Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation

Computing the shortest path between two given locations in a road network is an important problem that finds applications in various map services and commercial navigation products. The state-of-the-art solutions for the problem can be divided into two categories: spatial-coherence-based methods and vertex-importance-based approaches. The two categories of techniques, however, have not been compared systematically under the same experimental framework, as they were developed from two independent lines of research that do not refer to each other. This renders it difficult for a practitioner to decide which technique should be adopted for a specific application. Furthermore, the experimental evaluation of the existing techniques, as presented in previous work, falls short in several aspects. Some methods were tested only on small road networks with up to one hundred thousand vertices; some approaches were evaluated using distance queries (instead of shortest path queries), namely, queries that ask only for the length of the shortest path; a state-of-the-art technique was examined based on a faulty implementation that led to incorrect query results. To address the above issues, this paper presents a comprehensive comparison of the most advanced spatial-coherence-based and vertex-importance-based approaches. Using a variety of real road networks with up to twenty million vertices, we evaluated each technique in terms of its preprocessing time, space consumption, and query efficiency (for both shortest path and distance queries). Our experimental results reveal the characteristics of different techniques, based on which we provide guidelines on selecting appropriate methods for various scenarios.Comment: VLDB201

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

Spatial Network k-Nearest Neighbor: A Survey and Future Directives

Nearest neighbor algorithms play many roles in our daily lives. From facial recognition to networking applications, many of these are constantly improved for faster processing time and reliable memory management. There are many types of nearest neighbor algorithms. One of them is called k-nearest neighbor (k-NN), a technique that helps to find number of k closest objects from a user location within a specified range of area. k-NN road network algorithm studies have been through various query performance discussions. Each algorithm is usually judged based on query time over few selected parameters which are; number of k, network density and network size. Many studies have claimed different opinions over their techniques and with many results to prove better query performance than others. However, among these techniques, which k-NN road network algorithm has the highest rate of query performance based on the selected parameters? In this paper, reviews on several k nearest neighbor algorithms were made through series of journal extractions and experimentation in order to identify the algorithm that achieves highest query performance. It was found that with the experimentation method, we can identify not only the algorithm’s performance, but also its design flaws and possible future improvement. All methods were tested with some parameters such as varying number of k, road network density and network size. With the results collected, Incremental Expansion Restriction – Pruned Highway Labeling method (IER-PHL) proves to have the best query performance than other methods for most cases

Semantische Modellierung und Reasoning für Kontextinformationen in Infrastrukturnetzen

Infrastrukturen wie Verkehrs- und Energienetze bilden das Rückgrat unserer Gesellschaft und Wirtschaft. Präzises Wissen über den aktuellen technischen Zustand der Infrastrukturkomponenten gilt als Grundvoraussetzung zur Befriedigung des ständig wachsenden Kapazitätsbedarfs und zur Erhöhung der Kosteneffizienz, insbesondere bei der Instandhaltung. Zwar liefern Fernüberwachungssysteme verschiedener Organisationen bereits heute unterschiedlichste Statusinformationen. Es fehlt jedoch ein generischer Ansatz zur integrierten Auswertung dieser Daten, um komplexe Gesamtzustände der Infrastrukturkomponenten abzuleiten. Diese Arbeit versteht die Zustandsüberwachung für Infrastrukturnetze als ein kontextsensitives System im Sinne der Ambient Intelligence (Umgebungsintelligenz): Fernüberwachungssysteme liefern Kontextinformationen}, und anstelle der Situation einer Entität soll damit der Zustand eines Überwachungsobjekts ermittelt werden. Da sich hierfür bei kontextsensitiven Systemen wissensbasierte Ansätze bewährt haben, überträgt diese Arbeit einen solchen Ansatz auf die Zustandsüberwachung in Infrastrukturnetzen. Damit sollen generische Verfahren sowohl zur Integration als auch zur Auswertung (Reasoning) von Kontextinformationen in Infrastrukturnetzen konzipiert und umgesetzt werden. Eine Analyse von Schienen- und Stromnetzen identifiziert als Anforderungen unter anderem die Interoperabilität der Kontextinformationen zwischen Systemen und Betreibern sowie die Möglichkeit, auch komplexe Zustände ableiten zu können. Die Standards des Semantic Web auf Basis der Beschreibungslogik SHIN bieten hierfür eine attraktive Grundlage und gewährleisten sowohl die Umsetzbarkeit als auch die Zukunftstüchtigkeit. Für die automatisierte Auswertung (Reasoning) müssen die Besonderheiten von Infrastrukturnetzen berücksichtigt werden: Einerseits fallen Kontextinformationen von Überwachungssystemen räumlich verteilt und bei verschiedenen Organisationen an. Deshalb werden Verfahren entwickelt, die konjunktive Anfragen auch bei verteilten Wissensbasen korrekt und vollständig beantworten. Dies wird theoretisch gezeigt und praktisch evaluiert. Andererseits müssen topologiebezogene Anfragen beantwortet werden, wie die Suche nach optimalen Pfaden und k-nächsten Nachbarn. Dazu wird eine hierarchische Modellierung des Infrastrukturnetzes entwickelt. Ein generisches Konzept ermöglicht es, damit verschiedene Verfahren für topologiebezogene Anfragen umzusetzen. Zur praktischen Umsetzung dieser Konzepte in einem Zustandsüberwachungssystem wird eine geschichtete Systemarchitektur spezifiziert. Ein Fallbeispiel aus dem europäischen Schienenverkehr zeigt ihre Realisierung: Mehrere Organisationen stellen unter anderem Achslast-, Gleisgeometrie- und Schienenprofilmessungen als Kontextinformationen zur Verfügung. Unabhängig von deren Verteilung über ganz Europa werten die entwickelten Reasoningverfahren die Semantik der Systemontologie aus und demonstrieren so die zustandsorientierte Wartung des Schienennetzes

Efficient query processing on spatial networks

A framework for determining the shortest path and the distance between every pair of vertices on a spatial network is presented. The framework, termed SILC, uses path coherence between the shortest path and the spatial positions of vertices on the spatial network, thereby, resulting in an encoding that is compact in representation and fast in path and distance retrievals. Using this framework, a wide variety of spatial queries such as incremental nearest neighbor searches and spatial distance joins can be shown to work on datasets of locations residing on a spatial network of sufficiently large size. The suggested framework is suitable for both main memory and disk-resident datasets. Categories and Subject Descriptor

SILC: Efficient Query Processing on Spatial Networks – p.4/27

A spatial network is a graph with spatial components at vertices and/or edges. Most transportation networks can be modeled as spatial networks. e.g., Road networks Each intersection is a vertex of the graph, the position of the intersection is associated with the vertex. Each edge of the graph corresponds to a road segment. The weight of an edge corresponds to the cost of travel (i.e., distance or time) along the corresponding road segment. Airline route