Mark Hansen
Professor, Department of Civil and Environmental Engineering
Professor, Department of Civil and Environmental Engineering
114 McLaughlin Hall, UC Berkeley
Berkeley, California, 94720-1720
Phone: (510) 642-2880
Fax: (510) 643-8919
Berkeley, California, 94720-1720
Phone: (510) 642-2880
Fax: (510) 643-8919
Scenario-Based Air Traffic Flow Management: from Theory to Practice (with P. Barry Liu)
Recent developments in solving the single airport ground holding problem use static or dynamic optimization to manage uncertainty about how airport capacities will evolve. Scenario trees of airport arrival capacity profile provide the basis for formulating multistage recourse problems. We investigate methodologies for generating scenario trees from empirical data and examine the performance of scenario-based models in a real-world setting.
Probabilistic Forecasts of Convective Storm Activity for En Route Air Traffic Management: A Statistical Learning Approach (with P. Barry Liu)
Real-time aircraft routing decision support tools require probabilistic forecasts with high accuracy. However, the existing forecasts for en route convective weather have unsatisfactory performance. We use probabilistic graphical models in the hidden Markov model family to characterize the convective weather evolution process. Through statistical learning from historical data and existing forecasts, our model shows promise in providing forecasts with better quality.
Managing uncertainty in the Single Airport Ground Holding Problem using Markov Decision Process (with P. Barry Liu)
The goal of this research is to advance the support of decision-making in air traffic flow management under uncertainty. We develop optimization models that treat the airport capacity as a Markovian process. Unlike the stochastic programming models, the Markov decision process approach can give a far more comprehensive plan that responds to conditions dynamically. We investigate ways to maintain the computational tractability and evaluate the performance of the models in a real-world setting.
Slot Auction at U.S. Airport (With Yu Zhang)
The current demand management policy at LGA must be changed in 2007 under the provisions the Aviation Investment and Reform Act for the 21st Century (AIR-21). One of the objectives of this project is examining the likely impact that current and alternative allocation mechanisms on FAA, airlines, and airport operations. In addition, a practical proposal for using slot auctions at U.S. airports will be developed with special emphasis on New York's LaGuardia airport.
Analyzing the Competition Between a Charter/Un-shceduled Airline and Scheduled Airline (with Gautam Gupta)
This project started as assessing the feasibility of a dedicated charter service for student athlete travel. Our interaction with MVPAir (read the full article here) generated questions regarding the responses of a scheduled service to a charter service. The analysis of competition is begin done at three levels: a single link excluding all network effects due to limited fleet, a simplified network with constraints on fleet, and large real-world networks.
Commercial Vehicle Parking: An Evaluation of the Problem, Issues with Modeling Parking Demand, and Possible Solutions (with Megan Smirti)
Currently, California has a shortage of public and private commercial vehicle parking spaces, and demand is forecasted to increase annually by 1.9 percent. As a result, the incidence of trucks parking illegally on highway shoulders and ramps is increasing, creating safety hazards due to obscured motorist sight distance and dangerous speed differentials when parked trucks re-enter highways. This research reviews commercial vehicle truck parking problems in the U.S. and California and describes and analyzes a parking demand model. The of the model's overestimation is explored, along with the implications of the truck parking shortage. The impact of innovative truck mobility solutions, such as truck only facilities, on truck parking and mobility is determined.
Automation, Adoption and Adaptation in Air Traffic Control (with Tatjana Bolic)
The goal of this research is to draw lessons from the experience with technology insertion (for example, the User Request Evaluation, or URET, developed to help air traffic controllers to detect and resolve potential conflicts between aircraft and between aircraft and airspace) that can inform and assisst technology deployment in the future.
Aircraft Routing and Ground Holding under Convective Weather Uncertainty (with Wanjira Jirajaruporn)
The stochastic dynamic ground-holding and rerouting model has been developed to enhance strategic planning tools, Ground Delay Programs for ground holding, and National Playbook for rerouting. The model combines the ground-holding and rerouting and is formulated as a heuristic shortest path problem. In order to yield the better storm predictability from the weather forecast, the probabilistic model is used to generate an input for the stochastic dynamic ground-holding and rerouting model.
