Recent applications of big data analytics in railway transportation systems: A survey

Ghofrani, Faeze; He, Qing; Goverde, Rob M.P.; Liu, Xiang

doi:10.1016/j.trc.2018.03.010

Cited by 253 publications

(109 citation statements)

References 121 publications

Supporting

Mentioning

109

Contrasting

Order By: Relevance

“…For example, Hodge et al (2015) carried out a survey of wireless sensor network technology for surveillance railway systems, structures, vehicles, and machinery, and Ghofrani et al (2018) proposed a systematic framework for railway transportation big data analysis and how it could be applied [18,19]. A well-defined WSN installed in the London Underground tunnels has also been presented [20].…”

Section: Review Of Related Survey Articlesmentioning

confidence: 99%

“…Great amounts of data, such as timetable and train delays data, can be seen as the first applications of big data in railway operations [18]. Moreover, data mining was used to represent the records and retrieve train positions on the track, including the impact of timetable shortcomings and causes of secondary delayed trains and delays at stations online, which is another source of data used.…”

Section: Smart Railway Stationsmentioning

confidence: 99%

“…In recent times, data analytics has attracted a great deal of attention from researchers in railway engineering [18]. This is because data processing and integration are providing a full image of an organisation's condition [19,74].…”

Section: Machine Learning and The Railway Stationsmentioning

confidence: 99%

“…For a high-performance model, a deeper analysis of data comes from different devices, as long as more information is available for the design of the model [81]. This has resulted in an improvement of advanced technologies in the big data analysis (BDA), in both academia and industry [18,82].…”

Section: Machine Learning and The Railway Stationsmentioning

confidence: 99%

See 3 more Smart Citations

Wireless Sensor Networks: Toward Smarter Railway Stations

Alawad

Kaewunruen

2018

Infrastructures

View full text Add to dashboard Cite

Railway industry plays a critical role in transportation and transit systems attributed to the ever-growing demand for catering to both freight and passengers. However, owing to many challenges faced by railway stations such as harsh environments, traffic flow, safety and security risks, new and adaptive systems employing new technology are recommended. In this review, several wireless sensor networks (WSNs) applications are proposed for use in railway station systems, including advanced WSNs, which will enhance security, safety, and decision-making processes to achieve more cost-effective management in railway stations, as well as the development of integrated systems. The size, efficiency, and cost of WSNs are influential factors that attract the railway industry to adopt these devices. This paper presents a review of WSNs that have been designed for uses in monitoring and securing railway stations. This article will first briefly focus on the presence of different WSN applications in diverse applications. In addition, it is important to note that exploitation of the state-of-the-art tools and techniques such as WSNs to gain an enormous amount of data from a railway station is a new and novel concept requiring the development of artificial intelligence methods, such machine learning, which will be vital for the future of the railway industry.

show abstract

Section: Review Of Related Survey Articlesmentioning

confidence: 99%

Section: Smart Railway Stationsmentioning

confidence: 99%

Section: Machine Learning and The Railway Stationsmentioning

confidence: 99%

Section: Machine Learning and The Railway Stationsmentioning

confidence: 99%

See 2 more Smart Citations

Wireless Sensor Networks: Toward Smarter Railway Stations

Alawad

Kaewunruen

2018

Infrastructures

View full text Add to dashboard Cite

show abstract

“…Cheng, Jian, Maghrebi, Rashidi, & Waller, 2018;Ghofrani, He, Goverde, & Liu, 2018;Hashemi & Abdelghany, 2018;Maeda, Sekimoto, Seto, Kashiyama, & Omata, 2018;Medina & Benekohal, 2012;Nabian & Meidani, 2018). Cheng, Jian, Maghrebi, Rashidi, & Waller, 2018;Ghofrani, He, Goverde, & Liu, 2018;Hashemi & Abdelghany, 2018;Maeda, Sekimoto, Seto, Kashiyama, & Omata, 2018;Medina & Benekohal, 2012;Nabian & Meidani, 2018).…”

mentioning

confidence: 99%

An ensemble machine learning‐based modeling framework for analysis of traffic crash frequency

Zhang

Waller

Jiang

2019

Computer aided Civil Eng

View full text Add to dashboard Cite

This study is, to our knowledge, the first in the literature to introduce a modeling framework for analyzing traffic crash frequency based on a series of ensemble machine learning (EML) methods. The main objectives of this study are fourfold: (a) to design a systematic EML‐based framework for crash frequency analysis, (b) to comprehensively compare the performance in analyzing crash frequency by different optimized EML models, (c) to identify significant contributors to crash frequency, and (d) to propose the approach to construct schemes to reduce traffic crashes. To achieve the research goal, the Highway Safety Information System database that includes records of over 1.5 million crashes is employed for model estimation and validation. We first optimize the EML models for crash analysis via the k‐fold cross‐training, including the two averaging methods of random forest and extremely randomized trees, and the two boosting methods of adaptive boosting and gradient tree boosting. Then, we assess the behavior of the optimized models, and conduct a sensitivity test to validate the stability of model performance. Furthermore, we evaluate the relative importance of features to crash frequency by using the Gini diversity index. The results indicate that the two averaging EML models can achieve desirable performance in crash frequency analysis, which outperform the two boosting EML models, in terms of predictive accuracy, generalization ability, and stability. From the results, we explore new insights into the significance of contributors to crash occurrence. Finally, we present the approach of safety improvements for transport facilities.

show abstract

Distributed Data Management

Chbeir¹

2023

Distributed Systems

View full text Add to dashboard Cite

Recent applications of big data analytics in railway transportation systems: A survey

Cited by 253 publications

References 121 publications

Wireless Sensor Networks: Toward Smarter Railway Stations

Wireless Sensor Networks: Toward Smarter Railway Stations

An ensemble machine learning‐based modeling framework for analysis of traffic crash frequency

Distributed Data Management

Contact Info

Product

Resources

About