Ridesourcing Car Detection by Transfer Learning
نویسندگان
چکیده
Ridesourcing platforms like Uber and Didi are geing 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 rst 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) classier using trajectory features shared by taxis/buses and ridesourcing/other cars. en, we use the RF to label all the candidate cars. In Stage 2, leveraging the subset of high condent labels from the previous stage as input, we further learn a convolutional neural network (CNN) classier for ridesourcing detection, and iteratively rene 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. ACM Reference format: Leye Wang1, Xu Geng1, Jintao Ke1, Chen Peng1, Xiaojuan Ma1, Daqing Zhang2, Qiang Yang1. 2016. Ridesourcing Car Detection by Transfer Learning. In Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Conference’17), 9 pages.
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ورودعنوان ژورنال:
- CoRR
دوره abs/1705.08409 شماره
صفحات -
تاریخ انتشار 2017