Parallel computation of best connections in public transportation networks

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 so-called one-to-all profile-search problem in public transportation networks. It answers the question for all fastest connections between a given station S 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 multi-core 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 that second, only few connections from S 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-tostation 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 (Los Angeles) on a standard multi-core server in less than 55 ms on average.