Yutian Pang, Jueming Hu, Christopher S. Lieber, N. Cooke, Yongming Liu
{"title":"Air Traffic Controller Workload Level Prediction using Conformalized Dynamical Graph Learning","authors":"Yutian Pang, Jueming Hu, Christopher S. Lieber, N. Cooke, Yongming Liu","doi":"10.48550/arXiv.2307.10559","DOIUrl":"https://doi.org/10.48550/arXiv.2307.10559","url":null,"abstract":"Air traffic control (ATC) is a safety-critical service system that demands constant attention from ground air traffic controllers (ATCos) to maintain daily aviation operations. The workload of the ATCos can have negative effects on operational safety and airspace usage. To avoid overloading and ensure an acceptable workload level for the ATCos, it is important to predict the ATCos' workload accurately for mitigation actions. In this paper, we first perform a review of research on ATCo workload, mostly from the air traffic perspective. Then, we briefly introduce the setup of the human-in-the-loop (HITL) simulations with retired ATCos, where the air traffic data and workload labels are obtained. The simulations are conducted under three Phoenix approach scenarios while the human ATCos are requested to self-evaluate their workload ratings (i.e., low-1 to high-7). Preliminary data analysis is conducted. Next, we propose a graph-based deep-learning framework with conformal prediction to identify the ATCo workload levels. The number of aircraft under the controller's control varies both spatially and temporally, resulting in dynamically evolving graphs. The experiment results suggest that (a) besides the traffic density feature, the traffic conflict feature contributes to the workload prediction capabilities (i.e., minimum horizontal/vertical separation distance); (b) directly learning from the spatiotemporal graph layout of airspace with graph neural network can achieve higher prediction accuracy, compare to hand-crafted traffic complexity features; (c) conformal prediction is a valuable tool to further boost model prediction accuracy, resulting a range of predicted workload labels. The code used is available at href{https://github.com/ymlasu/para-atm-collection/blob/master/air-traffic-prediction/ATC-Workload-Prediction/}{$mathsf{Link}$}.","PeriodicalId":221764,"journal":{"name":"Adv. Eng. Informatics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125402688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Tamym, L. Benyoucef, A. Nait-Sidi-Moh, M. D. E. Ouadghiri
{"title":"Big data analytics-based approach for robust, flexible and sustainable collaborative networked enterprises","authors":"L. Tamym, L. Benyoucef, A. Nait-Sidi-Moh, M. D. E. Ouadghiri","doi":"10.2139/ssrn.4045870","DOIUrl":"https://doi.org/10.2139/ssrn.4045870","url":null,"abstract":"","PeriodicalId":221764,"journal":{"name":"Adv. Eng. Informatics","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121867001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamed H. Hamza, R. Polichshuk, Hyunseong Lee, Paul Parker, A. Campbell, A. Chattopadhyay
{"title":"Aircraft post-upset flight risk region prediction for aviation safety management","authors":"Mohamed H. Hamza, R. Polichshuk, Hyunseong Lee, Paul Parker, A. Campbell, A. Chattopadhyay","doi":"10.2139/ssrn.4089181","DOIUrl":"https://doi.org/10.2139/ssrn.4089181","url":null,"abstract":"","PeriodicalId":221764,"journal":{"name":"Adv. Eng. Informatics","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130164091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ipek Gursel Dino, Esat Kalfaoglu, O. K. Iseri, Bilge Erdogan, Sinan Kalkan, Aydin Alatan
{"title":"Vision-based estimation of the number of occupants using video cameras","authors":"Ipek Gursel Dino, Esat Kalfaoglu, O. K. Iseri, Bilge Erdogan, Sinan Kalkan, Aydin Alatan","doi":"10.1016/j.aei.2022.101662","DOIUrl":"https://doi.org/10.1016/j.aei.2022.101662","url":null,"abstract":"","PeriodicalId":221764,"journal":{"name":"Adv. Eng. Informatics","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117568536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Comprehensive understanding of smart product service system from multi-dimension and multi-perspective: An innovative service model for Customer-product Interaction Life Cycle (CILC)","authors":"Xianyu Zhang, X. Ming","doi":"10.1016/j.aei.2022.101619","DOIUrl":"https://doi.org/10.1016/j.aei.2022.101619","url":null,"abstract":"","PeriodicalId":221764,"journal":{"name":"Adv. Eng. Informatics","volume":"210 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"119205399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Saadallah, Felix Finkeldey, J. Buss, K. Morik, P. Wiederkehr, W. Rhode
{"title":"Simulation and sensor data fusion for machine learning application","authors":"A. Saadallah, Felix Finkeldey, J. Buss, K. Morik, P. Wiederkehr, W. Rhode","doi":"10.1016/j.aei.2022.101600","DOIUrl":"https://doi.org/10.1016/j.aei.2022.101600","url":null,"abstract":"","PeriodicalId":221764,"journal":{"name":"Adv. Eng. Informatics","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120283315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}