With the increase in urban population, city agencies have been struggling to manage their resources efficiently. In most of the metropolitan cities, number of people who use public transportation has increased drastically and city agencies don’t have enough resources to suffice every need. This project addresses one urban issue that has been a major challenge for most of the cities in the world. Since more and more people from the metropolitan cities are relying shared and solo cab services, we wanted to identify the areas that have the maximum number of trips either by Uber or the yellow and green cabs in NYC. By analyzing these hotspots, we can give recommendations for the optimization of trip routes, especially in the case of shared cab services. We also analyzed the ridership patterns and how weather can affect user preferences towards their commute options.
Keywords— Analytics, Uber, taxi, location intelligence, TLC, weather effect, ridership patterns, hot-spot analysis
Technologies used: Hadoop Map Reduce, Hive, GeoPandas, Python
Team Members: Isha Chaturvedi, Prince Abunku, Gaurav Bhardwaj