Optimal Allocation Strategy Based on Stackelberg Game for Inspecting Drunk Driving on Traffic Network

• Yingmo Jie,
• Mingchu Li,
• Tingting Tang,
• Cheng Guo,

Vol. 11, No. 12, December 30, 2017
10.3837/tiis.2017.12.005, Download Paper (Free):

• Stackelberg Game
• Resource Allocation
• Security
• Optimization
• drunk driving

Abstract

As the main means to cope with the stubborn problem of drunk driving, the inspection of drunk driving has already been paid more attention and thus reinforced. In this paper, we model this scenario as a Stackelberg game, where the police department (called defender) allocates resources dynamically in terms of the traffic situation on the traffic network to arrest drink drivers and drivers who drink (called attacker), whether choosing drunk driving or designated driving service, expect to minimize their cost for given travel routes. However, with the number of resources are limited, our goal is to calculate the optimal resource allocation strategy for the defender. Therefore, first, we provide an effective approach (named OISDD) to fulfill our goal, i.e., generate the optimal strategy to inspect drunk driving. Second, we apply OISDD to directed graphs (which are abstracted from Dalian traffic network) to analyze and test its correctness and rationality. The experimental results show that OISDD is feasible and efficient.

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