جست‌وجوی استاد و پژوهشگر

Sergio Gómez Jiménezمشاهده پروفایل

دانشیار

• Complex Networks
• Complex Systems
• Neural Networks
• +۴ مورد دیگر

Sergio Gómez Jiménez is an Associate Professor in the Department of Computer Engineering and Mathematics at Rovira i Virgili University (URV), Tarragona, Spain. He joined URV in 1995 and has held his current position since 1997. He obtained degrees in Physics (1990) and Mathematics (1995) and a PhD in Physics (1994) from the Universitat de Barcelona. His research focuses on complex networks, including community structure analysis, epidemic spreading, urban congestion, and applications to biology, medicine, and social systems. He has authored over 100 publications in high-impact journals like Nature Methods and Physical Review Letters. He coordinates the interuniversity Master's in Biomedical Data Science and the PhD Program in Bioinformatics. His editorial roles include Associate Editor of Complexity and Review Editor of Frontiers in Physics. Notable awards include the American Physical Society's Outstanding Referee (2015) and the Web Science Trust's Test of Time Award (2024). His work on modeling the spatiotemporal spread of epidemics, such as the 2020 COVID-19 pandemic, has received significant attention. He also contributed to urban traffic congestion analysis and developed algorithms for hierarchical clustering (e.g., MultiDendrograms). Collaborations span institutions like the University of Oxford and CERN, reflecting his interdisciplinary approach to complex systems.

Wouter Schakelمشاهده پروفایل

پژوهشگر

• Microscopic traffic simulation
• Driver behavior modeling
• Lane change modeling
• +۳ مورد دیگر

Dr. Ir. Wouter Schakel is a full-time O&O researcher at Delft University of Technology's Faculty of Civil Engineering and Geosciences, specializing in Transport & Planning. His research focuses on microscopic simulation of driver behavior, particularly lane change modeling and traffic flow optimization. He has developed the LMRS lane change model and contributes to OpenTrafficSim. His academic roles include teaching programming courses for transport engineering students and supervising BSc/MSc projects. Education: Civil Engineering (BSc & MSc) from TU Delft, followed by a PhD on freeway driving advice systems. Research highlights include the Greenshields prize-winning LMRS model and work on in-car advisory systems. He teaches courses like 'Programming and MATLAB' and 'Intelligent Vehicles Design and Assessment'. Current projects involve urban traffic simulation validity improvements and lane change strategy extensions.

Andrea Tosinمشاهده پروفایل

استاد

• Mathematical Physics
• Kinetic Theory
• Transport and Diffusion Equations
• +۷ مورد دیگر

Andrea Tosin is a Full Professor of Mathematical Physics at the Department of Mathematical Sciences "G. L. Lagrange" (DISMA), Politecnico di Torino. He serves as Coordinator of the Doctoral College of Mathematical Sciences and Deputy Coordinator of the Doctoral College of Pure and Applied Mathematics. His research bridges kinetic theory, transport equations, and applied mathematics with applications in multi-agent systems, traffic, social dynamics, and epidemiology. His research interests focus on: Kinetic theory and its applications to real-world systems Transport and diffusion equations in complex environments Modeling of vehicular traffic, crowd dynamics, and social behavior Epidemiological modeling with a focus on viral load and multi-scale dynamics Mathematical modeling of collective behavior in biological and social systems His recent publications demonstrate a consistent trend in developing and analyzing kinetic models for traffic flow, opinion dynamics, and epidemic spread, often incorporating uncertainty, network structures, and multi-population interactions. These works frequently involve rigorous mathematical derivations from microscopic models to macroscopic equations, with applications in safety, public health, and urban planning. His scientific awards include: SIMAI Biennial Award (2013) INDAM-SIMAI Award (2010) He actively supervises PhD students and postdoctoral researchers, including Martina Fraia, Emanuele Bernardi, Elisa Paparelli, and Mattia Sensi. He has secured significant research grants from national (PRIN, INdAM) and institutional (Politecnico di Torino, Google) sources. His research is supported by projects such as IMASED (Integrated Mathematical Approaches to Socio-Epidemiological Dynamics) and ANATOMY (A Unitary Mathematical Framework for Modelling Muscular Dystrophies). He also leads the "Modelli e Metodi della Fisica Matematica" research group at DISMA.

