Highly scalable trip grouping for large-scale collective transportation systems

Abstract

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 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.

A foundation for spatial data warehouses on the Semantic Web

“…We assume that the cube has the cube schema CS = (D, M, F ), with the fact members f ∈ F M as given in Def. 16 12). After applying the s-slice operator on cube C, the new (sub)set of fact members is defined for both cases respectively as follows;…”

“…We will also consider more efficient representations of the data, e.g., by removing redundancies. Furthermore, it would be interesting to extend QB4SOLAP and GeoSemOLAP [18] to handle highly dynamic spatio-temporal data and queries, as for instance, found in large-scale transport analytics [12]. Table 5 presents the query runtimes for the SOLAP operator examples (Ex.…”

Scalable Splitting of Massive Data Streams

“…The stream splitting proved to be very efficient for online spatio-temporal optimization of trip grouping [7], based on static or dynamic routing decisions. Similarly, GSDM [12] distributed its stream computations by selecting and composing distribution templates from a library, in which some basic templates were defined including both splitting and joining.…”

Collaborative Transportation Systems

“…In [5] Gidofalvi and Pedersen address the problem of cabsharing. Specifically, they propose a centralized cab-sharing system that provides door-to-door transportation service, while minimizing the transportation costs and increasing the efficiency of cab space utilization.…”