Hannah Bast

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.

Fast routing in very large public transportation networks using transfer patterns

We show how to route on very large public transportation networks (up to half a billion arcs) with average query times of a few milliseconds. We take into account many realistic features like: traffic days, walking between stations, queries between geographic locations instead of a source and a target station, and multi-criteria cost functions. Our algorithm is based on two key observations: (1) many shortest paths share the same transfer pattern, i.e., the sequence of stations where a change of vehicle occurs; (2) direct connections without change of vehicle can be looked up quickly. We precompute the respective data; in practice, this can be done in time linear in the network size, at the expense of a small fraction of non-optimal results. We have accelerated public transportation routing on Google Maps with a system based on our ideas. We report experimental results for three data sets of various kinds and sizes.

Car or Public Transport--Two Worlds

There are two kinds of people: those who travel by car, and those who use public transport. The topic of this article is to show that the algorithmic problem of computing the fastest way to get from A to B is also surprisingly different on road networks than on public transportation networks. On road networks, even very large ones like that of the whole of Western Europe, the shortest path from a given source to a given target can be computed in just a few microseconds. Lots of interesting speed-up techniques have been developed to this end, and we will give an overview over the most important ones. Public transportation networks can be modeled as graphs just like road networks, and most algorithms designed for road networks can be applied for public transportation networks as well. They just happen to perform not nearly as well, and to date we do not know how to route similarly fast on large public transportation networks as we can on large road networks. The reasons for this are interesting and non-obvious, and it took us a long time to fully comprehend them. Once understood, they are relatively easy to explain, however, and that is what we want to do in this article. Oh, and by the way, happy birthday, Kurt!

An index for efficient semantic full-text search

In this paper we present a novel index data structure tailored towards semantic full-text search. Semantic full-text search, as we call it, deeply integrates keyword-based full-text search with structured search in ontologies. Queries are SPARQL-like, with additional relations for specifying word-entity co-occurrences. In order to build such queries the user needs to be guided. We believe that incremental query construction with context-sensitive suggestions in every step serves that purpose well. Our index has to answer queries and provide such suggestions in real time. We achieve this through a novel kind of posting lists and query processing, avoiding very long (intermediate) result lists and expensive (non-local) operations on these lists. In an evaluation of 8000 queries on the full English Wikipedia (40 GB XML dump) and the YAGO ontology (26.6 million facts), we achieve average query and suggestion times of around 150ms.

Easy access to the freebase dataset

We demonstrate a system for fast and intuitive exploration of the Freebase dataset. This required solving several non-trivial problems, including: entity scores for proper ranking and name disambiguation, a unique meaningful name for every entity and every type, extraction of canonical binary relations from multi-way relations (which in Freebase are modeled via so-called mediator objects), computing the transitive hull of selected relations, and identifying and merging duplicates. Our contribution is two-fold. First, we provide for download an up-to-date version of the Freebase data, enriched and simplified as just sketched. Second, we offer a user interface for exploring and searching this data set. The data set, the user interface and a demo video are available from http://freebase-easy.cs.uni-freiburg.de.

Semantic full-text search with broccoli

We combine search in triple stores with full-text search into what we call \emph{semantic full-text search}. We provide a fully functional web application that allows the incremental construction of complex queries on the English Wikipedia combined with the facts from Freebase. The user is guided by context-sensitive suggestions of matching words, instances, classes, and relations after each keystroke. We also provide a powerful API, which may be used for research tasks or as a back end, e.g., for a question answering system. Our web application and public API are available under \url{http://broccoli.cs.uni-freiburg.de}.

Delay-Robustness of Transfer Patterns in Public Transportation Route Planning

Transfer pattern routing is a state-of-the-art speed-up technique for finding optimal paths which minimize multiple cost criteria in public transportation networks. It precomputes sequences of transfer stations along optimal paths. At query time, the optimal paths are searched among the stored transfer patterns, which allows for very fast response times even on very large networks. On the other hand, even a minor change to the timetables may affect many optimal paths, so that, in principle, a new computation of all optimal transfer patterns becomes necessary. In this paper, we examine the robustness of transfer pattern routing towards delay, which is the most common source of such updates. The intuition is that the deviating paths caused by typical updates are already covered by original transfer patterns. We perform experiments which show that the transfer patterns are remarkably robust even to large and many delays, which underlines the applicability and reliability of transfer pattern routing in realistic routing applications.

Real-time movement visualization of public transit data

We introduce a framework to create a world-wide live map of public transit, i.e. the real-time movement of all buses, subways, trains and ferries. Our system is based on freely available General Transit Feed Specification (GTFS) timetable data and also features real-time delay information (where available). The main problem of such a live tracker is the enormous amount of data that has to be handled (millions of vehicle movements). We present a highly efficient back-end that accepts temporal and spatial boundaries and returns all relevant trajectories and vehicles in a format that allows for easy rendering by the client. The real-time movement visualization of complete transit networks allows to observe the current state of the system, to estimate the transit coverage of certain areas, to display delays in a neat manner, and to inform a mobile user about near-by vehicles. Our system can be accessed via http://tracker.geops.ch/. The current implementation features over 80 transit networks, including the complete Netherlands (with real-time delay data), and various metropolitan areas in the US, Europe, Australia and New Zealand. We continuously integrate new data. Especially for Europe and North America we expect to achieve almost full coverage soon.

More Informative Open Information Extraction via Simple Inference

Recent Open Information Extraction OpenIE systems utilize grammatical structure to extract facts with very high recall and good precision. In this paper, we point out that a significant fraction of the extracted facts is, however, not informative. For example, for the sentence The ICRW is a non-profit organization headquartered in Washington, the extracted fact a non-profit organization is headquartered in Washington is not informative. This is a problem for semantic search applications utilizing these triples, which is hard to fix once the triple extraction is completed. We therefore propose to integrate a set of simple inference rules into the extraction process. Our evaluation shows that, even with these simple rules, the percentage of informative triples can be improved considerably and the already high recall can be improved even further. Both improvements directly increase the quality of search on these triples.

Frequency-based search for public transit

We consider the application of route planning in large public-transportation networks (buses, trains, subways, etc). Many connections in such networks are operated at periodic time intervals. When a set of connections has sufficient periodicity, it becomes more efficient to store the time range and frequency (e.g., every 15 minutes from 8:00am-6:00pm) instead of storing each of the time events separately. Identifying an optimal frequency-compression is NP-hard, so we present a time- and space-efficient heuristic. We show how we can use this compression to not only save space but also query time. We particularly consider profile queries, which ask for all optimal routes with departure times in a given interval (e.g., a whole day). In particular, we design a new version of Dijkstras algorithm that works with frequency-based labels and is suitable for profile queries. We evaluate the savings of our approach on two metropolitan and three country-wide public-transportation networks. On our largest network, we simultaneously achieve a better space consumption than all previous methods as well as profile query times that are about 5 times faster than the best previous method. We also improve Transfer Patterns, a state-of-the-art technique for fully realistic route planning in large public-transportation networks. In particular, we accelerate the expensive preprocessing by a factor of 60 compared to the original publication.