scholarly journals Simultaneous Incomplete Traffic Data Imputation and Similarity Pattern Discovery with Bayesian Nonparametric Tensor Decomposition

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yaxiong Han ◽  
Zhaocheng He
Keyword(s):  
Dirichlet Process ◽  
Traffic Management ◽  
Pattern Discovery ◽  
Tensor Decomposition ◽  
Data Imputation ◽  
Traffic Data ◽  
Bayesian Nonparametric ◽  
Dirichlet Process Mixture ◽  
Wide Range ◽  
Similarity Pattern

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.

scholarly journals Urban Traffic Data Imputation With Detrending and Tensor Decomposition

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 11124-11137 ◽  
Author(s):  
Chuanfei Gong ◽  
Yaying Zhang
Keyword(s):  
Tensor Decomposition ◽  
Urban Traffic ◽  
Data Imputation ◽  
Traffic Data

scholarly journals Nonparametric Bayesian inference for mean residual life functions in survival analysis

Biostatistics ◽  
2018 ◽  
Vol 20 (2) ◽  
pp. 240-255 ◽  
Author(s):  
Valerie Poynor ◽  
Athanasios Kottas
Keyword(s):  
Survival Analysis ◽  
Mixture Model ◽  
Dirichlet Process ◽  
Residual Life ◽  
Right Censoring ◽  
Mean Residual Life ◽  
Dirichlet Process Mixture ◽  
Data Set ◽  
Survival Distribution ◽  
Wide Range

SUMMARY Modeling and inference for survival analysis problems typically revolves around different functions related to the survival distribution. Here, we focus on the mean residual life (MRL) function, which provides the expected remaining lifetime given that a subject has survived (i.e. is event-free) up to a particular time. This function is of direct interest in reliability, medical, and actuarial fields. In addition to its practical interpretation, the MRL function characterizes the survival distribution. We develop general Bayesian nonparametric inference for MRL functions built from a Dirichlet process mixture model for the associated survival distribution. The resulting model for the MRL function admits a representation as a mixture of the kernel MRL functions with time-dependent mixture weights. This model structure allows for a wide range of shapes for the MRL function. Particular emphasis is placed on the selection of the mixture kernel, taken to be a gamma distribution, to obtain desirable properties for the MRL function arising from the mixture model. The inference method is illustrated with a data set of two experimental groups and a data set involving right censoring. The supplementary material available at Biostatistics online provides further results on empirical performance of the model, using simulated data examples.

scholarly journals Identifying damage on a bridge using rotation-based Bridge Weigh-In-Motion

2020 ◽  
Author(s):  
E. J. OBrien ◽  
J. M. W. Brownjohn ◽  
D. Hester ◽  
F. Huseynov ◽  
M. Casero
Keyword(s):  
Damage Detection ◽  
Traffic Management ◽  
Traffic Data ◽  
Simply Supported ◽  
Weigh In Motion ◽  
Bridge Model ◽  
Wide Range ◽  
Bridge Condition Assessment ◽  
Bridge Damage ◽  
Bridge Weigh In Motion

Abstract Bridge Weigh-in-Motion (B-WIM) systems use the bridge response under a traversing vehicle to estimate its axle weights. The information obtained from B-WIM systems has been used for a wide range of applications such as pre-selection for weight enforcement, traffic management/planning and for bridge and pavement design. However, it is less often used for bridge condition assessment purposes which is the main focus of this study. This paper presents a bridge damage detection concept using information provided by B-WIM systems. However, conventional B-WIM systems use strain measurements which are not sensitive to local damage. In this paper the authors present a B-WIM formulation that uses rotation measurements obtained at the bridge supports. There is a linear relationship between support rotation and axle weight and, unlike strain, rotation is sensitive to damage anywhere in the bridge. Initially, the sensitivity of rotation to damage is investigated using a hypothetical simply supported bridge model. Having seen that rotation is damage-sensitive, the influence of bridge damage on weight predictions is analysed. It is shown that if damage occurs, a rotation-based B-WIM system will continuously overestimate the weight of traversing vehicles. Finally, the statistical repeatability of ambient traffic is studied using real traffic data obtained from a Weigh-in-Motion site in the U.S. under the Federal Highway Administration’s Long-Term Pavement Performance programme and a damage indicator is proposed as the change in the mean weights of ambient traffic data. To test the robustness of the proposed damage detection methodology numerical analysis are carried out on a simply supported bridge model and results are presented within the scope of this study.

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