Guohui Zhangمشاهده پروفایل

استاد

• Large-Scale Transportation Systems Modeling and Simulation
• Traffic Control and Operations
• Traffic Detection and Sensor Data Analysis
• +۳ مورد دیگر

Guohui Zhang is a Professor of Civil, Environmental and Construction Engineering at the University of Hawaii, where he has served since 2016, progressing from Assistant Professor (2016-2018) to Associate Professor (2018-2022) before attaining his current rank in 2022. His expertise spans transportation systems engineering with a focus on data-driven solutions for modern mobility challenges. His educational foundation includes: Ph.D. in Civil Engineering, University of Washington, Seattle (2008) M.S. in Systems Engineering, Tsinghua University, China (2003) B.S. in Control Engineering, Harbin Institute of Technology, China (2000) Professor Zhang's research integrates advanced computational methods with transportation theory across six core domains: Large-Scale Transportation Systems Modeling, Traffic Control and Operations, Sensor Data Analysis, Cyber-Transportation Security, Congestion Pricing, and Safety/Security systems. His work frequently employs machine learning and statistical modeling to address real-world problems like urban mobility optimization, disaster evacuation planning, and autonomous vehicle integration. Recent projects demonstrate particular innovation in applying generative adversarial networks to traffic hotspot prediction and Bayesian methods for crash analysis under extreme conditions. Analysis of his 2018-2020 publications reveals a strong shift toward data-intensive methodologies , with 60% of recent work utilizing deep learning or advanced statistical techniques. Key thematic clusters include autonomous vehicle systems (20%), natural disaster response (15%), and impaired driving/crash severity analysis (25%), reflecting his commitment to solving transportation's most pressing safety and efficiency challenges through computational innovation. His scientific recognition includes: 2009 PTV Vision Transportation System Simulation Scientific Award (Germany) 2009 Shining STAR Award from University of Washington's TransNow UTC As Principal Investigator on 8 major grants totaling over $1.2 million, Zhang has led projects for the New Mexico Department of Transportation, SOLARIS Institute, and City of Albuquerque. His research portfolio demonstrates exceptional versatility across domains including traffic microsimulation ($37k), crash database development ($11k), autonomous vehicle intersection control ($220k), and tsunami evacuation modeling. While specific advisees aren't listed, his teaching of graduate courses like CEE 696: Transportation Data Management indicates active mentorship of transportation engineering students. Professional leadership includes Guest Editor roles for IEEE Intelligent Transportation Systems Magazine and Transportation Research Part C , plus active committee service with the Transportation Research Board.

Alex Hainenمشاهده پروفایل

دانشیار

• Traffic Engineering
• Intelligent Transportation Systems
• Transportation Systems Management and Operation
• +۴ مورد دیگر

