About

Mark Hansen is a Professor of Civil and Environmental Engineering at the University of California, Berkeley. He holds a PhD in Transportation Engineering from UC Berkeley, a Masters in City and Regional Planning from UC Berkeley, and a Bachelor's degree in Physics and Philosophy from Yale University. Since joining the Berkeley faculty in 1988, he has led numerous transportation research projects focusing on urban transportation planning, air transport systems modeling, air traffic flow management, aviation systems performance analysis, aviation safety, environmental impacts, and air transport economics. Professor Hansen has taught a variety of courses at both graduate and undergraduate levels, covering topics such as transportation economics, systems analysis, planning, probability and statistics, and air transportation. He is also the co-director of the National Center of Excellence in Aviation Operations Research at Berkeley, a multi-university consortium sponsored by the Federal Aviation Administration. His research interests include transportation economics, policy and planning, air transportation, and public transportation.

Research topics

• Computer Science
• Operations research
• Engineering
• Artificial Intelligence
• Economics
• Mathematics
• Mathematical optimization
• Distributed computing
• Econometrics
• Computer network
• Microeconomics
• Real-time computing

Selected publications

• Traffic management and resource allocation for UAV-based parcel delivery in low-altitude urban space

  Transportation Research Part C Emerging Technologies · 2022 · 78 citations

  • Computer Science
  • Computer Science
  • Real-time computing

  This research proposes a framework of Unmanned Aircraft Vehicles (UAV) system traffic management in the context of parcel delivery in low-altitude urban airspace, including clustering-based UAV path planning, Unmanned Aircraft System Traffic Management (UTM) with conflict detection and resolution (CD&R), and mechanism design for airspace resource allocation. For UAV path planning, we develop a procedure by first clustering a large variety of obstacles that arise from building heights and terrain topology and can impede UAV flying. Based on the clustered obstacles, Saturated Fast-Marching Square (Saturated FM2) algorithm is then employed to generate optimal and alternative paths for each UAV mission. While identifying the optimal and alternative paths does not consider UAV traffic interactions, several traffic management models are proposed to efficiently allocate spatial and temporal airspace resources to UAV missions. The UTM models determine the departure time and the path to take for each UAV flight while resolving path conflicts from different perspectives. Specifically, four UTM models are proposed: Sequential Delay (SD) Model, Sequential Delay/Reroute (SDR) Model, Full Optimization (FO) Model, and Batch Optimization (BO) Model. Among the four models, the BO model is of particular interest as it strikes a balance between seeking a system optimum solution and maintaining computational tractability. Given that traffic management requires private information from UAV operators, the Vickrey-Clarke-Groves (VCG) mechanism is further adapted to the UTM context, in which airspace resource allocation is performed in conjunction with a payment scheme to incentivize truthful private information reporting by UAV operators. Extensive numerical analysis is conducted with San Francisco as the case study area. The results show the effectiveness of the proposed framework, particularly the scalability of the BO model. We also find that payment by a UAV flight under the adapted VCG mechanism depends critically on traffic density and the extent of interaction the UAV flight has with other flights.

  DOI

• Air Traffic Flow Management Integrating Separation Management and Ground Holding: An Efficiency-Equity Bi-objective Perspective

  Transportation Research Part B Methodological · 2021 · 36 citations

  • Computer Science
  • Computer Science
  • Mathematical optimization
  DOI

• Quantity-Contingent Auctions and Allocation of Airport Slots

  Transportation Science · 2020 · 29 citations

  • Computer Science
  • Operations research
  • Computer Science

  In this paper, we define and investigate quantity-contingent auctions. Such auctions can be used when there exist multiple units of a single product and the value of a set of units depends on the total quantity sold. For example, a road network or airport will become congested as the number of users increase so that a permit for use becomes more valuable as the total number allocated decreases. A quantity-contingent auction determines both the number of items sold and an allocation of items to bidders. Because such auctions could be used by bidders to gain excessive market power, we impose constraints limiting market power. We focus on auctions that allocate airport arrival and departure slots. We propose a continuous model and an integer programming model for the associated winner determination problem. Using these models, we perform computational experiments that lend insights into the properties of the quantity-contingent auction.

  PublisherDOI

Frequent coauthors

• Bo Zou

  34 shared

• Yanjun Wang

  21 shared

• Megan S. Ryerson

  18 shared

• Geoffrey D. Gosling

  14 shared

• Michael O. Ball

  12 shared

• Manuel Soler

  Universidad Carlos III de Madrid

  10 shared

• Avijit Mukherjee

  10 shared

• Yi Liu

  9 shared

Education

• B.A., Physics and Philosophy

  Yale

  1980

• M.S., City and Regional Planning

  UC Berkeley

• Ph.D., Engineering Science

  UC Berkeley

Awards & honors

• Fellow of the Berkeley Space Center

Similar researchers at University of California, Berkeley

• Peidong Yangh-262
• Jennifer A. Doudnah-230
• Jeffrey R. Longh-189
• John F. Hartwigh-180
• Rasmus Nielsenh-180