TY - GEN

T1 - Highly Scalable Trip Grouping for Large Scale Collective Transportation Systems

A1 - Gidofalvi,Gyozo

A1 - Pedersen,Torben Bach

A1 - Risch,Tore

A1 - Zeitler,Erik

AU - Gidofalvi,Gyozo

AU - Pedersen,Torben Bach

AU - Risch,Tore

AU - Zeitler,Erik

PB - Association for Computing Machinery

PY - 2008

Y1 - 2008

N2 - <p>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.</p><p>This paper presents highly scalable <em>trip grouping algorithms</em>, 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 policies for dividing the input data stream into sub-streams are developed and implemented using continuous stream queries. Third, using the partitioning policies, parallel implementations of the grouping algorithm in a parallel computing environment are described. Extensive experimental results show that the parallel implementation using simple adaptive partitioning methods can achieve speed-ups of several orders of magnitude without significantly degrading the quality of the grouping.</p>

AB - <p>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.</p><p>This paper presents highly scalable <em>trip grouping algorithms</em>, 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 policies for dividing the input data stream into sub-streams are developed and implemented using continuous stream queries. Third, using the partitioning policies, parallel implementations of the grouping algorithm in a parallel computing environment are described. Extensive experimental results show that the parallel implementation using simple adaptive partitioning methods can achieve speed-ups of several orders of magnitude without significantly degrading the quality of the grouping.</p>

U2 - 10.1145/1353343.1353425

DO - 10.1145/1353343.1353425

SN - 978-1-59593-926-5

BT - Proceedings of the 11th international conference on Extending database technology

T2 - Proceedings of the 11th international conference on Extending database technology

SP - 678

EP - 689

ER -