| Fleet operational policies for automated mobility: A simulation assessment for Zurich S Hörl, C Ruch, F Becker, E Frazzoli, KW Axhausen Transportation Research Part C: Emerging Technologies 102, 20-31, 2019 | 193* | 2019 |
| Agent-based simulation of autonomous taxi services with dynamic demand responses S Hörl Procedia Computer Science 109, 899-904, 2017 | 120 | 2017 |
| Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data S Hörl, M Balac Transportation Research Part C: Emerging Technologies 130, 103291, 2021 | 82* | 2021 |
| Amodeus, a simulation-based testbed for autonomous mobility-on-demand systems C Ruch, S Hörl, E Frazzoli 2018 21st International Conference on Intelligent Transportation Systems …, 2018 | 79 | 2018 |
| Towards a testbed for dynamic vehicle routing algorithms M Maciejewski, J Bischoff, S Hörl, K Nagel Highlights of Practical Applications of Cyber-Physical Multi-Agent Systems …, 2017 | 78 | 2017 |
| Recent perspectives on the impact of autonomous vehicles S Hörl, F Ciari, KW Axhausen Arbeitsberichte Verkehrs-und Raumplanung 1216, 2016 | 68 | 2016 |
| Simulation of price, customer behaviour and system impact for a cost-covering automated taxi system in Zurich S Hörl, F Becker, KW Axhausen Transportation Research Part C: Emerging Technologies 123, 102974, 2021 | 54 | 2021 |
| Dynamic demand estimation for an AMoD system in Paris S Hörl, M Balac, KW Axhausen 2019 IEEE Intelligent Vehicles Symposium (IV), 260-266, 2019 | 53 | 2019 |
| Improved public transportation in rural areas with self-driving cars: A study on the operation of Swiss train lines L Sieber, C Ruch, S Hörl, KW Axhausen, E Frazzoli Transportation research part A: policy and practice 134, 35-51, 2020 | 49 | 2020 |
| Pairing discrete mode choice models and agent-based transport simulation with MATSim S Hörl, M Balać, KW Axhausen 2019 TRB Annual Meeting Online, 19-02409, 2019 | 49 | 2019 |
| A first look at bridging discrete choice modeling and agent-based microsimulation in MATSim S Hörl, M Balac, KW Axhausen Procedia computer science 130, 900-907, 2018 | 48 | 2018 |
| The prospects of on-demand urban air mobility in Zurich, Switzerland M Balac, RL Rothfeld, S Hörl 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 906-913, 2019 | 42 | 2019 |
| Induzierter Verkehr durch autonome Fahrzeuge: Eine Abschätzung S Hörl, F Becker, TJP Dubernet, KW Axhausen ETH Zurich, 2019 | 38 | 2019 |
| Simulation of autonomous taxis in a multi-modal traffic scenario with dynamic demand S Hörl, A Erath, KW Axhausen Arbeitsberichte Verkehrs-und Raumplanung 1184, 2016 | 38 | 2016 |
| Implementation of an autonomous taxi service in a multi-modal traffic simulation using MATSim S Hörl Department of Energy and Environment Chalmers University of Technology,(June) 82, 2016 | 32 | 2016 |
| Designing a large-scale public transport network using agent-based microsimulation P Manser, H Becker, S Hörl, KW Axhausen Transportation Research Part A: Policy and Practice 137, 1-15, 2020 | 31 | 2020 |
| Introducing the eqasim pipeline: From raw data to agent-based transport simulation S Hörl, M Balac Procedia Computer Science 184, 712-719, 2021 | 19 | 2021 |
| Synthetic population for the state of California based on open-data: examples of San Francisco Bay area and San Diego County M Balac, S Hörl 100th Annual Meeting of the Transportation Research Board (TRB), 2021 | 15 | 2021 |
| Relaxation–discretization algorithm for spatially constrained secondary location assignment S Hörl, KW Axhausen Transportmetrica A: Transport Science 19 (2), 1982068, 2023 | 12 | 2023 |
| Dynamic Demand Simulation for Automated Mobility on Demand S Hörl ETH Zurich, 2020 | 11 | 2020 |
Kay AxhausenProfessor for Transport Planning, ETH ZürichBestätigte E-Mail-Adresse bei ivt.baug.ethz.ch
