Speakers & Presentations

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Xiaopeng Li, University of Wisconsin-Madison, Director of the U.S. Tribal and Rural Autonomous Vehicles for Efficiency, Livability and Safety Program

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ABSTRACT 

Several studies have shown that female occupants are more susceptible to severe injuries and fatalities than males. For example, one study reported that females have a 22% higher risk of head injury, 45% higher risk of neck injury, 26% higher risk of chest injury, and 80% higher risk of leg injury with compared to males. The American Association of State Highway and Transportation Officials (AASHTO) Manual for Assessing Safety Hardware (MASH) provides guidelines for crash testing and evaluating roadside safety hardware. MASH safety criteria include occupant compartment intrusion limits and the flail space model when assessing roadside safety hardware. Occupant compartment intrusion limits, or the amount of interior vehicle crush toward an occupant, vary based upon location within the vehicle, such as the windshield, roof, floorboard, toe pan, door, etc. Intrusion limits established by MASH have been shown to strongly predict injury risk, with crashes exceeding these limits significantly more likely to result in injuries. However, even intrusions below the thresholds can cause significant injuries, especially for females.

The flail space model assumes unrestrained occupants with longitudinal and lateral traversal distances prior to occupant impact with stiff internal vehicle structures. Longitudinal and lateral traversal distances of 24 in. and 12 in. respectively, were determined with a 50th-percentile male dummy. The time at which the occupant traverses 24 in. longitudinally or 12 in. laterally, which is the theoretical time of occupant impact with interior vehicle structures, establishes the longitudinal and lateral occupant impact velocities (OIVs). The flail space model assumes that the occupant remains in contact with the vehicle after initial impact. Therefore, the occupant is subjected to the same accelerations and velocity changes as the vehicle. As such, the occupant ridedown acceleration (ORA) is determined from the peak longitudinal and lateral accelerations after the occupant impacts the vehicle interior. The limits for OIVs and ORAs are 40 ft/s and 20.49 g, respectively. 

Due to known differences in male/female injury and fatality risk in the event of a crash, it was desired to evaluate the flail space model with varying longitudinal and lateral traversal distances reflective of the 5th- and 50th-percentile female and 95th-percentile male seated positions. To do so, 244 previous crash tests across 11 system types were reevaluated with the alternative traversal distances. Of those tests, 26%, 44%, and 57% indicated 5th-percentile female, 50th-percentile female, and 95th-percentile male, respectively had same or less severe OIV when compared to 50th-percentile male. Further, 95%, 98%, and 89% of tests indicated the 5th-percentile female, 50th-percentile female, and 95th-percentile male, respectively had same or less severe ORA when compared to 50th-percentile male. Differences in which 5th- and 50th-percentile female exhibit higher OIVs and ORAs than 50th-percentile male were minor.

To further evaluate MASH occupant risk criteria, the Hybrid III 5th percentile female, 50th percentile male, and 95th percentile male dummy models were seated in vehicle models and impacts into roadside safety hardware were simulated, and dummy injury criteria were compared to MASH criteria. 

PRESENTER

Brandon Perry

Brandon Perry received his Bachelor's degree in Biological Systems Engineering at the University of Nebraska-Lincoln (UNL) and Master’s degree in Mechanical Engineering from the University of Virginia (UVA). He is currently a Research Engineer at UNL’s Midwest Roadside Safety Facility (MwRSF) where his focus is developing crashworthy roadside safety hardware and evaluating designs with computer simulations and full-scale crash tests.

ABSTRACT 

The American Association of State Highway and Transportation Officials (AASHTO) Manual for Assessing Safety Hardware (MASH) provides guidelines for crash testing and evaluating roadside safety hardware. Roadside safety hardware includes bridge rails, longitudinal barriers such as W-beam guardrail and cable barriers, end terminals for longitudinal barriers, crash cushions, sign supports, luminaire poles, etc. Evaluation criteria for full-scale vehicle crash testing are based on three factors: (1) structural adequacy, (2) occupant risk, and (3) post-impact vehicle trajectory. Criteria for structural adequacy are intended to evaluate the ability of roadside barrier to contain and redirect impacting vehicles. In addition, controlled lateral deflection of the test article is acceptable. Occupant risk evaluates the degree of hazard to occupants in the impacting vehicle. Post-impact vehicle trajectory is a measure of the potential of the vehicle to result in a secondary collision with other vehicles and/or fixed objects, thereby increasing the risk of injury to the occupants of the impacting vehicle and/or other vehicles. 

