In this highly competitive taxi service industry, anticipating the location of future customer requests and accordingly selecting routes is critical to gaining a competitive advantage. Such strategically selected routes would lead to shorter wait times for customers and reduced fuel costs for taxi drivers. In this talk, we will discuss algorithms to achieve this goal both for the traditional scenario where a customer hires the entire taxi as well as the more recent ride-sharing model. Through extensive empirical evaluation of real datasets, we will present evidence that the proposed strategies lead to up to 70% shorter waiting times for customers, 40% more customers, and a 20% lower rejection rate.

Key Takeaways:

• Efficient Route recommendation overview
• Understanding how apt models reduce cost and increase revenue
• Impact of Reinforcement learning on the cab-industry