A Real-Time Taxicab Recommendation System Using Big Trajectories Data. (25th July 2017)

• Record Type: Journal Article
• Authors: Chen, Pengpeng; Lv, Hongjin; Gao, Shouwan; Niu, Qiang; Xia, Shixiong
• Other Names: Bellavista Paolo Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2017(2017)
• Publication Date: 2017-07-25
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2017/5414930
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : Carpooling is becoming a more and more significant traffic choice, because it can provide additional service options, ease traffic congestion, and reduce total vehicle exhaust emissions. Although some recommendation systems have proposed taxicab carpooling services recently, they cannot fully utilize and understand the known information and essence of carpooling. This study proposes a novel recommendation algorithm, which provides either a vacant or an occupied taxicab in response to a passenger's request, called VOT. VOT recommends the closest vacant taxicab to passengers. Otherwise, VOT infers destinations of occupied taxicabs by similarity comparison and clustering algorithms and then recommends the occupied taxicab heading to a close destination to passengers. Using an efficient large data-processing framework, Spark, we greatly improve the efficiency of large data processing. This study evaluates VOT with a real-world dataset that contains 14747 taxicabs' GPS data. Results show that the ratio of range (between forecasted and actual destinations) of less than 900 M can reach 90.29%. The total mileage to deliver all passengers is significantly reduced (47.84% on average). Specifically, the reduced total mileage of nonrush hours outperforms other systems by 35%. VOT and others have similar performances in actual detour ratio, even better in rush hours.