An Unlicensed Taxi Identification Model Based on Big Data Analysis

Wei Yuan, Pan Deng, Tarik Taleb, Jiafu Wan*, Chaofan Bi

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

68 Citations (Scopus)


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 ((DITS)-I-2). Along with traffic growth, (DITS)-I-2 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.

Original languageEnglish
Pages (from-to)1703-1713
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number6
Publication statusPublished - Jun 2016
MoE publication typeA1 Journal article-refereed


  • Big data
  • intelligent transportation systems
  • machine learning
  • data-driven ITS
  • unlicensed taxi


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