講座時間：2019年5月27日（周一）9：30-11：30講座地點：25樓A座3層B教室主講人：Professor Heng Wei
主講人簡介：Dr. Heng Wei has extensive research expertise and industrial experience in intelligent transportation systems (ITS), Connected and Automated Vehicle (CAV) impacts, infrastructure-CAV nexus in traffic signal system design, and artificial intelligent (AI)-based Informatics and geographic information system (GIS) in smart-city-oriented travel demand and environmental analytics techniques. He has secured a great number of research projects from ODOT, FHWA, NSF, US EPA, USDOT UTCs, and UC URC/FDC. His research has resulted in over 184 peer-reviewed and referred papers, and nine book or chapters. He has been awarded with many professional prizes and honors. He has served as a member of numerous outstanding professional committees, such as TRB Committee on Artificial Intelligence and Advanced Computing Applications (ABJ70), User Information Systems (AND 20), Transportation in Developing Countries (ABE90), and ASCE T&DI Committees on i) Advanced Technology and Transportation Safety, ii) Sustainability and Environment; and iii) CAV Impacts. He is the Chair of IEEE ITSS Travel Information and Traffic Management Committee, Chair of Energy and Environment Committee under Urban Planning category for the World Transportation Convention (WTC), and Past President of Chinese Overseas Transportation Association (COTA). Dr. Heng Wei invented a video-capture-based method for extracting vehicle trajectory data on multiple lanes from video and developed a software tool, VEVID which was later combined with image processing technique into the a “hybrid” system (VIEW-TRAFIC) to make the video-based traffic extraction suitable for all traffic conditions. He has developed two GIS-based analysis systems, Traffic Air Environmental Health Impact Analysis (TAEHIA) and Air Impact Relating Scenario-based Urban Settings and Transportation Assets In Network (AIR-SUSTAIN) to facilitate analysis of the land-social-travel-environment nexus. Recently, Dr. Wei has created a model and algorithm that uses data from connected vehicles to adjust the timing of traffic lights for maximizing throughput, while formulating the infrastructure-CAV nexus.
講座內容：The vehicle-to-vehicle and vehicle-to-infrastructure communication technologies make the CAVs a floating-vehicle sensing data source that can be received ubiquitously in roadways through roadside equipment or Internet-of-Things technologies. This advancement makes it possible to resolve the challenges being faced off with adaptive signal control depending on the traditional loop or video based detection technologies, which cannot directly detect and measure the vehicle trajectory and identify traffic states. Data collected from the CAVs can be used to capture a much more complete picture of the traffic states, which can be utilized for effectively implementing the adaptive signal control. However, it is an imperative need of a synthetic mechanism with modeling algorithms that enables the real-time data exchange among vehicles and between vehicles and infrastructure to formulate the “brain intelligence” of implementing the adaptive control schemes. More significantly, the CAV’s capability of serving as the mobile trajectory sensors could help us to reduce the dependencies on conventional infrastructure-based vehicle detectors. The preliminary research resulting from the simulation evaluation of the developed synthetic models shows that the average control delay per vehicle reduced by 51% whereas throughput improved by around 29% when we implemented the optimization strategy at an isolated intersection. The results initially prove the developed concepts and modules and provide a solid foundation to deeply investigate how the adaptive signal control algorithms proposed in this research can improve the performance along an entire corridor.