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Due to the increasing number of vehicles in recent years, traffic congestion problem is a common issue for residents of metropolises. For a better understanding of traffic congestion, the analyzed data from big data technology can be provided as timeline information. However, a scalability problem would occur when we convert raw traffic data into the timeline information due to the volume and complexity...
We analyze the work of urban trip planners and the relevance of trips they recommend upon user queries. We propose to improve the planner recommendations by learning from choices made by travelers who use the transportation network on the daily basis. We analyze a large collection of individual travelers' trips collected from the automated fare collection systems; we convert the trips into pair-wise...
In recent years, the widespread adoption of GPS enabled vehicles brings the Location Based Services new opportunities. It benefits many related fields such as urban planning, city traffic modeling, personalized recommendations and driving suggestions. The service providers can understand their users better by modeling the mobility pattern and provide more personalized services by predicting the destination...
Lately, a large amount of traffic-related data, such as traffic statistics, accident statistics, road information, and drivers' and pedestrians' comments, has been collected through sensors and social media networks. In this paper, we propose a novel framework for mining traffic risk from such heterogeneous data. Traffic risk refers to the possibility of traffic accidents occurring. We specifically...
Traditional reliability analysis of complex machinery involves statistical modeling of historical data on part failures from warranty claims, using distributions from exponential family such as the Weibull or log-normal distribution. When observed failures (in one or more parts) across a population of machines exceed the number expected based on such a model, this may serve as an early warning of...
Modern industrial equipments of all kinds are instrumented with a large number of sensors that continuously transmit their readings wirelessly, giving rise to what is often referred to as the ‘industrial internet’. Such data are often explored by engineers to determine the different usage patterns and behavior of similar machines. In this paper we describe a technique to automatically summarize the...
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