Time-Location-Relationship Combined Service Recommendation based on Taxi Trajectory Data
Abstract
Recently, urban traffic management has encountered a paradoxical situation which is the empty carrying phenomenon for taxi drivers and the difficulty of taking a taxi for passengers. In this paper, through analyzing the quantitative relationship between passengers' getting on and off taxis, we propose a time-location-relationship (TLR) combined taxi service recommendation model to improve taxi drivers' profits, uncover the knowledge of human mobility patterns, and enhance passengers' travel experience. Moreover, the TLR model uses Gaussian process regression and statistical approaches to acquire passenger volume, mean trip distance, and average trip time in functional regions during every period on weekdays and weekends, and allows drivers to pick up more passengers within a short time frame. Finally, we compare our proposed model with the autoregressive integrated moving average model, the back-propagation neural network model, the support vector machine model, and the gradient boost decision tree model by using the real taxi GPS data in Beijing. The experimental results show that our optimizing taxi service recommendation can predict more accurately than others by considering the 3-D properties.
Recommended Citation
X. Kong et al., "Time-Location-Relationship Combined Service Recommendation based on Taxi Trajectory Data," IEEE Transactions on Industrial Informatics, vol. 13, no. 3, pp. 1202 - 1212, IEEE Computer Society, Jun 2017.
The definitive version is available at https://doi.org/10.1109/TII.2017.2684163
Department(s)
Computer Science
Research Center/Lab(s)
Intelligent Systems Center
Second Research Center/Lab
Center for High Performance Computing Research
Keywords and Phrases
Backpropagation; Decision trees; Neural networks; Taxicabs; Traffic control; Transportation; Trees (mathematics); Autoregressive integrated moving average models; Back propagation neural networks; Functional region; Gaussian process regression; Human mobility; recommendation; Support vector machine models; Trajectory data; Location based services; Taxi trajectory data
International Standard Serial Number (ISSN)
1551-3203; 1941-0050
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 IEEE Computer Society, All rights reserved.
Publication Date
01 Jun 2017
Comments
This work was partially supported by the National Natural Science Foundation of China under Grant 61572106, by the Natural Science Foundation of Liaoning Province, China under Grant 201602154, by the Dalian Science and Technology Planning Project (2015A11GX015, 2015R054), and by the U.S. National Science Foundation under Grant CNS-1545037, Grant CNS-1545050, Grant CCF-1533918, and Grant CBET-1609642.