Scanning the Issue

Azim Eskandarian
2020 IEEE transactions on intelligent transportation systems (Print)  
In tunnel lining surface images, cracks and linear seams have great similarities in both intensity value and texture features, and it is difficult for existing crack extraction methods to obtain accurate crack segmentation results. In this article, the authors proposed a novel algorithm to adaptively eliminate linear seams in tunnel lining crack images. By analyzing characteristics of linear seams and cracks, the idea of binning is used to classify those detected line edges with disordered
more » ... tions into multiple angle subintervals. Then, by calculating the ratio of statistical specific pixels on the expanded line edge, the length and binning information are used to select linear seam edges with the use of adaptive expansion algorithm. Finally, fragmented segmentations of linear seams are adopted so that cracks and linear seams can be clearly separated, and linear seams can be removed. Research on Analysis Method of Characteristics Generation of Urban Rail Transit Z. Cai, T. Li, X. Su, L. Guo, and Z. Ding In this article, the authors provide a novel method for urban rail station characteristics analysis in intelligent transportation considering city land usages. Initially, points of interest (POIs) are divided by the proposed colored R-tree (RC-tree)-based algorithm into the bounded areas for each station. Second, the diversity and proportion approaches are proposed to extract the top-k POIs from bounded areas based on their semantic and spatial characteristics. Then, classify the stations based on the similarity of the extracted top-k POIs. Moreover, they made a case study on real data set, including a large volume of AFC records for experimental evaluations, and the results show that the proposed method can verify the rationality of land use and provide support for the application of transportation model technology. In metro systems, a tactical train schedule with the rolling stock circulation plan aims to determine the movements of all physical trains. To utilize the regenerative energy as much as possible, this article proposes an integrated model to simultaneously generate the optimal train schedule and rolling stock circulation plan, in which the brake-traction overlapping time Digital Object Identifier 10.1109/TITS.2020.3015408 at stations is maximized. In particular, the proposed model rigorously considers the train turn-around constraints, train circulation constraints, and dynamic passenger demands to tackle the train loading capacity constraints. To eliminate the effect of nonlinear constraints, the original model is reformulated into its equivalent linear model that can be efficiently solved by linear programming solvers. Finally, numerical experiments based on the Beijing Yizhuang Metro Line are implemented to demonstrate the effectiveness of the proposed model. This article proposes a constrained mixture sequential Monte Carlo (CMSMC) method in which a mixture representation is incorporated in the estimated posterior distribution to maintain multimodality. Multiple targets can be tracked simultaneously within a unified framework without explicit data association between observations and tracking targets. The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of CMSMC method. An example in this article is a learning-based model for hierarchical timeseries prediction, which consists of a behavior recognition module and a state evolution module. Both modules in the proposed model are generic and flexible so as to be applied to a class of time-series prediction problems where behaviors can be separated into different levels. Finally, the proposed framework is applied to a numerical case study as well as a task of on-road vehicle tracking, behavior recognition, and prediction in highway scenarios. Using Approximate Dynamic Programming to Maximize Regenerative Energy Utilization for Metro J. Xun, T. Liu, B. Ning, and Y. Liu A new model is formulated to maximize the utilization of regenerative energy for a couple of trains. Also, an algorithm for maximizing the utilization of regenerative energy (MURE) is proposed by using the approximate dynamic programming (ADP) approach to adjust the speed curve of the accelerating train, where three approximation methods, rollout method, interpolation method, and neural network, are discussed. The rollout algorithm could improve the basic policy for train control with careful designing. The function approximation method using interpolation could further decrease the energy consumption with assuring punctuality. Neural network approximation usually cannot realize the effect superior to the interpolation strategy due to its complex structure, and it needs more computation time. The analysis of regenerative energy utilization is given by implementing numerical experiments
doi:10.1109/tits.2020.3015408 fatcat:4yuvdgqw3fd4zp5kj7sjvkxhea