Abstract
A crucial task in traffic data analysis is similarity pattern discovery, which is of great importance to urban mobility understanding and traffic management. Recently, a wide range of methods for similarities discovery have been proposed and the basic assumption of them is that traffic data is complete. However, missing data problem is inevitable in traffic data collection process due to a variety of reasons. In this paper, we propose the Bayesian nonparametric tensor decomposition (BNPTD) to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously. BNPTD is a hierarchical probabilistic model, which is comprised of Bayesian tensor decomposition and Dirichlet process mixture model. Furthermore, we develop an efficient variational inference algorithm to learn the model. Extensive experiments were conducted on a smart card dataset collected in Guangzhou, China, demonstrating the effectiveness of our methods. It should be noted that the proposed BNPTD is universal and can also be applied to other spatiotemporal traffic data.
Highlights
Recent advances in data acquisition technologies and mobile computing lead to a collection of large quantities of urban traffic data from various sources, such as loop detectors data, GPS data, and smart card data. ese datasets can capture rich spatial-temporal information of the whole transportation system and enable some traffic analysis
Inspired by the recent work of Bayesian tensor decomposition, we propose a novel framework named Bayesian nonparametric tensor decomposition (BNPTD) to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously. e BNPTD consists of two components: (1) Bayesian tensor decomposition and (2) Dirichlet process mixture model (DPMM)
We propose Bayesian nonparametric tensor decomposition that is comprised of Bayesian tensor decomposition and Dirichlet process mixture model via a hierarchical probabilistic model to achieve incomplete traffic data imputation and similarity pattern discovery simultaneously
Summary
Recent advances in data acquisition technologies and mobile computing lead to a collection of large quantities of urban traffic data from various sources, such as loop detectors data, GPS data, and smart card data. ese datasets can capture rich spatial-temporal information of the whole transportation system and enable some traffic analysis. Ese datasets can capture rich spatial-temporal information of the whole transportation system and enable some traffic analysis. A crucial task in a data-driven transportation system is similarity pattern discovery. Ese similarities are beneficial for urban mobility pattern understanding and the authorities’ policy-making. For aggregate-level, the classification management can be adopted in metro systems and the managers should pay more attention to station A and station B to prevent congestion during the morning peak. The similarities can be used for anomaly detection and improving the traffic prediction as a prior knowledge [2,3,4]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
