Ridesharing is a challenging topic in the urban computing paradigm, which utilizes urban sensors to generate a wealth of benefits and thus is an important branch in ubiquitous computing. Traditionally, ridesharing is achieved by mainly considering the received user ridesharing requests and then returns solutions to users. However, there lack research efforts of examining user acceptance to the proposed solutions. To our knowledge, user decisions in accepting/rejecting a rideshare is one of the crucial, yet not well studied, factors in the context of dynamic ridesharing. Moreover, existing research attention is mainly paid to find the nearest taxi, whilst in reality the nearest taxi may not be the optimal answer. In this paper, we tackle the above un-addressed issues while preserving the scalability of the system. We present a scalable framework, namely TRIPS, which supports the probability of accepting each request by the companion passengers and minimizes users’ efforts. In TRIPS, we propose three search techniques to increase the efficiency of the proposed ridesharing service. We also reformulate the criteria for searching and ranking ridesharing alternatives and propose indexing techniques to optimize the process. Our approach is validated using a real, large-scale dataset of 10,357 GPS-equipped taxis in the city of Beijing, China and showcases its effectiveness on the ridesharing task.
Zhang, W. E., Shemshadi, A., Sheng, M. Q. Z., Qin, Y., Xu, X., & Yang, J. (2018). A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data. IEEE Transactions on Big Data. https://doi.org/10.1109/TBDATA.2018.2872450