routing Algorithms

[سارة شاكر]

السلام عليكم ورحمة الله وبركاته

اخواني واخواتي
انا اريد ان اطور نظام معلومات جغرافي يقوم بعملية routing وتحديدي اقصر الطرق
وفي الحقيقة قرأت كثيرا عن ال Routing Algorithms ولكني لم استطع ان اتوصل الى ما يمكن تطبيقه او ما لا يمكن تطبيقه وكيفية التطبيق
فارجو ممن لديه الخبرة ان يقترح على algorithm استطيع فهمه وتطبيقه
وجزاكم الله خيرا

[بهجت يوسف الجعافرة]

برنامج routing او ما يسمى linear referencing وهو يخص العمل على المعلم الخطي من اضافة جداول خطية او نقطية
اما برنامج network analysis
فهو المختص بتحدل افضل مسار حسب الزمن او الطول او الازدحام او مجتمعة
يرجى الاطلاع على ال tutorial الخاص network analysis

[سارة شاكر]

جزاكم الله خيرا

ولكني عندي بالفعل برنامج Actual Map وهو ياتي لي باقصر واسرع الطرق ولكني لا اعرف الالجوريزم الذي يتبعه ولهذا اسأل على الجوريزمات لاقرأها

فهل ال tutorial الخاصة بال network Analysis بها شيء من هذا؟
وهل تفضلتم برفعها
بارك الله فيكم

[محمد عسيوي]

هناك مثلا
Dijkstra algorithm
وهو معروف
وللمزيد
راجع
graph theory

[نجد جمال]

Shortest Path Problem:I
n graph theory, the shortest path problem is the problem of finding a path between two vertices (or nodes) such that the sum of the weights of its constituent edges is minimized. An example is finding the quickest way to get from one dcidcidcidcilocation to another on a road map; in this case, the vertices represent dcidcidcidcilocations and the edges represent segments of road and are weighted by the time needed to travel that segment
Formally, given a weighted graph (that is, a set V of vertices, a set E of edges, and a real-valued weight function f : E → R), and one element v of V, find a path P from v to a v' of V so that

Is minimal among all paths connecting v to v’.
The problem is also sometimes called the single-pair shortest path problem, to distinguish it from the following generalizations:
• The single-source shortest path problem, in which we have to find shortest paths from a source vertex v to all other vertices in the graph.
• The single-destination shortest path problem, in which we have to find shortest paths from all vertices in the graph to a single destination vertex v. This can be reduced to the single-source shortest path problem by reversing the edges in the graph.
• The all-pairs shortest path problem, in which we have to find shortest paths between every pair of vertices v, v' in the graph.


5.4.3-Algorithms
The most important algorithms for solving this problem are:
1. Dijkstra's algorithm solves the single-pair, single-source, and single-destination shortest path problems.
2. Bellman-Ford algorithm solves single source problem if edge weights may be negative.
3. A* search algorithm solves for single pair shortest path using heuristics to try to speed up the search.
4. Floyd-Warshall algorithm solves all pair’s shortest paths.
5. Johnson's algorithm solves all pair’s shortest paths, and may be faster than Floyd-Warshall on sparse graphs.
6. Perturbation theory finds (at worst) the locally shortest path.





Dijkstra's algorithm

conceived by Dutch computer scientist Edsger Dijkstra in 1959, [1] is a graph search algorithm that solves the single-source shortest path problem for a graph with nonnegative edge path costs, producing a shortest path tree. This algorithm is often used in routing.
For a given source vertex (node) in the graph, the algorithm finds the path with lowest cost (i.e. the shortest path) between that vertex and every other vertex. It can also be used for finding costs of shortest paths from a single vertex to a single destination vertex by stopping the algorithm once the shortest path to the destination vertex has been determined. For example, if the vertices of the graph represent cities and edge path costs represent driving distances between pairs of cities connected by a direct road, Dijkstra's algorithm can be used to find the shortest route between one city and all other cities. As a result, the shortest path first is widely used in network routing.
This graph below show Dijkstra's algorithm*
1. Let's call the node we are starting with an initial node. Let a distance of a node X be the distance from the initial node to it. Dijkstra's algorithm will assign some initial distance values and will try to improve them step-by-step.
2. Assign to every node a distance value. Set it to zero for our initial node and to infinity for all other nodes.
3. Mark all nodes as unvisited. Set initial node as current.
4. For current node, consider all its unvisited neighbors and calculate their distance (from the initial node). For example, if current node (A) has distance of 6, and an edge connecting it with another node (B) is 2, the distance to B through A will be 6+2=8. If this distance is less than the previously recorded distance (infinity in the beginning, zero for the initial node), overwrite the distance When we are done considering all neighbors of the current node, mark it as visited.
5. A visited node will not be checked ever again; its distance recorded now is final and minimal.
6. Set the unvisited node with the smallest distance (from the initial node) as the next "current node" and continue from step 3



http://www.gisclub.net/vb/images/uploads/5406_12984c60e41f06fbf.bmp


وهذا تطبيق عملي عليه:

http://www.gisclub.net/vb/images/uploads/5406_7024c60e44f738c7.JPG


وهذا شرح لنموذج Dijkstra

WIDTH=600 HEIGHT=400

[سارة شاكر]

جزاكم الله جميعا كل خير

[محمد مزاحم]

اختي الكريمة هناك تولز قوية في الارك ماب هي Network Analysis وهي تقوم بعدة مهمام منها اقصر الطرق Short path

[سارة شاكر]

جزاكم الله خيرا اخانا