Latest revision as of 12:53, 12 February 2021

Abstract

Increasing the on-time rate of bus service can prompt the people’s willingness to travel by bus, which is an effective measure to mitigate the city traffic congestion. Performing queries on the bus arrival can be used to identify and analyze various kinds of non-on-time events that happened during the bus journey, which is helpful for detecting the factors of delaying events, and providing decision support for optimizing the bus schedules. We propose a data management model, called Bus-OLAP, for querying bus journey data, considering the characteristics of bus running and the scenarios of non-on-time analysis. While fulfilling typical requirements of bus journey data queries, Bus-OLAP not only provides a flexible way to manage the data and to implement multiple granularity data query and update, but it also supports distributed queries and computation. The experiments on real-world bus journey data verify that Bus-OLAP is effective and efficient.

Document type: Article

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The different versions of the original document can be found in:

• https://link.springer.com/content/pdf/10.1007%2Fs41019-018-0061-9.pdf under the license https://creativecommons.org/licenses/by

• https://trepo.tuni.fi/handle/10024/118794 under the license cc-by

• http://link.springer.com/article/10.1007/s41019-018-0061-9,

https://doaj.org/toc/2364-1185,

https://doaj.org/toc/2364-1541

• http://link.springer.com/article/10.1007/s41019-018-0061-9/fulltext.html,

http://link.springer.com/content/pdf/10.1007/s41019-018-0061-9.pdf,

http://dx.doi.org/10.1007/s41019-018-0061-9

• https://dblp.uni-trier.de/db/journals/dase/dase3.html#DuanPNZZT18,

https://trepo.tuni.fi/handle/10024/118794,

https://link.springer.com/article/10.1007/s41019-018-0061-9,

https://rd.springer.com/article/10.1007/s41019-018-0061-9,

https://academic.microsoft.com/#/detail/2794345047 under the license https://creativecommons.org/licenses/by/4.0