Dr. Nada Mahmoud, Kittelson and Associates, Inc. United States
Dr. Nada Mahmoud is a highly qualified professional with a Ph.D. in Civil Engineering from the University of Central Florida, earned between August 2018 and April 2021. Her research centered on the critical intersection of safety and operations of urban arterials, utilizing the innovative Context Classification System. Currently serving as an Engineering Associate at Kittelson and Associates Inc. since March 2023, Dr. Mahmoud excels as a traffic engineer, specializing in safety analysis, traffic operations, and data analytics. In her role, she actively collaborates with a Safety Review Working Group, which includes Caltrans representatives, to enhance and optimize safety review processes. 🚗🚦👩🔬
🎓Education :
Dr. Nada Mahmoud is a distinguished professional, holding a Doctor of Philosophy in Civil Engineering from the University of Central Florida (August 2018 – April 2021). Her doctoral research delved into the critical realm of “Safety and Operations of Urban Arterials Incorporating the Context Classification System.” 🏙️🚗 Additionally, she earned a Master of Science in Civil Engineering from Ain Shams University, Cairo, Egypt (February 2014 – January 2018), focusing on the innovative “Development of a Transit Signal Priority Algorithm for Urban Corridors.” 🚦🚇 Dr. Mahmoud’s academic journey reflects her commitment to advancing transportation engineering with a focus on safety and efficiency. 🎓
🌐 Profile : Google Scholar
🏆 Awards :
Dr. Nada Mahmoud’s commitment to excellence extends beyond her academic achievements, as evidenced by her notable recognitions. In 2021, she earned the SAS Data Mining Certificate from the University of Central Florida, showcasing her expertise in harnessing data for insightful analysis. 📊🎓 Additionally, her dedication to advancing women in transportation was acknowledged in 2020 with the Frankee Hellinger Graduate Scholarship from Women in Transportation in Central Florida. 🚺🛣️ Moreover, Dr. Mahmoud’s impactful contributions were further highlighted by the UCF College of Graduate Studies Presentation Fellowship in the same year, underscoring her commitment to knowledge dissemination and scholarly engagement. 🌟🔍
🧠 Research Interests 🔬🌐 :
Dr. Nada Mahmoud’s intellectual pursuits span a dynamic landscape within the realm of transportation engineering. Her inquisitive mind is deeply immersed in the intricate web of topics such as traffic safety, traffic operations, and the expansive domain of big data. 🚗🚦 Her research journey is marked by a keen interest in unraveling patterns and insights through the application of statistical and machine learning models, showcasing a commitment to leveraging cutting-edge technologies for impactful solutions. 🧠💻 In this rapidly evolving field, Dr. Mahmoud’s exploration extends globally, reflecting a holistic approach to understanding and shaping the future of transportation systems. 🌐🛣️
- All Time:
- Citations: 211 📖
- h-index: 9 📊
- i10-index: 7 🔍
- Since 2018:
- Citations: 209 📖
- h-index: 8 📊
- i10-index: 7 🔍
👨🏫 A prolific researcher with significant impact and contributions in the field, as evidenced by citation metrics. 🌐🔬
Publications ( Top Note ) :
1. CitySim: A drone-based vehicle trajectory dataset for safety oriented research and digital twins
Published Year: 2022, Journal/Source: arXiv preprint, Cited By: 46
2. Predicting cycle-level traffic movements at signalized intersections using machine learning models
Published Year: 2021, Journal/Source: Transportation research part C: emerging technologies, Cited By: 42
3. Sequence-to-sequence recurrent graph convolutional networks for traffic estimation and prediction using connected probe vehicle data
Published Year: 2022, Journal/Source: IEEE Transactions on Intelligent Transportation Systems, Cited By: 19
4. Vulnerable road users’ crash hotspot identification on multi-lane arterial roads using estimated exposure and considering context classification
Published Year: 2021, Journal/Source: Accident Analysis & Prevention, Cited By: 18
5. Estimating cycle-level real-time traffic movements at signalized intersections
Published Year: 2022, Journal/Source: Journal of Intelligent Transportation Systems, Cited By: 17
6. Using CNN-LSTM to predict signal phasing and timing aided by High-Resolution detector data
Published Year: 2022, Journal/Source: Transportation research part C: emerging technologies, Cited By: 11
7. Developing a grouped random parameter beta model to analyze drivers’ speeding behavior on urban and suburban arterials with probe speed data
Published Year: 2021, Journal/Source: Accident Analysis & Prevention, Cited By: 11
8. Freeway crash prediction models with variable speed limit/variable advisory speed
Published Year: 2023, Journal/Source: Journal of transportation engineering, Part A: Systems, Cited By: 9
9. Effect of various speed management strategies on bicycle crashes for urban roads in central Florida
Published Year: 2022, Journal/Source: Transportation research record, Cited By: 9
10. Multivariate Poisson-Lognormal Models for Predicting Peak-Period Crash Frequency of Joint On-Ramp and Merge Segments on Freeways
Published Year: 2023, Journal/Source: Transportation Research Record, Cited By: 6