Poster Sessions
Be sure to check out the poster session, which will take place after lunch in the Kansas Ballroom! The following posters will be presented.
ABSTRACT
Understanding crash patterns and identifying effective countermeasures have always been challenging as 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, 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.
This methodology was implemented in USDOT-funded Safe Streets for All (SS4A) projects for Eureka and El Dorado, Kansas, successfully identifying locally relevant countermeasures and revealing 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 Random Forest (RF). The selected factors—covering roadway type, control type, lighting, surface condition, and driver behaviors were used to group similar crashes through several unsupervised algorithms. Among those, K-Modes provided the most reliable representation of categorical crash attributes. Each resulting cluster represents a unique crash context or “crash typology,” where crashes within a cluster share 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.
Moreover, a severity-based scoring system was 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 cost relationships reported in the Highway Safety Manual and crash severity cost estimates used in Kansas Department of Transportation safety analyses, and was 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.
Integrating the cluster-level profiles with the HIN helped determine which crash types dominate each high-risk location. Moreover, this method allows to not only evaluate the countermeasures for HIN but also prioritize the countermeasures for each intersection and segment. For instance, if a site showed 50%, 30%, and 20% of 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 by Clusters 2 and 3. This proportional approach ensures that safety improvements directly reflect the dominant crash patterns present at each site, leading to more targeted and efficient countermeasure implementation. The approach provides a scalable, transferable, and reproducible framework for cities and counties 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.
Amirmohammad Sadeghnejad, Li Zhao, University of Nebraska-Lincoln
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Karla Diaz-Corro, University of Arkansas
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Samuel Durairaj, University of Kansas Medical Center
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Yuxi Shen, Michigan State University
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Christos Achillides, Iteris, Inc.
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Bahareh Bakhti, University of Kansas
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José Carlos Acedo Aguilar, University of Texas at El Paso
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Mahgam Tabatabaei, Siavash Shojaat, Alexandra Kondyli, University of Kansas
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Shuxia Pang, Saint Louis University
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Xinyuan Li, University of Kansas
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Yuhui Liu, Saint Louis University
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Yassine Ibork, Wichita State University
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Logan Pittman, University of Kansas
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Mansimran Singh, University of Iowa
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Samiha Karim Subah, Richa Bhattarai, Arif Mohaimin Sadri, University of Oklahoma
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ABSTRACT
Worn lane markings weaken the guidance that drivers and lane-keeping systems rely on and are associated with lane-departure crashes, so degraded lines must be identified and restriped before they become hazards. Vision-language models can read marking condition from dashcam imagery, but their reliability under uneven illumination is not established. This study evaluates GPT-5.4 and Claude Opus 4.8 on US-75 corridor frames where intact lane markings pass from daylight into bridge-underpass shadow, using the shadow as a natural experiment that separates the effect of lighting from that of wear. Three prompts are compared, and each frame is judged three times. Illumination is the main obstacle: across all prompts, intact markings in shadow are flagged as worn several times more often than in good light. The few-shot prompt gives the best balance, and routing only the frames where repeated queries disagree to human review yields a workflow suited to maintenance screening.
PRESENTER
Tianyang Cui
Tianyang Cui is a second-year PhD student in Mechanical Engineering at the University of Texas at Dallas, supervised by Dr. Zejiang Wang. His primary research focuses on scenario generation for intelligent transportation systems, developing methods to create and test-driving scenarios for safety evaluation. More broadly, his work applies artificial intelligence and computer vision to transportation problems, including the vision-language model approach to lane-marking condition assessment presented in this poster. He is interested in developing practical AI tools that advance transportation safety and infrastructure monitoring.
Haohua Chen, University of Kansas
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Oluwaseun Taiwo Ajadi, University of Kansas
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Nhat Ha Nguyen, Wichita State University
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Mehdi Zolali, University of Arkansas
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Amirmohammad Sadeghnejad, Li Zhao, University of Nebraska–Lincoln
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