TTI Competitive Research Program 2015

2014-2015 Awardees:

Tom Ferris

Tom Ferris                                           
Assistant Professor                               

Project Monitor: Joel Cooper, PhD, University of Utah Psychology Department and President of Precision Driving Research – Email:
Other Team Members: Youngbo Suh, Industrial and Systems Engineering PhD Student; Jeff Miles, Assistant Research Engineer
Project Title: Investigating the roles of touchscreen and physical control interface characteristics on driver distraction and multitasking performance


In-vehicle technologies are increasingly designed with touchscreen interface controls, replacing physical buttons/knobs. This has implications for the nature of attention orientation when interacting with common controls, often requiring a larger degree of visual feedback to substitute for the lack of haptic feedback. As a result, visual attention is diverted away from the roadway to a greater extent when interacting with these technologies, which may affect driving safety and performance. Synthetic feedback (auditory or vibrotactile cues such as “clicks”) may reduce the need for extended visual reorientation. Not much is known from existing research about the extent to which physical controls and touchscreens with various types of synthetic feedback affect one’s visual awareness of surroundings, and consequently, driving safety and performance. This study will investigate both a handheld (smartphone) and dashboard-mounted interface under various feedback conditions to determine the quality of interaction and also the effects on a real-world driving task. To this end, a TTI fleet vehicle will be used on a closed course track at the Texas A&M Riverside campus, and study participants will be tasked with safely navigating the prescribed course while conducting a secondary task on a smartphone and dashboard touchscreen. Proper safety precautions will be taken to ensure minimal risk to the driver, and the study team will seek approval from the TAMU Institutional Review Board prior to commencing the study. Driver performance measures such as speed and lane position will be collected, as well as measures related to performance on the interaction task, eyetracking, and a “visual awareness” task which will require visual recognition of and verbal acknowledgment of various targets of interest, such as colored cones placed next to buildings. The findings from the study will be informative to the designers of in-vehicle technologies which require manual driver interaction, such as GPS systems, and will also be informative for the design of handheld technologies meant to be interacted with while multitasking.

Kay Fizpatrick

Kay Fitzpatrick
Senior Research Engineer

Project Monitor: Elizabeth Hilton, P.E., Geometric Design Engineer, FHWA-HIPA-20 – Email:
Other Team Members: Subasish Das, Associate Transportation Researcher; Adrian Contreras, Graduate Student
Project Title: Is Age a Factor in Crashes at Channelized Right-Tum Lanes?


Several documents, including the recently published Federal Highway Administration 2014 Handbook for Designing Roadways for the Aging Population, states that older drivers have difficulties in turning their head to see upstream gaps in a merge situation. The objective of this study is to determine the relationship between crashes and channelized right-turn lane geometric characteristics with a specific consideration of the age of the driver.

Joan Hudson

Joan Hudson
Associate Research Engineer

Project Monitor: Will Bozeman, Transportation Operations, Texas Department of Transportation (TxDOT) – Email:
Other Team Members: Haotian Zhong; Maarit Moran; Vichika Iragavarapu; Vickie Vincent; Boya Dai, AICP
Project Title Best Practices for Addressing Pedestrian Crashes on High Speed Roadways


The objective of this project is to identify significant factors associated with pedestrian safety on high speed controlled access roadways and survey model Departments of Transportation in terms of strategies and regulations to lower pedestrian crash on high speed roadways. Document best practices in education, enforcement, engineering, and evaluation. The result is expected to have statewide applicability with particular focus on urban areas.

Dominique Lord2  Geedipally,Srinivas-2

Dominique Lord                             Srinivas Geedipally
Associate Professor                        Assistant Research Engineer                 

Project Monitor: Dr. John N. Ivan, Professor and Associate Head of Department of Civil & Environmental Engineering, University of Connecticut – Email:
Other Team Member: Mohammadali Shirazi
Project Title: Developing a procedure for estimating the required sample size for safety performance function calibration factors


The predictive procedures included in the Highway Safety Manual (HSM) are known as safety performance functions (SPFs) and are based on observed crashes for a select number of locations in the United States.  For a jurisdiction to be able to fully benefit from applying the SPFs to their region, it is necessary to calibrate these models for local conditions, being a state or one of its regions. Currently, existing manuals recommend a “one-size-fits” all sample size for calibration procedures that require crash data from 30 to 50 locations with a minimum number of approximately 100 crashes per year. This recommended sample size is not fully supported by documented research and several agencies that have initiated SPF calibration efforts indicate that this sample size is impossible to obtain due to infrequent crash conditions associated with certain facility types. The objectives of the proposed research are to (1) review and document issues with the existing calibrating method in the HSM, (2) develop a procedure for the calibration of crash count and severity-level models developed using a discrete choice model, (3) identify factors that influence the calibration procedures and the selection of the sample size, and (4) determine how frequently an agency should update their calibration factors.

