2026-09-19T16:19:40Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/295772022-01-13T07:54:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Amedeo R. Odoni.
El Alj, Yasmine, 1978-
Massachusetts Institute of Technology. Operations Research Center.
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
2006-03-24T16:04:11Z
2006-03-24T16:04:11Z
2003
2003
http://hdl.handle.net/1721.1/29577
52757166
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2003.
Includes bibliographical references (p. 129).
Most air traffic delay measures assess delays relative to schedule. Over the past decades, however, airline schedules have been adjusted to take into account airspace congestion and yield better on-time performance. In that context, delay measures that are using scheduled times as a benchmark are of very limited use in assessing airport and airspace system congestion, since delay has already been built into the schedule. The primary goal of this thesis is to develop a measure that will estimate "true" delays that are not sensitive to schedule adjustments. In order to calculate "true" delays, we compute the difference between the actual gate-to-gate time and a theoretical benchmark, the "baseline". The baseline time to be used is O-D specific and is defined here as the gate-to-gate time from origin to destination under optimal (non-congested) conditions. We choose the fifteenth percentile of reported statistics on gate-to-gate time as an estimator of the baseline. We then compute baseline times for 618 major O-D pairs. Using the baseline times, we compute "true delays" on these 618 O-D pairs and observe that they are about 40% to 60% larger than delays relative to schedule. We also develop two methods to attribute O-D delays to the origin and destination airports. Using these methods, we determine that airports incurred about 5 to 13 minutes of delay per operation in 2000, depending on the airport under consideration. Airport rankings according to "true" delays are compared to airport rankings obtained from OPSNET delay statistics. The comparison suggests that, although OPSNET statistics underestimate the magnitude of delays, they yield very comparable airport rankings and can therefore be used to rank airports with respect to congestion. Finally, we change perspective and look at the development of probabilistic models for designing flight schedules that minimize delays relative to schedule. We use the simple case of an airline scheduling an aircraft for a round trip to illustrate the complexities and uncertainties associated with optimal scheduling.
by Yasmine E. Alj.
S.M.
147 p.
7784152 bytes
7783960 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Civil and Environmental Engineering.
Operations Research Center.
Estimating the true extent of air traffic delays
Thesis
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<Abstract>Most air traffic delay measures assess delays relative to schedule. Over the past decades, however, airline schedules have been adjusted to take into account airspace congestion and yield better on-time performance. In that context, delay measures that are using scheduled times as a benchmark are of very limited use in assessing airport and airspace system congestion, since delay has already been built into the schedule. The primary goal of this thesis is to develop a measure that will estimate "true" delays that are not sensitive to schedule adjustments. In order to calculate "true" delays, we compute the difference between the actual gate-to-gate time and a theoretical benchmark, the "baseline". The baseline time to be used is O-D specific and is defined here as the gate-to-gate time from origin to destination under optimal (non-congested) conditions. We choose the fifteenth percentile of reported statistics on gate-to-gate time as an estimator of the baseline. We then compute baseline times for 618 major O-D pairs. Using the baseline times, we compute "true delays" on these 618 O-D pairs and observe that they are about 40% to 60% larger than delays relative to schedule. We also develop two methods to attribute O-D delays to the origin and destination airports. Using these methods, we determine that airports incurred about 5 to 13 minutes of delay per operation in 2000, depending on the airport under consideration. Airport rankings according to "true" delays are compared to airport rankings obtained from OPSNET delay statistics. The comparison suggests that, although OPSNET statistics underestimate the magnitude of delays, they yield very comparable airport rankings and can therefore be used to rank airports with respect to congestion. Finally, we change perspective and look at the development of probabilistic models for designing flight schedules that minimize delays relative to schedule. We use the simple case of an airline scheduling an aircraft for a round trip to illustrate the complexities and uncertainties associated with optimal scheduling.</Abstract>
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