A Matching Model for Vehicle Sharing Based on User Characteristics and Tolerated-Time

In the present age, transportation is humankind’s necessity. With the increasing population, it has produced adverse effects like rapid consumption of fuel resources, high carbon emissions, and global traffic issue. In such cases, vehicle sharing is gaining attraction as a possible candidate solution. We have implemented a vehicle sharing rider matching model which matches users reaching nearby destinations. The algorithm then undergoes another matching layer, which filters users based on user characteristics. Best-matched users are then added to a final itinerary forming the route for the commute. In our model, we have used New York City cab zone locations with real-time navigation using Google Maps. We have introduced the concept of “User Threshold Time (UTT),” the time riders are willing to spend to pick other riders. Our major motive is to complete the pool for the maximum number of trips based on user characteristics. On a global scale, our model aims at saving resources and improve overall global atmospheric conditions. Results show that our matching model can be achievable in a reasonable time constraint.