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Inhalt des Dokuments


Dominik Ziemke and Luk Knapen and Kai Nagel (forthcoming). Expanding the analysis scope of a MATSim transport simulation by integrating the FEATHERS activity-based demand model. Procedia Computer Science

Theresa Ziemke and Söhnke Braun (forthcoming). Automated generation of traffic signals and lanes for MATSim based on OpenStreetMap. Procedia Computer Science

Thomas Gable and Kai Martins-Turner and Kai Nagel (2022). Enhanced Emission Calculation for Freight Transport.

Chengqi Lu and Kai Martins-Turner and Kai Nagel (2022). Creating an agent-based long-haul freight transport model for Germany.

Simon Meinhardt and Tilmann Schlenther and Kai Martins-Turner and Michal Maciejewski (2022). Simulation of On-Demand Vehicles that Serve both Person and Freight Transport.

Tilmann Schlenther and Peter Wagner and Gregor Rybczak and Kai Nagel and Laura Bieker-Walz and Michael Ortgiese (2022). Simulation-based investigation of transport scenarios for Hamburg.

Rakow, Christian and Kaddoura, Ihab and Lu, Chengqi and Nippold, Ronald and Wagner, Peter (2021). Investigation of the system-wide effects of intelligent infrastructure concepts with microscopic and mesoscopic traffic simulation.

Dominik Ziemke and Billy Charlton and Sebastian Hörl and Kai Nagel (2021). An efficient approach to create agent-based transport simulation scenarios based on ubiquitous Big Data and a new, aspatial activity-scheduling model. Transportation Research Procedia. Elsevier BV, 613–620.

Theresa Ziemke and Lucas N. Alegre and Ana L. C. Bazzan (2021). Reinforcement learning vs. rule-based adaptive traffic signal control: A Fourier basis linear function approximation for traffic signal control. AI Communications, 89–103.

Theresa Ziemke and Leon Sering and Laura Vargas Koch and Max Zimmer and Kai Nagel and Martin Skutella (2021). Flows Over Time as Continuous Limit of Packet-Based Network Simulations. Transportation Research Procedia. Elsevier BV, 123–130.

Kaddoura, I. and Ewert, R. and Martins-Turner, K. (2021). Exhaust and non-exhaust emissions from today’s and future road transport: A simulation-based quantification for Berlin. TU Berlin, Transport Systems Planning and Transport Telematics

Ihab Kaddoura and Tilmann Schlenther (2021). The impact of trip density on the fleet size and pooling rate of ride-hailing services: A simulation study. Procedia Computer Science, 674–679.

Bieker-Walz, L. and I. Kaddoura and Z. Meng and M. Ortgiese and P. Wagner (2021). Future Mobility Scenarios for Hamburg, Germany.

Schlenther, T. and Leich, G. and Maciejewski, M. and Nagel, K. (2020). Addressing Spatial Service Provision Equity for Pooled Ride-Hailing Services through Rebalancing.

A. Agarwal and D. Ziemke and K. Nagel (2020). Bicycle superhighway: An environmentally sustainable policy for urban transport. Transportation Research Part A: Policy and Practice. Elsevier BV, 519–540.

Rico Kötschau and Kai Martins-Turner and Jan Fabian Ehmke and Kai Nagel (2020). Combining Simulation and Optimisation to Design ReliableTransportation Services with Autonomous Fleets.

Theresa Ziemke and Lucas N. Alegre and Ana L. C. Bazzan (2020). A reinforcement learning approach with Fourier basis linear function approximation for traffic signal control. CEUR Workshop Proceedings, 55–62.

Ihab Kaddoura and Joschka Bischoff and Kai Nagel (2020). Towards welfare optimal operation of innovative mobility concepts: External cost pricing in a world of shared autonomous vehicles. Transportation Research Part A: Policy and Practice, 48–63.

Ihab Kaddoura and Janek Laudan and Dominik Ziemke and Kai Nagel (2020). Verkehrsmodellierung für das Ruhrgebiet. Neue Dimensionen der Mobilität. Springer Fachmedien Wiesbaden, 361–386.

