A clustering based traffic flow prediction method with dynamic spatiotemporal correlation analysis

Ryu, Unsok; Pak, Unjin; Kwak, Sonil; Ri, Kwangchol; Jang, Junhyok; Sok, Kyongjin

doi:10.1007/s11116-021-10200-9

Cited by 9 publications

(3 citation statements)

References 58 publications

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“…There is no intersection between t 1 and t 2 in the two cases of (a,d) in Figure 6, and the length of b(t) is short, thus there is no correlation, i.e., P(t 1 ,t 2 ) = 0. There is an intersection between t 2 and t 1 in the benchmark time slice in (b,e), which can be calculated by using Equation (6). The case of (c,f) t 1 and t 2 exists in the same benchmark time, and it can be considered that P(t 1 ,t 2 ) = 1 at this time.…”

Section: • Adjacent and Disjointmentioning

confidence: 99%

“…For instance, Celik et al [5] introduced the fast mixed-drove co-occurrence pattern (FastMDCOP)-Miner algorithm which prunes temporal infrequent patterns during the mining of spatiotemporal co-occurrence patterns to improve efficiency. Ryu et al [6] employed a spatiotemporal correlation matrix to express short-term dependencies between adjacent road sections, resulting in improved accuracy for traffic flow prediction. Baer et al [7] developed a spatiotemporal hybrid Bayesian hierarchical model (STM BHM) that decomposes global random effects in space-time and classifies spatial regions to describe disease risk characteristics.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach for Mining Spatiotemporal Explicit and Implicit Information in Multiscale Spatiotemporal Data

Wang

Cao

2023

IJGI

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In the era of big data, a significant volume of spatiotemporal data exists in a multiscale format, describing diverse phenomena in the objective world across different spatial and temporal scales. While existing methods focus on analyzing the features and connections of spatiotemporal data at various scales, they often overlook the consideration of uncertainty in spatiotemporal information within the context of multiscale meaning. To effectively harness the potential of spatiotemporal data, it becomes crucial to capture the fuzzy spatiotemporal information inherent in multiscale datasets. This paper proposes a novel multiscale spatiotemporal correlation method that accounts for and quantifies the uncertainty of spatiotemporal information. Spatiotemporal information is categorized into two types, explicit information and implicit information, based on respective levels of uncertainty. The method employs spatiotemporal cubes to interpret the spatiotemporal items within the data, followed by the introduction of a benchmark scale to determine the certainty of each spatiotemporal item based on its range and topological relationships. Subsequently, spatiotemporal confidence and correlation index are proposed to gauge the significance of geographical elements and their interrelationships. To validate the proposed method, a multiscale spatiotemporal transaction dataset is generated and utilized in the experiment. The experimental results demonstrate that the proposed method effectively captures spatiotemporal implicit information and enables better utilization of multiscale spatiotemporal data. Notably, the importance of each object of study varies when analyzed using different benchmark scales, providing valuable insights for professionals to identify novel objects and associations worthy of consideration. The obtained results can be used to construct spatiotemporal knowledge graphs.

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Section: • Adjacent and Disjointmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Mining Spatiotemporal Explicit and Implicit Information in Multiscale Spatiotemporal Data

Wang

Cao

2023

IJGI

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show abstract

“…The study by Zhang et al [24] proposed a two-step K-Means clustering model to divide subway stations into different categories for passenger flow forecasting. Ryu et al [25] designed a clustering-based traffic flow prediction method by introducing spatial-temporal correlation matrices. These works took account of spatial heterogeneity but not temporal heterogeneity by clustering traffic nodes.…”

Section: Introductionmentioning

confidence: 99%

STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction

Yu,

Bao,

Shi

2023

Heliyon

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An adaptive model of optimal traffic flow prediction using adaptive wildfire optimization and spatial pattern super learning

Jain,

Dhingra,

Joshi

et al. 2024

Wireless Netw

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A clustering based traffic flow prediction method with dynamic spatiotemporal correlation analysis

Cited by 9 publications

References 58 publications

A Novel Approach for Mining Spatiotemporal Explicit and Implicit Information in Multiscale Spatiotemporal Data

A Novel Approach for Mining Spatiotemporal Explicit and Implicit Information in Multiscale Spatiotemporal Data

STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network for traffic flow prediction

An adaptive model of optimal traffic flow prediction using adaptive wildfire optimization and spatial pattern super learning

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