the node, commonly elapsed time, in keeping with the principle of friction of distance. For example, a node in a street network may require a different amount of time to make a particular left turn or right turn. Such costs can vary over time, such as the pattern of travel time along an urban street depending on diurnal cycles of traffic volume. Flow volume, measurements of the actual movement taking place. This may be specific time-encoded measurements collected using sensor networks such as traffic counters, or general trends over a period of time, such as Annual average daily traffic (AADT). Analysis Methods A wide range of methods, algorithms, and techniques have been developed for solving problems and tasks relating to network flow. Some of these are common to all types of transport networks, while others are specific to particular application domains. Many of these algorithms are implemented in commercial and open-source GIS software, such as GRASS GIS and the Network Analyst extension to Esri ArcGIS. Optimal routing One of the simplest and most common tasks in a network is to find the optimal route connecting two points along the network, with optimal defined as minimizing some form of cost, such as distance, energy expenditure, or time. A common example is finding directions in a street network, a feature of almost any web street mapping application such as Google Maps. The most popular method of solving this task, implemented in most GIS and mapping software, is Dijkstra's algorithm. In addition to the basic point-to-point routing, composite routing problems are also common. The Traveling salesman problem asks for the optimal (least distance/cost) ordering and route to reach a number of destinations; it is an NP-hard problem, but somewhat easier to solve in network space than unconstrained space due to the smaller solution set. The Vehicle routing problem is a generalization of this, allowing for multiple simultaneous routes to reach the destinations. The Route inspection or "Chinese Postman" problem asks for the optimal (least distance/cost) path that traverses every edge; a common application is the routing of garbage trucks. This turns out to be a much simpler problem to solve, with polynomial time algorithms. Location analysis This class of problems aims to find the optimal location for one or more facilities along the network, with optimal defined as minimizing the aggregate or mean travel cost to (or from) another set of points in