Time-Location-Relationship Combined Service Recommendation based on Taxi Trajectory Data


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.


Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Second Research Center/Lab

Center for High Performance Computing Research


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.

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


File Type





© 2017 IEEE Computer Society, All rights reserved.

Publication Date

01 Jun 2017