Discovering spatio-temporal dependencies based on time-lag in intelligent transportation data

Zhou, Xiabing; Hong, Haikun; Xing, Xingxing; Bian, Kaigui; Xie, Kun; Xu, Mingliang

doi:10.1016/j.neucom.2016.06.084

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…US Department of Transportation and ITS America had put forward the "10-year Development Plan for Intelligent Transportation Systems in the United States," and in this Plan, it is pointed out that the ITS in the US shall be a system to be planned and constructed as a whole, not by every region independently [18]. At present, US ITS includes 6 modules: traffic management, epayment operation, public transport management and operation, emergency treatment, vehicle control and management, and safety management system [19]. Early this century, Japan has set up many monitors and radars on roads so that it can monitor road conditions and collect information at any time; meanwhile, it has also actively promoted ETC (electronic toll collection) on highways, which improved the utilization of toll roads.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Research of Schema Evolution and Implementation Scheme Optimization in AI‐Enabled Embedded Systems

Xiao-juan

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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The demand of embedded artificial intelligence system for powerful computing power and diversified application scenarios will inevitably bring some new problems. This paper builds the system dynamics model of embedded system based on artificial intelligence (AI). By analyzing the causal relationship between the elements of the system dynamics model, the state equation is established, and the parameters are estimated and tested. At the same time, the influence of the model simulation experiment on the relevant factors is evaluated. The simulation results show that the proposed model is effective and efficient.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Research of Schema Evolution and Implementation Scheme Optimization in AI‐Enabled Embedded Systems

Xiao-juan

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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“…The l1-norm in the Lagrangian form is included in the objective function and ensures meaningful feature selection. Kamarianakis et al [51] applied LASSO to vector autoregressive models; Piatkowski et al [52] utilised LASSO and elastic net techniques to construct a graphical (random field) model; Li et al [53] and Zhou et al [54] executed preliminary feature selection and constructed LASSO-regularised autoregressive distributed lag models. Haworth and Cheng [55] analysed alternative regularisation techniques, maximum concave penalty (MCP) and smoothly clipped absolute deviation (SCAD), and found MCP beneficial with respect to the estimated STN sparsity.…”

Section: Class 4: Embedded Feature Selection Methodsmentioning

confidence: 99%

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Pavlyuk

2019

Eur. Transp. Res. Rev.

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A spatiotemporal approach that simultaneously utilises both spatial and temporal relationships is gaining scientific interest in the field of traffic flow forecasting. Accurate identification of the spatiotemporal structure (dependencies amongst traffic flows in space and time) plays a critical role in modern traffic forecasting methodologies, and recent developments of data-driven feature selection and extraction methods allow the identification of complex relationships. This paper systematically reviews studies that apply feature selection and extraction methods for spatiotemporal traffic forecasting. The reviewed bibliographic database includes 211 publications and covers the period from early 1984 to March 2018. A synthesis of bibliographic sources clarifies the advantages and disadvantages of different feature selection and extraction methods for learning the spatiotemporal structure and discovers trends in their applications. We conclude that there is a clear need for development of comprehensive guidelines for selecting appropriate spatiotemporal feature selection and extraction methods for urban traffic forecasting.

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“…They form different types of networks such as in transportation (Zhou et al. 2017 ; Han et al. 2015 ), cellular (Krishnan and Dhillon 2017 ), wireless sensors (Alipio et al.…”

Section: General Stdm Challenges and Research Gapsmentioning

confidence: 99%

“…( 2014 ); Zhou et al. ( 2017 ), V-Hochman and Schwartz ( 2012 ); Tsou ( 2015 ) VA-Cao et al. ( 2015 ) PS-Bogomolov et al.…”

Section: Summary Of Stdm General Challengesmentioning

confidence: 99%

Spatiotemporal data mining: a survey on challenges and open problems

et al. 2021

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Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

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Discovering spatio-temporal dependencies based on time-lag in intelligent transportation data

Cited by 14 publications

References 18 publications

[Retracted] Research of Schema Evolution and Implementation Scheme Optimization in AI‐Enabled Embedded Systems

[Retracted] Research of Schema Evolution and Implementation Scheme Optimization in AI‐Enabled Embedded Systems

Feature selection and extraction in spatiotemporal traffic forecasting: a systematic literature review

Spatiotemporal data mining: a survey on challenges and open problems

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