Recent developments in solving the single airport ground holding problem use static or dynamic optimization to manage uncertainty about how airport capacities will evolve. Scenario trees of airport arrival capacity profile provide the basis for formulating multistage recourse problems. We investigate methodologies for generating scenario trees from empirical data and examine the performance of scenario-based models in a real-world setting.
Probabilistic Forecasts of Convective Storm Activity for En Route Air Traffic Management: A Statistical Learning Approach (with P. Barry Liu)
Real-time aircraft routing decision support tools require probabilistic forecasts with high accuracy. However, the existing forecasts for en route convective weather have unsatisfactory performance. We use probabilistic graphical models in the hidden Markov model family to characterize the convective weather evolution process. Through statistical learning from historical data and existing forecasts, our model shows promise in providing forecasts with better quality.
Managing uncertainty in the Single Airport Ground Holding Problem using Markov Decision Process (with P. Barry Liu)
The goal of this research is to advance the support of decision-making in air traffic flow management under uncertainty. We develop optimization models that treat the airport capacity as a Markovian process. Unlike the stochastic programming models, the Markov decision process approach can give a far more comprehensive plan that responds to conditions dynamically. We investigate ways to maintain the computational tractability and evaluate the performance of the models in a real-world setting.
Slot Auction at U.S. Airport (With Yu Zhang)
The current demand management policy at LGA must be changed in 2007 under the provisions the Aviation Investment and Reform Act for the 21st Century (AIR-21). One of the objectives of this project is examining the likely impact that current and alternative allocation mechanisms on FAA, airlines, and airport operations. In addition, a practical proposal for using slot auctions at U.S. airports will be developed with special emphasis on New York's LaGuardia airport.
Analyzing the Competition Between a Charter/Un-shceduled Airline and Scheduled Airline (with Gautam Gupta)
This project started as assessing the feasibility of a dedicated charter service for student athlete travel. Our interaction with MVPAir (read the full article here) generated questions regarding the responses of a scheduled service to a charter service. The analysis of competition is begin done at three levels: a single link excluding all network effects due to limited fleet, a simplified network with constraints on fleet, and large real-world networks.
Commercial Vehicle Parking: An Evaluation of the Problem, Issues with Modeling Parking Demand, and Possible Solutions (with Megan Smirti)
Currently, California has a shortage of public and private commercial vehicle parking spaces, and demand is forecasted to increase annually by 1.9 percent. As a result, the incidence of trucks parking illegally on highway shoulders and ramps is increasing, creating safety hazards due to obscured motorist sight distance and dangerous speed differentials when parked trucks re-enter highways. This research reviews commercial vehicle truck parking problems in the U.S. and California and describes and analyzes a parking demand model. The of the model's overestimation is explored, along with the implications of the truck parking shortage. The impact of innovative truck mobility solutions, such as truck only facilities, on truck parking and mobility is determined.
Automation, Adoption and Adaptation in Air Traffic Control (with Tatjana Bolic)
The goal of this research is to draw lessons from the experience with technology insertion (for example, the User Request Evaluation, or URET, developed to help air traffic controllers to detect and resolve potential conflicts between aircraft and between aircraft and airspace) that can inform and assisst technology deployment in the future.
Aircraft Routing and Ground Holding under Convective Weather Uncertainty (with Wanjira Jirajaruporn)
The stochastic dynamic ground-holding and rerouting model has been developed to enhance strategic planning tools, Ground Delay Programs for ground holding, and National Playbook for rerouting. The model combines the ground-holding and rerouting and is formulated as a heuristic shortest path problem. In order to yield the better storm predictability from the weather forecast, the probabilistic model is used to generate an input for the stochastic dynamic ground-holding and rerouting model.