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StarLee 2016-07-07 19:35:26 +08:00
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\includegraphics[width=8cm]{min_dis.jpg}
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path(p)表示从起始节点到当前节点的路径min\_dis(a,p)指p到路径a上最短距离。通过这种方式后效果比之前的要好了一些但是对于一些地点它还是会处理的不好比如对于位于底部的博餐来说如果距离函数用的还是直线距离那么当算法走到位于其左上角的丁字路口时无论是直走还是右拐其值不变但是对于其他值来说右拐似乎更好因此路径就会被诱导的右拐了即使博餐的人群量再大在这个路口也不会直走。因此我就想着如何规避这个陷阱 最后决定用A*算法算出的最短路径来代替直线距离这样的h(n)更能反映真实的距离。这样一来这个算法有点贪心算法的性质,并且直观上讲起效率有点低下。在这里我进行了优化,利用动态规划后的思想对地点到路径节点的最短路径值进行缓存,这样的做法大大的提高了效率,实验对比会在实验结果中给出。
\subsection{站点规划}
为更有效的运行该线路,可以在上一节得到的线路中,对其设置一些校车停靠点,
为更有效的运行该线路可以在上一节得到的线路中对其设置一些校车停靠点这个问题相对来说比较好解决些。其目标就是要让各地点的人到停靠点的距离越小越好。同样这个问题也要考虑人群量的影响。我的解决思路是利用深度优先遍历取最优值。比如想要设置8个站点除去图书馆和北门外两个子问题得到的线路各自应该有3个站点。在该子路线中按照深度优先的策略对每三个节点进行测试计算各个地点到该规划站点的距离其计算公式为\\
\begin{figure}[h!]
\centering
\includegraphics[width=8cm]{sdis.jpg}
\label{sdis}
\end{figure}
stops是规划得到的停靠点集合min_sdis(stops,p)用来计算p到stops中每个站点的最短路径的最小值。因为在线路规划中最短路径值都已经缓存过了因此这里的计算速度还可以。因为我一开始设计的站点数k是可以动态设计的因此这部分的难点在于如何高效率动态地遍历出所有k个节点的组合多层for循环是不现实的因为k是变化的我用了一个类似数值逐位求余的思想组合的代码效果还可以。
\section{实验结果}
数据渲染效果已经在前面有所展示了,下面的章节从剩余的三个方面给出实验结果。
\subsection{最短路径}
\begin{table}[!h]
\renewcommand\arraystretch{1.1}
\centering

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