Data Analytics & Simulation Research / Methodologies Data Analytics & Simulation — how we research INSPIRE Lab combines agent-based simulation, empirical data analytics, and real-world sensing to study how transportation and energy systems behave at scale. Our work draws on large datasets — market data, trip records, loop detectors, transit sensors — to generate evidence for infrastructure planning and policy. Key Approaches — three core directions Theme 01 Agent-Based Simulation Multi-agent simulation of mobility, charging, and infrastructure interactions. Theme 02 Empirical Data Analytics Statistical analysis of market, trip, and operational data at scale. Theme 03 Sensing & Pilot Studies Real-world sensor evaluation and transit infrastructure pilots. Featured Publications — recent highlights Transport Letters · 2022 Optimization-Based Trip Chain Emulation for Electrified Ride-Sourcing Charging Demand Analyses Alam, Hou, Aeschliman, Zhou & Guo Energy Policy · 2018 Residual Value Analysis of Plug-in Vehicles in the United States Guo & Zhou TRR · 2012 Estimating Traffic Speed with Single Inductive Loop Event Data Lu, Varaiya, Horowitz, Guo & Palen Full Publication List → Highlighted Projects Co-PI FDOT Emerging Data-as-a-Service for Sustainable Transit Service Planning PI DOE · Argonne Estimation of Ride-Sourcing OD Demand from Traditional Household Travel Survey
