جستجوی استاد راهنمای دانشگاهی

David Noyce is a Professor and Executive Director of the Traffic Operations and Safety (TOPS) Laboratory at the University of Wisconsin-Madison's Department of Civil & Environmental Engineering. He also serves as Associate Director of the Safety Research Using Simulation (SaferSIM) Center, a University Transportation Center. His work focuses on transportation safety, traffic operations, and automated/connected vehicle systems. Key roles include leading over $35M in research since 2003 through the TOPS Lab and advancing technologies like V2V/V2I communication, winter maintenance strategies, and work zone safety. Education: PhD (Texas A&M, 1999), MS (Civil Engineering, UW-Madison, 1995), MBA (UW-Whitewater, 1994), BS (UW-Madison, 1984). Research interests span traffic control devices, driver behavior analysis, crash reconstruction, and multi-modal transportation systems. He pioneered the national implementation of flashing yellow arrow signals and developed the Wisconsin Connected and Automated Transportation Consortium. Current projects include C-V2X localization frameworks, automated vehicle safety testing, and winter maintenance efficiency. Recognized with 20+ awards, including ITE and ASCE Fellowships and the Dr. Arthur F. Hawnn Professorship. His labs offer full-scale driving simulators and field-testing capabilities for real-world validation of innovations.

• Transportation Safety
• Traffic Operations
• Vehicle-to-Infrastructure Communication
• +۵ مورد دیگر

Wendy Ju is an Associate Professor of Information Science at Cornell Tech, with appointments in the Cornell Ann S. Bowers College of Computing and Information Science, the Jacobs Technion-Cornell Institute, and the Technion-Israel Institute of Technology. Previously, she served as executive director of interaction design research at Stanford University's Center for Design Research and as an associate professor of interaction design at the California College of the Arts. Her work bridges human-computer interaction, design, and robotics with a focus on how interactive devices can communicate with people without interrupting them. PhD in Mechanical Engineering from Stanford University Master's degree in Media Arts and Sciences from MIT Professor Ju's research centers on implicit interactions, human-robot collaboration, and automotive interfaces. She investigates how people interact with automated systems in natural contexts, develops methods for early-stage prototyping of autonomous technologies, and examines the social implications of robotics in urban environments. Her work spans from theoretical frameworks to practical applications, with particular emphasis on designing systems that integrate seamlessly into human activities without demanding constant attention. Her recent publications reveal a strong trajectory toward understanding human-robot interaction in public urban spaces, with increasing focus on robot navigation in city streets, the social implications of autonomous vehicles, and the integration of generative AI in design processes. Her work consistently bridges theoretical HCI frameworks with practical applications in transportation, urban design, and everyday robotics. Inducted into the ACM SIGCHI Academy (2025) Multiple Honorable Mention Awards at ACM CHI and DIS conferences Best Paper Award at Multimodal Technologies and Interaction (2023) Best Student Paper Award at IEEE Intelligent Vehicles Symposium (2017) Best Demonstration Award at HRI (2017) Professor Ju actively mentors numerous PhD and master's students, many of whom have become leading researchers in HCI and robotics. Her research has been supported by significant grants from NSF and industry partners, enabling extensive field studies of human-robot interaction in real-world settings. She has pioneered methodologies for studying autonomous vehicle interactions through both simulated and naturalistic driving environments. Her work with the Jacobs Technion-Cornell Institute supports interdisciplinary research at the intersection of computing, design, and urban technology. She has established research partnerships with transportation authorities, automotive companies, and urban planning organizations to study how emerging technologies can enhance urban mobility while respecting human needs and social contexts.

• Interaction with Automation
• Interaction Design Research
• Human Robot Interaction
• +۲ مورد دیگر

