Ridesourcing Car Detection by Transfer Learning

by   Leye Wang, et al.

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.


page 1

page 2

page 3

page 4


How much is my car worth? A methodology for predicting used cars prices using Random Forest

Cars are being sold more than ever. Developing countries adopt the lease...

Automated detection of vulnerable plaque in intravascular ultrasound images

Acute Coronary Syndrome (ACS) is a syndrome caused by a decrease in bloo...

Deep Bayesian Learning for Car Hacking Detection

With the rise of self-drive cars and connected vehicles, cars are equipp...

Improving Botnet Detection with Recurrent Neural Network and Transfer Learning

Botnet detection is a critical step in stopping the spread of botnets an...

Public Parking Spot Detection And Geo-localization Using Transfer Learning

In cities around the world, locating public parking lots with vacant par...

Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks

Idling vehicles waste energy and pollute the environment through exhaust...

The Transfer Student Experience: It's A Lot Like Buying a Used Car

The experience transfer students encounter as they navigate their journe...

Please sign up or login with your details

Forgot password? Click here to reset