Traffic signal phase and timing (TSPaT) information is valuable for various applications, such as velocity advisory systems, navigation systems, collision warning systems, and so forth. In this paper, we focus on learning baseline timing cycle lengths for fixed-time traffic signals. The cycle length is the most important parameter among all timing parameters, such as green lengths. We formulate the cycle length learning problem as a period estimation problem using a sparse set of noisy observations, and propose the most frequent approximate greatest common divisor (MFAGCD) algorithms to solve the problem. The accuracy performance of our proposed algorithms is experimentally evaluated on both simulation data and the real taxi GPS trajectory data collected in Shanghai, China. Experimental results show that the MFAGCD algorithms have better sparsity and outliers tolerant capabilities than existing cycle length estimation algorithms.

This paper discusses several practical issues related to the provision of video-on-demand (VOD) services, focusing on retrieval of video data from disk on the server. First, with regard to system design, the pros and cons of cycle-based scheduling algorithms for VOD servers are compared, and an adequate policy according to system configuration is presented. Second, we present a way to tune the cycle-based scheduling algorithm so that it maximizes profit. Third, a method to overcome the cons of cycle-based scheduling algorithms is proposed, and its cost is analyzed.