Ayşe Tuğba Yapıcı | Electric Vehicles | Best Research Article Award

Posted on by Automobile Award

Ms. Ayşe Tuğba Yapıcı | Electric Vehicles | Best Research Article Award

Doctoral Researcher  |  Kocaeli University  |  Turkey

Ms. Ayşe Tuğba Yapıcı is a dedicated doctoral researcher whose academic journey is strongly rooted in cutting-edge technologies for electric vehicles, smart energy systems, and intelligent power electronics. She has cultivated significant professional experience through her active involvement in research addressing real-world problems such as electric vehicle charging optimization, grid-integrated charging infrastructures, and advanced modeling of power converter systems. Throughout her career, she has contributed to impactful scientific studies focusing on induction heating systems, charging time prediction using deep learning, and data-driven forecasting on electric vehicle adoption and infrastructure planning. Her research interests include electric vehicle technologies, charging station design, renewable-integrated smart grids, artificial intelligence–based energy forecasting, machine learning and deep learning applications in power systems, and IoT-enhanced smart mobility frameworks. She possesses strong research skills in Python-based deep learning toolkits, MATLAB/Simulink, DigSilent PowerFactory modeling, statistical evaluation metrics, time-series forecasting, optimization algorithms, and performance analysis of intelligent systems. She has published multiple peer-reviewed articles in international SCI/Scopus-indexed journals, delivering innovative research outcomes that offer comprehensive and practical solutions for the sustainable development of electric transportation. Her research achievements include proposing an intelligent deep learning–based framework for EV charging time prediction, integrating spatial–temporal mobility parameters, and enhancing operational efficiency for fast-charging infrastructures. Her work stands out for its interdisciplinary approach and technological significance, supporting the transition toward cleaner mobility, optimized charging networks, and smart energy management. In addition to research excellence, she continues to contribute to academic and scientific communities through conference participation, collaborations, and knowledge dissemination. She aims to advance secure, intelligent, and scalable charging automation systems that support next-generation autonomous electric mobility. Her long-term vision is to shape energy-efficient transportation ecosystems, reduce environmental impacts, and contribute to the global sustainability agenda through innovation and scientific leadership. She has achieved  3 Citations , 2 Documents,  1 h-index.

Featured Publications

Yapıcı, A. T., & Abut, N. (2025, November 23). An intelligent and secure IoT-based framework for predicting charging and travel duration in autonomous electric taxi systems. Applied Sciences.

Yapıcı, A. T., Abut, N., & Yıldırım, A. (2025, October 27). Estimation of future number of electric vehicles and charging stations: Analysis of Sakarya Province with LSTM, GRU and multiple linear regression approaches. Applied Sciences.

Yapıcı, A. T., & Abut, N. (2025, August 21). Geleceğe yönelik elektrikli araç ve şarj istasyonu sayılarının LSTM ve GRU derin öğrenme yöntemleri kullanılarak tahmin edilmesi: Kocaeli ili örneği. Politeknik Dergisi.

Yapıcı, A. T., Abut, N., & Erfidan, T. (2025, April 11). Comparing the effectiveness of deep learning approaches for charging time prediction in electric vehicles: Kocaeli example. Energies.

Yapıcı, A. T., & Abut, N. (2024, September 15). Elektrikli araç şarj istasyonu konum tasarımında, Digsilent yazılımı kullanılarak Kocaeli Üniversitesi Umuttepe Kampüsü için örnek uygulama. Black Sea Journal of Engineering and Science.

Ayşe Tuğba Yapıcı’s research advances intelligent and sustainable electric mobility by integrating deep learning, smart grid technologies, and IoT-based predictive frameworks to optimize charging infrastructure and energy management. Her work supports the transition toward autonomous electric transportation, reducing environmental impacts, improving urban mobility planning, and contributing to global innovation in smart energy systems. She envisions scalable, reliable, and human-centered smart mobility ecosystems that accelerate the adoption of clean transportation worldwide.

Mohammad Anis | Transportation Engineering | Best Researcher Award

Posted on by Automobile Award

Mr. Mohammad Anis | Transportation Engineering | Best Researcher Award

PhD Student | Texas A&M University| United States

Mr. Mohammad Anis is a dedicated Ph.D. candidate in Civil and Environmental Engineering at Texas A&M University, specializing in traffic safety, autonomous vehicle safety, crash risk modeling, pedestrian safety, and digital twin applications. He previously earned an M.S. in Civil Engineering from the University of Texas Rio Grande Valley (2021), where he conducted pioneering research on electrically heated rigid pavements, and a B.S. in Civil Engineering from Dhaka University of Engineering & Technology, Bangladesh (2018). With over four years of research experience, he has worked extensively on federally and state-funded projects with agencies such as FHWA, TxDOT, NCHRP, FMCSA, and ODOT, contributing to crash prediction models, pedestrian safety analysis, driver distraction studies, and systemic roadway design improvements. His dissertation integrates physics-informed near-miss data with hierarchical Bayesian frameworks for real-time crash occurrence risk estimation, pushing the boundaries of data-driven traffic safety planning. His professional experience includes roles as a doctoral researcher at Texas A&M University, a graduate research assistant at the Texas A&M Transportation Institute, and a graduate teaching assistant at both Texas A&M University and UTRGV, where he mentored students in transportation engineering and civil materials. His research interests lie in real-time safety modeling, AI and machine learning applications in transportation, spatiotemporal crash risk prediction, and sustainable roadway infrastructure. He is skilled in programming (Python, R, MATLAB), statistical modeling (MCMC, machine learning, time-series analysis), traffic simulation tools (SUMO, VISSIM, CARLA), and GIS platforms (ArcGIS, QGIS). He has published widely in high-impact journals such as Accident Analysis & Prevention and Transportation Research Record, along with multiple IEEE and Scopus-indexed conferences. Among his many accolades are the Keese-Wootan Transportation Fellowship (Top 5%), Zachry Excellence Fellowship, Terracon Foundation Scholarship, and Graduate Student Travel Awards. With a strong record of publications, collaborations, and peer-review service, Mr. Anis demonstrates outstanding potential to lead future research in traffic safety and intelligent mobility systems. He has achieved 30 citations across 27 documents, with 8 publications and an h-index of 2.

Profiles:  Scopus | ORCID

Featured Publications

Anis, M., Geedipally, S. R., & Lord, D. (2025). Pedestrian crash causation analysis near bus stops: Insights from random parameters Negative Binomial–Lindley model. Accident Analysis & Prevention, 220, 108137.

Zhang, H., Li, S., Li, Z., Anis, M., Lord, D., & Zhou, Y. (2025). Why anticipatory sensing matters in commercial ACC systems under cut-in scenarios: A perspective from stochastic safety analysis. Accident Analysis & Prevention, 218, 108064

Anis, M., Li, S., Geedipally, S. R., Zhou, Y., & Lord, D. (2025). Real-time risk estimation for active road safety: Leveraging Waymo AV sensor data with hierarchical Bayesian extreme value models. Accident Analysis & Prevention, 211, 107880.

Abdel-Raheem, M., & Anis, M. (2025). Toward sustainability: A new construction method for electrically heated rigid pavement systems. Transportation Research Record: Journal of the Transportation Research Board, 2679(3), 281–303.

Anis, M., & Abdel-Raheem, M. (2024). A review of electrically conductive cement concrete pavement for sustainable snow-removal and deicing: Road safety in cold regions. Transportation Research Record: Journal of the Transportation Research Board, 2678(9), 50–71.