Transportation Shortest Path Search Area Model

Although many studies on shortest-path algorithms have been conducted in the past, few of them have considered the time and effort required to obtain and update the weight property of the network arcs. For transportation-related problems – due to the size and complexity of the network – preparing, updating, and transmitting the network database on which the shortest-path algorithms perform can be a challenge. This study designed a Transportation Shortest Path Search Area (TSPSA) model to enhance the database preparation and updating step before any shortest-path search algorithm can start processing. Taking advantage of the characteristics of the transportation networks, this new TSPSA model divides a transportation network into hierarchical levels of areas, and uses an elliptical search area to reduce the amount of data required by existing methods. For testing the designed TSPSA model, the DC-Baltimore metropolitan area roadway network was selected as a performance case study. The network GIS map was obtained by translating Census 2000 Topologically Integrated Geographic Encoding and Referencing system (TIGER) files into GIS shape files. Using the TSPSA model, when the Origin and Destination (OD) Euclidean distance increases, the amount of data saving increases; concurrently, the maximum percent error between the TSPSA model and other traditional models rapidly decreases. The percentage of the data saving is around 75% to 85%, which means the data transmitting time is reduced about 80%. Moreover, the maximum percent error between the TSPSA model and other traditional models reduces to less than 5% when the Euclidean distance between the original and destination points (ED) is greater than 1.8 miles in urban areas. Similarly, the maximum percent error reduces to less than 5% when ED is greater than 4 miles in suburban areas, and less than 5% when ED is greater than 9 miles in rural areas. The study concludes that the TSPSA model greatly reduces shortest-path search area data size, and increases the data transmitting speed between the information control center and its clients. It contributes to speeding up the shortest-path search process as a whole, as well as reducing the congestion obstacles in data transmission

Efficient shortest distance query processing and indexing on large road network

University of Technology Sydney. Faculty of Engineering and Information Technology.Computing the shortest distance between two vertices is a fundamental problem on road networks. State-of-the-art indexing-based solutions can be categorized into hierarchy-based solutions and hop-based solutions. However, the hierarchy-based solutions require a large search space for long-distance queries while the hop-based solutions result in a high computational waste for short-distance queries. Moreover, in real life, the weight of edges changes frequently. For example, building a road need several months, but the travel time of road changes frequently such as traffic jam in the morning peak. We model this problem as the shortest path problem on a dynamic road network. The existing solutions are not efficient to update the index for the dynamic condition. Shor-test path query on bicriteria road network is another important and practical problem in real life. To compute shortest path between any two vertices, we can get the shortest path set which is called path skyline. We propose an efficient exploring strategy to accelerate path skyline computing. We propose a novel hierarchical 2-hop index (H2H-Index) which assigns a label for each vertex and at the same time preserves a hierarchy among all vertices. With the H2H-Index, we design an efficient query processing algorithm with performance guarantees by visiting part of the labels for the source and destination based on the vertex hierarchy. We also propose an algorithm to construct the H2H-Index based on distance preserved graphs. The algorithm is further optimized by computing the labels based on the partially computed labels of other vertices. We use dynamic road network to define the graph model whose topological structure is stable and weight of edges changes frequently. In this model, we have two processing, shortest path query and road update processing, to do on road network. We use Contraction Hierarchies which is one of art-of-the-state index algorithm for shortest path problem to answer queries. And propose an efficient index updating algorithm to update CH index for road updating processing. In contrast to vertex centric algorithm, our shortcut centric algorithm has better theoretical bound. In the literature, PSQ is a fundamental algorithm for path skyline query and is also used as a building block for the afterwards proposed algorithms. In PSQ, a key operation is to record the skyline paths for each node that is possible on the skyline paths from to . However, to obtain the skyline paths for , PSQ has to maintain other paths that are not skyline paths for , which makes PSQ inefficient. Motivated by this, in this chapter, we propose a new algorithm PSQ⁺ for the path skyline query. By adopting an ordered path exploring strategy, our algorithm can totally avoid the fruitless path maintenance problem in PSQ. We conducted extensive performance studies using large real road networks including the whole USA road network. The experimental results demonstrate that our approach can make significant improvement to every problem

Fully-dynamic Approximation of Betweenness Centrality

Betweenness is a well-known centrality measure that ranks the nodes of a network according to their participation in shortest paths. Since an exact computation is prohibitive in large networks, several approximation algorithms have been proposed. Besides that, recent years have seen the publication of dynamic algorithms for efficient recomputation of betweenness in evolving networks. In previous work we proposed the first semi-dynamic algorithms that recompute an approximation of betweenness in connected graphs after batches of edge insertions. In this paper we propose the first fully-dynamic approximation algorithms (for weighted and unweighted undirected graphs that need not to be connected) with a provable guarantee on the maximum approximation error. The transfer to fully-dynamic and disconnected graphs implies additional algorithmic problems that could be of independent interest. In particular, we propose a new upper bound on the vertex diameter for weighted undirected graphs. For both weighted and unweighted graphs, we also propose the first fully-dynamic algorithms that keep track of such upper bound. In addition, we extend our former algorithm for semi-dynamic BFS to batches of both edge insertions and deletions. Using approximation, our algorithms are the first to make in-memory computation of betweenness in fully-dynamic networks with millions of edges feasible. Our experiments show that they can achieve substantial speedups compared to recomputation, up to several orders of magnitude