An Automated Framework for Shortest Path Computation

Shortest path computation is one of the most common queries in location-based services that involve transportation networks. Motivated by scalability challenges faced in the mobile network industry, we propose adopting the wireless broadcast model for such location-dependent applications. Existing work develop a framework called live traffic index (LTI) which enables drivers to quickly and effectively collect the live traffic information on the broadcasting channel. An impressive result is that the driver can compute/update their shortest path result by receiving only a small fraction of the index. As such, huge communication cost will be spent on sending result paths on this model. We will extend our solution on time dependent networks. This is a very interesting topic since the decision of a shortest path depends not only on current traffic data but also based on the predicted traffic circumstances. I.INTRODUCTION: Computing fastest routes in road networks is one of the showpieces of real-world applications of algorithmic. In principle we could use Dijkstra’s algorithm. But for large road networks this would be far too slow. Therefore, in recent years, there has been considerable interest in speedup techniques for route planning. This makes it possible to preprocess some information once and for all that can be used to accelerate all subsequent point-to-point queries. Today, the shortest path computation problem in road networks can be regarded as largely solved. However, real road networks change all the time. In this paper, we address two such dynamic scenarios: We present two novel methods, namely Elliptic Boundary (EB) and Next Region (NR)e.g., due to traffic jams, and switching between different cost functions that take vehicle type, road restrictions, or driver preferences into account. For example, in Google Maps, once a shortest path from A to B has been obtained which passes through C, users can simply change the query to find the shortest path from A to B which is constrained to pass through D instead of C and the new shortest path is presented to the user instantly. Requiring that the result be obtained in real time (or almost real time) precludes the use of conventional algorithms that are graph-based (e.g., the INE and IER methods and improvements) which usually incorporate Dijkstra’s algorithm in at least some parts of the solution LITERATURE SURVEY: IN TRANSIT TO CONSTANT TIME SHORTEST-PATH QUERIES IN ROAD NETWORKS When you drive to somewhere ‘far away’, you will leave your current location via one of only a few ‘important’ traffic junctions. Starting from this informal observation, we develop an algorithmic approach transit node routing that allows us to reduce quickest-path queries in road networks to a small number of table lookups. We present two implementations of this idea, one based on a simple grid data structure and one based on highway hierarchies. For the road map of the United States, our best query times improve over the best previously published figures by two orders of magnitude. Our results exhibit various trade-offs between average query time (5 μs to 63 μs), preprocessing time (59 min to 1200 min), and storage overhead (21 bytes/node to 244 bytes/node). ENGINEERING HIGHWAY HIERARCHIES We presented a shortest path algorithm that allows fast point-to-point queries in graphs using preprocessed data. Here, we give an extensive revision of our method. It allows faster query and preprocessing times, it reduces the size of the data obtained during the preprocessing and it deals with directed graphs. Some important concepts like the neighbourhood radii and the contraction of a network have been generalized and are now more flexible. The query algorithm has been simplified: it differs only by a few lines from the bidirectional version of DIJKSTRA'S algorithm. We can prove that our algorithm is correct even if the graph contains several paths of the same length. Experiments with real-world road networks confirm the effectiveness of our approach. Preprocessing the network of Western Europe, which consists of about 18 million nodes, takes 15 minutes and yields 68 bytes of additional data per node. Then, random queries take 0.76 ms on average. If we are willing to accept slower query times (1.38 ms), the memory usage can be decreased to 17 bytes per node. For the European and the US road networks, we can guarantee that at most 0.05% of all nodes are visited during any query. REACH-BASED ROUTING: A NEW APPROACH TO SHORTEST PATH ALGORITHMS OPTIMIZED FOR ROAD NETWORKS We study the point-to-point shortest path problem in a setting where preprocessing is allowed. We improve the reach-based approach of Gutman in several ways. In particular, we introduce a bidirectional version of the algorithm that uses implicit lower bounds and we add shortcut arcs which reduce vertex reaches. Our modifications greatly reduce both preprocessing and query times. The resulting algorithm is as fast as the best previous method, due to Sanders and Schultes. However, our R.Kamatchi et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (2) , 2015, 1526-1534