Abstract
Public bus service plays an indispensable role in modern urban traffic system. With the bus running data, the detection of the statistically significant aggregations of bus delay is useful for optimizing the bus timetable, so that the service quality can be improved. However, previous studies have not considered how to detect bus delay aggregation using statistical hypothesis testing. To fill that gap, this paper considers the detection of bus delay aggregation from bus running data. We present RSTV-Miner, a mining method using statistical hypothesis testing, for detecting statistically significant bus delay aggregation. Our empirical study on real data demonstrates that RSTV-Miner is effective and efficient.
This work was supported in part by NSFC 61572332, the Fundamental Research Funds for the Central Universities 2016SCU04A22, and China Postdoctoral Science Foundation 2014M552371, 2016T90850.
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Wu, X., Duan, L., Pang, T., Nummenmaa, J. (2016). Detection of Statistically Significant Bus Delay Aggregation by Spatial-Temporal Scanning. In: Morishima, A., et al. Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9865. Springer, Cham. https://doi.org/10.1007/978-3-319-45835-9_24
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DOI: https://doi.org/10.1007/978-3-319-45835-9_24
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