Abstract

Traffic police faces the problem of enforcing speed limits under restricted budget. Implementing high Enforcement Thresholds (ET) will ease the workload on the police but will also intensify the problem of speeding. We model this as a game between the police, which wish that drivers obey the speed limits and the drivers who wish to speed without getting caught. For the police we construct a multi-stage strategy in which at each stage the ET is randomized between low and high values. This confuses the drivers who now need to consider the worst case of low ET. We establish analytically and by simulations that this strategy gradually reduces the ET until it converges to the desired speed limit without increasing the workload along the process. Importantly, this method works even if the strategy is known to the drivers. We study the effect of several factors on the convergence rate of the process. Interestingly, we find that increasing the frequency of randomization is more effective in expediting the process than raising the average amount of fines.

中文翻译：

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困惑的驾驶员是更好的驾驶员：使用随机策略强制执行速度限制

摘要

交警面临着在预算有限的情况下强制实施限速的问题。实施高执法阈值（ET）将减轻警察的工作量，但也会加剧超速驾驶的问题。我们将此模型模拟为警察与警察之间的游戏，警察希望驾驶员遵守速度限制，而驾驶员希望在不被抓到的情况下超速行驶。对于警察，我们构建了一个多阶段策略，其中在每个阶段，ET均在低值和高值之间随机分配。这使现在需要考虑低ET的最坏情况的驾驶员感到困惑。通过分析和仿真，我们确定了该策略会逐渐减小ET，直到收敛到所需的速度极限，而不会增加整个过程的工作量。重要的是，即使驾驶员知道该策略，此方法也可以工作。我们研究了几个因素对过程收敛速度的影响。有趣的是，我们发现增加随机化频率比加快平均罚款额更有效地加快了流程。