Routing services on the web and on hand-held devices have become ubiquitous in the past couple of years. Websites like Bing or Google Maps allow users to find routes between arbitrary locations comfortably in no time. Likewise onboard navigation units belong to the off-the-shelf equipment of virtually any new car.The amount of volunteered spatial data of the OpenStreetMap project has increased rapidly in the past five years. In many areas, the data quality already matches that of commercial map data, if not outright surpass it.We demonstrate both a server and a hand-held device based implementation working with OpenStreetMap data. Both applications provide real-time and exact shortest path computation on continental sized networks with millions of street segments.We also demonstrate sophisticated real-time features like draggable routes and round-trip planning.

Abstract. 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 Contraction Hierarchies (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.

Server based route planning in road networks is now powerful enough to find quickest paths in a matter of milliseconds, even if detailed information on time-dependent travel times is taken into account. However this requires huge amounts of memory on each query server and hours of preprocessing even for a medium sized country like Germany. This is a problem since global internet companies would like to work with transcontinental networks, detailed models of intersections, and regular repreprocessing that takes the current traffic situation into account. By giving a distributed memory parallelization of the arguably best current technique-time-dependent contraction hierarchies, we remove these bottlenecks. For example, on a medium size network 64 processes accelerate preprocessing by a factor of 28 to 160 seconds, reduce per process memory consumption by a factor of 10.5 and increase query throughput by a factor of 25.

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