Kaddoura, I. and Leich, G. and Nagel, K. (2020). The impact of pricing and service area design on the modal shift towards demand responsive transit. Procedia Computer Science, 807–812.

Kaddoura, I. and Leich, G. and Neumann, A. and Nagel, K. (2020). A Simulation-based heuristic for the improvement on-demand mobility services.

Simoni, M. D. AND Kockelman, K. M. AND Gurumurthy, K. M. and Bischoff, J. (2019). Congestion Pricing in a World of Self-driving vehicles: an Analysis of Different Strategies in Alternative Future Scenarios. Transportation Research Part C: Emerging Technologies, 167–185.

Ricardo Ewert (2019). Modellierung und Simulation des städtischen Abfallwirtschaftsverkehrs am Beispiel Berlins. TU Berlin, Institute for Land and Sea Transport Systems

Theresa Thunig and Nico Kühnel and Kai Nagel (2019). Adaptive traffic signal control for real-world scenarios in agent-based transport simulations. Transportation Research Procedia. Elsevier BV, 481–488.

Thunig, T. and Nagel, K. (2019). Effects of user adaption on traffic-responsive signal control in agent-based transport simulations. 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 1–7.

Theresa Thunig and Robert Scheffler and Martin Strehler and Kai Nagel (2019). Optimization and simulation of fixed-time traffic signal control in real-world applications. Procedia Computer Science. Elsevier BV, 826–833.

Ziemke, D. and Agarwal, A. and Kaddoura, I. (2019). Entwicklung eines regionalen, agentenbasierten Verkehrssimulationsmodells zur Analyse von Mobilitätszenarien für die Region Ruhr. 10. Wissenschaftsforum Mobilität : Mobility in Times of Change – Past, Present, Future. Springer Gabler, Wiesbaden, 383–410.

Ziemke, D. and Kaddoura, I. and Nagel, K. (2019). The MATSim Open Berlin Scenario: A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data. Procedia Computer Science, 870–877.

Leich, G. and Bischoff, J. (2019). Should autonomous shared taxis replace buses? A simulation study. Transportation Research Procedia, 450–460.

Kaddoura, I. and Nagel, K. (2019). Congestion pricing in a real-world oriented agent-based simulation context. Research in Transportation Economics, 40–51.

Kühnel, N. and Kaddoura, I. and Möckel (2019). Incorporation of noise shielding in an agent-based transport model by using volunteered geographic data. Procedia Computer Science, 808–813.

Singh, D. and Padgham, L. and Nagel, K. (2019). Using MATSim as a Component in Dynamic Agent-based Micro-Simulations.

Livingston, C.L. and Beyer Bartana, I. and Ziemke, D. and Bahamonde-Birke, F. (2019). The Influence of the Route Environment on the Route Choice of Bicyclists – A Preliminary Study.

Bischoff, J. AND Führer, K. AND Maciejewski, M. (2019). Impact assessment of autonomous DRT systems. Transportation Research Procedia, 440–446.

Bischoff, J. AND Maciejewski, M. AND Schlenther, T. AND Nagel, K. (2019). Autonomous vehicles and their impact on parking search. IEEE Intelligent Transportation Systems

Dominik Ziemke and Simon Metzler and Kai Nagel (2018). Bicycle traffic and its interaction with motorized traffic in an agent-based transport simulation framework. Future Generation Computer Systems

Kaddoura, I. and Nagel, K. (2018). Simultaneous internalization of traffic congestion and noise exposure costs. Transportation, 1579–1600.

Kaddoura, I. AND Nagel, K. (2018). Using real-world traffic incident data in transport modeling. Procedia Computer Science. Elsevier BV, 880–885.

Kickhöfer, B. and Agarwal, A. and Nagel, K. (2018). Mind the price gap: how optimal emission pricing relates to the EU CO\textsubscript2 reduction targets. International Journal of Sustainable Transportation

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