12 papers
From Questions to Queries to Insights: A Schema-Grounded Agentic Workflow for Natural-Language Crash Data Analysis
Jingwen Zhu, Keshu Wu, Steven T. Parker +2
Transportation agencies maintain extensive crash databases to identify safety priorities, evaluate countermeasures, and guide resource allocation. Making these data accessible to p…
SAGE: A hybrid vision-language and geometry framework for work-zone pathway prediction under temporary traffic control
Jiaxi Liu, Keshu Wu, Weizhe Tang +5
Temporary traffic control redirects vehicles through work zones, requiring automated vehicles to connect an interpretation of the visible devices to a continuous passing path. Geom…
Large Language Model-Assisted Preparation of Transportation Management Plans: A Case Study with WisDOT WisTMP System
Zihao Sheng, Pei Li, Zilin Huang +6
Work zones are critical yet hazardous components of transportation infrastructure, requiring carefully designed Transportation Management Plans (TMPs) to ensure safety and mobility…
Forecasting the Emergence and Evolution of Crash Hotspots: A Unified Deep Learning Framework for Proactive Traffic Safety
Jingwen Zhu, Keshu Wu, Pei Li +3
Road crashes remain among the gravest threats to public safety, and preventing them is a defining task of transportation systems worldwide. Much of that harm concentrates at hotspo…
Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction
Weizhe Tang, Jiaxi Liu, Junwei you +5
Accurate work-zone geometry perception is critical for intelligent transportation systems, and ultra-wideband sensing offers a low-cost approach for infrastructure-aided reconstruc…
V2I Work Zone Geometry Reconstruction with Pose-Conditioned UWB Range Denoising
Jiaxi Liu, Hangyu Li, Yang Cheng +7
Reliable work zone mapping is important for connected and autonomous vehicles (CAVs) to navigate safely and smoothly through work zone areas. Cone-mounted ultra-wideband (UWB) road…
An Agentic Workflow for Detecting Personally Identifiable Information in Crash Narratives
Junyi Ma, Pei Li, Rui Gan +3
Crash narratives in crash reports provide crucial contextual information for traffic safety analysis. Yet, their broader use is hindered by the presence of personally identifiable…
V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors
Keshu Wu, Pei Li, Yang Zhou +8
The advancement of Connected and Automated Vehicles (CAVs) and Vehicle-to-Everything (V2X) offers significant potential for enhancing transportation safety, mobility, and sustainab…
A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors
Keshu Wu, Pei Li, Yang Cheng +4
Transportation Cyber-Physical Systems (T-CPS) enhance safety and mobility by integrating cyber and physical transportation systems. A key component of T-CPS is the Digital Twin (DT…
Real-World Data Inspired Interactive Connected Traffic Scenario Generation
Junwei You, Pei Li, Yang Cheng +4
Simulation is a crucial step in ensuring accurate, efficient, and realistic Connected and Autonomous Vehicles (CAVs) testing and validation. As the adoption of CAV accelerates, the…
Truck Parking Usage Prediction with Decomposed Graph Neural Networks
Rei Tamaru, Yang Cheng, Steven Parker +3
Truck parking on freight corridors faces the major challenge of insufficient parking spaces. This is exacerbated by the Hour-of-Service (HOS) regulations, which often result in una…
Developing a Conceptual Tribal Crash Safety Dashboard: Data-Driven Strategies for Identifying High-Risk Areas and Enhancing Tribal Safety Programs
Tianyi Chen, Haotian Shi, Steven T. Parker +3
Tribal lands in the United States have consistently exhibited higher crash rates and injury severities compared to other regions. To address this issue, effective data-driven safet…