我已经在路由方面工作了几年，最近由于客户的需求而引起了大量的活动，我发现a *很容易就足够快了;真的没有必要去寻找优化或更复杂的算法。在一个巨大的图上路由不是问题。

但是速度取决于整个路由网络，我指的是在内存中分别表示路由段和节点的有向图。主要的时间开销是创建这个网络所花费的时间。基于一台运行Windows系统的普通笔记本电脑，并在整个西班牙进行路由的一些粗略数字:创建网络所需时间:10-15秒;计算路线所花费的时间:太短而无法测量。

The other important thing is to be able to re-use the network for as many routing calculations as you like. If your algorithm has marked the nodes in some way to record the best route (total cost to current node, and best arc to it) - as it has to in A* - you have to reset or clear out this old information. Rather than going through hundreds of thousands of nodes, it's easier to use a generation number system. Mark each node with the generation number of its data; increment the generation number when you calculate a new route; any node with an older generation number is stale and its information can be ignored.

像Dijkstra算法这样的图算法将无法工作，因为图是巨大的。

这个论点并不一定成立，因为Dijkstra通常不会查看完整的图，而只是一个非常小的子集(图的互联性越好，这个子集就越小)。

对于行为良好的图，Dijkstra实际上可能表现得相当好。另一方面，通过仔细的参数化，A*总是表现得一样好，甚至更好。您是否已经尝试过它对数据的处理方式?

也就是说，我也很有兴趣听听其他人的经历。当然，像谷歌Map搜索这样的突出例子是特别有趣的。我可以想象类似于有向近邻启发式的东西。

我以前没有在谷歌或微软或雅虎地图工作过，所以我不能告诉你他们是如何工作的。

然而，我确实为一家能源公司设计了一个定制的供应链优化系统，其中包括为他们的卡车车队提供调度和路由应用程序。然而，我们对路线的标准远比建筑、交通减速或车道封闭的地方更具体。

我们采用了一种称为ACO(蚁群优化)的技术来调度和路线卡车。该技术是一种人工智能技术，应用于旅行推销员问题来解决路由问题。ACO的技巧是基于路由的已知事实构建错误计算，以便图求解模型知道何时退出(当错误足够小时)。

你可以谷歌ACO或TSP找到更多关于这个技术。然而，我没有使用过任何开源AI工具，所以不能推荐一个(尽管我听说SWARM非常全面)。

以下是世界上最快的路由算法的比较和正确性:

http://algo2.iti.uka.de/schultes/hwy/schultes_diss.pdf

下面是谷歌关于这个主题的技术演讲:

http://www.youtube.com/watch?v=-0ErpE8tQbw

以下是schultes所讨论的高速公路层次算法的实现(目前仅在柏林，我正在编写界面，移动版本也正在开发中):

http://tom.mapsforge.org/

作为一个在地图公司工作了18个月的人，其中包括研究路由算法……是的，Dijkstra的方法确实有效，只是做了一些修改:

Instead of doing Dijkstra's once from source to dest, you start at each end, and expand both sides until they meet in the middle. This eliminates roughly half the work (2*pi*(r/2)^2 vs pi*r^2). To avoid exploring the back-alleys of every city between your source and destination, you can have several layers of map data: A 'highways' layer that contains only highways, a 'secondary' layer that contains only secondary streets, and so forth. Then, you explore only smaller sections of the more detailed layers, expanding as necessary. Obviously this description leaves out a lot of detail, but you get the idea.

通过沿着这些路线进行修改，您甚至可以在非常合理的时间范围内完成跨国家路由。

Probably similar to the answer on pre-computed routes between major locations and layered maps, but my understanding is that in games, to speed up A*, you have a map that is very coarse for macro navigation, and a fine-grained map for navigation to the boundary of macro directions. So you have 2 small paths to calculate, and hence your search space is much much smaller than simply doing a single path to the destination. And if you're in the business of doing this a lot, you'd have a lot of that data pre-computed so at least part of the search is a search for pre-computed data, rather than a search for a path.