Mohamed Ahmed

Professor, and Director of the CEAS Transportation Center

University of Cincinnati

United States

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Professor Mohamed Ahmed is the Director of the CEAS Transportation Center at the University of Cincinnati, USA. His research focuses on transportation safety, autonomous vehicles, and the impact of weather on driving behavior. Recent articles examine topics such as crash severity prediction using deep learning and the analysis of pedestrian and bike crashes in school zones. He is actively involved in advancing methodologies to enhance roadway safety and traffic management.

Articles (13)

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2025

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2025

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2024

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2023

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2023

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

Collaborators (5)

Sherif Gaweesh

Assistant Professor

North Dakota State University

UNITED STATES

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Md Nasim Khan

Texas State University

UNITED STATES

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Shaun S. Wulff

University of Wyoming

UNITED STATES

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Steven Lavrenz

Wayne State University

UNITED STATES

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Jiaqi Ma

University of California

UNITED STATES

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Mohamed Ahmed

Research Interests

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Articles (13)

Severity Analysis of Pedestrian and Bike Crashes in School Buffer Zones

Assessing the efficacy of pre-trained large language models in analyzing autonomous vehicle field test disengagements

Prediction of Truck-Involved Crash Severity on a Rural Mountainous Freeway Using Transfer Learning with ResNet-50 Deep Neural Network

Analysis Framework to Assess Crash Severity for Large Trucks on Rural Interstate Roads Utilizing the Latent Class and Random Parameter Model

A novel deep learning approach to predict crash severity in adverse weather on rural mountainous freeway

Does random slope hierarchical modeling always outperform random intercept counterpart? Accounting for unobserved heterogeneity in a real-time empirical analysis of critical crash occurrence

Evaluating connected vehicle-based weather responsive management strategies using weather-sensitive microscopic simulation

Deep Learning Approach for Detecting Lane Change Maneuvers Using SHRP2 Naturalistic Driving Data

Developing Statewide Safety Performance Functions for Commercial Trucks Transporting Hazardous Materials on Interstate Rural Roads in Wyoming

Machine and Deep Learning Techniques for Daytime Fog Detection in Real Time with In-Vehicle Vision Systems Using the SHRP 2 Naturalistic Driving Study Data

Structural equation modeling approach for investigating drivers’ risky behavior in clear and adverse weather using SHRP2 naturalistic driving data

Cluster analysis and multi-level modeling for evaluating the impact of rain on aggressive lane-changing characteristics utilizing naturalistic driving data

Weather-Based Lane-Change Microsimulation Parameters for Safety and Operational Performance Evaluation of Weaving and Basic Freeway Segments

Collaborators (5)

Sherif Gaweesh

Md Nasim Khan

Shaun S. Wulff

Steven Lavrenz

Jiaqi Ma

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