Railway Traffic Accident Forecast Based on an Optimized Deep Auto-encoder

Feng, Fenling; Wan, Li; Jiang, Qiwei

doi:10.7307/ptt.v30i4.2568

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Prediction of congestion was analysed in 11 different studies. Meanwhile, traffic accidents were predicted in 12 studies, out of which only one study focused on railway accidents (Feng et al 2018). Meanwhile, others analysed road traffic accidents.…”

Section: Areas Of Applicationmentioning

confidence: 99%

Deep Learning in Transport Studies: A Meta-analysis on the Prediction Accuracy

Varghese

Chikaraishi

Urata

2020

J. Big Data Anal. Transp.

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Deep learning methods are being increasingly applied in transport studies, while the methods require modellers to go through a try-and-error model tuning process particularly on choosing neural network structure. Moreover, the accuracy level also depends on other factors such as the type of data, sample size, region of data collection, and time of prediction. To efficiently facilitate such a model tuning process, this study attempts to summarize the relationship between the prediction accuracy of deep learning models and the factors which influence it. We conducted a comprehensive review of the literature by adopting a detailed search strategy, followed by a meta-analysis on prediction accuracy. Four separate linear mixed effects models, taking into account unobserved heterogeneities in prediction accuracy across studies, were developed to statistically test the impacts of influential factors on prediction accuracy for (a) all observations (136 studies; 2314 cases), (b) studies with MAPE, MRE, and average accuracy indicators (86 studies; 1,878 cases), (c) classification-based studies with accuracy indicator (29 studies; 220 cases), and (d) traffic forecasting studies with MAPE, MRE, and average accuracy indicators (36 studies, 991 cases). The final model includes additional factors to test the influence of sample size and time horizon of prediction variables. The findings showed that, as expected, deep learning models, particularly ones that consider spatiotemporal dependencies of transport phenomena, show better prediction accuracies compared to conventional machine learning models. We also found that, on average, the prediction accuracy is improved by 5.90% with 100 million additional data, while the accuracy is reduced by 5.28% with 100 min increase in time horizon of prediction in traffic forecasting studies. We concluded this paper with a comprehensive summary of the existing findings on the applications of deep learning to transport studies.

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Section: Areas Of Applicationmentioning

confidence: 99%

Deep Learning in Transport Studies: A Meta-analysis on the Prediction Accuracy

Varghese

Chikaraishi

Urata

2020

J. Big Data Anal. Transp.

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“…Table 3). Railway traffic accident prediction is the main issue of safety management (Feng et al, 2018). Many methods have been used for railway accident predictions.…”

Section: Data and Research Methodologymentioning

confidence: 99%

An Estimate of the Number of Accidents and Serious Accidents in Railway Traffic

Pupavac,

Marinac,

Knežević

2023

Period. Polytech. Transp. Eng.

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The aim of this paper is to investigate the relationship between the number of extraordinary events and the total number of accidents and serious accidents in railway traffic. The purpose of the paper is to estimate the total number of accidents and serious accidents on the track network of Croatian Railway Infrastructure (HŽI) until 2027. The results of the research are based on secondary data that are the subject of processing using statistical methods of correlation and regression analysis. The main finding of this paper points to the existence of a strong and positive relationship between the number of extraordinary events and the total number of accidents and serious accidents in railway traffic. The estimated number of extraordinary events and total number of accidents and serious accidents until 2027 indicates a trend of their further reduction.

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“…To this end, many methods have been proposed to improve this situation. In recent years, the usage of deep learning models [42][43][44], deep belief network [45,46] and deep autoencoder [47][48][49] have changed these weaknesses to a certain extent. These models were mainly used for image recognition, classification task and so on.…”

Section: Research On the Traffic Demand Forecast Methodsmentioning

confidence: 99%

“…Through Spearman rank correlation analysis, the relational degrees between search terms and railway passenger demand could be obtained. The correlation degree range is [−1, 1], the closer to 1 the relational degree is, the higher the positive similarity degree of two-time series is; the closer to 0 the relational degree is, the lower the similarity degree is; the closer to −1 the relational degree is, the higher the dissimilarity degree of two-time series is [49]. The stronger correlations between search terms and Beijing railway passenger demand, the better the predictive performance.…”

Section: Data Analysis Of Web Search Termsmentioning

confidence: 99%

Short‐term railway passenger demand forecast using improved Wasserstein generative adversarial nets and web search terms

Feng

Zhang

Liu

et al. 2021

IET Intelligent Trans Sys

Self Cite

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Accurately predicting railway passenger demand is conducive for managers to quickly adjust strategies. It is time-consuming and expensive to collect large-scale traffic data. With the digitization of railway tickets, a large amount of user data has been accumulated. We propose a method to predict railway passenger demand using web search terms data. In order to improve the prediction accuracy, we improved Wasserstein Generative Adversarial Nets (WGAN), which were good at generating and identifying data, by adding a predictor and supervised learning adversarial training to predict railway passenger demand. The improved WGAN could generate virtual data to expand real data, and use parallel data to predict railway passenger demand. We used search times of web search terms on different devices as training data to predict railway passenger demand in Beijing. The results show that the change in demand for railway passenger lags behind the change in the data of web search terms by one month. It is suitable for forecasting in advance. Compared with other forecasting methods, the improved WGAN performance is better, and the mean absolute percentage error is 1.98%. Because it can use mixed data for training and prediction, it has stronger adaptability when data scale decreases. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Railway Traffic Accident Forecast Based on an Optimized Deep Auto-encoder

Cited by 4 publications

References 20 publications

Deep Learning in Transport Studies: A Meta-analysis on the Prediction Accuracy

Deep Learning in Transport Studies: A Meta-analysis on the Prediction Accuracy

An Estimate of the Number of Accidents and Serious Accidents in Railway Traffic

Short‐term railway passenger demand forecast using improved Wasserstein generative adversarial nets and web search terms

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