House Number Interpolation For Route Planning Applications

Nowadays users expect door-to-door routing from a navigation system; for a given address including street name and house number this requires at least an approximate location on the routable part of the OSM data. Of course, eventually the goal is to have every single building mapped and tagged in OSM with its housenumber. For most regions this is still a pipe dream, though. Often, only a small subset of the buildings have been mapped, and even if mapped, quite frequently their housenumber is missing. In particular buildings mapped automatically from aerial imagery typically lack more detailed tags. And even though there are explicit interpolation tags recommended to be used, they are not frequently used, hence many addresses with house numbers cannot be located even approximately. In this paper we show how to automatically infer house numbers based on the data already present in OpenStreetMap. The result of our algorithm is a street graph where every road segment (part of a way between two consecutive nodes) is associated with a range/subset of house numbers. On one hand, this yields an interpolation of house numbers that are not explicitly present in the OSM data. On the other hand this allows route planning applications to perform door-to-door routing up to OSM road segment level (which seems sufficient given that a typical OSM road segment between consecutive nodes is rather short) without the need to look up house numbers in respective building data etc. This is an important advantage in particular in offline scenarios where the route planning data is stored locally e.g. on a mobile device. For example, in a small (but rather well mapped) excerpt of the OpenStreetMap data of Germany, we have counted about 494k buildings, but only around 241k with a housenumber provided (either at the building outline/way or at an entrance node). For most of these buildings our methods allow for the association of the correct range of house numbers including these missing house numbers (and potentially housenumbers of still unmapped buildings). The addr:interpolation tag has only been used 81 times in this excerpt. Our implementation is efficient enough such that a country-sized excerpt can be processed within few minutes (e.g. in preparation for offline storage on a mobile route planning device)

Automatic Improvement of Point-of-Interest Tags For OpenStreetMap Data

The user experience of any OpenStreetMap (OSM) based service heavily depends on the quality of the underlying data. If the service deals with points-of-interest (POIs), consistent and comprehensive tagging of the respective map elements is a necessary condition for a satisfying service. In this paper, we develop methods that can automatically infer tags characterizing POIs solely based on the POI names. The idea being that many POI names already contain sufficient information for tagging. For example, \u27Pizzeria Bella Italia\u27 most certainly indicates an Italian restaurant. As the OSM data contains hundred of thousands POIs for Germany alone, we aim for a tool that can accomplish tag extrapolation in an automated way. In a first step, we automatically extract typical words and phrases that occur in names associated with a certain tag. For example, learning indicators for ‘shop=hairdresser’ on German OSM tags led to high scores for ‘fris’, ‘cut’, hair’ and ‘haar’. Having available such indicator phrases, we use standard machine learning techniques to derive the probability for a POI to exhibit a certain tag. If this probability exceeds a certain threshold, we assign the tag to the POI in an automated fashion. We used our extrapolation framework to create new amenity, shop, tourism, and leisure tags. The accuracy of our approach was over 85% for all considered tags. Moreover, for POIs tagged with amenity=restaurant, we aimed for extrapolating the respective cuisine tag. For more than 19 thousand out of 28 thousand restaurants in Germany lacking the cuisine-tag, our approach assigned a cuisine. In a random sample of those assignments 98% of these appeared to be true

Efficiently Computing All Delaunay Triangles Occurring over All Contiguous Subsequences

p_n}, we are interested in computing T, the set of distinct triangles occurring over all Delaunay triangulations of contiguous subsequences within P. We present a deterministic algorithm for this purpose with near-optimal time complexity O(|T|log n). Additionally, we prove that for an arbitrary point set in random order, the expected number of Delaunay triangles occurring over all contiguous subsequences is ?(nlog n)

An Upper Bound on the Number of Extreme Shortest Paths in Arbitrary Dimensions

Graphs with multiple edge costs arise naturally in the route planning domain when apart from travel time other criteria like fuel consumption or positive height difference are also objectives to be minimized. In such a scenario, this paper investigates the number of extreme shortest paths between a given source-target pair s, t. We show that for a fixed but arbitrary number of cost types d ? 1 the number of extreme shortest paths is in n^O(log^{d-1}n) in graphs G with n nodes. This is a generalization of known upper bounds for d = 2 and d = 3

Preference-Based Trajectory Clustering - An Application of Geometric Hitting Sets

In a road network with multicriteria edge costs we consider the problem of computing a minimum number of driving preferences such that a given set of paths/trajectories is optimal under at least one of these preferences. While the exact formulation and solution of this problem appears theoretically hard, we show that in practice one can solve the problem exactly even for non-homeopathic instance sizes of several thousand trajectories in a road network of several million nodes. We also present a parameterized guaranteed-polynomial-time scheme with very good practical performance

Constant Time Queries for Energy Efficient Paths in Multi-hop Wireless Networks

We investigate algorithms for computing energy efficient paths in ad-hoc radio networks. We demonstrate how advanced data structures from computational geometry can be employed to preprocess the position of radio stations in such a way that approximately energy optimal paths can be retrieved in constant time, i.e., independent of the network size. We put particular emphasis on actual implementations which demonstrate that large constant factors hidden in the theoretical analysis are not a big problem in practice

Combinatorial curve reconstruction and the efficient exact implementation of geometric algorithms

This thesis has two main parts. The first part deals with the problem of curve reconstruction. Given a finite sample set S from an unknown collection of curves Gamma, the task is to compute the graph G(S, Gamma) which has vertex set S and an edge between exactly those pairs of vertices that are adjacent on some curve in Gamma. We present a purely combinatorial algorithm that solves the curve reconstruction problem in polynomial time. It is the first algorithm which provably handles collections of curves with corners and endpoints. In the second part of this thesis, we will be concerned with the exact and efficient im plementation of geometric algorithms. First, we develop a generalized filtering scheme to speed-up exact geometric computation and then discuss the design of an object-oriented kernel for geometric computation.Diese Dissertation besteht aus zwei Teilen. Der erste Teil beschäftigt sich mit den Problemen der Kurvenrekonstruktion. Gegeben eine endliche Menge von Stichprobenpunkten S von einer Menge von unbekannten Kurven Gamma, besteht die Aufgabe darin, den Graphen G(S, Gamma) zu konstruieren, welcher die Knotenmenge S und Kanten zwischen genau den Knotenpaaren besitzt, welche auf einer der Kurven in Gamma adjazent sind. Wir präsentieren einen rein kombinatorischen Algorithmus, der das Kurevenkonstruktionsproblem in polynomieller Zeit löst. Es ist der erste Algorithmus, der beweisbar Mengen von Kurven rekonstruieren kann, wenn diese auch Ecken und Endpunkte beinhalten dürfen. Der zweite Teil dieser Dissertation handelt von der exakten und effizienten Implementierung von Geometrischen Algorithmen. Wir entwickeln zunächst ein generalisiertes Filterschema, um exakte geometrische Berechnungen zu beschleunigen, und entwerfen dann das Design eines objektorientierten Kernels für geometrische Berechnungen