Gretchen A. Macht is an Associate Professor in the Department of Mechanical, Industrial and Systems Engineering at the University of Rhode Island, College of Engineering. She specializes in Human Systems Engineering, with research emphasizing team performance, sustainability, electric vehicles, solar energy systems, and voting systems. Her work bridges engineering, ergonomics, and public policy. Her research interests focus on understanding and optimizing human-centered systems. She investigates team communication and performance through fuzzy cognitive maps and entropy modeling, develops sustainable design practices for products and energy systems, and analyzes user behavior in electric vehicles and solar installations. A significant portion of her research is dedicated to election systems, where she uses simulation to improve accessibility, safety, and efficiency of in-person voting, particularly during public health crises. Her recent publications span topics such as EV charging behavior, solar installation safety, voting simulation, and team dynamics. These works reflect interdisciplinary trends combining systems engineering, data analytics, ergonomics, and sustainability science. She frequently employs simulation, naturalistic data analysis, and human factors methodologies. Dr. Macht has secured substantial grant funding from organizations including the U.S. Election Assistance Commission, National Science Foundation, MIT, and Democracy Fund. She serves as Principal Investigator (PI) or Co-PI on projects related to election systems (e.g., SMILE, URI VOTES), ergonomic redesign of protective gear, and community solar development. She leads the SIS Lab and the URI VOTES initiative, fostering collaborative research on simulation and system design. Her lab supports graduate students in conducting impactful research at the intersection of engineering and societal challenges. Notable grants include: PI, MIT: “Voting in Person - White Paper” (2023) PI, US EAC: “SMILE 2.0” (2022) PI, Democracy Fund: “URI VOTES Pocket Guide Tool” (2022) Co-PI, NSF: “Election Science: Convergence Accelerator Workshop Proposal” (2021) PI, US EAC: “SMILE: Simulation Models for an Immersive Learning Environment” (2021) She has advised several graduate students, including Mehrsa Khaleghikarahrodi, Nicholas D. Bernardo Jr., and Tim Jonas, whose research aligns closely with her focus areas in EVs, elections, and team performance.

• Human Systems Engineering
• Team Performance
• Sustainability
• +۷ مورد دیگر

Shrikanth (Shri) Narayanan is University Professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California (USC), with appointments spanning Electrical & Computer Engineering, Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head & Neck Surgery. He serves as Research Director of the Information Sciences Institute and Director of the Ming Hsieh Institute. PhD in Electrical Engineering (UCLA, 1995) Engineer and MS in Electrical Engineering (UCLA, 1992 and 1990) BE in Electrical Engineering (Anna University, India, 1988) His interdisciplinary research focuses on human-centered signal processing and machine intelligence , addressing societal challenges in health, education, defense, and media arts. Key areas include: Behavioral signal processing Affective computing Multimodal signal processing Computational speech science Biomedical applications Scientific Awards : IEEE James L. Flanagan Speech and Audio Processing Award (2025) Edward J. McCluskey Technical Achievement Award (2024) ISCA Medal for Scientific Achievement (2023) Claude Shannon-Harry Nyquist Technical Achievement Award (2023) ACM ICMI Sustained Accomplishment Award (2020) USC Distinguished Faculty Service Award With over 1,000 publications and 19 patents , his work has been commercialized through startups like Behavioral Signals Technologies and Lyssn . He leads transformative university initiatives and has served in editorial roles for top journals including Computer Speech and Language and IEEE Transactions on Affective Computing .

• Human-centered signal processing
• Machine intelligence
• Affective computing
• +۱۰ مورد دیگر

Duminda Wijesekera serves as Professor in the Department of Cyber Security Engineering and Department of Computer Science at George Mason University, where he was inaugural chairman of the Cyber Security Engineering Department until December 2022. He concurrently held the position of visiting research scientist at the National Institute of Standards and Technology (NIST) from 2007-2022 and maintains status as a fellow at the Potomac Institute of Policy Studies. He leads the Mason Innovation Laboratory at Mason Square, driving translational research in cyber-physical security. His educational foundation includes: PhD in Computer Science, University of Minnesota (1997) PhD in Mathematical Logic, Cornell University (1990) BSc in Mathematics, University of Colombo Professor Wijesekera's research centers on cyber-physical system security , with pioneering work in Intelligent Transportation Systems spanning trains, aircraft, and connected vehicles. His digital forensics innovations establish frameworks for evidence-based scenario reconstruction and error management, while his formal methods research provides mathematical guarantees for safety-critical systems. Current projects address Next G-based edge services, digital twin vulnerability detection, and healthcare security architectures, consistently bridging theoretical rigor with real-world infrastructure protection. Analysis of his 2022-2025 publications reveals intense focus on autonomous vehicle security (38% of recent output), including traffic signal control optimization, ramming attack countermeasures, and CARLA-based scenario validation. Digital forensics using AI (20%) and secure manufacturing/edge computing (27%) constitute other major thrusts, demonstrating how formal verification and machine learning converge to solve complex cyber-physical security challenges across transportation, energy, and healthcare domains. His scientific recognition includes: CCI Impact Award (2022) for groundbreaking cyber-physical security contributions Fellowship at the Potomac Institute of Policy Studies for cybersecurity policy leadership Professor Wijesekera has secured substantial research funding through: NIST grants for health record security frameworks (2014-2015) US Department of Transportation projects on wireless frequency mapping for high-speed rail (2013-2014) Cyber Security Research Alliance funding for trust architectures in cyber-physical systems (2014) Commonwealth Cyber Initiative awards for autonomous vehicle security and energy-efficient manufacturing His industry partnerships with Honeywell and NIST ensure practical impact of theoretical research. The Mason Innovation Laboratory under his direction serves as an interdisciplinary hub for cyber-physical security, integrating researchers from computer science, electrical engineering, and policy studies to develop deployable solutions for transportation networks, power grids, and critical infrastructure protection.

