Learning to Calibrate Hybrid Hyperparameters: a Study on Traffic Simulation

Author:

Xu Wanpeng1ORCID,Wei Hua2ORCID

Affiliation:

1. Gabelli School of Business, Fordham University, USA

2. Department of Informatics, New Jersey Institute of Technology, USA

Funder

National Science Foundation

Publisher

ACM

Reference18 articles.

1. Comparison of Stochastic Optimization Algorithms in Hydrological Model Calibration

2. Johannes Asamer , Henk  J van Zuylen , and Bernhard Heilmann . 2013. Calibrating car-following parameters for snowy road conditions in the microscopic traffic simulator VISSIM. IET Intelligent Transport Systems 7, 1 ( 2013 ). Johannes Asamer, Henk J van Zuylen, and Bernhard Heilmann. 2013. Calibrating car-following parameters for snowy road conditions in the microscopic traffic simulator VISSIM. IET Intelligent Transport Systems 7, 1 (2013).

3. Keith Beven and Andrew Binley . 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological processes 6, 3 ( 1992 ), 279–298. Keith Beven and Andrew Binley. 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological processes 6, 3 (1992), 279–298.

4. Raunak  P Bhattacharyya , Derek  J Phillips , 2018 . Multi-agent imitation learning for driving simulation . In IROS 2018. IEEE. Raunak P Bhattacharyya, Derek J Phillips, 2018. Multi-agent imitation learning for driving simulation. In IROS 2018. IEEE.

5. Surrogate Safety Measures from Traffic Simulation Models

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