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- Gidofalvi, Gyözö, 1975-, et al.
(författare)
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Highly scalable trip grouping for large-scale collective transportation systems
- 2008
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Ingår i: Advances in Database Technology - EDBT 2008 - 11th International Conference on Extending Database Technology, Proceedings. - New York, NY, USA : ACM Press. - 9781595939265 ; , s. 678-689
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Konferensbidrag (refereegranskat)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.
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- Gidófalvi, Gyözö, et al.
(författare)
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Highly Scalable Trip Grouping for Large Scale Collective Transportation Systems
- 2008
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Ingår i: Proc. 11th International Conference on Extending Database Technology, EDBT 2008.
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Konferensbidrag (refereegranskat)abstract
- Transportation–related problems, like road congestion, park-ing, and pollution are increasing in most cities. In order toreduce traffic, recent work has proposed methods for vehiclesharing, for example for sharing cabs by grouping “closeby”cab requests and thus minimizing transportation cost andutilizing cab space. However, the methods proposed so fardo not scale to large data volumes, which is necessary tofacilitate large scale collective transportation systems, e.g.,ride–sharing systems for large cities.This paper presents highly scalable “trip grouping” algo-rithms, that generalize previous techniques and support in-put rates that can be orders of magnitude larger. The follow-ing three contributions make the grouping algorithms scal-able. First, the basic grouping algorithm is expressed as acontinuous stream query in a data stream management sys-tem to allow for very large flows of requests. Second, follow-ing the divide–and–conquer paradigm, four space–partition-ing policies for dividing the input data stream into sub–streams are developed and implemented using continuousstream queries. Third, using the partitioning policies, par-allel implementations of the grouping algorithm in a paral-lel computing environment are described. Extensive experi-mental results show that the parallel implementation usingsimple adaptive partitioning methods can achieve speed–upsof several orders of magnitudes without significantly effect-ing the quality of the grouping.
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- Pedersen, Tore, 1961-
(författare)
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Affective Forecasting: Predicting Future Satisfaction with Public Transport
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Affective forecasting refers to the process of predicting future emotions in response to future events. The overall aim of the present thesis was to investigate, by applying the framework of Affective forecasting, how car users predict their satisfaction with public transport services. Study 1, Part 1 revealed a satisfaction gap between users and non-users of public transport, whereby non-users reported lower satisfaction than users, in overall satisfaction as well as in two quality factors resulting from a factor analysis of a major survey on satisfaction with public transport. It was hypothesized that non-users were biased in their satisfaction reports, something which was subsequently investigated in Study 1, Part 2, where a field experiment revealed that car users suffer from an impact bias in their predictions about future satisfaction with public transport due to being more satisfied with the services after a trial period than they initially predicted they would. Addressing the question of whether or not a focusing illusion is the psychological mechanism responsible for the impact bias, two experiments containing critical incidents were conducted during Study 2, in order to investigate whether or not car users exaggerate the impact of specific incidents upon their future satisfaction with public transport. For car users with a stated intention to change their current travel mode, in Study 2, Part 1, as well as for car users with no stated intention to change their travel mode, in Study 2, Part 2, the negative critical incident generated lower predicted satisfaction with public transport, in support of the hypothesis that the impact bias in car users’ predictions about future satisfaction with public transport is caused by a focusing illusion.
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