다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

In this Chapter, we imagine a decision maker (or a group of experts) trying to establish or examine fair procedures to combine opinions about alternatives related to different points of view.

Multiple criteria decision making

Since the early 1980s, short-term traffic forecasting has been an integral part of most Intelligent Transportation Systems (ITS) research and applications; most effort has gone into developing methodologies that can be used to model traffic characteristics and produce anticipated traffic conditions. Existing literature is voluminous, and has largely used single point data from motorways and has employed univariate mathematical models to predict traffic volumes or travel times. Recent developments in technology and the widespread use of powerful computers and mathematical models allow researchers an unprecedented opportunity to expand horizons and direct work in 10 challenging, yet relatively under researched, directions. It is these existing challenges that we review in this paper and offer suggestions for future work.

Short-term traffic forecasting: Where we are and where we’re going

The matrix completion problem is to recover a low-rank matrix from a subset of its entries. The main solution strategy for this problem has been based on nuclear-norm minimization which requires computing singular value decompositions—a task that is increasingly costly as matrix sizes and ranks increase. To improve the capacity of solving large-scale problems, we propose a low-rank factorization model and construct a nonlinear successive over-relaxation (SOR) algorithm that only requires solving a linear least squares problem per iteration. Extensive numerical experiments show that the algorithm can reliably solve a wide range of problems at a speed at least several times faster than many nuclear-norm minimization algorithms. In addition, convergence of this nonlinear SOR algorithm to a stationary point is analyzed.

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Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm

Missing and suspicious traffic data are inevitable due to detector and communication malfunctions, which adversely affect the transportation management system (TMS). In this paper, a tensor pattern which is an extension of matrix is introduced into modeling the traffic data for the first time, which can give full play to traffic spatial–temporal information and preserve the multi-way nature of traffic data. To estimate the missing value, a tensor decomposition based Imputation method has been developed. This approach not only inherits the advantages of imputation methods based on matrix pattern for estimating missing points, but also well mines the multi-dimensional inherent correlation of traffic data. Experiments demonstrate that the proposed method achieves a better imputation performance than the state-of-the-art imputation approach even when the missing ratio is up to 90%. Furthermore, the experimental results show that the proposed method can address the extreme case where the data of one or several days are completely missing, and additionally it can be employed to recover the missing traffic data in adverse weather as well.

A Tensor Based Method for Missing Traffic Data Completion

In order to understand driver’s safety-related approaching behaviour during car-following process in more depth, it is necessary to achieve the comprehensive analysis of vehicle-to-vehicle dynamic interactions. Based on qualitative description of driving shaping behaviour associated with driving human factors of influencing driver’s car-following behaviour, this paper presents briefly the fundamentals of simulation modelling of driver’s safety approaching behaviour in urban traffic operation. The emphasis on our research is placed on the development of a driver’s safety approaching behavioural model by considering the variability of following vehicle’s speed to the leading vehicle’s one and the relative distances among vehicles. Furthermore, we have carried out simulation and analysis of driver’s deceleration and acceleration behaviour under different driving situations after identified the key safety-related parameters. Finally the developed model has been validated by using detailed vehicle trajectory data that was collected in naturalistic driving environment. The results show that the safety-based approaching behavioural model could be used to analyze driver’s car-following behaviour for driving support and to reveal the essence of traffic flow characteristics at the microscopic level.

Wuhong Wang

Papers

A Tensor Based Method for Missing Traffic Data Completion

A safety-based approaching behavioural model with various driving characteristics

Reliability Analysis of Pedestrian Safety Crossing in Urban Traffic Environment

Triboelectric nanogenerator as a highly sensitive self-powered sensor for driver behavior monitoring

Tensor completion via a multi-linear low-n-rank factorization model