Ride-sharing service customers look for and deserve fair fares. However, with the use of the Internet to access competing fares when booking shared rides, how should ride-sharing providers forecast passenger demands in order to remain competitive? Wang et al. offer a new framework for predicting ride-sharing demands originating from different data sources.

The authors concisely review literature on fair prediction algorithms. The existing algorithms--ride-finding, passenger-finding, and global dispatching--are deficient because they (a) undersupply rides that satisfy the timely demands of passengers, and (b) oversupply scheduled rides that increase the delay times for drivers to pick up new passengers. Consequently, the authors present a framework for investigating “the bias between the trajectory data and the real ground truth.”

The authors offer unique contributions for effectively studying the ride-sharing patterns: (1) a parameter for differentiating ride time rates in alternative areas, and (2) a machine learning model, predicated on a variety of global positioning system (GPS) meteorological datasets, to effectively target fare-sharing riders and drivers based on big data analyses.

Numerous experiments were performed with real-life datasets to ascertain the effectiveness of the proposed model. The experimental results compare favorably with the well-known results of statistical and machine learning algorithms in the literature. Even though the experimental dataset is limited to one region, there is no doubt that researchers can replicate the experiments given the continued challenges and research opportunities related to the Internet of Things (IoT).