• Cybersecurity
• Cyber-Physical Systems
• Digital Forensics
• +۵ مورد دیگر

Suining He is an Assistant Professor at the University of Connecticut (UConn)'s School of Computing, leading the Ubiquitous and Urban Computing Lab since 2019. Previously, he was a postdoctoral research fellow at the University of Michigan's Real-Time Computing Lab (2016–2019). He holds a Ph.D. in Computer Science from the Hong Kong University of Science and Technology (2016) and a B.Eng. in Mechanical Design from Huazhong University of Science and Technology (2012). His research focuses on Cyber-Physical Systems (CPS), Smart & Connected Communities, Human-Centered Computing, and Urban Computing Cyberinfrastructure, with emphasis on mobility, equity, and AI-driven solutions. He has received prestigious awards including the NSF CAREER Award (2023), Google Research Scholar Program Award (2021), and recognition as a Stanford Top 2% Scientist (2020–2024). His work spans interdisciplinary grants from NSF, USDA, Google, NVIDIA, and industry partners. Recent publications explore autonomous driving simulation, equity-aware mobility prediction, and urban crowd activity modeling. Teaching excellence is reflected in his 2020 UConn Provost Award. He advises on reinforcement learning, CPS, and mobile computing, with openings for 2025/2026 PhD students. His lab collaborates on socially-conscious AI, privacy-preserving learning, and location-based services with industrial impact.

• Cyber-Physical Systems
• Smart & Connected Communities
• Human-Centered Computing
• +۱۰ مورد دیگر

Prof. Dr.-Ing. Christoph Stiller is a full professor at the Karlsruher Institut für Technologie (KIT) and serves as the director of the Institute of Measurement and Control Technology (Institut für Mess- und Regelungstechnik, MRT). His work focuses on autonomous driving, sensor fusion, probabilistic estimation, HD mapping, motion planning, and intelligent transportation systems. Education: Details on his academic degrees are not provided in the text, but he holds the title of Dr.-Ing. indicating a doctoral degree in engineering. Research Interests: Prof. Stiller's research spans a wide array of topics critical to the development of autonomous vehicles. His work includes: Sensor Fusion: Integrating data from LiDAR, cameras, and radar to create robust perception systems. HD Mapping & Localization: Developing high-definition maps and precise localization techniques for urban and highway environments. Motion Planning & Decision Making: Creating algorithms for safe and efficient trajectory planning under uncertainty. Machine Learning & AI: Applying deep learning and reinforcement learning to perception, prediction, and control tasks. Publication Trends: His recent publications (2023–2025) emphasize robust traffic light detection, image stitching for panoramic views, motion prediction using redundancy reduction, and safety-enhanced model predictive control. The work increasingly integrates learning-based methods with classical control and estimation theory. Scientific Awards: No specific awards are listed in the provided text. Teaching & Supervision: Prof. Stiller teaches foundational and advanced courses in measurement and control systems, probabilistic estimation, and autonomous driving. He holds regular office hours during both summer and winter semesters and is actively involved in advising students and researchers. Labs & Teams: He leads the Institute of Measurement and Control Technology (MRT) at KIT, which is engaged in cutting-edge research in autonomous systems. The institute collaborates with industry and academia on large-scale projects such as UNICARagil and various European initiatives.

