Identifying Modes of Driving Railway Trains from GPS Trajectory Data: An Ensemble Classifier-Based Approach

Zheng, Han; Cui, Zanyang; Zhang, Xingchen

doi:10.3390/ijgi7080308

Cited by 8 publications

(20 citation statements)

References 49 publications

(107 reference statements)

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“…Five issue papers analyzed human mobility related issues from various human-centric perspectives [60][61][62][63][64]: railway and public transport, emergencies, nursing equity, and crowd flows. Zheng et al [60] developed machine learning techniques to efficiently recognize modes of driving railway trains. Maeda et al [64] created an index based on human mobility data, making it possible to predict the influence of urban development on future residential movements.…”

Section: The Contributions Of This Special Issuementioning

confidence: 99%

Human-Centric Data Science for Urban Studies

Resch

Szell

2019

IJGI

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Due to the wide-spread use of disruptive digital technologies like mobile phones, cities have transitioned from data-scarce to data-rich environments. As a result, the field of geoinformatics is being reshaped and challenged to develop adequate data-driven methods. At the same time, the term "smart city" is increasingly being applied in urban planning, reflecting the aims of different stakeholders to create value out of the new data sets. However, many smart city research initiatives are promoting techno-positivistic approaches which do not account enough for the citizens' needs. In this paper, we review the state of quantitative urban studies under this new perspective, and critically discuss the development of smart city programs. We conclude with a call for a new anti-disciplinary, human-centric urban data science, and a well-reflected use of technology and data collection in smart city planning. Finally, we introduce the papers of this special issue which focus on providing a more human-centric view on data-driven urban studies, spanning topics from cycling and wellbeing, to mobility and land use.

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Section: The Contributions Of This Special Issuementioning

confidence: 99%

Human-Centric Data Science for Urban Studies

Resch

Szell

2019

IJGI

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“…Recently, new ensemble learning algorithms, such as canonical correlation forest (CCF) (2015), extreme gradient boosting (XgBoost) (2016), and Light Gradient Boosting Machine (LightGBM) (2017), have been introduced to the machine learning community [23][24][25]. A very limited number of papers have been published regarding CCF [26,27] and XgBoost [28][29][30] for classification purposes in remote sensing; however, no study has been published yet using the recently launched LightGBM, which is a highly efficient gradient boosting decision tree that was developed by Microsoft Research in the field of remote sensing for classification purposes. Only in one paper by Liu, Ji, and Buchroithner [31] has LightGBM been tested, in this case for soil property retrieval by combining partial least squares.…”

Section: Introductionmentioning

confidence: 99%

Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

Üstüner

Şanlı

2019

IJGI

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In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification. Besides the polarimetric target decomposed parameters, the original polarimetric features (linear backscatter coefficients, coherency, and covariance matrices) were also incorporated for crop classification. The experimental results demonstrated that polarimetric target decompositions, with the exception of Cloude–Pottier, were found to be superior to the original features in terms of overall classification accuracy. The highest classification accuracy (92.07%) was achieved by Yamaguchi, whereas the lowest (75.99%) was achieved by the covariance matrix. Model-based decompositions achieved higher performance with respect to eigenvector-based decompositions in terms of class-based accuracies. Furthermore, the results emphasize the added benefits of model-based decompositions for crop classification using PolSAR data.

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“…Modes of driving railway trains (MDRTs) represent the potential patterns characterizing drivers' behaviors when driving railway trains. MDRTs are an important concept in the research field of transportation and play a vital role in the understanding of the stochastic nature of railway systems [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…For example, MDRTs existing in sections (the connecting parts of adjacent stations) make the running time multivariate, even under the same running plan. If operators have minimal understanding of MDRTs and use poor models (e.g., constant values) to represent the distributions of parameters (e.g., running time and additional operating time), the resulting plans would be inaccurate and unenforceable in the future [2,[10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Discovery of Railway Train Driving Modes Using Unsupervised Deep Learning

Zheng

Cui

Zhang

2019

IJGI

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Driving modes play vital roles in understanding the stochastic nature of a railway system and can support studies of automatic driving and capacity utilization optimization. Integrated trajectory data containing information such as GPS trajectories and gear changes can be good proxies in the study of driving modes. However, in the absence of labeled data, discovering driving modes is challenging. In this paper, instead of classical models (railway-specified feature extraction and classical clustering), we used five deep unsupervised learning models to overcome this difficulty. In these models, adversarial autoencoders and stacked autoencoders are used as feature extractors, along with generative adversarial network-based and Kullback–Leibler (KL) divergence-based networks as clustering models. An experiment based on real and artificial datasets showed the following: (i) The proposed deep learning models outperform the classical models by 27.64% on average. (ii) Integrated trajectory data can improve the accuracy of unsupervised learning by approximately 13.78%. (iii) The different performance rankings of models based on indices with labeled data and indices without labeled data demonstrate the insufficiency of people’s understanding of the existing modes. This study also analyzes the relationship between the discovered modes and railway carrying capacity.

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Identifying Modes of Driving Railway Trains from GPS Trajectory Data: An Ensemble Classifier-Based Approach

Cited by 8 publications

References 49 publications

Human-Centric Data Science for Urban Studies

Human-Centric Data Science for Urban Studies

Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

Automatic Discovery of Railway Train Driving Modes Using Unsupervised Deep Learning

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