Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways

Shi, Qixin; Abdel-Aty, Mohamed

doi:10.1016/j.trc.2015.02.022

Cited by 341 publications

(165 citation statements)

References 28 publications

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“…Product R&D Bae and Kim, 2011;Do, 2014;Lei and Moon, 2015;Opresnik and Taisch, 2015;Zhang et al, 2017 Production planning & control Chien et al, 2014;Krumeich et al, 2016;Zhang et al, 2017;Shu et al, 2016;Dai et al, 2012;Zhong et al, 2016;Helo and Hao, 2017 Quality management Krumeich et al, 2016;Zhang et al, 2017; Maintenance & diagnosis Shu et al, 2016;Guo et al, 2016;Kumar et al, 2016;Zhang et al, 2017;Wang et al, 2015 Warehousing Storage assignment Chuang et al, 2014;Li, Moghaddam, et al, 2016;Tsai and Huang, 2015;Chiang et al, 2011;Chiang et al, 2014 Order picking Ballestín et al, 2013;Chuang et al, 2014; Inventory control Alyahya et al, 2016;Hofmann, 2015;Huang and Van Mieghem, 2014;Lee et al, 2016;Stefanovic, 2015 Logistics and transportation Cui et al, 2016;Li et al, 2015;Shi and Abdel-Aty, 2015;StAubin et al, 2015;Toole et al, 2015;Xia et al, 2016;Yu and Abdel-Aty, 2014;Zangenehpour et al, 2015;…”

Section: Manufacturingmentioning

confidence: 99%

Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen

Zhou

Spiegler

et al. 2018

Computers & Operations Research

400

238

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Abstract:The rapidly growing interest from both academics and practitioners in the application of big data analytics (BDA) in supply chain management (SCM) has urged the need for review of up-to-date research development in order to develop a new agenda. This review responds to the call by proposing a novel classification framework that provides a full picture of current literature on where and how BDA has been applied within the SCM context. The classification framework is structurally based on the content analysis method of Mayring (2008) techniques are employed to develop these models? The discussion tackling these four questions reveals a number of research gaps, which leads to future research directions.

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Section: Manufacturingmentioning

confidence: 99%

Big data analytics in supply chain management: A state-of-the-art literature review

Nguyen

Zhou

Spiegler

et al. 2018

Computers & Operations Research

400

238

View full text Add to dashboard Cite

show abstract

“…Shi and Abdel-Aty [8] applied the random forest data mining technique and Bayesian inference to process large volumes of data from a microwave vehicle detection system, with the main goal of identifying the contributing factors to crashes in realtime. Rear-end crashes were studied because they have a straightforward relation with congestion.…”

Section: Distributed Computing For Processing Traffic Datamentioning

confidence: 99%

“…BusInfo includes values for remarkable locations, timerange, trip number, and timestamp, needed to apply a secondary sorting (see Sorting). 8 …”

mentioning

confidence: 99%

A distributed platform for big data analysis in smart cities: combining Intelligent Transportation Systems and socioeconomic data for Montevideo, Uruguay

Nesmachnow¹,

Baña²,

Массобрио³

2017

EAI Endorsed Transactions on Smart Cities

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This article proposes a platform for distributed big data analysis in the context of smart cities. Extracting useful mobility information from large volumes of data is crucial to improve decision-making processes in smart cities. This article introduces a framework for mobility analysis in smart cities combining Intelligent Transportation Systems and socioeconomic data for the city of Montevideo, Uruguay. The efficiency of the proposed system is analyzed over a distributed computing infrastructure, demonstrating that the system scales properly for processing large volumes of data for both off-line and on-line scenarios. Applications of the proposed platform and case studies using real data are presented, as examples of the valuable information that can be offered to both citizens and authorities. The proposed model for big data processing can also be extended to allow using other distributed (e.g. grid, cloud, fog, edge) computing infrastructures.

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“…Some research has been done lately in this field and various research projects have studied sensors, technologies and connections. Shi and Abdel-Aty claim that data mining methods have high accuracy in predicting traffic events, such as car crashes [17]. The processes that refine this huge amount of data must be fast, efficient and reliable, in addition to the above mentioned data security issues.…”

Section: Related Research On Data Gathering Sharing and Presentationmentioning

confidence: 99%

Enhancing Traffic Safety with Mobile Applications

Kaartinen

Jamsa

2016

Acta Tech Jaur

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Intelligent Transportation Systems (ITS) have great potential and market on modern traffic environment. Technologies of the day enable the real-time data transfer and presentation for the actors in traffic and outside of it. Intercognitive communication is a form of communication where an information system gathers data and processes it to a form of which users can benefit on their decision making. In this paper we will present how deploying new cognitive elements on mobile applications can increase traffic safety. The most important point of view in sharing the traffic data is how to present it for the driver and how to make the data transfer reliable and safe. New vehicles have built-in solutions, such as comprehensive infotainment systems, to present the information and warnings, but older vehicles do not have this option. Therefore the modern devices, such as smartphones and tablet computers can be utilized for these purposes. This paper describes Centria's research work on developing mobile applications for improving the traffic flow and safety by real-time support for the driver's decision making. Also, the data security has been studied and tested at Centria, and will be reported in this paper.

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Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways

Cited by 341 publications

References 28 publications

Big data analytics in supply chain management: A state-of-the-art literature review

Big data analytics in supply chain management: A state-of-the-art literature review

A distributed platform for big data analysis in smart cities: combining Intelligent Transportation Systems and socioeconomic data for Montevideo, Uruguay

Enhancing Traffic Safety with Mobile Applications

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