Busy stations recognition of Hangzhou public free-bicycle system based on sixth order polynomial smoothing support vector machine

2011 International Conference on Machine Learning and Cybernetics Pub Date : 2011-07-10 DOI:10.1109/ICMLC.2011.6016818

Haitao Xu, Hao Wu, Wanjun Zhang, Ning Zheng

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引用次数: 4

Abstract

In China, Hangzhou is the first city to set up the Public Free-Bicycle System. There are many and many technology problems in the decision of intelligent dispatch. Among of these problems, how to list the busy station is very important to design the location of storehouses. Now, there are near 4000 stations in Hangzhou. In this paper, a new data classification method is used to recognize the busy station, which is called Support vector machine (SVM). The original model is a quadratical programming with linear inequalities constraints. In order to get the optimal solution, a new solution method is proposed. The constraints are moved away from the original optimization model by using the approximation solution in the feasible space. Three points under one control parameter smoothing function is used to smoothen the objective function of unconstrained model. It is a sixth order polynomial function. The smoothing performance is investigated. Actually, the busy stations can be recognized from the given data set.

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基于六阶多项式平滑支持向量机的杭州公共自由自行车系统繁忙站点识别

在中国，杭州是第一个建立公共免费自行车系统的城市。智能调度决策中存在着许多技术问题。在这些问题中，如何列出繁忙车站对仓库选址的设计是非常重要的。现在，杭州有近4000个车站。本文提出了一种新的数据分类方法——支持向量机(SVM)来识别忙站。原模型是一个具有线性不等式约束的二次规划模型。为了得到最优解，提出了一种新的求解方法。利用可行空间的近似解将约束从原优化模型中移开。采用一个控制参数平滑函数下的三点对无约束模型的目标函数进行平滑。它是一个六阶多项式函数。对其平滑性能进行了研究。实际上，从给定的数据集可以识别出繁忙的站点。

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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2011 International Conference on Machine Learning and Cybernetics

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