Mr. Amr Shafik | Engineering | Best Researcher Award
Civil Engineering Department at Virginia Tech, United States
Amr Shafik is a dedicated researcher specializing in transportation systems engineering, with over seven years of academic and industry experience in transportation planning, traffic engineering, and intelligent mobility solutions. Currently a Ph.D. candidate in Civil and Environmental Engineering at Virginia Tech, his research focuses on optimizing eco-driving systems for connected and automated vehicles, stochastic traffic signal control, and predictive modeling. He has published extensively in IEEE Transactions on Intelligent Transportation Systems and presented at prestigious conferences such as the IEEE Smart Mobility Conference and the Transportation Research Board Annual Meetings. Amr has collaborated with global organizations like the World Bank and EBRD on large-scale mobility projects. With expertise in simulation modeling, data science, and machine learning, he contributes to sustainable transportation innovations. Additionally, he has extensive teaching experience, mentoring students in traffic engineering and transportation planning. His technical skills include Python, R, AutoCAD, GIS, and advanced traffic simulation tools.
Professional Profile
Education
Amr Shafik holds a strong academic background in transportation engineering and data-driven mobility solutions. He is currently pursuing a Ph.D. in Civil and Environmental Engineering at Virginia Tech, where his research focuses on eco-driving optimization for connected and automated vehicles, stochastic traffic signal control, and predictive modeling. He earned his Master’s degree in Transportation Engineering from Cairo University, where he specialized in traffic flow theory, simulation modeling, and intelligent transportation systems. His thesis explored data-driven approaches to optimizing urban traffic networks. Prior to that, he completed his Bachelor’s degree in Civil Engineering from Cairo University with distinction, laying the foundation for his expertise in infrastructure design, traffic analysis, and sustainable mobility. Throughout his academic journey, he has engaged in interdisciplinary research, collaborated with global institutions, and honed advanced technical skills in Python, GIS, and transportation simulation tools. His education equips him to tackle real-world transportation challenges with innovative solutions.
Professional Experience
Amr Shafik has extensive professional experience in transportation engineering, data-driven mobility solutions, and intelligent transportation systems. He has worked as a Research Assistant at Virginia Tech, contributing to projects on eco-driving optimization, stochastic traffic signal control, and predictive modeling for connected and automated vehicles. Prior to this, he served as a Transportation Engineer at a leading consultancy, where he specialized in traffic flow analysis, microsimulation modeling, and urban mobility planning. His expertise extends to working with big data analytics, GIS applications, and machine learning for transportation systems. He has collaborated with government agencies and research institutions to develop sustainable and efficient mobility solutions. Additionally, he has experience in teaching and mentoring students in transportation engineering concepts. His strong analytical skills, combined with his hands-on experience in software tools like Python, MATLAB, and traffic simulation platforms, position him as a key contributor to the advancement of smart and sustainable transportation networks.
Research Interest
Amr Shafik’s research interests lie at the intersection of transportation engineering, intelligent mobility, and data-driven traffic management. He focuses on optimizing traffic flow and enhancing transportation efficiency through connected and automated vehicle technologies, eco-driving strategies, and stochastic traffic signal control. His work integrates machine learning, big data analytics, and artificial intelligence to develop predictive models for traffic behavior and mobility patterns. He is particularly interested in sustainable urban transportation, leveraging smart mobility solutions to reduce congestion, emissions, and energy consumption. His research also explores the application of Geographic Information Systems (GIS) and simulation modeling in transportation planning. By collaborating with industry partners and academic institutions, he aims to contribute to the development of next-generation intelligent transportation systems that improve safety, efficiency, and environmental sustainability. His passion for innovation and interdisciplinary research drives him to address real-world transportation challenges through advanced computational and analytical techniques.
Awards and honor
Amr Shafik has received numerous awards and honors in recognition of his contributions to transportation engineering and intelligent mobility research. He has been honored with prestigious research grants and fellowships for his work on data-driven traffic management and sustainable transportation solutions. His innovative research has earned him accolades at international conferences, where he has received Best Paper and Outstanding Research awards. He has also been recognized by professional engineering societies for his significant advancements in traffic optimization and eco-driving strategies. Additionally, he has been awarded competitive scholarships for academic excellence and leadership in the field of intelligent transportation systems. His contributions to collaborative projects with industry and government agencies have further solidified his reputation as a leading researcher in the field. Through his dedication to advancing transportation science, Amr Shafik continues to receive recognition for his impactful work in shaping the future of smart and sustainable mobility solutions.
