@article{li_wan_2021, title={A fuel savings and benefit analysis of reducing separation standards in the oceanic airspace managed by the New York Air Route Traffic Control Center}, volume={152}, ISSN={["1878-5794"]}, DOI={10.1016/j.tre.2021.102407}, abstractNote={New or improved satellite-based technologies are being introduced to improve the surveillance and communication capabilities in remote airspace. We study the benefits of reducing the separation standards among flights in the oceanic airspace managed by the New York Air Route Traffic Control Center (New York Oceanic) based on these technologies. We develop a model that simulates the activities of aircraft, pilots, and air traffic controllers (ATC) at the microscopic level to study the benefits of doing so in 2020 and 2025. With pessimistic assumptions on the reduced separation standards, the system-wide fuel savings within New York Oceanic are about (in million gallons) 2.25 in 2020 and 3.21 in 2025. After excluding additional variable cost, the monetary value of the fuel savings is about (in million 2018 US dollars) 3.65 and 6.38, respectively. The fuel benefits are more significant for aircraft with light or medium maximum takeoff weight. Some determinants of the workload of ATC and pilots can reduce by about 10% to 20%. With optimistic assumptions on the reduced standards, the corresponding statistics are about 2 to 3 times as high. This study can be used, for example, by air traffic control agencies to conduct benefit-cost analyses of adopting new/improved technologies, by airlines to develop strategies to make the best use of satellite services, and by satellite service providers to design service charging schemes and conduct market analysis.}, journal={TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW}, author={Li, Tao and Wan, Yan}, year={2021}, month={Aug} }
 @article{li_2021, title={An Optimization Model for Selecting Sample Days}, volume={38}, ISSN={["1793-7019"]}, DOI={10.1142/S0217595920500529}, abstractNote={ Sample day selection method plays an important role in managerial decisions which require analyses that are prohibitively expensive to apply to a large number of days. We develop a general sample day selection model that selects sample days based on the cumulative distributions of airspace conditions and characteristics (C&C) by considering factors such as sampling targets, degree of diversity and coverage of the selected days. We introduce indicators that capture the airspace C&C of the North Atlantic region (NAT) and apply the model to select sample days for the NAT. The results show that the model outperforms the methods used by the U.S. Federal Aviation Administration. }, number={04}, journal={ASIA-PACIFIC JOURNAL OF OPERATIONAL RESEARCH}, author={Li, Tao}, year={2021}, month={Aug} }
 @article{li_wan_2019, title={Estimating the geographic distribution of originating air travel demand using a bi-level optimization model}, volume={131}, ISSN={["1366-5545"]}, DOI={10.1016/j.tre.2019.09.018}, abstractNote={The historical air travel demand plays an important role in the analysis of, for example, major changes in airport use, airport and airline marketing, and airport planning. We provide a bi-level optimization model as a relatively quick and less expensive alternative to survey method to estimate the originating air travel demand and its geographic distribution at an airport. The lower-level model estimates the geographic distribution of originating air travel demand and model coefficients. The upper-level model estimates the airport access distance threshold which is used to model traveler’s airport choice behaviors in the lower-level model. We adopt an Evolutionary Algorithm (EA) to solve the bi-level optimization model. A General Reduced Gradient solution algorithm is used within the EA to solve the lower-level model. We present a real-world case study in which we apply the model to estimate the originating air travel demand and its geographic distribution on the contiguous United States. The model estimates are generally close to the statistics from the American Travel Survey. The comparisons of the model estimates with the statistics from the Washington-Baltimore regional air passenger survey show mixed results. Possible reasons for the estimation errors are identified.}, journal={TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW}, author={Li, Tao and Wan, Yan}, year={2019}, month={Nov}, pages={267–291} }