• Autonomous Driving
• Sensor Fusion
• Intelligent Transportation Systems
• +۵ مورد دیگر

Lingxi Li is a Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University's Indianapolis campus. His research focuses on modeling complex systems, connected and automated vehicles, intelligent transportation systems, and parallel intelligence. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2008), and master's and bachelor's degrees from the Chinese Academy of Sciences (2003) and Tsinghua University (2000). Research Interests: Dr. Li's work bridges control systems, transportation engineering, and AI, with emphasis on human-machine interaction, autonomous vehicle systems, and scenario-based traffic modeling. His projects include developing frameworks for Industry 5.0 collaboration, enhancing traffic flow prediction through parallel learning, and advancing safety in micro-mobility systems like e-scooters. Recent Publications: Over 15+ articles (2023-2025) explore topics such as game-theoretic vehicle interaction modeling, vision-language systems for autonomous driving, and acoustic SLAM technologies. These studies reflect a focus on real-world validation and system integration in smart transportation. Labs & Initiatives: Leads research in autonomous mining systems and scenario engineering for intelligent vehicles, leveraging parallel intelligence concepts. Collaborates on projects like ParallelWorkforce (Industry 5.0 frameworks) and SceNDD++ (naturalistic driving datasets).

• Intelligent Transportation Systems
• Automated Vehicles
• Control Systems
• +۶ مورد دیگر

Evita Papazikou serves as a Lecturer in Transport Engineering at the School of Engineering, University of the West of England (UWE Bristol), where she contributes to the Centre for Transport and Society and collaborates with the Bristol Robotics Laboratory's Connected & Autonomous Vehicles Centre. Her academic qualifications include: Civil Engineering (BEng and MEng) from Aristotle University of Thessaloniki MSc in Planning, Organisation, and Management of Transport Systems, Aristotle University of Thessaloniki PhD in Automated Systems and Driver Behaviour (Road Safety) from Loughborough University, sponsored by the Insurance Institute for Highway Safety with access to SHRP2 NDS data Dr. Papazikou's research focuses on road safety, connected and automated vehicles, driver behaviour analysis, and smart infrastructure. She investigates accident causation through statistical modeling, develops driver monitoring systems, and explores human factors in transportation. Her work integrates traffic simulation with mobility data fusion from vehicles, sensors, and infrastructure to enhance safety in future mobility systems, particularly in cooperative, connected, and automated environments. Her recent publications (2023-2025) reveal a concentrated research trajectory examining safety impacts of dedicated lanes for autonomous vehicles, parking policy implications in automated eras, and driver fatigue management. She consistently employs naturalistic driving data and traffic microsimulation to analyze driver-vehicle-environment interactions, with increasing emphasis on real-world intervention effectiveness and environmental sustainability in mobility systems. Scientific Awards: No specific awards were mentioned in the provided information. Dr. Papazikou has secured significant research funding through competitive programs including Horizon 2020, Innovate UK, and the Department for Transport. Her project portfolio demonstrates substantial industry collaboration, particularly with Ford, and includes: LEVITATE: Assessing societal impacts of Connected and Automated Vehicles SafetyCube: Developing an innovative road safety decision support tool i-DREAMS: Creating a smart driver and road environment assessment system DDRST: Building a data-driven road safety tool for hotspot identification TRIP: Developing a driver culpability assignment tool for road injury prevention She actively contributes to interdisciplinary research through her affiliations with the Centre for Transport and Society and the Bristol Robotics Laboratory's Connected & Autonomous Vehicles Centre, where she bridges engineering, human factors, and policy development for next-generation transportation systems.

