15 citations · 25 across the 16 of their papers we have counts for
8 papers · 1 filter
A Prescriptive Framework for Determining Optimal Days for Short-Term Traffic Counts
Arthur Mukwaya, Nancy Kasamala, Nana Kankam Gyimah +5
The Federal Highway Administration (FHWA) mandates that state Departments of Transportation (DOTs) collect reliable Annual Average Daily Traffic (AADT) data. However, many U.S. DOT…
Real-Time Conflict Prediction for Large Truck Merging in Mixed Traffic at Work Zone Lane Closures
Abyad Enan, Abdullah Al Mamun, Gurcan Comert +4
Large trucks substantially contribute to work zone-related crashes, primarily due to their large size and blind spots. When approaching a work zone, large trucks often need to merg…
Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign Classification
Reek Majumder, Mashrur Chowdhury, Sakib Mahmud Khan +6
Deep learning (DL)-based image classification models are essential for autonomous vehicle (AV) perception modules since incorrect categorization might have severe repercussions. Ad…
Unraveling Pedestrian Fatality Patterns: A Comparative Study with Explainable AI
Methusela Sulle, Judith Mwakalonge, Gurcan Comert +2
Road fatalities pose significant public safety and health challenges worldwide, with pedestrians being particularly vulnerable in vehicle-pedestrian crashes due to disparities in p…
Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems
Hannah Musau, Nana Kankam Gyimah, Judith Mwakalonge +2
Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowled…
Crash Severity Risk Modeling Strategies under Data Imbalance
Abdullah Al Mamun, Abyad Enan, Debbie A. Indah +3
This study investigates crash severity risk modeling strategies for work zones involving large vehicles (i.e., trucks, buses, and vans) under crash data imbalance between low-sever…
An AutoML-based approach for Network Intrusion Detection
Nana Kankam Gyimah, Judith Mwakalonge, Gurcan Comert +6
In this paper, we present an automated machine learning (AutoML) approach for network intrusion detection, leveraging a stacked ensemble model developed using the MLJAR AutoML fram…
Hybrid Quantum-Classical Neural Network for Incident Detection
Zadid Khan, Sakib Mahmud Khan, Jean Michel Tine +7
The efficiency and reliability of real-time incident detection models directly impact the affected corridors' traffic safety and operational conditions. The recent emergence of clo…