Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2015
Abstract
Taxi bookings are events where requests for taxis are made by passengers either over voice calls or mobile apps. As the demand for taxis changes with space and time, it is important to model both the space and temporal dimensions in dynamic booking data. Several applications can benefit from a good taxi booking model. These include the prediction of number of bookings at certain location and time of the day, and the detection of anomalous booking events. In this paper, we propose a Grid-based Gaussian Mixture Model (GGMM) with spatio-temporal dimensions that groups booking data into a number of spatio-temporal clusters by observing the bookings occurring at different time of the day in each spatial grid cell. Using a large-scale real-world dataset consisting of over millions of booking records, we show that GGMM outperforms two strong baselines: a Gaussian Mixture Model (GMM) and the state-of-the-art spatio-temporal behavior model, Periodic Mobility Model (PMM), in estimating the spatio-temporal distribution of bookings at specific grid cells during specific time intervals. GGMM can achieve up to 95.8% (96.5%) reduction in perplexity compared against GMM (PMM). Further, we apply GGMM to detect anomalous bookings and successfully relate the anomalies with some known events, demonstrating GGMM's effectiveness in this task.
Keywords
Spatial-temporal dynamics, Taxi demand modeling, Unified grid-based gaussian mixure model
Discipline
Databases and Information Systems | Transportation
Research Areas
Data Science and Engineering
Publication
SIGSPATIAL '15: Proceedings of the 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: November 3-6, 2015, Seattle, Washington
First Page
32:1
Last Page
10
ISBN
9781450339674
Identifier
10.1145/2820783.2820807
Publisher
ACM
City or Country
New York
Citation
CHIANG, Meng-Fen; HOANG, Tuan Anh; and LIM, Ee-Peng.
Where are the passengers? A Grid-Based Gaussian Mixture Model for taxi bookings. (2015). SIGSPATIAL '15: Proceedings of the 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: November 3-6, 2015, Seattle, Washington. 32:1-10.
Available at: https://ink.library.smu.edu.sg/sis_research/3170
Copyright Owner and License
Publisher/LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/2820783.2820807