The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads.
Some people may opt for the ride sharing merely for cost savings. Individuals not owning private cars have limited access to public transportation options can be other targets of ridesharing. People are likely to participate in ridesharing arrangements if they pay less fare in comparison with taking a taxi alone. Also, they have to accept a deviation from their shortest path too. There are different methods to share the travel-expenses among entities. The most straightforward intuitive way is to devide the costs evenly between passengers @cite_3 . This approach would not be fair as the portion of the shared path could be different. To overcome this, passengers may divide the related costs proportional to the distances they ride autonomously and the part they shared the car.
@cite_3
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Todays ride sharing services still mimic a better billboard. They list the offers and allow to search for the source and target city, sometimes enriched with radial search. So finding a connection between big cities is quite easy. These places are on a list of designated origin and distination points. But when you want to go from a small town to another small town, even when they are next to a freeway, you run into problems. You can't find offers that would or could pass by the town easily with little or no detour. We solve this interesting problem by presenting a fast algorithm that computes the offers with the smallest detours w.r.t. a request. Our experiments show that the problem is efficiently solvable in times suitable for a web service implementation. For realistic database size we achieve lookup times of about 5ms and a matching rate of 90 instead of just 70 for the simple matching algorithms used today.