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Indexed by:Journal Papers

Date of Publication:2020-07-01

Journal:APPLIED MATHEMATICS AND COMPUTATION

Included Journals:EI、SCIE

Volume:376

ISSN No.:0096-3003

Key Words:Game theory; Resource allocation; Stackelberg game; Artificial intelligence; Traffic network; Drunk driving; Traffic safety

Abstract:To reduce the number of deaths and injuries due to drunk driving (also referred to as drink driving, driving while intoxicated, and driving under the influence of alcohol in the literature), many countries have deployed public safety resources to inspect traffic network. However, challenges remain in allocating limited public safety resources to the significantly large traffic networks. In this paper, we propose an optimal public safety resource allocation scheme to inspect drunk driving. To highlight the utilization of limited public safety resources, first, we model the issue of drunk driving as a defender-attacker Stackelberg game. In the game, the law enforcement agency (the defender) allocates public safety resources in a traffic network to arrest drunk drivers (the attackers), and the attacker seeks to choose a feasible route given the defender's strategy to maximize the escape probability. Second, we develop an effective approach to compute the optimal defender strategy based on a double oracle framework. Third, we analyze the complexity of the defender oracle problem. Then, we conduct simulations on directed graphs, which are abstracted from the city traffic network in Dalian, China, to demonstrate that our scheme achieves a robust solution and higher utility, and is capable of scaling up to handle realistic-sized drunk-driving problems. (C) 2020 Elsevier Inc. All rights reserved.