Publications

(2023). Intermediate Service Facility Planning in a Stochastic and Competitive Market: Incorporating Agent-infrastructure Interactions over Networks. Transportation Research Part C: Emerging Technologies.

Project

(2023). System-level impacts of en-route information sharing considering adaptive routing. Transportation Research Part C: Emerging Technologies.

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(2023). Private Electric Vehicles for Emergency Load Pickup: A Multi-Network Stochastic Equilibrium Approach. Transportation Research Record: Journal of the Transportation Research Board.

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(2022). Co-optimization of Charging Scheduling and Platooning for Long-haul Electric Freight Vehicles. Transportation Research Part C: Emerging Technologies.

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(2022). Electric Vehicles for Distribution System Load Pickup Under Stressed Conditions: A Network Equilibrium Approach. IEEE Transactions on Power Systems.

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(2022). Spatial Pricing of Ride-sourcing Services in a Congested Transportation Network. Transportation Research Part C: Emerging Technologies.

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(2022). Optimization-based trip chain emulation for electrified ride-sourcing charging demand analyses. Transportation Letters.

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(2021). Data Mining of Plug-in Electric Vehicles Charging Behavior using Supply-Side Data. Energy Policy.

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(2021). Analyzing the Travel and Charging Behavior of ElectricVehicles - A Data-driven Approach. 2021 IEEE Kansas Power and Energy Conference.

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(2021). A Stochastic Multi-Agent Optimization Framework for Interdependent Transportation and Power System Analyses. IEEE Transactions on Transportation Electrification.

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(2019). Impacts of Integrating Topology Reconfiguration and Vehicle-to-Grid Technologies on Distribution System Operation. IEEE Transactions on Sustainable Energy.

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(2017). Contingency-Constrained Unit Commitment With Intervening Time for System Adjustments. IEEE Transactions on Power Systems.

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(2016). Infrastructure Planning for Fast Charging Stations in a Competitive Market. Transportation Research Part C: Emerging Technologies.

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(2012). Estimating Traffic Speed with Single Inductive Loop Event Data. Transportation Research Record: Journal of the Transportation Research Board.

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Projects

Distributed Reinforcement Learning for Optimal Speed Limit Control Over Network, sponsor: Safer-Sim

The goal is to optimize variable speed limit control (VSLC) strategies over network to improve both traffic safety and mobility. The proposed research will advance the current knowledge and practice of VSLC in three aspects. First, this research will enlarge the scope of VSLC from link­based to network­ based control to bring a new understanding about its system­level safety implications. Second, it will optimize the impact of VSLC using multi­objective learning approaches considering both safety and mobility. Third, distributed artificial intelligence approaches proposed in this research introduce new opportunities for network control effectiveness and scalability compared with traditional model/rule based approaches.

Optimizing Information Value in Heterogeneous Multi-agent Transportation Systems (OPTIMA), sponsor: NSF, Safer-Sim(seed)

This project addresses the use of advanced sensing, communications, and computing technologies in studying value of information in transportation systems made up of heterogeneous traffic (cars, autonomous and connected vehicles, buses, bicycles, etc.) The wealth of data available on these systems enables new approaches to information provisioning that have the potential to improve transportation system efficiency, reliability, and resilience. The project develops a unified modeling framework for information provision considering heterogeneous non-cooperative stakeholders and addresses three critical questions: (1) How do we model adaptive behavior of different traffic components in response to evolving information updates? (2) What are the limits of positive and negative information on systems efficiency, reliability, and resilience? (3) Given limited resources, what, when, and where should the information be communicated with which groups of stakeholders to optimize system performance?

Agnet-base Simulation for Complex Infrastructure Systems, sponsor: Argonne/DOE

Due to an increasing connectivity, interdependence, and complexity of infrastructure systems, an analytical model may not be feasible to reflect the interactions within/between systems. As an alternative, agent-base modeling is found to be effective to capture complicated agent interactions and visualize system dynamics, especially with increasing data availability. This project develops an agent-base modeling platform to capture the interactions between key decision makers in transportation and power systems, including drivers, households and infrastructure investors, and simulate their decision making over both short-term and long-term horizon. This platform has broad applications in the study of EV charging, ride-sharing and power-transportation interdependency.

Cascading Failures and Robust Planning/Operation of Power System

Cascading failures have become one of the biggest challenges for increasingly connected infrastructure systems, such as power network. This project investigates effective and robust planning and operation strategies for power system considering the possibility of cascading failures.

