Bicycle‐ or bike‐sharing systems (BSSs) have received increasing attention as a secondary transportation mode due to their advantages, for example, accessibility, prevention of air pollution, and health promotion. However, in BSSs, due to bias in bike demands, the bike rebalancing problem should be solved. Various methods have been proposed to solve this problem; however, it is difficult to apply such methods to small cities because bike demand is sparse, and there are many practical issues to solve. Thus, we propose a demand prediction model using multiple classifiers, time grouping, categorization, weather analysis, and station correlation information. In addition, we analyze real‐world relocation data by relocation managers and propose a relocation algorithm based on the analytical results to solve the bike rebalancing problem. The proposed system is compared experimentally with the results obtained by the real relocation managers.

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The popularity of mobile and multimedia applications made real-time support a mandatory feature for embedded operating systems. However, the current situation is that the overall performance is significantly degraded due to the real-time support. This paper suggests a novel scheme to minimize the performance degradation in embedded operating systems with real-time support. Especially, we propose transparent and selective real-time interrupt services which transparently monitor the system and postpone interrupt handling that are not relevant to real-time tasks. The proposed scheme was implemented on the Linux 2.6 kernel and the experimental results show that our scheme improves the throughput by up to 86% for Hackbench benchmark while providing almost the same scheduling latency compared to the previous work.

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