@conference {1503006,
	title = {STREETS: Game-Theoretic Traffic Patrolling with Exploration and Exploitation },
	booktitle = {Innovative applications of Artificial Intelligence (IAAI)},
	year = {2014},
	abstract = {To dissuade reckless driving and mitigate accidents,
cities deploy resources to patrol roads. In this paper, we
present STREETS, an application developed for the city
of Singapore, which models the problem of computing randomized traffic patrol strategies as a defenderattacker Stackelberg game. Previous work on Stackelberg security games has focused extensively on counterterrorism settings. STREETS moves beyond counterterrorism and represents the first use of Stackelberg
games for traffic patrolling, in the process providing a
novel algorithm for solving such games that addresses
three major challenges in modeling and scale-up. First,
there exists a high degree of unpredictability in travel
times through road networks, which we capture using
a Markov Decision Process for planning the patrols of
the defender (the police) in the game. Second, modeling all possible police patrols and their interactions
with a large number of adversaries (drivers) introduces
a significant scalability challenge. To address this challenge we apply a compact game representation in a
novel fashion combined with adversary and state sampling. Third, patrol strategies must balance exploitation
(minimizing violations) with exploration (maximizing
omnipresence), a tradeoff we model by solving a biobjective optimization problem. We present experimental results using real-world traffic data from Singapore.
This work is done in collaboration with the Singapore
Ministry of Home Affairs and is currently being evaluated by the Singapore Police Force.},
	author = {Brown, Matthew and Saisubramanian, Sandhya and Varakantham, Pradeep and Tambe, Milind}
}
