My research is profoundly motivated and predominantly fueled by the burgeoning advancements in Artificial Intelligence (AI) and Machine Learning (ML) methods and techniques, alongside their vast applications in the context of the unprecedented availability of Big Data spanning various fields and domains.
- Sustainable and Resilient Infrastructure Systems
- Monitoring, modeling and management (e.g., digital twins)
- Sensor network and remote sensing (e.g., edge/cloud computing and IoT applications)
- Infrastructure planning, design, and non-destructive evaluation
- Intelligent Transportation Systems
- Freeway operations (e.g., active traffic management, computer vision, and artificial intelligence applications)
- Arterial operations (e.g., traffic signal optimization, detection of diverse road users)
- Simulation-based studies
- Transportation Safety
- Quantitative methods in safety analysis (e.g., statistical and econometric methods, machine learning/deep learning methods and applications)
- Multimodal sensing and monitoring (e.g., vision-based and vehicle-borne sensing for real-time monitoring)
- Multisource data fusion and analytics (Integrating data from connected vehicles, crowdsourcing, and other emerging sources for enhanced safety insights)
- Predictive crash modeling (Focusing on spatiotemporal modeling with high-dimensional features)
- Smart Mobility Systems
- Transportation demand management
- Modular bus rapid transit systems
- Electric mobility (e.g., optimizing EV charging network and operation)
- Connected and Autonomous Vehicles (CAV) enabled systems
- Mobility as a Service (MaaS)
- Big Data analytics
The COVID-19 pandemic wreaked havoc on our society and completely changed the way we live and travel. We have to rethink about how to address safety and mobility challenges in the post-COVID-19 era.