| Spatio-temporal congestion patterns in urban traffic networks F Rempe, G Huber, K Bogenberger Transportation Research Procedia 15, 513-524, 2016 | 58 | 2016 |
| A phase-based smoothing method for accurate traffic speed estimation with floating car data F Rempe, P Franeck, U Fastenrath, K Bogenberger Transportation Research Part C: Emerging Technologies 85, 644-663, 2017 | 26 | 2017 |
| Online freeway traffic estimation with real floating car data F Rempe, P Franeck, U Fastenrath, K Bogenberger 2016 IEEE 19th International Conference on Intelligent Transportation …, 2016 | 26 | 2016 |
| Fusing probe speed and flow data for robust short-term congestion front forecasts F Rempe, L Kessler, K Bogenberger 2017 5th IEEE International Conference on Models and Technologies for …, 2017 | 8 | 2017 |
| Assessing the probability of arriving on time using historical travel time data in a road network A Samara, F Rempe, U Fastenrath, S Göttlich 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 1343-1348, 2019 | 4 | 2019 |
| Travel time prediction in partitioned road networks based on floating car data F Rempe, G Huber, K Bogenberger 2016 IEEE 19th International Conference on Intelligent Transportation …, 2016 | 4 | 2016 |
| A CEP technology stack for situation recognition on the gumstix embedded controller S Grimm, T Hubauer, T Runkler, C Pachajoa, F Rempe, M Seravalli, ... INFORMATIK 2013–Informatik angepasst an Mensch, Organisation und Umwelt, 2013 | 3 | 2013 |
| On the estimation of traffic speeds with Deep Convolutional Neural Networks given probe data F Rempe, P Franeck, K Bogenberger Transportation research part C: emerging technologies 134, 103448, 2022 | 2 | 2022 |
| A novel approach for vehicle travel time distribution: copula-based dependent discrete convolution A Samara, F Rempe, S Gottlich Transportation Letters, 1-12, 2021 | 2 | 2021 |
| Estimating traffic speeds using probe data: A deep neural network approach F Rempe, P Franeck, K Bogenberger arXiv preprint arXiv:2104.09686, 2021 | 2 | 2021 |
| Modelling arterial travel time distribution using copulas A Samara, F Rempe, S Göttlich 2020 IEEE 23rd International Conference on Intelligent Transportation …, 2020 | 2 | 2020 |
| Traffic speed estimation and prediction using floating car data F Rempe Universitätsbibliothek der Universität der Bundeswehr München, 2018 | 2 | 2018 |
| Multi-Sensor Data Fusion for Accurate Traffic Speed and Travel Time Reconstruction L Kessler, F Rempe, K Bogenberger Frontiers in Future Transportation 2, 27, 2021 | 1 | 2021 |
| Estimating motorway traffic states with data fusion and physics-informed deep learning F Rempe, A Loder, K Bogenberger 2021 IEEE International Intelligent Transportation Systems Conference (ITSC …, 2021 | 1 | 2021 |
| Multi-Sensor Data Fusion for Accurate Traffic Speed and Travel Time Reconstruction L Kessler, F Rempe, K Bogenberger Annual Meeting of the Transportation Research Board, 2021, 2021 | 1 | 2021 |
| Temporal Aggregated Analysis of GPS Trajectory Data using Two-fluid Model Y Zhang, A Loder, F Rempe, K Bogenberger Transportation Research Board 101st Annual MeetingTransportation Research Board, 2022 | | 2022 |
| TUM Chair of Traffic Engineering and Control K Bogenberger, RL Bertini, A Keler, M Margreiter, G Grigoropoulos, ... Transportation Research, 2022 | | 2022 |
| Methods and Devices Arranged for Routing Autonomous Driving S Duym, F Rempe US Patent App. 16/972,179, 2021 | | 2021 |
| Vehicle Routing Optimized for Autonomous Driving A Samara, F Rempe, S Göttlieh 2021 IEEE Intelligent Vehicles Symposium (IV), 1162-1167, 2021 | | 2021 |
| PhD thesis: Traffic Speed Estimation and Prediction using Floating Car Data F Rempe https://athene-forschung.unibw.de/127445, 2019 | | 2019 |
Klaus BogenbergerTUM - Technical University MunichVerified email at tum.de
