously damaged
or collapse in natural disasters, e.g., earthquakes and hurricanes. Since 1960s, major structural damage
has caused millions of dollars of economic losses in a number of states, including Alaska, California,
Washington, and Oregon. Due to the limited retrofitting resources, it is neither practical nor economical
to retrofit all bridges to their full health. This research work focuses on developing sustainable
transportation network in hedging against extreme events. Traditional ways of handling this situation is
to minimize investment cost or replacement cost. However, a low investment cost may results a high
life-cycle cost. A system approach is required that considers all components of costs in the long term,
including investment cost, maintenance cost and system travel cost. A mean-risk stochastic
programming model is developed in this study to optimally allocate limited retrofit resources. The
model considers risk preference of decision makers and provides sustainable solutions that will minimize
the long term social economic losses in extreme events. The core of this sustainable transportation
infrastructure planning is optimization, a general method that can be applied to other fields including
renewable energy supply chain problem, sustainable energy infrastructure systems planning, and
transportation planning in climate change adaptation.
Bio: Jie Lu is a Ph.D. student at Clemson University, with a major in civil engineering and a special
interest in sustainable transportation system rehabilitation, transportation system modeling and
optimization. He got his undergraduate degree in Civil Engineering from Tsinghua University, China and
master degree in Civil Engineering department at University of Florida. He’s interested in using
integrative modeling and solution skills to solve large-scale infrastructure systems protection problems.
He has conducted research developing sustainable transportation network with emphasis on decision
making under uncertainties. His research is supported by “Transforming Robust Design Concept into a
Novel Geotechnical Design Tool” project funded by NSF. During his study at Clemson University, he has
presented his research at Transportation Research Board (TRB) annual meetings and the Institute for
Operations Research and the Management Sciences (INFORMS) annual meetings. Jie is preparing
himself for a career in transportation engineering with a focus on system planning and optimization. His
future research plans include developing sustainable energy infrastructure systems, improving the
resiliency of transportation systems under uncertain disruptions and network design and applications in
climate change adaptation.
Thursday, October 29, 3:-4:30 PM, Traffic and Transportation Lab, H353
Dr. Maaza Mecuria, Hawaii DOT
Transit Stop Spacing Optimization on a Realistic Street Network
Abstract: Transit stop spacing is one of the tools used to improve service along an existing transit route.
The cost components that are essential for a proper evaluation of service on a transit route are walk
time, ride time and operational time. These three can further be compared in $/hr using appropriate
cost factors. This presentation describes a methodology that employs evaluation alternatives and
optimization of an existing transit line with parcel level disaggregation of demand, while using the real
street network to model walk access. The modeling paradigm allows in-depth impact exploration and
interaction of all the stops within the stop set. The optimization procedure employs network theory to
link a scenario (alternative stop arrangement) with a path, and an arc with an immediate cost of a triplet
or quintuplet, etc. The maximum-flow / minimum-cut theorem is used to find the minimum number of
paths (scenarios) needed to cover all the arcs (immediate costs) in the flow network. A case study
evaluation and optimization of a transit route is presented.
Bio: Maaza Mekuria is the Highway Performance Monitoring System (HPMS) coordinator for the Hawaii
Department of Transportation. He has over 25 years of experience in consulting practice, research,
education and public sectors. He a research associate with the Mineta Institute of Transportation (MTI)
at San Jose State University. He was the co-author and principal investigator of the Low Stress Bicycle
Network Modeling project for MTI. He is principal investigator of Multi-Modal Transit Access project for
MTI, applying the network modeling and optimization methodology he developed. He has
authored/coauthored several papers and presented nationwide. Maaza previously taught at Evergreen
Valley College, San Jose, California and at Bunker Hill Community College, Boston, MA. He has a BSCE
from Anna University, Chennai, India, MSc and PhD in Civil Engineering from Northeastern University,
Boston, Massachusetts. His work and research interests include Transport network modeling and
analysis, design, and simulation applications. Maaza is registered Professional Engineer in Hawaii,
Massachusetts, Maryland, and California.
Friday, November 6, 3:-4:30 PM, Traffic and Transportation Lab, H353
Dr. Roger Chen, Rochester Institute of Technology (RIT)
Identifying Market Segments for Plug-in Electric Vehicle Adoption
Abstract: In transportation planning and engineering, market segments are often identified in order to
define a set of policies and strategies that effectively target each segment. Examples include residential
location choices, electric vehicle adoption and the marketing of public transit options. In marketing and
consumer studies, market segments are commonly defined based on observed socioeconomic
attributes, such as gender and income. However, travelers may also be segmented based on variations
in their observed travel and activity patterns. The activity-based approach to travel analysis
acknowledges the need to analyze travel conceptualized as a trip chain or tour, as opposed to individual
trip segments. This has implications for identifying market segments based on travel patterns that need
to distinguish between the sequencing and timing of travel choices and activities, in addition to the
actual travel choices and activities. One approach that holds promise are pattern recognition methods
which have seen wide application in image analysis, speech recognition and physiological signal
processing. In this study, pattern recognition methods are applied to observed daily travel and activity
patterns from Oregon to identify travel market segments for plug-in electric vehicles (EV). These
patterns are specified along three dimensions (i) distance from home; (ii) activity type; and (iii) and
vehicle occupancy levels that are time-dependent and exhibit a waveform. To examine the implications
for plug-in EV adoption, the classified patterns are tested under different EV charging scenarios that vary
in their assumptions. Inversion of the extracted features for each segment provides representative
travel and activity patterns that can be related to their socioeconomic and urban form characteristics.
Bio: Dr. Roger Chen is currently an Assistant Professor at the Golisano Institute for Sustainability at the
Rochester Institute of Technology (RIT). He has specific expertise on the design of statistical
experiments, simulation and econometrics. His research focuses on understanding and modeling the
dynamics of user responses to real-time information systems and new communication technologies in
transportation systems. He has worked on models of route and departure time choice, as well as
activity-based models of travel demand. He has expertise in the development, estimation and
application of advanced travel demand models, including his recent work on developing choice models
with randomly distributed values of time (user heterogeneity), which have been integrated within
dynamic traffic assignment modeling platforms for predicting responses to congestion pricing and
varying weather patterns. Dr. Chen is currently an active member of the standing TRB Committee
ADB20, on Information and Communication Technologies (ICT) and Travel. Dr. Chen received his PhD
from the University of Maryland, College Park, and BS and MS degrees from the University of Texas at
Austin, all in Civil and Environmental Engineering.
Friday, November 13, 3:-4:30 PM, Traffic and Transportation Lab, H353
Dr. Guohui Zhang, University of New Mexico
TBD
Abstract: TBD
Bio: Dr. Guohui is currently an Assistant Professor at the University of New Mexico.