The increased availability of large-scale trajectory data provides rich information for the study of urban dynamics. For example, New York City Taxi & Limousine Commission regularly releases source/destination information of taxi trips, where 173 million taxi trips released for Year 2013 [1]. Such a big dataset provides us potential new perspectives to address the traditional traffic problems. In this paper, we study the travel time estimation problem. Instead of following the traditional route-based travel time estimation, we propose to simply use a large amount of taxi trips without using the intermediate trajectory points to estimate the travel time between source and destination. Our experiments show very promising results. The proposed big data-driven approach significantly outperforms both state-of-the-art route-based method and online map services. Our study indicates that novel simple approaches could be empowered by the big data and these approaches could serve as new baselines for some traditional computational problems.

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi & Limousine Commission regularly releases source/destination information about trips in the taxis they regulate [1]. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps [2] and Baidu Maps [3] (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A * search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).

We consider the problem of privately releasing a class of queries that we call hierarchical count-of-counts histograms.

The aim of this study was to investigate the effects of local (LIPC) and remote (RIPC) ischemic preconditioning on sprint interval exercise (SIE) performance. Fifteen male collegiate basketball players underwent a LIPC, RIPC, sham (SHAM), or control (CON) trial before conducting six sets of a 30-s Wingate-based SIE test. The oxygen uptake and heart rate were continuously measured during SIE test. The total work in the LIPC (+2.2%) and RIPC (+2.5%) conditions was significantly higher than that in the CON condition (p < 0.05). The mean power output (MPO) at the third and fourth sprint in the LIPC (+4.5%) and RIPC (+4.9%) conditions was significantly higher than that in the CON condition (p < 0.05). The percentage decrement score for MPO in the LIPC and RIPC condition was significantly lower than that in the CON condition (p < 0.05). No significant interaction effects were found in pH and blood lactate concentrations. There were no significant differences in the accumulated exercise time at ≥80%, 90%, and 100% of maximal oxygen uptake during SIE. Overall, both LIPC and RIPC could improve metabolic efficiency and performance during SIE in athletes.

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Advances in sensor technology have enabled the collection of largescale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that result from sensor malfunction or human operation faults. In order to utilize such data for real-world applications, it is critical to detect outliers so that models built from these datasets will not be skewed by outliers.In this paper, we propose a new outlier detection method that utilizes the correlations in the data (e.g., taxi trip distance vs. trip time). Different from existing outlier detection methods, we build a robust regression model that explicitly models the outliers and detects outliers simultaneously with the model fitting.We validate our approach on real-world datasets against methods specifically designed for each dataset as well as the state of the art outlier detectors. Our outlier detection method achieves better performances, demonstrating the robustness and generality of our method. Last, we report interesting case studies on some outliers that result from atypical events.

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