Charging Infrastructure Evolvement in a Competitive Market

Charging infrastructure is a critical element for the success of transportation electrification. But charging infrastructure is not developed by a single central planner. This project investigates the potential evolvement of PEV charging infrastructure in a competitive market.

Data Analytics in Transportation System, sponsor: Argonne/DOE

Data has arguably become one of the most powerful tools to understand system in a timely fashion. In transportation, multiple data sources (e.g. social media, transaction, economics, sensors, etc.) have become readily available. This project collects and analyzes large-scale transportation data, including vehicle market value, on-demand transportaiton service, travel survey, and charging data.

Interdependency of Power and Transportation, sponsor: UCF ORC Seed

Power and transportation networks have various similarity and interdependency. This project looks at different linkages between these two critical infrastructure systems, including methodological and functional aspects

Teaching

University of Central Florida

  • TTE 3405: Applied Numerical Methods (Fall 2022-present)
  • TTE 3810: Highway Engineering (2018-present, 1-2 sessions per year )
  • CGN 5617: Infrastrastructure Systems Optimization (Spring 2019-present)
  • TTE 6938: Transportation Network (Fall 2021-present)

University of California Davis

  • ECI 16: Spatial Data Analysis (Spring 2016)
  • ECI 253: Dynamic Programming and Multi-stage Decision Processes (Spring 2015)
  • ECI 153: Introduction to Operations Research (Fall 2014)

Team

Md Nafees Fuad Rafi

Graduate Student Researcher, PhD Student

MohamadReza GhorbanaliZadegan

Graduate Student Researcher, PhD Student

Nima Rashgi Shishvan

Graduate Student Researcher, PhD Student

Saman Mazaheri Khameneh

Graduate Student Researcher, PhD Student

Miguel Angel Venero Yupanqui

Undergraduate Researcher

Fatima Afifah

Graduate Student Researcher, PhD Student

Md Rakibul Alam

Graduate Student Researcher, PhD Student

Sina Baghali

Graduate Student Researcher, PhD Student

Amani McFarlane

Undergraduate Researcher, RAMP Scholar

Chuang Hou

Visiting Scholar (2020), Software Engineer at Alibaba Group

Enrique Sara Cueto

Undergraduate Researcher

Hongjing Shi

Volunteer, High School Student

Kyle Cooke

Undergraduate Researcher

Sean Morrisey

Undergraduate Researcher

Tadeas Lobreis

Undergraduate Researcher

Lab News

More Posts

INSPIRE lab will present three of recent research at TRB 2024: (1)Estimating Bus Occupancy in Real-time from Wi-Fi Frames with Randomized MAC Addresses; (2) Optimal Speed Limit Control for Network Mobility and Safety: A Twin-Delayed Deep Deterministic Policy Gradient Approach; (3) Estimating Bus Occupancy in Real-time from Wi-Fi Frames with Randomized MAC Addresses

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I’m thrilled to receive the NSF CAREER Award this year. Especially thanks my mentors, students, and friends who have helped greatly to this achievement. The topic of my CAREER proposal is on “A Decentralized Optimization Framework for Next-Gen Transportation and Power Systems with Large-scale Transportation Electrification”, aiming to contribute to the efficiency, sustainability, and resilience of these two critical infrastructure systems leveraging electric vehicles. Look forward to broad collaboration and discussion on this topics.

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Dr. Guo will serve as the panelist on the NSF-sponsored workshop: Cyber-enabled to Support Carbon-Neutral Electricity and Mobility at TAMU from Apr. 24-25, 2023. The title of his talk is on Opportunities and Challenges for Large-scale Transportation Electrification.

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INSPIRE LAB’s paper on Multi-stage Charging Station and Distributed Generator Planning in Decentralized Power Distribution and Transportation Systems received the 3rd place in the best student paper award competition in Energy System Track in IISE Annual Conference 2022. Congratulations Sina and Rakibul!

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We will have three presentations at IISE Annual Conference 2022 at Seattle from May 21 to May 24, 2022. Sina will present the work, coauthored with Rakibul Alam and Dr. Guo, on multi-stage multi-agent charging infrastructure and distributed generators planning in coupled transportation and power systems. Dr. Guo will present two of the works on the charging infrastructure planning for ride-sourcing systems (coauthored with Rakibul) and network modeling with en-route information updates for transportation systems (coauthored with Fatima).

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