The drivers have limited parameters on which they can decide the areas where they can maximize their chance of finding rides. We intend to provide drivers with insights that improve their chance of a pickup, real-time. An efficient demand model based on multiple key points drawn from different clusters of a city helps drivers navigate to get more pickups.

Users suffer when there are insufficient cabs available, both in terms of time and money. We intend to alleviate this problem by distributing cabs based on real-time demand. So our service hits both ways, demand, and supply.

We intend to keep the human touch in a way that achieves a synergy effect. An operator can override any functionality or rating and has tools like Areas of Interest, Holiday Calendar, etc for assistance to do this.

What it does

We are offering two solutions: one for drivers which help them find riders and the second, which helps the operator to supervise and intervene when needed. Even though we don't offer users any direct interaction, both the solutions help the users in better pickups.

How we built it

With mind, body, hand and soul. And of course, Seasonal and non-seasonal ARIMA.

Challenges we ran into

Working out the details of the idea and finding relevant real-life data wasn't trivial. Building out the whole system in limited time has its own challenges.

Accomplishments that we're proud of

We were able to decide on a common idea—out of the many we came up, that targets a common problem in delivery/logistics/mobility.

What we learned

Agreeing on an idea by discussion, deciding what are the important features and distributing the work load among ourselves in the limited time we have.

What's next for Hunter

Building a stronger demand model, if we had real-time pickups. Add features like reassigning drivers based on the nearest pickup, clubbing multiple pickups...

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