At some point or the other almost each one of us has used an Ola or Uber for taking a ride.
Ride hailing services are services that use online-enabled platforms to connect between passengers and local drivers using their personal vehicles. In most cases they are a comfortable method for door-to-door transport. Usually they are cheaper than using licensed taxicabs. Examples of ride hailing services include Uber and Lyft.
To improve the efficiency of taxi dispatching systems for such services, it is important to be able to predict how long a driver will have his taxi occupied. If a dispatcher knew approximately when a taxi driver would be ending their current ride, they would be better able to identify which driver to assign to each pickup request.
In this Project, we are challenged to build a model that predicts the total ride duration of taxi trips in New York City.
Data files:
https://drive.google.com/file/d/1Nywj7RhL-dNOPtqS929MRz7fVoPmR5cn/view?usp=sharing
https://drive.google.com/file/d/16W18M6fbisz3OGcJ58ihjfFEpMnuAFGR/view?usp=sharing
https://drive.google.com/file/d/1QU8XoNCGed7dEscq_bH835eYtPFqRFoD/view?usp=sharing
Sometimes, adding external information can be crucial for improving the model. Here we will use data extracted from The Open Source Routing Machine or OSRM for each trip in our original dataset. OSRM is a C++ implementation of a high-performance routing engine for shortest paths in road networks. This will give us a very good estimate of distances between pickup and dropoff Points
Source: http://project-osrm.org/