The study objectives will be accomplished using simulated and observed data.  The anticipated guidelines will include a discussion on (1) the minimum sample size, (2) the characteristics of the data used for the calibration procedure, (3) potential issues with the calibration procedure, and (4) when or how often models need to be re-calibrated.


Michael Manser
Senior Research Scientist

Project Monitor: Tom Ferris, Industrial and Systems Engineering, Texas A&M University – Email:
Other Team Member: Laura Higgins, M.S.
Project Title: Older Driver Support System


A significant factor impinging on older drivers’ ability to maintain safe and efficient mobility as they age is the decline in behavioral, cognitive, and perceptual functions. The provision of real-time information and warnings regarding unsafe and risky driving behaviors has been shown to be effective for improving safe driving in other high risk populations. The goal of this research study is to develop and demonstrate a smartphone-based application that supports older driver safety through the provision of real-time information and warnings to address the behavioral, cognitive, and perceptual factors associated with older driver crashes. This will be accomplished through the completion of four tasks that include the identification of needed information and warnings, designing a smartphone-based application, building the application, and then pilot testing the application with older drivers. Results of the work will serve as the basis for future on-road research studies examining the utility of smartphone-based older driver support systems.

Chiara Silvestri Dobrovolny2      dfblower

Chiara Silvestri-Dobrovolny             Daniel Blower
Associate Research Scientist, TTI    Associate Research Scientist, UMTRI  

jrupp  Jingwen Hu

Jonathan Rupp                                                   Jingwen Hu
Research Associate Professor, UMTRI          Associate Research Scientist, UMTRI                                    

Project Title: Project Investigation of the Correlation between Roadside Safety Hardware and Vehicle Safety Standards Evaluation Criteria


The Manual for Assessing Safety Hardware (MASH) defines crash tests to assess the impact performance of highway safety features in frontal and oblique impact events using criteria that are based on the structural adequacy of the safety feature, post-impact behavior of the test vehicle, and risks of injury for the occupants of the impacting vehicle. MASH occupant risk criteria, however, are considered conservative in their nature, due to the fact that they are based on unrestraint occupant accelerations.   Due to increased enforcement, significantly more occupants are belted in real world crashes.  In addition, large improvements to occupant protection, whose contribution is not being considered within MASH occupant risk criteria, can significantly reduce the risk of injury to an occupant in a collision.   Therefore, there is potential for increasing the maximum limits dictated in MASH for occupant risk evaluation.

Praprut Songchitruksa2      Yunlong Zhang2
Praprut Songchitruksa                      Yunlong Zhang
Associate Research Engineer           Associate Professor                   

Project Monitor: Zong Tian, PhD, P.E., Associate Professor and Director Center for Advanced Transportation Education and Research (CATER), University of Nevada – Email:
Other Team Members: Apoorba Bibeka; Lu (Irene) Lin
Project Title: A Framework for Incorporating Driver Behaviors into Simulation Technology for Evaluating Connected Vehicle Applications


The adoption of connected vehicle technology is anticipated at various levels of development and deployment over the next decade. One primary challenge with these new technologies is the lack of platform to enable a robust and reliable evaluation of their safety and mobility benefits given the complexity of wireless communications, algorithms, and range of human behaviors that will interact with and impact upon the system. A simulation technology is commonly accepted as a tool for providing transportation professionals with impact assessment of various operation and control strategies. Simulation models rely on algorithms for microscopic driver behaviors such as car-following and lane-changing models. While these algorithms are sufficient for traditional operation evaluation, they are not always well-suited for modern applications using connected vehicle technology. This project will design a driver modeling framework to enable realistic adjustment of driver behaviors and implement driver modeling modules to facilitate the performance evaluation of connected vehicle applications within the simulation environment. The project will select two connected vehicle applications for prototyping and proof-of-concept testing within the simulation environment. The testing results will be used to refine the modules and demonstrate how the framework can be used to improve the simulation assessment of safety and mobility benefits from connected vehicle applications.



The ATLAS Center is a collaboration between the University of Michigan (U-M) Transportation Research Institute (UMTRI) and Texas A&M Transportation Institute