Highly scalable trip grouping for large-scale collective transportation systems

Transportation?related problems, like road congestion, parking, and pollution, are increasing in most cities. In order to reduce traffic, recent work has proposed methods for vehicle sharing, for example for sharing cabs by grouping "closeby" cab requests and thus minimizing transportation cost and utilizing cab space. However, the methods published so far do not scale to large data volumes, which is necessary to facilitate large?scale collective transportation systems, e.g., ride?sharing systems for large cities. This paper presents highly scalable trip grouping algorithms, which generalize previous techniques and support input rates that can be orders of magnitude larger. The following three contributions make the grouping algorithms scalable. First, the basic grouping algorithm is expressed as a continuous stream query in a data stream management system to allow for a very large flow of requests. Second, following the divide?and?conquer paradigm, four space?partitioning p...

• Type: Conference
• Year: 2008
• Where: EDBT
• Authors: Gyözö Gidófalvi, Torben Bach Pedersen, Tore Risch, Erik Zeitler
• Keywords: Basic Grouping Algorithm, Continuous Stream Queries, Database, EDBT 2008, Trip Grouping Algorithms