1,698 research outputs found
Transit Node Routing Reconsidered
Transit Node Routing (TNR) is a fast and exact distance oracle for road
networks. We show several new results for TNR. First, we give a surprisingly
simple implementation fully based on Contraction Hierarchies that speeds up
preprocessing by an order of magnitude approaching the time for just finding a
CH (which alone has two orders of magnitude larger query time). We also develop
a very effective purely graph theoretical locality filter without any
compromise in query times. Finally, we show that a specialization to the online
many-to-one (or one-to-many) shortest path further speeds up query time by an
order of magnitude. This variant even has better query time than the fastest
known previous methods which need much more space.Comment: 19 pages, submitted to SEA'201
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Trip-Based Public Transit Routing Using Condensed Search Trees
We study the problem of planning Pareto-optimal journeys in public transit
networks. Most existing algorithms and speed-up techniques work by computing
subjourneys to intermediary stops until the destination is reached. In
contrast, the trip-based model focuses on trips and transfers between them,
constructing journeys as a sequence of trips. In this paper, we develop a
speed-up technique for this model inspired by principles behind existing
state-of-the-art speed-up techniques, Transfer Pattern and Hub Labelling. The
resulting algorithm allows us to compute Pareto-optimal (with respect to
arrival time and number of transfers) 24-hour profiles on very large real-world
networks in less than half a millisecond. Compared to the current state of the
art for bicriteria queries on public transit networks, this is up to two orders
of magnitude faster, while increasing preprocessing overhead by at most one
order of magnitude
An Efficient Index for Reachability Queries in Public Transport Networks
Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential building blocks in many applications, for example, group recommendations, ranking spatial queries, or geomarketing. We propose an efficient solution for reachability queries in public transport networks. Currently, there are two options to solve reachability queries. (1) Execute a modified version of Dijkstra’s algorithm that supports time-dependent edge traversal costs; this solution is slow since it must expand edge by edge and does not use an index. (2) Issue a separate path query for each single POI, i.e., a single reachability query requires answering many path queries. None of these solutions scales to large networks with many POIs. We propose a novel and lightweight reachability index. The key idea is to partition the network into cells. Then, in contrast to other approaches, we expand the network cell by cell. Empirical evaluations on synthetic and real-world networks confirm the efficiency and the effectiveness of our index-based reachability query solution
Scalable big data systems: Architectures and optimizations
Big data analytics has become not just a popular buzzword but also a strategic direction in information technology for many enterprises and government organizations. Even though many new computing and storage systems have been developed for big data analytics, scalable big data processing has become more and more challenging as a result of the huge and rapidly growing size of real-world data. Dedicated to the development of architectures and optimization techniques for scaling big data processing systems, especially in the era of cloud computing, this dissertation makes three unique contributions. First, it introduces a suite of graph partitioning algorithms that can run much faster than existing data distribution methods and inherently scale to the growth of big data. The main idea of these approaches is to partition a big graph by preserving the core computational data structure as much as possible to maximize intra-server computation and minimize inter-server communication. In addition, it proposes a distributed iterative graph computation framework that effectively utilizes secondary storage to maximize access locality and speed up distributed iterative graph computations. The framework not only considerably reduces memory requirements for iterative graph algorithms but also significantly improves the performance of iterative graph computations. Last but not the least, it establishes a suite of optimization techniques for scalable spatial data processing along with three orthogonal dimensions: (i) scalable processing of spatial alarms for mobile users traveling on road networks, (ii) scalable location tagging for improving the quality of Twitter data analytics and prediction accuracy, and (iii) lightweight spatial indexing for enhancing the performance of big spatial data queries.Ph.D
- …