First steps towards real-world traffic signal control optimisation by reinforcement learning-Reference-Cited by-同舟云学术

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First steps towards real-world traffic signal control optimisation by reinforcement learning

Published:2024-06-25 Issue: Volume: Page:1-16
ISSN:1747-7778
Container-title:Journal of Simulation
language:en
Short-container-title:Journal of Simulation

Author:

Meess Henri¹,Gerner Jeremias²,Hein Daniel³,Schmidtner Stefanie²,Elger Gordon¹,Bogenberger Klaus⁴

Affiliation:

1. Application Center »Connected Mobility and Infrastructure«, Fraunhofer Institute for Transportation and Infrastructure Systems IVI, Ingolstadt, Germany

2. AImotion Bavaria, Technische Hochschule Ingolstadt, Ingolstadt, Germany

3. Traffic Control, GEVAS software GmbH, Munich, Germany

4. Chair of Traffic Engineering and Control, Technical University of Munich, Munich, Germany

Funder

German Federal Ministry of Transport and Digital Infrastructure [BMVI]

Bavarian State Ministry for Economic Affairs, Regional Development and Energy

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17477778.2024.2364715

Reference53 articles.

1. Learning dexterous in-hand manipulation

2. Adaptive traffic signal control with actor-critic methods in a real-world traffic network with different traffic disruption events

3. Detection and classification of sensor anomalies for simulating urban traffic scenarios

4. Reinforcement learning with long short-term memory;Bakker B.;Advances in Neural Information Processing Systems,2001

5. Behrisch, M., Bieker, L., Erdmann, J., & Krajzewicz, D. (2011). Sumo–simulation of urban mobility: An overview. In Proceedings of Simul 2011, the third International Conference on Advances in System Simulation, Barcelona, Spain.

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