Parallel computation of best connections in public transportation networks

Abstract

Exploiting parallelism in route planning algorithms is a challenging algorithmic problem with obvious applications in mobile navigation and timetable information systems. In this work, we present a novel algorithm for the one-to-all profile-search problem in public transportation networks. It answers the question for all fastest connections between a given station <it>S</it> and any other station at any time of the day in a single query. This algorithm allows for a very natural parallelization, yielding excellent speed-ups on standard multicore servers. Our approach exploits the facts that, first, time-dependent travel-time functions in such networks can be represented as a special class of piecewise linear functions and, second, only few connections from <it>S</it> are useful to travel far away. Introducing the connection-setting property, we are able to extend Dijkstra's algorithm in a sound manner. Furthermore, we also accelerate station-to-station queries by preprocessing important connections within the public transportation network. As a result, we are able to compute all relevant connections between two random stations in a complete public transportation network of a big city (New York) on a standard multi-core server in real time.