Transportation Engineering and Science Program
SSMC Research Areas
Areas of potential focus include: Incident management/emergency vehicle management, Freight mobility/commercial vehicles, En-route driver information, Disaster response and evacuation, Archived "big data", and Environmental/eco-friendly applications.
The UT Consortium members are nationally recognized experts in intelligent transportation systems. The team members have a combination of in-depth familiarity with various areas related to ITS deployment and evaluation. Additionally, they have expertise in proof of concept, test-beds and operational tests, incident management, work zone management, multi-modal transportation systems, advanced traveler information systems and big data modeling and simulation, energy/emissions, embedded systems, wireless sensor networks, software/apps, algorithms, computing (cloud), and cyber-security.
- Driving styles can be broadly characterized as calm or volatile, with significant implications for traffic safety, energy consumption and emissions. How to quantify the extent of calm or volatile driving and explore its correlates is a key research question investigated in the study. This study contributes by leveraging a large-scale behavioral database to analyze short-term driving decisions and develop a new driver volatility index to measure the extent of variations in driving. The index captures variation in driving behavior constrained by the performance of the vehicle from a decision-making perspective. Specifically, instantaneous driving decisions include maintaining speed, accelerating, decelerating, maintaining acceleration/deceleration, or jerks to vehicle, i.e., the decision to change marginal rate of acceleration or deceleration. A fundamental understanding of instantaneous driving behavior is developed by categorizing vehicular jerk reversals (acceleration followed by deceleration), jerk enhancements (increasing accelerations or decelerations), and jerk mitigations (decreasing accelerations or decelerations). Volatility in driving decisions, captured by jerky movements, is quantified using data collected in Atlanta, GA during 2011. The database contains 51,370 trips and their associated second-by-second speed data, totaling 36 million seconds. Rigorous statistical models explore correlates of volatility that include socioeconomic variables, travel context variables, and vehicle types. The study contributes by proposing a framework that is based on defining instantaneous driving decisions in a quantifiable way using big data generated by in-vehicle GPS devices and behavioral surveys.
- Lane change behaviors have been suspected as a critical factor that can affect free traffic flow. Specially, lane change behaviors can occur at relative high speed or without a turn signal, which may potentially lead to a crash event. With advanced wireless communication technology under connected vehicle environment, vehicles are able to communicate or exchange sub-second (10 Hz) data with each other (vehicle-to-vehicle) and with infrastructure (vehicle-to-infrastructure). This data can be used to provide a more complete picture of vehicle status for identifying real-time lane change events and then help drivers to make better decisions or to implement vehicle control assist systems to avoid high risk lane change situations, thereby improving safety. Basic Safety Messages (BSMs) is the major exchanged data type. The core part of BSMs includes vehicle position (longitude, latitude), motion (speed, heading, and acceleration) and control (brake, turn signal). This study presents a new approach to identify aggressive lane change behaviors by using BSMs generated in real world by vehicles equipped with data acquisition systems (DAS) devices that participated in the Safety Pilot Model Deployment deployed in Ann Arbor Michigan. A total of 2,160,912 samples generated from 246 trips by 51 vehicles are used for analysis. The proposed approach have successfully identified lane change behaviors using a sub-sample (652,071) generated from 51 trips and the results have been successfully validated by using Google Earth.
- While analysis of the overall crash samples (based on overall crash and injury distribution) is important, it is also imperative to rigorously analyze the tail of injury distributions (large-scale events),
given the huge monetary impact of such crash events on society, and to identify covariates and potential countermeasures. This project aims at, 1) What are the factors that can have direct effects on injury outcomes
of large scale crashes (either in terms of injury involved or truck/bus involvement)?, 2) What factors contribute significantly to injury outcomes of large scale crashes via a mediating effect through pre-crash critical
behavioral errors? (both from temporal and geographically diverse perspective), 3) Given that a driver is observed to be "at-fault", which type of behavioral errors (intentional/unintentional) constitute the event specific
taxonomies of errors?, 4) What implications do these taxonomies of errors of "at-fault" drivers have on injury outcomes of other "Not-At-Fault" crash involved vehicles (drivers/passengers). The research team will utilize
rigorous analytical and econometric tools such as path analysis, geographically weighted count data regressions, and time-series models to address afore-mentioned objectives. Ultimately, the detailed proposed framework is
expected to provide fruitful guidelines for policy making and educational and enforcement programs, and thus likely helping the nation in achieving the challenging "zero-fatality" national goal.
- Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while
reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers' vehicle purchase and use choices. The Environmental Protection Agency's fuel economy ratings are a key source of potential benefits of using AFVs.
However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be
unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that
can better represent consumers' real-world driving practices instead of using pre-designed standard driving cycles. In this project, a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based
on drivers' characteristics and contemporary real-world driving has been developed. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically,
using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more
than 60,000 trips, made by 3,000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. The AFV driving cycles, created from real-world driving data, show significant
differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisio
- It has been shown that due to variations in real-life driving, pre-designed standard driving cycles are not precise to obtain accurate fuel consumption estimations for individual customers using specific types of vehicles (Liu et al. 2016).
In this project the objective a comparative analysis of the fuel consumption for three different vehicles models is performed, by using customized driving profiles based on real driving data along and pre-designed standard driving cycles.
The characteristics of the standard driving profiles are compared to the customized driving profiles. Then, three fuel consumption estimation models are used to compare the fuel consumption for the standard and the customized driving profiles.
- The Safety Pilot Model Deployment (SPMD) underway in Ann Arbor, Michigan is intended to demonstrate vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies in a real-world environment.
This study attempts to utilize Basic Safety Messages (frequency of 10 Hz) through Data Acquisition Systems (967, 270 samples from 154 unique trips undertaken by 48 vehicles) to understand the process that
governs the time at which driving behavior transitions between different regimes/states i.e. longitudinal acceleration and deceleration, jerky movements. Recognizing the time-series driving behavior profiles
that are believed to transition over finite set of unobserved regimes/states (acceleration/deceleration), a dynamic Markov-switching regression framework will be developed for understanding the persistency and
volatility of two different states in driving behaviors. The proposed framework will allow the evolution of specific driving regimes separately and to conceptualize factors that may be associated with regime(s) evolution.
The analysis is expected to reveal several factors or situations that may be differently associated with, 1) driver's tendency to stay in same state and 2) the conditions in which they switch between different driving states i.e.
acceleration and deceleration. From safety perspective in connected vehicle environment, such information can help us understand the specific situations (e.g. factors related to host vehicle's motion, vehicle component status,
and instantaneous driving contexts) that may be associated with drivers' tendency to behave differently, thus helping us in development of preemptive warnings/alert and control assist applications.
- Traffic incidents always post a threat to the traffic flow along the freeway by introducing negative impacts to the overall freeway transportation system, such as congestion, reduced safety level,
increased energy consumption and fuel, increased greenhouse gas emissions and drivers' increasing anxiety, etc. In this study, we are trying to find out the associated or causal factors that will induce
the occurrence of the traffic incidents by using several regional incident data, and other incident related databases. Specifically, one objective of our study is to develop a model for the incident duration through
applying several duration prediction approaches. Another objective of this study is to develop algorithms and models to detect the secondary incidents that can occur as a consequence. By studying the independent and
dependent variables in the database, we further analyze the operational defects of the procedures of regional traffic incident management teams. Also simulation analysis is applied to warn the traffic incident site if a
queue is formed during the incident in order to further reduce the possibility of secondary incident occurrence. Through this study, we expect to be able to provide more useful information to traffic incident managers to
further improve current operations, mobility, safety, energy use and environment.
- TCAVs represent the opportunity to greatly enhance safety, and reduce both congestion and emissions. Adoption of new technologies is often messy,
even if they follow the familiar S-shaped adoption curve. Among the challenges is how will partial adoption of automated technologies, characterized by
levels 0 to 5, work in a transportation network? In this work we are developing simulations to help us understand the impacts of CAVs in transportation networks.
Specifically, our research is focusing on developing network simulations and algorithms to understand how variations in driving control will impact safety and congestion.
Specifically, in this exploratory study we are using novel tools to understand the implications of partial automation on the traffic network performance.
This problem is made complex by the unpredictable nature of partial automation, where humans have different levels of involvement. In this study,
we are developing simulations and algorithms to better understand how variations in driving control will impact safety and congestion.
