A distributed spatial–temporal weighted model on MapReduce for short-term traffic flow forecasting

Xia, Dawen; Wang, Binfeng; Li, Huaqing; Li, Yantao; Zhang, Zili

doi:10.1016/j.neucom.2015.12.013

Cited by 162 publications

(78 citation statements)

References 51 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

403

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

403

238

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“…Since MR-PFP can achieve exactly the same accuracy as PFP, we focus on evaluating their efficiency and scalability using three evaluation metrics (i.e., speedup, sizeup, and scaleup [12,13,[36][37][38]) in this experiment. …”

Section: Evaluation On Mr-pfpmentioning

confidence: 99%

“…In particular, taxi trajectory data is becoming one of the most significant data sources of mobile trajectory data. For example, the big taxi trajectory data used in this work is composed of 47,991 small files (each file with a size of 1.1 MB) [12] and contains a large number of GPS points which can generate the road network of Beijing as illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

A MapReduce‐Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data

Xia

et al. 2018

Complexity

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Frequent pattern mining is an effective approach for spatiotemporal association analysis of mobile trajectory big data in datadriven intelligent transportation systems. While existing parallel algorithms have been successfully applied to frequent pattern mining of large-scale trajectory data, two major challenges are how to overcome the inherent defects of Hadoop to cope with taxi trajectory big data including massive small files and how to discover the implicitly spatiotemporal frequent patterns with MapReduce. To conquer these challenges, this paper presents a MapReduce-based Parallel Frequent Pattern growth (MR-PFP) algorithm to analyze the spatiotemporal characteristics of taxi operating using large-scale taxi trajectories with massive small file processing strategies on a Hadoop platform. More specifically, we first implement three methods, that is, Hadoop Archives (HAR), CombineFileInputFormat (CFIF), and Sequence Files (SF), to overcome the existing defects of Hadoop and then propose two strategies based on their performance evaluations. Next, we incorporate SF into Frequent Pattern growth (FP-growth) algorithm and then implement the optimized FP-growth algorithm on a MapReduce framework. Finally, we analyze the characteristics of taxi operating in both spatial and temporal dimensions by MR-PFP in parallel. The results demonstrate that MR-PFP is superior to existing Parallel FP-growth (PFP) algorithm in efficiency and scalability.

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“…Xia et al [11] studied the real-time short-term traffic flow forecasting problem. To solve the problem, the authors proposed using the k nearest neighbor algorithm in a distributed environment, following the Map-Reduce model implemented over the Hadoop framework.…”

Section: Distributed Computing For Processing Traffic Datamentioning

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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A distributed spatial–temporal weighted model on MapReduce for short-term traffic flow forecasting

Cited by 162 publications

References 51 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 MapReduce‐Based Parallel Frequent Pattern Growth Algorithm for Spatiotemporal Association Analysis of Mobile Trajectory Big Data

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

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