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Locating inspection facilities in traffic networks: an artificial intelligence approach

机译:在交通网络中定位检查设施:一种人工智能方法

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摘要

In order for traffic authorities to attempt to prevent drink driving, check truck weight limits, driver hours and service regulations, hazardous leaks from trucks, and vehicle equipment safety, we need to find answers to the following questions: (a) What should be the total number of inspection stations in the traffic network? And (b) Where should these facilities be located? This paper develops a model to determine the locations of uncapacitated inspection stations in a traffic network. We analyze two different model formulations: a single-objective optimization problem and a multi-objective optimization problem. The problems are solved by the Bee Colony Optimization (BCO) method. The BCO algorithm belongs to the class of stochastic swarm optimization methods, inspired by the foraging habits of bees in the natural environment. The BCO algorithm is able to obtain the optimal value of objective functions in all test problems. The CPU times required to find the best solutions by the BCO are found to be acceptable.
机译:为了使交通管理当局能够防止酒后驾驶,检查卡车的重量限制,驾驶员的工作时间和服务规定,卡车的危险泄漏以及车辆设备的安全性,我们需要找到以下问题的答案:(a)应该采取什么措施?交通网络中的检查站总数是多少? (b)这些设施应位于何处?本文建立了一个模型来确定交通网络中无能力的检查站的位置。我们分析了两种不同的模型公式:单目标优化问题和多目标优化问题。通过蜂群优化(BCO)方法解决了这些问题。 BCO算法是受自然环境中蜜蜂觅食习性启发的一类随机群优化方法。 BCO算法能够在所有测试问题中获得目标函数的最优值。发现BCO寻求最佳解决方案所需的CPU时间是可以接受的。

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