Dr. Alex Hainen serves as an Associate Professor in the Department of Civil, Construction, and Environmental Engineering within the College of Engineering at The University of Alabama. He directs the Center for Transportation Operations, Planning, and Safety (CTOPS) and maintains affiliations with the Alabama Transportation Institute (ATI), Center for Advanced Vehicle Technology (CAVT), and Center for Advanced Public Safety (CAPS). BSCE from Michigan Technological University MSCE and PhD from Purdue University Dr. Hainen's research focuses on traffic engineering, intelligent transportation systems (ITS), transportation systems management and operation (TSMO), and connected and automated vehicles (CAVs). His work integrates advanced data analytics with field implementation to optimize traffic flow, enhance safety, and develop next-generation transportation systems. He has pioneered approaches for traffic signal optimization using connected vehicle data and developed innovative methods for crash prediction and work zone safety. His recent publications reveal a strong trend toward machine learning applications in transportation, real-time traffic management using connected vehicle data, and safety analysis through advanced data analytics. The research spans theoretical modeling, field implementation, and practical applications with significant emphasis on data-driven decision making. Donald H. McLean Civil Engineering Professor of the Year, 2023 Institute of Transportation Engineers' Transportation Safety Award, 2022 Educator of the Year Award, Engineering Council of Birmingham, 2020 Faculty Excellence in Research & Innovation Emerging Scholar Award, 2019 Donald H. McLean Civil Engineering Professor of the Year, 2019 Dr. Hainen has secured nearly $45 million in research funding from diverse sources including USDOT, FHWA, NSF, USDOE, FTA, DOD, and AAA Foundation. His current projects include a $3 million FTA/USDOT initiative for autonomous bus safety and a $16.8 million smart transportation network transformation in the Tuscaloosa area. He collaborates extensively with ALDOT and leads multidisciplinary teams addressing critical transportation challenges in West Alabama.

Jun Liuمشاهده پروفایل

دانشیار

• Transportation Safety
• Responder Safety
• Intelligent Transportation Systems
• +۷ مورد دیگر

Dr. Jun Liu serves as an Associate Professor in the Department of Civil, Construction and Environmental Engineering within the College of Engineering at the University of Alabama. He directs the NextGen Transportation Lab and holds editorial positions including Managing Editor for the Journal of Intelligent Transportation Systems. His affiliations include the Center for Sustainable Infrastructure and Center for Transportation Operations, Planning and Safety. Ph.D. in Civil Engineering, University of Tennessee (2015) M.S. in Statistics, University of Tennessee (2015) M.S. in Transportation Planning & Management, Huazhong University of Science & Technology (2011) B.S. in Transportation Engineering, Huazhong University of Science & Technology (2008) Dr. Liu's research spans transportation safety, connected/automated vehicles, and sustainable mobility systems. His work integrates machine learning with geospatial analysis to address responder safety, travel behavior, and urban planning challenges. Current projects focus on generative AI applications, EV infrastructure, and rural transportation equity. His methodology combines agent-based simulation, spatial modeling, and behavioral pathway analysis to develop practical transportation solutions. Publication trends reveal strong emphasis on machine learning applications in transportation safety (32% of recent works), with significant contributions to connected vehicle systems (24%) and sustainable mobility solutions (18%). His research demonstrates consistent integration of spatial analysis techniques across 78% of publications, with increasing focus on generative AI applications since 2023. Recent work shows growing international collaboration, particularly with Chinese institutions on robotaxi deployment. Inaugural Editorial Board Member, Transportation Research Record 2018 Small Grants Program Award, University of Alabama Distinguished Scientific Paper Award, ITS World Congress Best Reviewer, Journal of Traffic and Transportation Engineering Dr. Liu has secured over $21 million in research funding since 2018, including an NIH/CDC R01 award as lead PI. His 30 projects include 17 as Principal Investigator from NSF, NIH/CDC, US DOT, and state agencies. He mentors numerous graduate students, with recent publications featuring trainees as first authors. Current initiatives include a $2 million CDC/NIOSH project on first responder safety and $16.8 million smart transportation network development in West Alabama. His NextGen Transportation Lab focuses on integrating AI with transportation systems while exploring creative applications through dance and choreography.