Occupant compartment intrusion limits, or the amount of interior vehicle crush toward an occupant, vary based upon location within the vehicle: 

• Roof: ≤ 4.0 in.,
• Windshield: must not tear or deform more than 3 in.,
• Side window: must remain intact (no shattering),
• A- and B-pillars: ≤ 5 in. resultant deformation and ≤ 3 in. lateral deformation,
• Toe pan, and front side door area (above the seat): ≤ 9 in.,
• Side front panel, front side door (below the seat), floor pan, transmission tunnel: ≤ 12 in.

The flail space model assumes unrestrained occupants with longitudinal and lateral traversal distances prior to occupant impact with stiff internal vehicle structures. Longitudinal and lateral traversal distances of 24 in. and 12 in., respectively. The time at which the occupant traverses 24 in. longitudinally or 12 in. laterally, establishes the longitudinal and lateral occupant impact velocities (OIVs). The flail space model assumes that the occupant remains in contact with the vehicle after initial impact. Therefore, the occupant is subjected to the same accelerations and velocity changes as the vehicle. As such, the occupant ridedown acceleration (ORA) is determined from the peak longitudinal and lateral accelerations after the occupant impacts the vehicle interior. The limits for OIVs and ORAs are 40 ft/s and 20.49 g, respectively. 

Challenges and examples of roadside safety hardware design, testing, and evaluation, the evolution of the vehicle fleet, and compatibility of battery electric vehicles (BEVs) with current roadside safety systems will be discussed. 

PRESENTER

Brandon Perry

Brandon Perry received his Bachelor's degree in Biological Systems Engineering at the University of Nebraska-Lincoln (UNL) and Master’s degree in Mechanical Engineering from the University of Virginia (UVA). He is currently a Research Engineer at UNL’s Midwest Roadside Safety Facility (MwRSF) where his focus is developing crashworthy roadside safety hardware and evaluating designs with computer simulations and full-scale crash tests.

MM Shakiul Haque, Jon Camenzind, and Aemal J. Khattak, University of Nebraska-Lincoln 

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Aemal Khattak, MM Shakiul Haque, Mujahid Ali Bahadur, Mid-America Transportation Center, University of Nebraska-Lincoln 

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Aobo Wang, Oregon State University 

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Christos Achillides, Iteris, Inc. 

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MM Shakiul Haque, Aemal J. Khattak, University of Nebraska-Lincoln 

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Tsigereda Mossie and Aobo Wang, Oregon State University 

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Li Zhao, University of Nebraska-Lincoln 

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Trilce Encarnación, University of Missouri-St. Louis 

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Geoffery Eyram Agorku, University of Arkansas 

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Omar Ahmad, University of Iowa 

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Caleb Knerr and Ian Waters, HDR, Engineering Inc. 

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Omid Armantalab, William Brown, Li Zhao, Wissam Kontar, University of Nebraska-Lincoln 

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ABSTRACT

Overview

Understanding crash patterns and identifying effective countermeasures have always been challenging. It requires a deeper look into the roadway, behavioral, and environmental contexts that shape crash outcomes. This study proposes a novel data-driven methodology that combines machine learning (ML), clustering, and spatial risk scoring to evaluate and prioritize countermeasures at both intersection and corridor levels. The approach was designed to be practical and transferable so that cities and counties can easily apply it under the Safe System Approach framework.

Methodology

This methodology was implemented in USDOT-funded Safe Streets for All (SS4A) projects in Eureka, Kansas, and El Dorado, Kansas. Findings successfully identified locally relevant countermeasures and revealed behavioral and roadway risk patterns that traditional screening methods often overlook.