Dr. Asad Khattak
Dr. Khattak is Beaman Professor of Civil & Environmental Engineering, University of Tennessee, Knoxville and Transportation Program Coordinator in the Department. Dr. Khattak received his Masters and Ph.D. degrees in Civil Engineering from Northwestern University in 1988 and 1991, respectively. Dr. Khattak's research focuses on various types of innovations related to 1) intelligent transportation systems, 2) transportation safety, and 3) sustainable transportation.
Read more about Dr. Khattak and his recent work.....
Dr. Chris Cherry
Dr. Chris Cherry is an Associate Professor at the University of Tennessee. He received his BS and MS in Civil Engineering from the University of Arizona and received his PhD in Civil and Environmental Engineering from the University of California, Berkeley in 2007.His research interests include multimodal transportation planning and operations, public transportation systems, travel behavior and demand, transportation economics, sustainable transportation and transit security. Read more about Dr. Cherry and his recent work.....
Dr. Airton Kohls
Research Associate Airton Kohls - Dr. Airton G. Kohls - Dr. Kohls is currently appointed as a Research Associate at the University of Tennessee Center for Transportation Research. He received his Ph.D. in Civil and Environmental Engineering from the University of Tennessee in 2010. His Ph.D. research focus on Adaptive Traffic Signal Control. Dr. Kohls is the developer and lead instructor of the Traffic Signal Academy, a program recommended by FHWA's Arterial Management Program since 2012. Dr. Kohls has program management experience from his 10 years of work in Brazil and was a Principal Investigator for the FHWA Upgrade of Geographic Information System project in 2014, Principal Investigator for TDOT Extreme Weather Vulnerability Assessment in 2013 and FHWA NHI Highway Traffic Noise in 2012.Dr. Kohls is a member of ITE and a reviewer for the TRB Traffic Signal Committee.
Dr. Subhadeep Chakraborty
Assistant Professor Subhadeep Chakraborty - Mechanical engineering, travel behavior. Dr. Chakraborty is currently appointed as an Assistant Professor in the Mechanical Aerospace and Biomedical Engineering Department at the University of Tennessee. He received his Ph.D. in Mechanical Engineering and a dual M.S. in Electrical Engineering from the Pennsylvania State University in 2010. His Ph.D. research focused on data-driven techniques for monitoring complex systems. He further developed this topic as a post-doctoral research associate at the University of Tennessee. Dr. Chakraborty's research encompasses the broad topics of collaborative decision making in the context of connected vehicles. More specifically, he focuses on modeling and learning individual driver's decision-making as a function of group driving behavior. He uses GPS and IMU sensor data for deploying learning algorithms. His research also involves multi-agent optimization of groups of vehicles to minimize damage due to secondary crashes. With grants from the Volkswagen Research Initiative, Dr. Chakraborty has instrumented a car with lasers, 360-degree camera, GPS and IMU units and is conducting studies for learning driver behavior in a connected environment. His group is also developing a distributed platform for simulating the connected vehicle environment, which will be useful for investigating the role of optimization algorithms and message transfer delays in deploying driver assist systems. His relevant professional affiliations include membership of IEEE and IEEE Robotics associations, and he has served as reviewer and session chair for numerous IEEE and ASME conferences.
Dr. Mingzhou Jin
Associate Professor Mingzhou Jin - industrial engineering, systems engineering. Dr. Mingzhou Jin is the Associate Professor and Associate Department Head of the Department of Industrial and Systems Engineering at UT and also directing both the Logistics, Transportation, and Supply Chain Engineering (LTS) lab and the Reliability and Maintainability Engineering (RME) program for the College of Engineering. He has done about 40 projects in the areas of transportation, optimization, systems engineering, and logistics with total funding of more than $8M. His sponsors include USDOT, Department of Homeland Security, three State DOTs, American Trucking Associations, several University Transportation Centers, and various companies. He has been a consultant for the Chief technical consultant for New Global Systems for Intelligent Transportation Management (NGSIM) since 2009. With more than $2M funding from USDOT, NGSIM is developing a microscopic simulation package to support connected vehicles research and education. Dr. Jin has been also working on how to use connected vehicles technology to improve the safety and mobility performance at signalized intersections and to improve battery performance and driver experience for electric vehicles. Dr. Jin is currently the president of the Engineering Economy division of the Institute for Industrial Engineers (IIE), the president-to-elect of the IIE Logistics and Supply Chain Division, and the secretary of the INFORMS Railway Application Society. He is serving on the editorial boards of the world-famous journals of the Engineering Economists, the Journal of Cleaner Production, and the International Journal of Production Economics. He has published more than 80 research papers in journals and refereed conferences, such as Transportation Research Part B, Transportation Research Record, Naval Research Logistics, Operations Research Letters, European Journal of Operational Research, and Journal of Transportation Research Forum.
