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 monitoring data, considering the characteristics of bus monitoring data and the scenarios of on-time analysis. While fulfilling typical requirements of bus monitoring data analysis, Bus-OLAP not only provides a flexible way to manage the data and to implement multiple granularity data query and update, but also supports distributed query and computation. The experiments on real-world bus monitoring data verify that Bus-OLAP is effective and efficient.
Keywords
This work was supported in part by NSFC 61572332, the Fundamental Research Funds for the Central Universities 2016SCU04A22, the China Postdoctoral Science Foundation 2016T90850, and the Academy of Finland Foundation 295694.
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Pang, T., Duan, L., Nummenmaa, J., Zuo, J., Zhang, P. (2017). Bus-OLAP: A Bus Journey Data Management Model for Non-on-time Events Query. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_15
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