Nikola Markovic portrait
  • Assistant Professor, Civil And Environmental Engin


  • Mohamadreza Sheibani, Yinhu Wang, Gaby Ou & Nikola Markovic (2022). Efficient Structural Reconnaissance Surveying for Regional Postseismic Damage Inference with Optimal Inspection Scheduling. Journal of Engineering Mechanics. Published, 02/01/2022.
  • Mohammad Farhadmanesh, Abbas Rashidi & Nikola Markovic (2022). An Image Processing Method for Light Aircraft Tail Number Detection in General Aviation Airports. Transportation Research Board. Published, 01/09/2022.
  • Bahar Azin, Terry Yang, Nikola Markovic & Mingxi Liu (2021). Infrastructure enabled and electrified automation: Charging facility planning for cleaner smart mobility. Transportation Research Part D: Transport and Environment. Published, 12/01/2021.

Research Statement

Operations Research

I am interested in development and application of operations research models that help improve efficiency of transportation systems. In my dissertation I developed models for the optimal location of violator-intercepting facilities in large-scale transportation networks. This work won the Glover-Klingman prize for the best paper published in Networks. My ride-sharing algorithms are used by half a dozen companies transporting seniors and people with disabilities. Currently, my group is developing a software for routing snowplow trucks in Utah. 


Data Science

I am interested in applications of machine learning and data visualization techniques that either help inform decision-making or help automate relevant processes. I have worked extensively with millions of GPS trajectories and used them to estimate statewide traffic patterns and network performance. Currently, my group is developing a computer vision system to automatically collect aircraft operations data at Utah airports. 


Interface of Optimization and Data Science

Traditional optimization approaches do not account for the cost of acquiring input data. My current research is concerned with developing methods for optimizing the amounts of data acquired for calibrating network optimization models. Similarly, traditional statistical methods for experimental design do not account for resource constraints that arise in any field collection of information. As part of multiple collaborative efforts, my group is currently working on algorithms to embed resource constraints into optimal learning procedures.

Research Keywords

  • Transportation Engineering
  • Operations Research

Research Groups

  • Seth Miller, Graduate Student. MS non-thesis. 01/07/2019 - 12/31/2019.