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Andrea TosinView profile

Professor

Polytechnic University of TurinItaly

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.

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Tamás TettamantiView profile

Professor

Budapest University of Technology and EconomicsHungary

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.

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Balázs VargaView profile

Research Fellow

Budapest University of Technology and EconomicsHungary

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.

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Varga IstvánView profile

Professor

Budapest University of Technology and EconomicsHungary

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.

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Yuanchang XieView profile

Professor

University of Massachusetts LowellUnited States

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.

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Hatem Abou-SennaView profile

Associate Professor

University of Central FloridaUnited States

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.

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Maria Paz Linares HerrerosView profile

Senior Lecturer

Polytechnic University of CataloniaSpain

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 .

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Sue AhnView profile

Professor

University of Wisconsin-MadisonUnited States

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.

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Song WeiguoView profile

Professor

University of Science and Technology of ChinaChina

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.

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Yasir AliView profile

Senior Lecturer

Loughborough UniversityUnited Kingdom

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

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Ben WatersonView profile

Professor

University of SouthamptonUnited Kingdom

Ben Waterson is a Professor in Transportation at the University of Southampton within the Faculty of Engineering and Physical Sciences. He has been a member of the Transportation Research Group since 1997, specializing in traffic control, traveler behavior, and AI applications. His research focuses on improving urban traffic systems, air quality, and safety using VR and AI. Education: BSc (1st Class Honours) in Statistics, University of Reading (1996) MSc in Operational Research (Distinction), University of Southampton (1997) PhD in Data Fusion for Real-Time Traffic Monitoring (2005) Research Interests: His work spans road transport, AI-driven traffic control, traveler behavior analysis, and VR applications. Recent projects include narrow passage driving simulations and coordinated signal control algorithms. Teaching: He teaches modules such as Transport Modelling (CENV6153), Highway and Traffic Engineering (CENV3060/CENV6171), and Transport Management and Safety (CENV6168). He supervises projects on topics like self-calibrating traffic lights and virtual reality train stations. Awards: William W. Millar Prize (2017) Grants/Projects: EPSRC-funded IVP Signals project Siemens-funded Future SCOOT Algorithm AI Development Project Labs/Groups: Member of the Transportation Research Group and Southampton Marine and Maritime Institute.

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Alex HainenView profile

Associate Professor

University of AlabamaUnited States

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.

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Markus OeserView profile

Professor

RWTH Aachen UniversityGermany

Markus Oeser is a full professor at the Chair and Institute of Highway Engineering, affiliated with the Federal Highway Research Institute (BASt). His research advances pavement technology, smart infrastructure, and sustainable materials through computational modeling and experimental characterization. Research Focus His work spans: Advanced asphalt materials (self-healing, inductive, nano-enhanced) Digital twins for real-time pavement monitoring Traffic flow modeling and AI-driven infrastructure management Sustainable recycling of construction waste Piezoresistive sensors for smart roads Publication Trends (2024-2025) Recent articles emphasize multi-scale modeling (atomic to structural levels), integration of AI/ML in pavement systems, and sustainability-driven material innovations. Dominant themes include nanomaterial-enhanced composites, digital twins for infrastructure, and traffic microsimulation. Affiliations & Infrastructure Leads research at BASt's highway engineering facilities, leveraging laboratories for materials testing, computational modeling, and full-scale pavement validation.

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Haitao HeView profile

Associate Professor

Loughborough UniversityUnited Kingdom

Haitao He is a Reader in Transport and AI at Loughborough University, UK, and leads the Transport AI Innovation Centre (TRAICE). He holds a BSc (First-Class Honours), MPhil, and PhD, and is a Fellow of the Higher Education Academy (FHEA). His research focuses on AI-driven solutions for smart cities and sustainable urban mobility, including traffic simulation, machine learning, and digital twinning. He develops methodologies in traffic flow theory, agent-based modeling, and deep learning to address challenges in automation, electrification, and shared mobility. Research Interests: AI in transportation, micro-mobility, sustainability, and data-driven urban planning. Projects: UKRI Future Leaders Fellowship on net-zero urban transport, TraffEase (Manchester Prize), and BusMONITOR (KTP with Vectare). His expertise spans predictive traffic analytics, micro-mobility patterns, and infrastructure optimization. He has been awarded the UKRI Future Leaders Fellowship and has contributed to projects like SignBus (Swiss National Science Foundation) and WEAVE (highway weaving section design). Key Achievements: Developed TRAICE to advance transport AI, published over 30 articles on topics like traffic flow modeling and pandemic mobility impacts. Haitao advises on PhD and postdoctoral research, focusing on AI and transport intersections. His work integrates academic and industry collaborations, driving innovation in smart mobility and policy-making.

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Casey BowmanView profile

Associate Professor

University of North GeorgiaUnited States

Dr. Casey Bowman is an Associate Professor in the Mathematics department at the University of North Georgia (UNG). He specializes in traffic simulation, machine learning, and big data analytics. His research focuses on integrating microscopic discrete-event simulation with real-world data to model transportation systems and optimize traffic flow. Bowman's work often bridges theoretical models with practical applications, such as calibrating vehicular traffic simulations and developing hybrid approaches combining simulation with machine learning for traffic forecasting. He holds a PhD in a related field (details unspecified) and has published extensively on topics like sensor data-driven traffic optimization and open-source big data frameworks. His research has been published in journals and conferences, including works on traffic light synchronization and service network analysis. Bowman is reachable at casey.bowman@ung.edu and can be found at the Faculty Center, 736, Oconee campus.

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