![](/images/graphics-bg.png)
A Real-Time Taxicab Recommendation System Using Big Trajectories Data
Joint Authors
Xia, Shixiong
Niu, Qiang
Lv, Hongjin
Gao, Shouwan
Chen, Pengpeng
Source
Wireless Communications and Mobile Computing
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-07-25
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Chen, Pengpeng& Lv, Hongjin& Gao, Shouwan& Niu, Qiang& Xia, Shixiong. 2017. A Real-Time Taxicab Recommendation System Using Big Trajectories Data. Wireless Communications and Mobile Computing،Vol. 2017, no. 2017, pp.1-18.
https://search.emarefa.net/detail/BIM-1205960
Modern Language Association (MLA)
Chen, Pengpeng…[et al.]. A Real-Time Taxicab Recommendation System Using Big Trajectories Data. Wireless Communications and Mobile Computing No. 2017 (2017), pp.1-18.
https://search.emarefa.net/detail/BIM-1205960
American Medical Association (AMA)
Chen, Pengpeng& Lv, Hongjin& Gao, Shouwan& Niu, Qiang& Xia, Shixiong. A Real-Time Taxicab Recommendation System Using Big Trajectories Data. Wireless Communications and Mobile Computing. 2017. Vol. 2017, no. 2017, pp.1-18.
https://search.emarefa.net/detail/BIM-1205960
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1205960