Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 30, Issue 3, Pages 585-598Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2017.2772907
Keywords
Extreme learning machine; passenger-finding potential; recommender system; taxi trajectory data analytics
Categories
Funding
- National Natural Science Foundation of China [61772344, 61402460, 61732011, 61472257, 61373092]
- Guangdong Provincial Science and Technology Plan Project [2013B040403005]
- HD Video R&D Platform for Intelligent Analysis and Processing in the Guangdong Engineering Technology Research Centre of Colleges and Universities [GCZX-A1409]
- Natural Science Foundation of SZU [2017060]
- CityU research grants (CityU) [9231131]
- NSF CRII [CNS-1657350]
- Pitney Bowes Inc. % Generated by IEEEtran.bst [1.13]
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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.
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