Ridesharing is popular among travellers because it can reduce their travel costs, and it also holds the potential to reduce travel time, congestion, air pollution, and overall fuel consumption. Existing ridesharing systems (e.g.. lyft, uberPOOL) often offer each traveler only one choice that aims to minimize system-wide vehicle travel distance or time. In this demonstration, we present a price-and-time-aware ridesharing system, termed as PTRider, which provides more options. It considers both pick-up time and price, so that travellers are able to choose the vehicle matching their preferences best. To answer the ridesharing request in real time, PTRider builds indexes on the road network and vehicles separately, and utilizes corresponding efficient matching methods. A real-life dataset that contains 432.327 trips extracted from 17.000 Shanghai taxis for one day (May 29. 2009) is used to demonstrate that PTRider can return various options for every ridesharing request in real time.
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