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Real-Time City-Scale Taxi Ridesharing

         ABSTRACT

We proposed and developed a taxi-sharing system that accepts taxi passengers’ real-time ride requests sent from smart phones and schedules proper taxis to pick up them via ridesharing, subject to time, capacity, and monetary constraints. The

monetary constraints provide incentives for both passengers and taxi drivers: passengers will not pay more compared with no ride sharing and get compensated if their travel time is lengthened due to ridesharing; taxi drivers will make money for all the detour distance due to ridesharing. Taxi riders and taxi drivers use the taxi-sharing service provided by the system via a smart phone App. The Cloud first finds candidate taxis quickly for a taxi ride request using a taxi searching algorithm supported by a spatio-temporal index. A scheduling process is then performed in the cloud to select a taxi that satisfies the request with minimum

increase in travel distance. We built an experimental platform using the GPS trajectories generated by over 33,000 taxis over a period of three months. A ride request generator is developed in terms of the stochastic process modelling real ride requests learned from the data set. Tested on this platform with extensive experiments, our proposed

system demonstrated its efficiency, effectiveness and scalability.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING SYSTEM:- In contrast to existing ridesharing, real-time taxi-sharing is more challenging because both ride requests and positions of taxis are highly dynamic and difficult to predict. First, passengers are often lazy to plan a taxi trip in advance, and usually submit a ride request shortly before the departure. Second, a taxi constantly travels on roads, picking up and dropping off passengers. Its destination depends on that of passengers, while passengers could go anywhere in a city.	PROPOSED SYSTEM:- We proposed and developed a taxi-sharing system that accepts taxi passengers’ real-time ride requests sent from smart phones and schedules proper taxis to pick up them via ridesharing, subject to time, capacity, and monetary constraints.  The monetary constraints provide incentives for both passengers and taxi drivers: passengers will not pay more compared with no ridesharing and get compensated if their travel time is lengthened due to ridesharing.
EXISTING ALGORITHM :- Searching Algorithm	PROPOSED ALGORITHM :- Single and Dual side searching Algorithm
ALGORITHM   DEFINITION:- The Cloud first finds candidate taxis quickly for a taxi ride request using a taxi searching algorithm supported by a spatio-temporal index. A scheduling process is then performed in the cloud to select a taxi that satisfies the request with minimum increase in travel distance.	ALGORITHM DEFINITION:- This algorithm only considers taxis currently “near” the pickup point of a query (thus called single-side search). The dual-side searching is a bi-directional searching process which selects grid cells and taxis from the origin side and the destination side of a query simultaneously.
DRAWBACKS:- It increases the overall computation cost, which is certainly not desirable for a rigid real-time system like taxi-sharing. This is not efficient to use middle classes and brings some certainity problem.	ADVANTAGES:- In this propose system we can able to reduce the passengers expensive and increases the driver’s income by using our techniques. It can help to reduces the traffic while we using the taxi sharing.