Title: Ridesourcing Car Detection by Transfer Learning,
Abstract: Ridesourcing platforms like Uber and Didi are getting more and more popular
around the world. However, unauthorized ridesourcing activities taking
advantages of the sharing economy can greatly impair the healthy development of
this emerging industry. As the first step to regulate on-demand ride services
and eliminate black market, we design a method to detect ridesourcing cars from
a pool of cars based on their trajectories. Since licensed ridesourcing car
traces are not openly available and may be completely missing in some cities
due to legal issues, we turn to transferring knowledge from public transport
open data, i.e, taxis and buses, to ridesourcing detection among ordinary
vehicles. We propose a two-stage transfer learning framework. In Stage 1, we
take taxi and bus data as input to learn a random forest (RF) classifier using
trajectory features shared by taxis/buses and ridesourcing/other cars. Then, we
use the RF to label all the candidate cars. In Stage 2, leveraging the subset
of high confident labels from the previous stage as input, we further learn a
convolutional neural network (CNN) classifier for ridesourcing detection, and
iteratively refine RF and CNN, as well as the feature set, via a co-training
process. Finally, we use the resulting ensemble of RF and CNN to identify the
ridesourcing cars in the candidate pool. Experiments on real car, taxi and bus
traces show that our transfer learning framework, with no need of a pre-labeled
ridesourcing dataset, can achieve similar accuracy as the supervised learning
methods.