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TaxiRec: Recommending Road Clusters to Taxi Drivers Using Ranking-Based Extreme Learning Machines.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . Mar2018, Vol. 30 Issue 3, p585-598. 14p. - Publication Year :
- 2018
-
Abstract
- Utilizing large-scale GPS data to improve taxi services has become a popular research problem in the areas of data mining, intelligent transportation, geographical information systems, and the Internet of Things. In this paper, we utilize a large-scale GPS data set generated by over 7,000 taxis in a period of one month in Nanjing, China, and propose TaxiRec: a framework for evaluating and discovering the passenger-finding potentials of road clusters, which is incorporated into a recommender system for taxi drivers to seek passengers. In TaxiRec, the underlying road network is first segmented into a number of road clusters, a set of features for each road cluster is extracted from real-life data sets, and then a ranking-based extreme learning machine (ELM) model is proposed to evaluate the passenger-finding potential of each road cluster. In addition, TaxiRec can use this model with a training cluster selection algorithm to provide road cluster recommendations when taxi trajectory data is incomplete or unavailable. Experimental results demonstrate the feasibility and effectiveness of TaxiRec. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 30
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 127814370
- Full Text :
- https://doi.org/10.1109/TKDE.2017.2772907