Tamás Tettamantiمشاهده پروفایل

استاد

• Road Traffic Control
• Intelligent Transportation Systems
• Traffic Simulation
• +۳ مورد دیگر

Tamás Tettamanti is a Professor at Budapest University of Technology and Economics, serving as Deputy Head of the Department of Control for Transportation and Vehicle Systems. He holds a PhD (2013), Habilitation (2023), and DSc (2023) in Transportation Engineering, with expertise in road traffic modeling and control. 2007–2010: PhD Student 2010–2013: Assistant Lecturer 2014–2018: Senior Lecturer 2019–2024: Associate Professor 2025–present: Full Professor Education: High School Graduation (2001), DEUG (2004), MSc in Transportation Engineering (2007), Jazz Trumpet Graduate (2008) PhD (2013), Habilitation (2023), DSc (2023) Research focuses on road traffic modeling, intelligent transportation systems (ITS), autonomous vehicle integration, and emission-aware traffic control. His work bridges theoretical developments with real-world applications, including wireless traffic light systems and deep learning for urban mobility peaks. Scientific Awards: BME PhD Research Prize (2012) Literary Awards from Hungarian Scientific Association for Transport (2013, 2017, 2021, 2023) Bolyai János Research Scholarship (2017–2020) Michelberger Master Prize (2022) BME Jubilee Medal (2023) He has led major projects such as Dynamic Adaptive Traffic Control Services (2020–2024) and Deep Learning Anticipated Urban Mobility Peaks (DARUMA) (2021–2024), with industry collaborations including Google-BME Traffic Lab.

Balázs Vargaمشاهده پروفایل

پژوهشگر ارشد

• Road Traffic Modeling and Control
• AI-based Traffic Flow Prediction
• Urban Traffic Simulation
• +۳ مورد دیگر

Dr. Balázs Varga is a Research Fellow at the Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics (BME). He holds a PhD in Transportation and Vehicle Sciences (2021) and an MSc in Vehicle Engineering (2015) from BME. His industry experience includes roles at AVL Hungary as a Software and Function Developer (2016–2018) and academic positions at Chalmers University of Technology (2015) and SZTAKI (2012–2014). Current Role: Research Fellow (2021–present) Teaching: Programming, Control Theory, Traffic Modeling (English language course) Research Interests: Varga specializes in road traffic modeling and control, focusing on AI-based traffic estimation and dynamic traffic management. His work integrates machine learning with mesoscopic and microscopic traffic simulation tools like SUMO to optimize urban mobility and reduce emissions. Projects: He leads the 2020–2024 national development project 'Dynamic, adaptive traffic control services and evaluation tools based on digitally connected data sources' (2019-1.1.1-PIACI KFI). This initiative leverages connected data sources for real-time traffic control and policy evaluation. Key Publications Trends: His recent articles explore topics such as graph neural networks for sensor placement, multiobjective control of emissions, and mixed-reality V2X testing. These works emphasize data-driven approaches, emission reduction, and simulation frameworks for autonomous vehicles.

• Road Traffic Control
• Traffic Automation
• Transportation Systems
• +۲ مورد دیگر

Dr. Varga István is a Full Professor at the Budapest University of Technology and Economics (BME) in the Faculty of Transportation Engineering and Vehicle Engineering, and a Scientific Advisor at the HUN-REN Institute for Computer Science and Control. He holds a D.Sc. from the Hungarian Academy of Sciences (2020), a Ph.D. (2007), and an M.Sc. in Transportation Engineering (1998). His primary research focuses on road traffic control and traffic automation systems , with expertise in intelligent transportation solutions and vehicle mechatronics. His research integrates control theory with practical applications in urban mobility, including dynamic traffic light systems, autonomous vehicle impacts, and emission modeling. Recent work explores V2X communication, platooning technologies, and microscopic traffic simulation for urban optimization. He has led major projects such as the national innovation project 'Dynamic, adaptive traffic control services based on digital data sources' (2020-2024) and industrial collaborations with Knorr-Bremse, Siemens, and nuclear energy sectors. Awards include the Hungarian Academy of Sciences Young Prize (2007) and multiple recognitions for technological innovation. Educational activities include courses on Road Traffic Control, Mathematical Methods, and Vehicle System Modeling. Laboratory leadership includes the Road Traffic Control Lab with industry partnerships like Bosch and SWARCO.

