Optimizing Car Pooling Supply Through Real Time Demand Prediction

Optimizing Car Pooling Supply Through Real Time Demand Prediction

Car Pooling and Ride & Share System are new medium of transportation which is increasingly gaining popularity among commuters. With the rise, there always arises new and novel issues. Timely supply of vehicles in high demand region is important strategy to fulfil commuters demand.
Think Transportation provides services to Car Pooling Ventures. We develop an algorithm which learn tempo spatial commuter demand pattern from historical data day wise for whole year for Karachi. This Model predicts the potential demand of car pool services based on spatial and time of year. Our method uses dynamic clustering to create real time demand zones so as to assign vehicles to specific zones before demand arises. This optimises distance between potential demand and vehicle which results in reduced time for vehicle to reach commuters along with satisfying high demand in region.