Big City Taxicity taxi
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Airport Taxi Rhode Island : Divine Providence at Boston Airport: Airport Transport RI
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New York City Taxi Driver and Driver Big Data Mobile Services
Summary: In recent years, New York City cabs have been fitted with Global Positioning System (GPS) technology (GPS) to detect the starting and ending points of each journey, in combination with fares, journey times, fares and distances. These papers analyze this huge set of datasets using large computing power. Use MapReduce and Hive to help us better grasp transport and journey habits, identify correlations between dates, and make forecasts about the taxiways.
In addition, we suggest a competency level modeling approach that combines the results of our analytics with smart phone apps to provide various valuation related tools to our customers using the results of our big analytics. With more and more taxi information being collected and available, we can perform various data-driven analytics that result in enhancements to our local community and daily life.
Taxi cabs are precious detectors and information associated with taxi rides can give an idea of many different facets of city living. New York cabs are all fitted with global positioning and payment technology that collects and uploads information from each journey to a central location. This information will be published on the NYC Taxi & Limousine Commission website.
Contains voyage logs of all journeys made in New York in New York between 2009 and 2015 in either red, blue or blue cabs. It contains a lot of useful information like place and date of pick-up and return, travelling times, etc. We describe a research in which we analyse this set of datas in order to gain an understanding of transport and journey pattern, to make forecasts for the taxi net and to find correlations between the two.
The analysis of this extensive set of information presents many challenging tasks. It is a highly sophisticated set of information containing geographic and chronological elements and several associated variable values for each journey.