Interactive Assistive Framework for Maximum Profit Routing in Public Transportation in Smart Cities

B. Armaselu, O. Daescu
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引用次数: 4

Abstract

We design an interactive framework for public transportation route planning in order to maximize the total profit (i.e. revenue minus costs). The framework allows specification of fixed points of interest (urban or tourism), each with a time avalability constraint (in hours), as well as a fleet count of public transportation vehicles. The main contributions of the framework are two approximation algorithms. The first algorithm, based on bin packing, has an approximation ratio of ⋍ 26 log T, where T is a constant denoting the latest deadline (in hours). The second algorithm is based on well-separated pair decompositions and has an approximation ratio of ⋍ 15 log T. While our algorithms may seem to have rather high approximation ratios, in practice they work well and, in the majority of cases, the profit obtained is at least 80% of the optimum. Our framework can be used to simulate the route planning in a Google Maps API environment. The algorithms were tested on a real-world dataset, and we also present the experimental results in this dataset.
智慧城市公共交通利润最大化路径的交互式辅助框架
我们设计了一个交互式的公共交通路线规划框架,以最大化总利润(即收入减去成本)。该框架允许指定固定的兴趣点(城市或旅游),每个兴趣点都有时间限制(以小时为单位),以及公共交通车辆的数量。该框架的主要贡献是两种近似算法。第一种算法基于装箱,其近似比为⋍26 log T,其中T是一个常数,表示最后期限(以小时为单位)。第二种算法基于分离良好的对分解,近似比为⋍15 log t。虽然我们的算法似乎具有相当高的近似比,但实际上它们工作得很好,并且在大多数情况下,获得的利润至少是最优值的80%。我们的框架可以用来模拟谷歌Maps API环境中的路由规划。算法在一个真实数据集上进行了测试,并给出了该数据集的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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