Daniel Klosa
Wissenschaftlicher Mitarbeiter der AG Optimierung und Optimale SteuerungRaum: NEOS 3240
E-Mail: dklosa@uni-bremen.de
Telefon: (0421) 218-64362
ORCID iD: 0000-0003-0904-0597
E-Mail: dklosa@uni-bremen.de
Telefon: (0421) 218-64362
ORCID iD: 0000-0003-0904-0597
Projekte
- hyBit: Hydrogen for Bremen's industrial transformation (01.09.2022 - 28.02.2026)
- DiSCO2-Bremen: Datenbasierte und intelligente Simulation des Verkehrs zur CO2-Reduktion in Bremen (01.07.2020 - 31.12.2022)
Abschlussarbeiten (Auswahl)
- Differenzierbare Architektursuche für die Vorhersage von Verkehrsdaten (Celina Groth)
Publikationen (Auswahl)
- D. Klosa, C. Büskens.
Low Cost Evolutionary Neural Architecture Search (LENAS) Applied to Traffic Forecasting.
21st IEEE International Conference on Machine Learning and Applications (ICMLA), 12.12.-14.12.2022, Nassau, Bahamas.
Mach. Learn. Knowl. Extr., 5(3):830-846, 2023.DOI: 10.3390/make5030044
- A. Mallek, D. Klosa, C. Büskens.
Enhanced K-Nearest Neighbor Model For Multi-steps Traffic Flow Forecast in Urban Roads.
8th IEEE International Smart Cities Conference, 26.09-29.09.2022.
Paphos, Cyprus, IEEE Xplore, S. 1-5, 2022. - A. Mallek, D. Klosa, C. Büskens.
Impact of Data Loss on Multi-Step Forecast of Traffic Flow in Urban Roads Using K-Nearest Neighbors.
Sustainability, 14(18), 11232, 2022. - D. Klosa, A. Mallek, C. Büskens.
Short-Term Traffic Flow Forecast Using Regression Analysis and Graph Convolutional Neural Networks.
2021 IEEE 23rd Int Conf on High Performance Computing & Communications; 7th Int Conf on Data Science & Systems; 19th Int Conf on Smart City; 7th Int Conf on Dependability in Sensor, Cloud & Big Data Systems & Application (HPCC/DSS/SmartCity/DependSys), 20.12-22.12.2021.
Haikou, Hainan, China, IEEE Xplore, S. 1413-1418, 2021.