Dr. Qing Cao
Assistant Professor Qing Cao - Electrical engineering, mobile sensor systems. Professor Cao is currently an assistant professor at the EECS department of the University of Tennessee. He obtained his Ph.D. in computer science from the University of Illinois in 2008. He has published more than 50 papers with more than 2,600 citations, including two best paper awards, and one best paper runner-up since 2008. He also has considerable experience in developing large-scale software packages, where he has previously led the development of an embedded operating system called LiteOS, an operating system that exports user-friendly abstractions for operating, programming, and deploying software on sensor network. Cao has worked on previous projects sponsored by NSF and DOE, such as his NSF CAREER project, "CAREER: Operating System based Energy Virtualization for Sensor Networks". Through these projects, Cao has accumulated rich experience in networking, embedded systems, and sensor networks. He has applied such expertise to connected vehicle networks in recent years, focusing on its software and networking challenges. Through these previously funded projects, Dr. Cao participated into the activities of advising 6 Ph.D. students and 5 Master students, including two female students. Cao is a member of IEEE, ACM, and the IEEE computer society.
Dr. Mark Dean
Distinguished Professor Mark Dean - Electrical engineering, security and large data. Dr. Dean is a John Fisher Distinguished Professor at the University Of Tennessee, College Of Engineering. His research focus is in advanced computer architecture (beyond Von Neumann systems), data centric computing, computational sciences and sensor array systems for environmental monitoring and analysis. Prior to UT Dr. Dean worked for IBM for 34 years, retiring as an IBM Fellow and vice president of Research. Of the 39 patents issued to Dr. Dean, he holds three of the nine patents for the original IBM PC. Dr. Dean's degrees include: a BSEE from the University of Tennessee, a MSEE from Florida Atlantic University, and a Ph.D. in Electrical Engineering from Stanford University. Dr. Dean's honors include: National Institute of Science Outstanding Scientist Award, AAAS member, NAE member, IEEE Fellow, Black Engineering of the Year, Ronald H. Brown American Innovators Award, and the National Inventor's Hall of Fame.
Dr. Shashi Nambisan
Professor Shashi Nambisan - Civil & environmental engineering, education, training and outreach. Dr. Shashi Nambisan received his Ph.D. from University of California at Berkeley in 1989 and MS from Virginia Tech in 1985. Dr. Nambisan is a Professor of Civil Engineering at University of Tennessee, Knoxville (UT). He was previously at Iowa State University in Ames, Iowa, where he was professor of Civil Engineering and formerly the director of the Institute for Transportation. He is a registered Professional Engineer in Nevada. Dr. Nambisan has more than 25 years of experience in transportation including more than 20 years leading the development and growth of research enterprises. He has led over 150 projects on a broad range of issues in transportation and infrastructure systems management related to policy, planning, operations, safety, and risk analysis. His research projects have involved local, statewide, regional, and national issues in transportation and information systems management, primarily related to the development and deployment of decision support tools. He has more than 125 peer reviewed journal and conference publications, and made over 220 technical presentations. Over the past 2 decades Dr. Nambisan has been an active in leader in several professional organizations such as the American Society of Civil Engineers, American Society for Engineering Education, Council of University Transportation Centers, Institute of Transportation Engineers, and Transportation Research Board. Among the awards and honors received by Dr. Nambisan is a proclamation by the Governor of the state of Nevada designating January 31, 2007 as the "Professor Shashi Nambisan Day" to recognize his leadership and contributions to enhancing transportation safety.