• Road Safety
• Connected and Automated Vehicles
• Driver Behaviour
• +۵ مورد دیگر

Dr. Otto Lappi is a Research Fellow at the University of Helsinki's Department of Digital Humanities, Faculty of Arts. His work bridges cognitive science and human performance in complex dynamic tasks, particularly focusing on predictive processing in visuomotor control. PhD in Cognitive Science Title of Docent at University of Helsinki Academy Research Fellow (2020-2025) His research explores: Cognitive mechanisms in high-speed sports and driving Predictive gaze strategies and steering control Flow states in skill acquisition VR and eye-tracking applications in archaeomusicology Neural substrates of domain expertise Recent publications analyze: Flow-state dynamics in visuomotor learning Predictive gaze behavior in automated driving Computational models of attention in distracted driving Chunking mechanisms in high-speed sports performance Scientific contributions include: Academy Research Fellow (Research Council of Finland) Editorial work on high-performance cognition Organizing cognitive science workshops As doctoral supervisor, he has guided: Samuel Tuhkanen (Predictive Gaze and Steering) Jesse Kuokkanen (Mechanistic Computationalism) Ida Maasalo (Driver Behavior) Markus Mattsson (Psychometrics)

• Cognitive Science
• Human Performance
• Gaze Control
• +۳ مورد دیگر

Niklas Grabbe is a postdoctoral researcher at the Chair of Ergonomics, Technical University of Munich (TUM), where he leads the research group on "Automated Driving and Mobility Systems" and is establishing a new working group focused on "Modelling complex socio-technical systems" with emphasis on Resilience Engineering and the Functional Resonance Analysis Method (FRAM). His research centers on human factors in automated and teleoperated driving, mobility systems, and behavior modeling. He applies systems engineering principles to analyze safety and usability, particularly using FRAM to study performance variability and resilience in socio-technical systems. His work addresses critical challenges in urban automated driving, teleoperation, and multi-driver interactions through rigorous quantitative modeling and field studies. Analysis of Grabbe's publications reveals a dominant trend in applying FRAM to driving scenarios, with recurring themes of safety enhancement, human-automation interaction, and usability evaluation. His interdisciplinary work bridges transportation psychology, safety engineering, and human-computer interaction, consistently emphasizing systems thinking to solve complex problems in automated vehicle technology. Grabbe contributes to academic instruction through courses like "Modelling Complex Sociotechnical Systems" (Winter 2025/26). He operates within TUM's Chair of Ergonomics infrastructure, utilizing specialized labs including driving simulators and ergonomic mockups to support experimental research in human factors and automated systems.

• Human Factors Engineering
• Automated Driving
• Teleoperated Driving
• +۵ مورد دیگر

Carryl Baldwin is the Carl and Rozina Cassat Distinguished Professor of Aging & Regional Institute on Aging and Director of the Wichita Auditory Research Group (WARG) at Wichita State University's Fairmount College of Liberal Arts and Sciences. Her work spans neuroergonomics, aging research, auditory cognition, and human-vehicle interface design, with funding from NHTSA, NIH, NASA, and private industry partners. Education : PhD in Human Factors Psychology (1997), MA in Human Factors Psychology (1994), BA in Psychology/Asian Studies (1987) Dr. Baldwin's research focuses on human interaction with automation , particularly in transportation contexts. She investigates driver attention, mind wandering, cognitive workload, and multimodal alarm systems using physiological metrics like EEG. Her lab explores aging-related cognitive changes and develops technologies to enhance safety and performance in semi-autonomous systems. The 15 most recent publications reveal trends in neuroergonomics , aging populations , and automated vehicle safety . Key themes include attention management in automation, auditory alarm optimization, and sustainability applications of human factors, with methodologies spanning EEG analysis, eye-tracking, and multimodal interface evaluation. Dr. Baldwin's lab has secured external funding from the National Highway Traffic Safety Administration, NIH-NIA, NASA centers, and defense contractors like Northrop Grumman. Current research includes driver vigilance in Level 2/3 vehicles, pandemic-related social isolation in older adults, and FAA-funded training technology evaluation.

• Neuroergonomics
• Aging Studies
• Human Factors Psychology
• +۵ مورد دیگر

Xiao Wang is a research assistant and PhD student in the Cyber-Physical Systems Group at the Technical University of Munich since 2019. She holds a Master of Science in Mechanical Engineering from the same university (2018) and a Bachelor of Engineering in Vehicle Engineering from Tongji University, China. Her research focuses on Motion Planning for Autonomous Vehicles , Formal Methods , and Safe Reinforcement Learning . She has supervised multiple theses exploring topics like constrained RL, online verification, imitation learning, and safety falsification for autonomous systems. Her teaching roles include exercises and practical courses on Artificial Intelligence and Motion Planning for Autonomous Vehicles since 2018. Her publications (2020–2023) span journals like Transactions on Machine Learning Research and conferences such as ITSC and FISITA , addressing challenges in safe RL, control barrier functions, and naturalistic traffic rule violations. She has also contributed to integrating the Apollo framework with the CommonRoad motion planning environment. Key research areas: Safe Reinforcement Learning, Motion Planning, Formal Verification, Autonomous Driving, Control Barrier Functions, Trajectory Prediction