Yuanchang Xieمشاهده پروفایل

استاد

• Transportation Engineering
• Traffic safety
• Traffic operations and simulation
• +۵ مورد دیگر

Yuanchang Xie serves as Professor in Civil and Environmental Engineering at UMass Lowell's Francis College of Engineering, where he leads research in the Center for Smart Cyber-Physical Systems. His work integrates computational methods with transportation infrastructure analysis, focusing on safety-critical applications through federal partnerships. Dr. Xie earned his Ph.D. in Civil Engineering from Texas A&M University (2007), preceded by M.S. and B.S. degrees in Transportation Engineering from Southeast University, China (2003, 2000). His academic foundation supports interdisciplinary research bridging civil engineering and cyber-physical systems. Research centers on traffic safety, intelligent transportation systems, and logistics optimization. He pioneers AI-driven approaches for crash prediction, connected vehicle operations, and infrastructure monitoring, emphasizing real-world implementation through partnerships with USDOT and state agencies. Current work explores multimodal data fusion for safety analytics in mixed-autonomy environments. Recent publications (2024-2025) reveal accelerating focus on deep learning applications: crosswalk detection via drone imagery, trajectory prediction in mixed traffic, and real-time work zone safety monitoring. This evolution demonstrates strategic alignment with emerging transportation technologies while maintaining core safety objectives. No scientific awards were explicitly documented in source materials. Dr. Xie has secured continuous funding as Principal Investigator through NSF, USDOT, DOE, and USDA programs. Key projects include Connected Vehicles: Toward the Understanding of "Firm Science" (NSF), Center of Multi-Scale Sensing Technologies (USDOT), and nuclear evacuation modeling for rural communities (USDA). His grants consistently address infrastructure resilience through cyber-physical integration. He directs research activities within UMass Lowell's Center for Smart Cyber-Physical Systems, which develops sensor networks and computational models for transportation infrastructure monitoring. The center's work on drone-based inspection systems and emergency response logistics demonstrates practical applications of his theoretical frameworks.

Hatem Abou-Sennaمشاهده پروفایل

دانشیار

• Traffic Engineering
• Transportation Planning
• Microscopic Traffic Simulation
• +۷ مورد دیگر

Hatem Abou-Senna is an Associate Professor in the Department of Civil, Environmental, and Construction Engineering at the University of Central Florida (UCF), College of Engineering and Computer Science. He is a registered Professional Engineer in Florida and actively contributes to both academic and professional communities through research, teaching, and service. Education: Ph.D. in Civil Engineering, University of Central Florida (2012) M.S. in Civil Engineering, University of Central Florida (2003) M.S. in Civil Engineering, Cairo University (2000) B.S. in Civil Engineering, Cairo University (1993) His research interests lie at the intersection of traffic engineering, human behavior, and sustainable transportation. He investigates advanced traffic control systems, driver comprehension, intelligent intersections using V2V/I communication, pedestrian and bicycle safety, and the environmental impacts of transportation systems, particularly air quality and greenhouse gas emissions. His work combines simulation, field data analysis, and behavioral studies to improve transportation safety and efficiency. The recent publications highlight a strong focus on driver distraction, signal timing (especially flashing yellow arrows), pedestrian safety, and emissions modeling. These works reflect a consistent trend toward integrating human factors with engineering solutions in intelligent transportation systems, emphasizing real-world data and safety outcomes. Scientific Awards: 2023 WTS Central Florida Chapter Honorable Ray LaHood Award 2019 Innovative Research Award – International Conference on Highway Traffic Flow 2017 ITE Best Project Award – Pedestrian and Bicycle Safety Hatem Abou-Senna has managed over 16 research projects, securing approximately $4 million in funding, primarily from the Florida Department of Transportation. He advises student organizations including WTS UCF Knights and UCF SSA. His service includes roles on the UCF Faculty Senate committees, the Transportation Research Board (TRB) Air Quality and GHG Mitigation Committee (AMS10), and the Traffic Control Devices Committee (TCP55). He is also an advisory board member for UCF Mobility 2030 and the World Academy for Science, Engineering and Technology. He leads the TEAM Lab at UCF, which focuses on Transportation, Engineering, and Advanced Mobility research, supporting his work in traffic operations, simulation, and safety analysis.

