Research Area:  Big Data

Abstract:

Social networks and mobile networks are exposing human beings to a big data era. With the support of big data analytics, conventional intelligent transportation systems (ITS) are gradually changing into data-driven ITS (D 2 ITS). Along with traffic growth, D 2 ITS need to solve more real-life problems, including the issue of unlicensed taxis and their identification, which potentially disrupts the taxi business sector and endangers society safety. As a remedy to this issue, a smart model is proposed in this paper to identify unlicensed taxis. The proposed model consists of two submodel components, namely, candidate selection model and candidate refined model. The former is used to screen out a coarse-grained suspected unlicensed taxi candidate list. The list is taken as an input for the candidate refined model, which is based on machine learning to get a fine-grained list of suspected unlicensed taxis. The proposed model is evaluated using real-life data, and the obtained results are encouraging, demonstrating its efficiency and accuracy in identifying unlicensed taxis, helping governments to better regulate the traffic operation and reduce associated costs.

Keywords:  

Author(s) Name:  Wei Yuan; Pan Deng; Tarik Taleb; Jiafu Wan and Chaofan Bi

Publisher name:  IEEE

DOI:  10.1109/TITS.2015.2498180

Volume Information:  Volume: 17, Issue: 6, June 2016,Page(s): 1703 - 1713

Paper Link:   https://ieeexplore.ieee.org/document/7336538