• Crash data from local transportation networks were analyzed to identify variables most associated with crash severity using ML model (random forest).
• Using the selected factors derived from ML model—roadway type, control type, lighting, surface conditions, and driver behaviors—similar types of crashes were grouped together through several unsupervised clustering algorithms.
• Each resulting crash cluster represented a unique crash context or “crash typology” where crashes shared similar characteristics while remaining distinct from other clusters.
• Detailed examination of cluster profiles allowed identifying the dominant contributing factors, crash scenarios, and overrepresented conditions for each cluster.
• Countermeasures were then developed for each cluster by combining insights from relevant literature, FHWA and NCHRP guidance, and practical engineering judgment and field experience.

A severity-based scoring system was also developed to quantify crash risk by assigning greater importance to more severe crashes while still considering less severe and property-damage-only crashes. The weighting structure was derived from crash severity cost estimates used in Kansas Department of Transportation safety analyses and engineering judgement further calibrated with local crash characteristics.

• Fatal and disabling injuries were combined because both often share similar contributing conditions.
• To capture the spatial dimension of risk, DBSCAN clustering was used to aggregate crashes at intersections and roadway segments separately. The cumulative risk score at each location was normalized by its crash proportion within the same facility type to create a ranked High-Injury Network (HIN) highlighting the top high-risk intersections and corridors.

Results

Integrating the cluster-level profiles with the HIN helped determine which crash types dominated each high-risk location. Moreover, this method evaluated the countermeasures for HIN while also prioritizing the countermeasures for each intersection and segment. For example, if a site showed 50%, 30%, and 20% of total crashes from clusters 1, 2, and 3 respectively, countermeasure priorities would follow the same order—giving highest priority to strategies effective for Cluster 1, followed the other clusters. This proportional approach ensured that safety improvements directly reflected the dominant crash patterns present at each site, leading to more targeted and efficient countermeasure implementation.

Conclusion

The approach provides a scalable, transferable, and reproducible framework for cities seeking to implement data-informed, human-centered safety improvements consistent with Vision Zero and the Safe System principles.

PRESENTER

Saumik Sakib Bin Masud

Dr. Saumik Sakib Bin Masud is a Traffic Engineer at JEO Consulting Group specializing in transportation safety, the Safe System Approach, and data-driven decision making. His work focuses on developing Safe Streets and Roads for All (SS4A) Safety Action Plans, conducting highway safety and operational analyses, evaluating roadway and interchange alternatives, and applying predictive safety methods, machine learning, and advanced analytics to support evidence-based transportation planning, infrastructure investment, and policy decisions. He earned his Ph.D. in Transportation Engineering from the University of Kansas, where his research advanced the understanding of human factors and automated driving systems through machine learning and driving simulation. He continues to conduct applied research in transportation safety, intelligent transportation systems, and artificial intelligence, bridging research and practice to improve roadway safety and mobility. Dr. Masud is an active member of the ITE Safety Council, the ITE Vision Zero Standing Committee, and ASCE, and regularly presents his work at national and international conferences while contributing to innovative, data-driven transportation safety solutions.

Mingfeng Shang, Rochester Institute of Technology 

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Mohammad Elayan and Wissam Kontar, University of Nebraska-Lincoln 

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Tianyi Li, Saint Louis University 

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ABSTRACT

A review of the literature identifies eight distinct categories of models used to simulate traffic flow: safe-distance, stimulus-response, optimal velocity, desired measures, gas-kinetic, kinematic, multi-class, and momentum-based. This presentation connects the fundamentals of fluid mechanics to each model, highlighting the equations typically solved, whether the model is applied to single or multiple vehicles, and the solver type required for computation. The discussion covers both microscopic and macroscopic traffic flow, distinguishing between models that involve straightforward computation using ordinary differential equations and those that require more complex numerical methods to solve partial differential equations.

