When Security Games Hit Traffic: Optimal Traffic Enforcement Under One Sided Uncertainty
Abstract
Efficient traffic enforcement is an essential, yet complex, component in preventing road accidents. In this paper, we present a novel model and an optimizing algorithm for mitigating some of the computational challenges of real-world traffic enforcement allocation in large road networks. Our approach allows for scalable, coupled and non-Markovian optimization of multiple police units and guarantees optimality. In an extensive empirical evaluation we show that our approach favorably compares to several baseline solutions achieving a significant speed-up, using both synthetic and real-world road networks.
Cite
Text
Rosenfeld and Kraus. "When Security Games Hit Traffic: Optimal Traffic Enforcement Under One Sided Uncertainty." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/533Markdown
[Rosenfeld and Kraus. "When Security Games Hit Traffic: Optimal Traffic Enforcement Under One Sided Uncertainty." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/rosenfeld2017ijcai-security/) doi:10.24963/IJCAI.2017/533BibTeX
@inproceedings{rosenfeld2017ijcai-security,
title = {{When Security Games Hit Traffic: Optimal Traffic Enforcement Under One Sided Uncertainty}},
author = {Rosenfeld, Ariel and Kraus, Sarit},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {3814-3822},
doi = {10.24963/IJCAI.2017/533},
url = {https://mlanthology.org/ijcai/2017/rosenfeld2017ijcai-security/}
}