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Infrastructure Systems

Vision and Background

The rapid developments of emerging transportation technologies (e.g., electric vehicles (EV), automated vehicles (AV), electric automated vehicles (EAV), and shared mobility) present unprecedented research opportunities in infrastructure design, fleet management, and system impacts. The focuses of our research in this direction are threefolds:

  • Infrastructure design and fleet management interfacing shared EV/AV/EAV systems and power systems.
  • EV applications to satisfy transportation needs during and after emergency situations such as hurricanes.
  • The potential equity impacts of emerging mobility systems, particularly on disadvantaged population groups.

    Objective and Progress

    (1) In terms of infrastructure design, we propose a simulation-based dynamic optimization model to seek near optimal design of charging station location and EAV deployment.

  • By discretizing the model, we propose a Monte Carlo simulation model to evaluate the total system cost for a given location and vehicle deployment design.
  • A heuristic approach based on the Genetic Algorithm is developed to solve the system design of station location and vehicle deployment.
  • A numerical test in Yantai city, China is conducted to illustrate the effectiveness of the proposed model and to draw managerial insights into how the key parameters affect the system design. The figure on the right-hand side shows examples of the optimal results for Batong Line in Beijing Subway System.
  • (2) In terms of fleet management, we propose the new concept of “energy sponge” service by designing an SEV system serving both a transportation system and a power market. With this concept, a fleet of SEVs will serve the transportation system when the transportation demand is high or return electricity to the grid when the power price shoots high. We propose an integer progamming model and a Markov decision process (MDP) model to design optimal fleet operation policy in this new "energy sponge" system.

  • An integer programming model is proposed to determine the optimal policy on time allocation of the SEV fleet.
  • For the case where the vehicls are autoamted, we propose a novel MDP model to optimally design the operation policy of the vacant SEV fleet. The proposed model optimizes the total profit of the SEV system by finding the optimal rebalancing, charging and discharging policy of vancant SEVs in a transportation network. We utilize the matching probability of vacant SEVs with charging stations, discharging stations and travel demands when estimating the state value of the vacant SEV fleets to reduce network dimension of the MDP model, .
  • New York taxi data are utilized test the proposed models. As shown in the figure below (left), the EAV fleet with discharging services always has higher profit (B_dis) than without discharging services (B_nodis). Further, the EAV fleet with MDP operation policy always obtains highest profit comparing to random walk policy and the policy without discharging services, as the figure below (right) shows.
  • CAVs

    (3) We are currently investigating EV operations in emergency circumstances. We optimize assignment between users and available EV charging stations with minimum cost in emergency circumstances and display the optimal assignment via a webpage tool. Such assignment can help charging station providers to better plan and prepare for emergency situations.

  • We have proposed a flow-based model for designing the optimal user charging plan and we are currently testing the model with real-world data.
  • We have created a framework on the web-based tool based on MapQuest and created the sign up/log in pages for the users with My SQL and PHP.
  • (4) Apart from the above engineering problems, our group is also interested in the potential equity imapcts of emerging transportation systems. Many emerging transportation systems are expected to bring substantial benefits to society, but increasing evidence has shown that these benefits may not be proportionally distributed among different population groups in society. Our efforts in this direction aim to propose data-driven methods to better understand the distributive equity impacts of transportation systems. Recent research outcomes include:

  • A new disaggregated method using individual-level data to analyze how bike-sharing accessibilty is distributed among the population in southern Tampa.
  • An empirical proof of the unobserved heterogeneity in transportation equity analysis and its equity implications. The figure below shows the spatial distribution of bike-sharing accessibility in southern Tampa. Dots represent land parcels. The left figure contains all Traffic analysis zones (TAZ) and parcels with bike-sharing accessibility greater than 0.001. The right figure contains all TAZs and parcels with bike-sharing accessibility less than and equal to 0.001. We see that within a large portion of TAZs, there are parcels with bike-sharing accessibility beyond the accessibility interval of these TAZs (e.g., red dots in TAZs shaded blue).
  • A large-scale simulation model integrating agent-based simulation and high-performance computing to analyze the impacts of connected and automated vehicles on system users in terms of accessibility, mobility, affordability, and public health impacts.
  • CAVs


    Related Projects

  • CTECH: Vehicle-based Sensing for Energy and Emission Reduction. Link: []
  • NSF CRISP Type 1: Self-Organized Infrastructure-Population Nexus ─ A Distributed Heterogeneous Flow-based Modeling Framework. Link: []
  • NSF CRISP Type 2: Collaborative Research: Harnessing Interdependency for Resilience: Creating an "Energy Sponge" with Cloud Electric Vehicle Sharing. Link: []
  • CTECH: Measuring Impact of Emerging Transportation Technologies on Community Equity in Economy, Environment and Public Health. Link: []
  • Federal Highway Administration: Automated Vehicle Access, Mobility, and Affordability for System Users.

    References from Our Work

  • Zhao, D., Li, X. and Cui, J., 2019. A simulation‐based optimization model for infrastructure planning for electric autonomous vehicle sharing. Computer‐Aided Civil and Infrastructure Engineering.
  • Zhao., D., Li, X., Wang, X., (2019) Electric Vehicle Sharing Based "Energy Sponge" Service Interfacing Transport and Power Systems. TR Part C, under review.
  • Zhao, M., Li, X., Yin, J., Cui., J., Yang, L., & An, S. (2018). An integrated framework for electric vehicle rebalancing and staff rebalancing in one-way carsharing systems: model formulation and Lagrangian relaxation-based solution approaches. TR Part B, 117(a), 542-572.
  • Chen, Z., Guo, Y., Stuart, A. L., Zhang, Y., & Li, X. (2019). Exploring the equity performance of bike-sharing systems with disaggregated data: A story of southern Tampa. Transportation research part A: policy and practice, 130, 529-545. []
  • Chen, Z., & Li, X. (2021). Unobserved heterogeneity in transportation equity analysis: Evidence from a bike-sharing system in southern Tampa. Journal of Transport Geography, 91, 102956. []