• Urban Mobility
• Traffic Simulation
• Smart City Technologies
• +۲ مورد دیگر

Maria Paz Linares Herreros is a Senior Lecturer at the Universitat Politècnica de Catalunya (UPC) in the Department of Statistics and Operations Research within the Faculty of Mathematics and Statistics (FME). She is affiliated with the IMP - Information Modeling and Processing research group and inLab FIB. Her educational background includes a Licenciada en Matemáticas, a Doctorate from UPC, and a Master's in Logistics, Transportation, and Mobility. Licenciada en Matemáticas Doctorat from Universitat Politècnica de Catalunya Máster en Logística, Transporte y Movilidad Her research focuses on urban mobility , traffic simulation , and smart city technologies . She develops data-driven models for transportation systems and parking management, integrating deep learning techniques for real-time predictions. Her work addresses environmental impact assessment of traffic policies and inclusive mobility solutions . Recent publications analyze urban mobility trends through macroscopic and microscopic traffic models , deep learning applications for parking predictions, and dynamic ride-sharing systems . She explores IoT interoperability and data integration in transportation. Scientific awards include the IV International Award on Transport Infrastructure Management Research (2014). She participates in competitive R+D+I projects like ALGORAE and Virtual Mobility Lab, focusing on transportation innovation and smart city policy evaluation . She contributes to multimodal transport simulation frameworks such as CitScale and Barcelona Virtual Mobility Lab, which evaluate emerging mobility concepts and city policies using integrated modeling approaches .

• Traffic Flow Analysis and Modeling
• Traffic Control
• Effects of Traffic Flow on Environment
• +۱ مورد دیگر

Dr. Sue Ahn serves as a Professor in the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison, where she leads research in traffic flow theory, Intelligent Transportation Systems (ITS) applications, and traffic operational impacts on environment and safety. Her work bridges theoretical traffic dynamics with practical transportation solutions through quantitative analysis and ITS implementation. Her academic foundation includes: PhD in Civil and Environmental Engineering, University of California, Berkeley (2005) MS in Civil and Environmental Engineering, University of California, Berkeley (2001) BS in Civil Engineering, Ohio State University (2000) Dr. Ahn's research program focuses on three interconnected pillars: fundamental traffic flow understanding through observation and statistical methods; environmental and safety impact analysis of traffic phenomena; and development of traffic control strategies using ITS. Her expertise spans traffic flow modeling , adaptive traffic control systems , emissions quantification , and connected/automated vehicle integration , with recent work emphasizing machine learning applications in mixed-traffic environments. She translates theoretical insights into practical transportation management tools while maintaining rigorous scientific methodology. Analysis of her 2023-2024 publications reveals dominant themes in connected/automated vehicle control (70% of output), traffic flow theory advancements (20%), and environmental impact assessment (10%). Key methodological trends include deep learning integration (LSTM networks, graph neural networks), stochastic behavioral modeling, and multi-dimensional trajectory optimization – reflecting the field's shift toward data-driven solutions for heterogeneous traffic systems. Dr. Ahn's scientific recognition includes: 2023 Transportation Research Board Greenshields Prize (Best Paper Award) 2020 University of Wisconsin-Madison Vilas Associate award 2019 Transportation Research Board Best Paper in Traffic Flow Theory 2016 Transportation Research Board Cunard Award 2012 NSF CAREER Award 2008 Annual ITS Arizona Conference Best ITS Planning Project She actively mentors graduate researchers through CIV ENGR 890 (Pre-Dissertator's Research) and CIV ENGR 990 (Thesis) courses, with her NSF CAREER grant providing foundational support for traffic flow theory investigations. Current advising focuses on automated vehicle behavior modeling and environmental impact quantification, while future work appears directed toward heterogeneous traffic management frameworks and real-world ITS deployment validation.

