The users' cancelling rental data in the bike-sharing system (BSS) is usually regarded as abnormal trip data and is ignored. Abnormal trip data may have implicit information about the availability of shared bikes. So this paper presents an approach based on functional principal components analysis (FPCA) and clustering to advance the shared-bike availability analysis and maintenance strategy optimization using the abnormal trip data. In the proposed approach, the ratio of the cancelling rental number to the total rental number is scored as an index. Their values reflect a smooth variation in availability. The FPCA method is performed to explore the long-term availability variation modes of shared bikes. Then the dominant modes of availability variations are determined using the k-means algorithm. The effectiveness of the proposed approach is illustrated on the real-world trip data of a BSS. The analysis result indicates that the long-term availability level of the referred BSS has decreased from the initial 0.907 to 0.861. In the definite availability variation modes, the availability of one of the variation modes even has decreased to 0.709. Finally, the preventive maintenance model is presented to prevent the deterioration or availability decrease of shared bikes based on the mean functions of availability variation modes.
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