Research
Progression 1: AI-Enabled Logistics Digital Twin for Sustainable Freight and Hydrogen Transportation 
Dr. Wang’s group is advancing AI-enabled logistics and digital twin technologies to reduce greenhouse gas (GHG) emissions and improve supply chain efficiency. The AI-Logit framework integrates data-driven forecasting, delivery operations optimization, and digital twin modeling to support sustainable logistics decision-making.
The research is organized into three core modules:
- Data Integration and Forecasting Module: combining logistics system information, socioeconomic factors, and AI-enabled predictions to anticipate demand and environmental impacts.
- Delivery Operations and Route Optimization Module: improving facility location, delivery service types, and route design under varying traffic and operational conditions.
- Digital Twin Integration Module: creating virtual replicas of physical systems to test “what-if” scenarios, model system-wide interactions, and evaluate equity and environmental justice impacts.
The group applies these tools to real-world logistics systems, delivering comprehensive, data-driven solutions. Case studies include warehouse–e-commerce co-location patterns across Texas metropolitan areas and a hydrogen transportation framework for the future (2026–2050), demonstrating how alternative fuels and infrastructure design can achieve emissions reductions.
The anticipated impacts include significant energy savings, reduced emissions, and improved customer satisfaction, showing how AI and digital twins can serve as powerful enablers of sustainable freight and logistics futures.
Progression 2: A Multi-dimensional Assessment to Enhance Transportation Cybersecurity 
Dr. Wang’s group advances the resilience of transportation systems by addressing the growing risks of cyberattacks and extreme weather events. Their work focuses on generating synthetic cyberattacks and hacking scenarios to test system vulnerabilities, while also building predictive models that leverage crowdsourced mobility platforms (e.g., StreetLight, Waze, INRIX) and remote sensing data. These tools allow for real-world, data-driven assessments of threats to both physical and digital infrastructure.
The group designs explainable AI frameworks to study visitor flows, industry clustering, and the spatial dynamics of transportation cybersecurity–related activities. Within this spatial dynamics analysis, they conceptualize the transportation cybersecurity industry as an ecosystem, comprising three dimensions: infrastructure, social capital, and institutions. This framework reveals how regional assets, socio-economic conditions, and institutional capacities interact to shape the industry’s growth and resilience.
A complementary strand of this research is a nationwide study of cybersecurity risk responses among public transit agencies, offering insights into preparedness, response strategies, and policy implications. Collectively, this work provides a deeper understanding of how cyber-physical risks unfold across space and institutions, informing strategies for building more resilient mobility systems.
Progression 3: Social, Behavioral, and Economic Impacts of Remote Work and Mobility Futures
Dr. Wang’s group investigates how the rise of digitalization and remote work is reshaping daily activity–travel behaviors and transforming urban systems. Our research examines the impacts of extended time spent online—whether through telework, online shopping, or digital leisure—on mobility patterns, transit demand, and spatial development.
We focus on four interconnected challenges: mental health and work–life balance, spatial regeneration of urban cores, uneven economic geographies, and governmental stress in adapting policy and infrastructure. To address these, we design human-centered AI systems such as spatio-temporal neural networks, behavioral digital twins, and optimization-based scheduling and siting models. By integrating transportation planning, behavioral economics, and public policy, our work generates solutions including synthetic mobility patterns, decentralized hub design, CBD regeneration strategies, and labor market analysis. These outcomes aim to support employees, service providers, employers, and policymakers in navigating the future of work and mobility.
Through education and outreach, Dr. Wang’s group translates research into the classroom and community with modules on Next-Generation Transport & Logistics, Sensing & Learning the Built Environment, and Digital Economy & Policy, preparing future professionals to navigate rapid societal and technological change.
Progression 4: 15-Minute City and Active Travel Research
Dr. Wang’s group investigates the transformative potential of the 15-minute city framework to enhance accessibility, sustainability, and equity in urban environments. Through comparative analyses across major U.S. metropolitan areas, we examine how walking, cycling, and public transit can collectively meet residents’ daily activity needs within a short travel radius.
Our work evaluates the feasibility of building walkable and bikeable 15-minute cities by upgrading infrastructure across urban, suburban, and even rural contexts. Persistent disparities in accessibility across regions underscore the need for targeted interventions. We also assess the potential of shifting toward active and shared modes within a 15-minute travel shed to reduce carbon emissions, with results showing that walking, cycling, and transit can achieve meaningful reductions, though the effectiveness varies by city and mode.
To capture on-the-ground realities, we conduct pedestrian and bicyclist friendliness assessments, integrating field audits, safety perception mapping, and infrastructure evaluations. Visual documentation highlights street features, crosswalk designs, and protective buffers that shape everyday walkability and bikeability. In addition, we introduce a scalable framework for estimating Bicycle Level of Traffic Stress (LTS) in contexts with limited data availability, leveraging simplified street network representations and algorithmic classification to evaluate cycling safety and comfort.