Demand forecasting is the art as well as the science of predicting the likely demand for a product or service in the future. This prediction is based on past behaviour patterns and the continuing trends in the present.
There are roughly 200 million taxi rides in New York City each year. Exploiting an understanding of taxi supply and demand could increase the efficiency of the city’s taxi system. In New York City, people use a taxi frequently and more than any other cities of US. Instead of booking a taxi by phone one day ahead of time, New York taxi drivers pick up passengers on the street. The ability to predict taxi ridership could present valuable insights to city planners and taxi dispatchers in answering questions such as how to position cabs where they are most needed, how many taxis to dispatch, and how ridership varies over time. Our project focuses on predicting the number of taxi pickups given a one-hour time window and a location within New York City.
It is not merely guessing the future demand but is estimating the demand scientifically and objectively. There are various methods of demand forecasting, and we are using one of the statistical methods called as Trend Projection method in our project in which a sufficient amount of accumulated past data of the taxi rides. This date is arranged chronologically to obtain a time series. Thus, the time series depicts the past trend, and on the basis of it, the future ridership can be predicted. It is assumed that the past trend will continue in the future. Thus, on the basis of the predicted future trend, the demand for a service is forecasted.
— Aastha Jain
Hadoop is an open-source framework of programs that is used to store and process big data. Hadoop uses multiple clusters of computers to analyze big data sets in parallel. The distributed processing of data sets can