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论文类型：期刊论文

发表时间：2015-08-01

发表刊物：IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS

收录刊物：SCIE、EI、Scopus

卷号：16

期号：4

页面范围：1716-1727

ISSN号：1524-9050

关键字：Big data; frequent trajectory graph (FTG); recommendation algorithm; taxi Global Positioning System (GPS) data; Taxi-hunting Recommendation System (Taxi-RS)

摘要：Recommender systems are constructed to search the content of interest from overloaded information by acquiring useful knowledge from massive and complex data. Since the amount of information and the complexity of the data structure grow, it has become a more interesting and challenging topic to find an efficient way to process, model, and analyze the information. Due to the Global Positioning System (GPS) data recording the taxi's driving time and location, the GPS-equipped taxi can be regarded as the detector of an urban transport system. This paper proposes a Taxi-hunting Recommendation System (Taxi-RS) processing the large-scale taxi trajectory data, in order to provide passengers with a waiting time to get a taxi ride in a particular location. We formulated the data offline processing system based on HotSpotScan and Preference Trajectory Scan algorithms. We also proposed a new data structure for frequent trajectory graph. Finally, we provided an optimized online querying subsystem to calculate the probability and the waiting time of getting a taxi. Taxi-RS is built based on the real-world trajectory data set generated by 12 000 taxis in one month. Under the condition of guaranteeing the accuracy, the experimental results show that our system can provide more accurate waiting time in a given location compared with a naive algorithm.