The DOI matches its Crossref record; 24 of the 25 references it cites were found in a registry or an index, and 1 are not checked yet.
Prescriptive analytics in airline operations: Arrival time prediction and cost index optimization for short-haul flights
Open accessAbstract
In this paper, we provide arrival time prediction combined with a cost index optimization model for short haul flights. Our work is based on flight data of a European network carrier. We focus on predicting the arrival time for incoming flights at two hub locations. Airlines focus on two aspects in their operations: Minimizing cost while ensuring on-time arrivals. Especially network carriers with hub connections need to ensure that incoming flights are on time for passenger, crew and aircraft transfer. The cost index is a tool for optimizing the aircraft’s speed. A high cost index implies a faster flight. The cost of time is set in relation to the cost of fuel. Today there is no model for arrival time prediction and integrated cost index optimization. We consider three different flight distances to model the impact of cost index changes on gate arrival time. With our model airlines are able to reduce the cost index without any tangible impact on their overall schedule. We conclude that the optimal cost index level heavily depends on a flight’s distance, fuel costs and delay costs. Especially for short haul flights we recommend lowering the cost index as a high cost index has limited impact on gate arrival time.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The objective most often considered is the operational cost to be minimized (Achenbach & Spinler, 2018).”
From Prescriptive analytics: Literature review and research challenges (2020) · cites this paper as Achenbach & Spinler, 2018
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
A Neural Probabilistic Language Model
Bengio, Y., Ducharme, R., Vincent, P., & Janvin, C. (2003) · Journal of machine learning research · doi:10.1162/153244303322533223 · Cited by 8,257
(2012) · Cited by 8,009
Characterization and prediction of air traffic delays
Rebollo, J. J., & Balakrishnan, H. (2014) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2014.04.007 · Cited by 330
Tu, Y., Ball, M. O., & Jank, W. (2008) · Journal of the American Statistical Association · doi:10.1198/016214507000000257 · Cited by 228
Ball, M. O., Barnhart, C., Dresner, M., Hansen, M. M., Neels, K., Odoni, A. R., Peterson, E., Sherry, L., Trani, A. A., & Zou, B. (2010) · ROSA P · Cited by 192
An integrated decision support tool for airlines schedule recovery during irregular operations
Abdelghany, K. F., Abdelghany, A. F., & Ekollu, G. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2006.12.045 · Cited by 151
European airline delay cost reference values
Cook, A. J., & Tanner, G. (2011) · WestminsterResearch (University of Westminster) · Cited by 128
Aircraft Rescheduling with Cruise Speed Control
Aktürk, M. S., Atamtürk, A., & Gürel, S. (2014) · Operations Research · doi:10.1287/opre.2014.1279 · Cited by 110
Research works printed but not linked: 1
- Beatty R., Hsu R., Berry L., Rome J.. Preliminary evaluation of flight delay propagation through and airline schedule. In: Proceedings of the 2nd USA Europe Air Traffic Management R&D Semin1998;(December):1–9.
Cited by
Sorted by citations
Prescriptive analytics: Literature review and research challenges
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.003 · Cited by 501
Autonomous robot-driven deliveries: A review of recent developments and future directions
Srinivas, S., Ramachandiran, S., & Rajendran, S. (2022) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2022.102834 · Cited by 190
Bongo, M. F., & Seva, R. R. (2023) · Aerospace · doi:10.3390/aerospace10030252 · Cited by 28
A deep learning-based approach for predicting in-flight estimated time of arrival
SILVESTRE, J., Martínez‐Prieto, M. A., Bregón, A., & Álvarez-Esteban, P. C. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06060-6 · Cited by 26
A data analytic-based logistics modelling framework for E-commerce enterprise
Verma, A., Kuo, Y., Kumar, M., Pratap, S., & Chen, V. (2022) · Enterprise Information Systems · doi:10.1080/17517575.2022.2028195 · Cited by 15
Metadata
- Authors
- Achenbach, A., & Spinler, S.
- Affiliations
- WHU – Otto Beisheim School of Management (DE)
- Corresponding author
- Achenbach, Anna
- Venue
- Operations Research Perspectives
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 5
- Pages
- 265-279
- ISSN
- 2214-7160 (ISSN-L)
- OpenAlex
- W2888763990
- Cited by
- 23
- References
- 25
- Citations by year
- 2025: 3 · 2024: 5 · 2023: 4 · 2022: 3 · 2021: 4 · 2020: 1 · 2019: 3
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Operations Research Perspectives (publishedVersion, cc-by-nc-nd)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Econstor (Econstor) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Air Traffic Management and Optimization
- Social Sciences › Decision Sciences › Management Science and Operations Research › Healthcare Operations and Scheduling Optimization
- Social Sciences › Transportation › Railway Systems and Energy Efficiency
- Keywords
- prescriptive analytics, arrival time prediction, short-haul flights, hub-and-spoke networks, fuel costs, delay cost, gate arrival time, airline operations, on-time arrival
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Prescriptive analytics in airline operations: Arrival time prediction and cost index optimization for short-haul flights. https://app.cochat.ai/papers/10.1016/j.orp.2018.08.004
- The paper
- Achenbach, A., & Spinler, S. (2018). Prescriptive analytics in airline operations: Arrival time prediction and cost index optimization for short-haul flights. Operations Research Perspectives. https://doi.org/10.1016/j.orp.2018.08.004