Research skill
Amr Shafik possesses a diverse set of research skills that contribute to his expertise in transportation engineering and intelligent mobility solutions. He excels in data analysis, statistical modeling, and machine learning applications for traffic flow optimization and predictive analytics. His proficiency in programming languages such as Python, MATLAB, and R enables him to develop advanced algorithms for real-time traffic monitoring and control. He is skilled in using Geographic Information Systems (GIS) and simulation software like VISSIM and SUMO to model transportation networks and assess the impact of smart mobility solutions. Additionally, he has a strong background in sensor data processing and Internet of Things (IoT) applications for connected and autonomous vehicles. His ability to conduct interdisciplinary research, collaborate with industry stakeholders, and publish high-impact studies demonstrates his analytical thinking, problem-solving abilities, and dedication to innovation in the field of intelligent transportation systems and sustainable urban mobility.
Conclusion
Amr Shafik is a strong candidate for the Best Researcher Award due to his extensive contributions to transportation engineering, expertise in traffic optimization, and impactful research in connected and automated vehicles. His impressive academic and industry experience, along with publications in top-tier conferences and journals, showcases his research excellence. To further strengthen his profile, expanding interdisciplinary collaborations, securing independent research funding, and pursuing patents or industry partnerships would be beneficial.
Publications Top Noted
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Optimization of vehicle trajectories considering uncertainty in actuated traffic signal timings
- Authors: AK Shafik, S Eteifa, HA Rakha
- Year: 2023
- Citations: 19
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Queue Length Estimation and Optimal Vehicle Trajectory Planning Considering Queue Effects at Actuated Traffic Signal Controlled Intersections
- Authors: A Shafik, H Rakha
- Year: 2024
- Citations: 5
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Environmental Impacts of MSW Collection Route Optimization Using GIS: A Case Study of 10th of Ramadan City, Egypt
- Authors: A Shafik, M Elkhedr, D Said, A Hassan
- Year: 2022
- Citations: 4
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Integrated Back of Queue Estimation and Vehicle Trajectory Optimization Considering Uncertainty in Traffic Signal Timings
- Authors: AK Shafik, HA Rakha
- Year: 2024
- Citations: 3
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Optimal Trajectory Planning Algorithm for Connected and Autonomous Vehicles Towards Uncertainty of Actuated Traffic Signals
- Authors: A Shafik, S Eteifa, HA Rakha, E Center
- Year: 2023
- Citations: 3
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Development of Online VISSIM Traffic Microscopic Calibration Framework Using Artificial Intelligence for Cairo CBD Area
- Authors: AK Shafik, A Hassan, AM Saied, AE & Abdelmegeed
- Year: 2022
- Citations: 2
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Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Traffic Signal Switch Times
- Authors: S Eteifa, AK Shafik, H Eldardiry, HA Rakha
- Year: 2024
- Citations: 1
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Kalman Filter-based Real-Time Traffic State Estimation and Prediction using Vehicle Probe Data
- Authors: AK Shafik, HA Rakha
- Year: 2024
- Citations: 1
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Enhancing and Evaluating a Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Controller on an Isolated Signalized Intersection
- Authors: AK Shafik, HA Rakha
- Year: 2024
- Citations: 1
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Real-Time Turning Movement, Queue Length, and Traffic Density Estimation and Prediction Using Vehicle Trajectory and Stationary Sensor Data
- Authors: AK Shafik, HA Rakha
- Year: 2025
- Citations: N/A
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Deep Learning Ensemble Approach for Predicting Expected and Confidence Levels of Signal Phase and Timing Information at Actuated Traffic Signals
- Authors: S Eteifa, A Shafik, H Eldardiry, HA Rakha
- Year: 2025
- Citations: N/A
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Real-Time Turning Movement, Queue Length and Traffic Density Estimation and Prediction from Probe Vehicle Data: A Kalman Filter Approach
- Authors: A Shafik, HA Rakha
- Year: 2025
- Citations: N/A
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Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Control: Model Enhancement and Testing on Isolated Signalized Intersections
- Authors: AK Shafik, HA Rakha
- Year: 2024
- Citations: N/A
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Real-Time Traffic State Estimation and Short-Term Prediction Using Probe Vehicle Data: A Kalman Filter Approach
- Authors: A Shafik, H Rakha
- Year: 2024
- Citations: N/A
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Queue Estimation and Consideration in Vehicle Trajectory Optimization at Actuated Signalized Intersections
- Authors: AK Shafik, HA Rakha
- Year: 2024
- Citations: N/A