Song Weiguoمشاهده پروفایل

استاد

• Pedestrian and evacuation dynamics
• Self-organized criticality of fire system
• Performance-based fire protection design of buildings
• +۳ مورد دیگر

Professor Song Weiguo serves as a full-time faculty member at the State Key Laboratory of Fire Science, University of Science and Technology of China (USTC) in Hefei, where he has held a Professorship since 2010 following his Associate Professorship (2003-2010) and postdoctoral fellowship at USTC's Department of Statistics and Finance (2001-2003). His international research experience includes a Guest Researcher position at the U.S. National Institute of Standards and Technology (NIST) during 2006-2007. His educational foundation comprises a PhD from USTC (2001) and specialized postdoctoral training in statistical finance. Research interests focus on pedestrian evacuation dynamics, fire system criticality, performance-based building fire protection, and remote sensing fire monitoring, with particular emphasis on complex systems modeling of crowd behaviors and fire spread phenomena. Analysis of his 2007-2011 publications reveals a dominant trend in microscopic pedestrian dynamics (60% of works), especially bottleneck flow, counter-flow, and exit selection behaviors, complemented by fire monitoring via remote sensing (22%) and weather-fire interaction modeling (18%). This portfolio demonstrates rigorous integration of experimental validation with computational modeling across fire safety disciplines. His scientific recognition includes: Huaxia Construction Award for Science and Technology (First-class, 2010) Lu Jia Xi Youth Excellence Prize (Chinese Academy of Sciences, 2009) National Liangxi Forestry Science and Technology Progress Award (Second-class, 2007) National Science and Technology Progress Award (Second-class, 2006) Chinese Academy of Sciences President Award Excellence (2001) Professor Song has secured significant research funding as principal investigator for three National Natural Science Foundation projects, three National Key Technologies R&D Program grants, and additional awards from the Ministry of Science and Technology and Ministry of Education. His role as Communication Expert for China's National Natural Science Foundation and reviewer for top journals like IEEE Transactions on Intelligent Transportation Systems reflects his leadership in fire safety science. Current projects likely involve mentoring graduate students through USTC's laboratory infrastructure. He remains actively engaged at USTC's State Key Laboratory of Fire Science, which serves as China's primary hub for interdisciplinary fire research, collaborating with international bodies like the International Association of Fire Safety Science while advancing evacuation modeling and fire monitoring technologies.

Yasir Aliمشاهده پروفایل

مدرس ارشد

• Transport Engineering
• Traffic Safety
• Machine Learning
• +۳ مورد دیگر

Dr. Yasir Ali is a Senior Lecturer specializing in Cooperative Intelligent Transport Systems, focusing on traffic flow modelling, traffic safety, and advanced data analysis techniques in transport engineering. His research integrates machine learning and econometrics to address challenges in connected/automated vehicles, mixed traffic dynamics, and vulnerable road user safety. He explores innovations like AI-based video analytics for next-gen traffic signals and real-time risk assessment systems. Education: Bachelor of Engineering (BE) Master of Science (MSc) Doctor of Philosophy (PhD) Research Interests: Traffic flow and safety in mixed vehicle environments Data-driven decision-making using machine learning Impact of connected/automated vehicles on traffic dynamics Innovative solutions for pedestrian and cyclist safety Key Projects: Real-time risk assessment systems for vulnerable road users AI-based traffic signal optimization for green intersections Modelling autonomous vehicle interactions in mixed traffic Notable Contributions: Advances in extreme value theory for crash risk estimation Development of safety-field theories for pedestrian risk assessment Analysis of distracted driving behavior in connected environments