This review, combined with an evaluation of microscopic leader-follower data from the Waymo Open Dataset, leads to new insight into simulating the follower vehicle. Specifically, observed oscillatory behavior in follower velocity relative to spacing motivates the inclusion of a bounded diffusion term in a safe-distance model. Historical context is provided on the use of diffusion in car-following models, with emphasis on how this approach aligns with and diverges from prior work. The modified model is evaluated across human-driven vehicle (HDV)-HDV, HDV-autonomous vehicle (AV), and AV-HDV datasets, with model predictions of distance, velocity, and acceleration compared to the data.

Connecting fluid mechanics with traffic flow theory enables a direct mapping between microscopic and macroscopic models. Results highlighting the translation of microscopic driver behavior to macroscopic traffic flow patterns are presented. By the end of this presentation, the audience will gain a clearer understanding of the interconnectedness of these models and how a fluid mechanics perspective can enhance the interpretation of traffic flow data.

PRESENTER

Christopher Depcik

Dr. Christopher Depcik is a Professor of Mechanical Engineering at the University of Kansas (KU), with a courtesy appointment in Aerospace Engineering. His research focuses on reacting flow modeling and energy systems, with an emphasis on reduced-dimensional (1-D and 1+1-D) formulations of chemically reacting flows and fluid mechanics. His work spans topics including catalytic reactors, desiccant wheels, traffic flow, and spirit maturation. His research group has published more than 130 peer-reviewed papers, and he has been recognized among the top 2% of researchers worldwide in the Elsevier Scopus rankings. He has received numerous departmental, school, university, national, and international awards for service, research, and teaching. He is a Fellow of ASME and SAE.

ABSTRACT

Resilience frameworks in transportation largely inherit their assumptions from natural hazards, which are random, bounded, and reasonably well characterized by historical data. Digitization strains that inheritance. Signal control, traffic management, connected vehicles, and electronic payment now depend on networked systems whose failures are chosen rather than drawn from a distribution. This presentation argues that cybersecurity does not simply add another hazard to existing resilience models but unsettles the premises on which they rest. An adversary adapts, selects targets, and may remain undetected, complicating both probabilistic reasoning and the recovery timelines that most resilience metrics assume. Drawing on work in cyber-physical systems and infrastructure resilience, the talk considers what changes when intelligent threats are treated as a design case: how disruption cascades across digital and physical boundaries, why organizational capacity may matter as much as technical hardening, and where research and agency practice should turn next.

PRESENTER

Kevin Heaslip

Kevin Heaslip, Ph.D., P.E., is Professor of Civil and Environmental Engineering and Director of the Center for Transportation Research (CTR) at the University of Tennessee, Knoxville. He directs FERSC, a $10M USDOT Tier 1 University Transportation Center focused on freight, energy, resilience, safety, and connected infrastructure, and co-leads the UT-ORNL Transportation Convergent Research Initiative. Under his leadership, CTR has grown to $15M in annual expenditures with over 150 personnel across four research thrusts: electrified connected automated transport, transportation operations, cybersecurity and resilience, and safety. His research spans transportation cybersecurity, intelligent transportation systems, infrastructure resilience, and automated and electric vehicle systems. He has secured over $40M in funded research across three institutions and published 73 journal articles. He is a licensed Professional Engineer and serves on TRB committees for connected/automated vehicles.

Richa Bhattarai, Samiha Karim Subah, Dominique Pittenger, Arif Sadri, University of Oklahoma 

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Samiha Karim Subah, University of Oklahoma 

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Juana Jaramillo-Rios, University of Missouri-St. Louis 

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Keshu Wu, Texas A&M University 

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Lokesh Das, Wichita State University 

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Sara Reed, University of Kansas 

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Efthymia Kostopoulou, Michigan State University 

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Shelley B. Bhattacharya, University of Kansas Medical Center 

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Marshall L. Mabry, University of Minnesota Twin Cities 

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Blessing Agyei Kyem, North Dakota State University 

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Jian Li, University of Kansas 

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Sarper Demirdogen, University of Kansas 

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Sherbaz Khan, University of Louisiana at Lafayette 

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