• Motion Planning for Autonomous Vehicles
• Formal Methods
• Safe Reinforcement Learning
• +۳ مورد دیگر

Peiyi Wang is an Assistant Professor at Peking University's School of Electronics Engineering and Computer Science, Institute for Artificial Intelligence. With strong research output spanning both natural language processing and robotics, Wang maintains significant collaborations with Southern University of Science and Technology and National University of Singapore, particularly in soft robotics research with Professor Cecilia Laschi. Additionally, Wang is actively involved with DeepSeek-AI, contributing to several major language model initiatives including DeepSeek-R1 and DeepSeek-V2. Peking University, School of EECS, Institute for Artificial Intelligence (Primary) Southern University of Science and Technology (Collaborative) National University of Singapore (Collaborative) DeepSeek-AI Research Organization Dr. Wang's research spans two primary domains with significant intersection points. In natural language processing, Wang focuses on large language model reasoning capabilities, mathematical verification, uncertainty estimation, and preference alignment. The robotics work centers on soft robotics, particularly origami-inspired designs, strain-based modeling, and control systems for continuum manipulators. These domains converge in Wang's work on vision-language models, embodied AI, and multimodal reasoning systems. Recent work demonstrates particular innovation in mathematical reasoning verification (Math-Shepherd), soft robotic control systems, and red teaming frameworks for language model safety. Wang's publication record shows remarkable productivity, with over 40 publications between 2021-2025 across top-tier venues including ACL, EMNLP, CVPR, and IEEE Transactions on Robotics. The work demonstrates consistent progression from foundational NLP tasks to increasingly sophisticated multimodal and reasoning systems. The most recent publications (2024-2025) show particular emphasis on mathematical reasoning verification, soft robotics control, and language model safety evaluation. While specific awards aren't documented in the provided materials, Wang's work has clearly gained significant recognition through acceptance at top-tier conferences and collaborations with leading researchers in both NLP and robotics fields. Wang's research demonstrates strong interdisciplinary connections, bridging theoretical NLP work with practical robotics applications. The work with DeepSeek-AI suggests active industry collaboration while maintaining strong academic research output. Current research directions appear focused on improving language model reasoning reliability while developing novel soft robotic systems that can interact safely and effectively with complex environments.

• Natural Language Processing
• Large Language Models
• Soft Robotics
• +۴ مورد دیگر

Pasi Lautala is a Professor in the Department of Civil, Environmental, and Geospatial Engineering at Michigan Technological University (Michigan Tech) and currently serves as the Associate Dean for Research. He holds a BS from Tampere University of Technology (Finland) and MS/PhD from Michigan Tech. His research focuses on rail and highway transportation engineering, with emphasis on grade crossing safety, multimodal logistics, sustainability, railway capacity analysis, and engineering education development. Since 2007, Lautala has directed the Rail Transportation Program (RTP) within the Michigan Tech Transportation Institute (MTTI), expanding rail research collaborations across disciplines. He leads over $10M in external research funding, including projects on trespasser safety, freight logistics, and lifecycle analysis. Lautala is a key figure in rail education revitalization, serving as Rail Group Chair at the Transportation Research Board (TRB) and advising the Michigan Commission for Supply Chain Logistics. His teaching spans courses like Transportation Engineering, Railroad Design, and Logistics Management. Lautala has advised numerous undergraduate and graduate projects, emphasizing industry partnerships. Recent contributions include developing in-vehicle auditory alerts for rail crossings and AI-driven safety systems like RAIILS. Key collaborations include the Federal Railroad Administration (FRA) on grade crossing safety ($641K+ projects), U.S. DOT on rail modal analysis, and Battelle on connected vehicle systems. He mentors the Tracks to the Future youth program and co-leads international rail education initiatives.

• Rail Transportation Engineering
• Highway-Rail Grade Crossing Safety
• Multimodal Transportation Logistics
• +۴ مورد دیگر