1 citations · 4 across the 8 of their papers we have counts for
8 papers
Mode Choice Heterogeneity Among Zero-Vehicle Households: A Latent Class Cluster Approach
Nancy Kasamala, Arthur Mukwaya, Nana Kankam Gyimah +4
In transportation planning, Zero-Vehicle Households (ZVHs) are often treated as a uniform group with limited mobility options and assumed to rely heavily on walking or public trans…
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…
Harnessing ADAS for Pedestrian Safety: A Data-Driven Exploration of Fatality Reduction
Methusela Sulle, Judith Mwakalonge, Gurcan Comert +2
Pedestrian fatalities continue to rise in the United States, driven by factors such as human distraction, increased vehicle size, and complex traffic environments. Advanced Driver…
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…
Analysis of Distracted Pedestrians Crossing Behavior: An Immersive Virtual Reality Application
Methusela Sulle, Judith Mwakalonge, Gurcan Comert +4
Pedestrian safety is a critical public health priority, with pedestrian fatalities accounting for 18% of all U.S. traffic deaths in 2022. The rising prevalence of distracted walkin…
Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT
Robert Akinie, Nana Kankam Brym Gyimah, Mansi Bhavsar +1
The rapid advancement of machine learning (ML) and on-device computing has revolutionized various industries, including transportation, through the development of Connected and Aut…
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…