A Spatiotemporal Apriori Approach to Capture Dynamic Associations of Regional Traffic Congestion

Xie, Da; Wang, Mei-Hong; Zhao, Xiaomei

doi:10.1109/access.2019.2962619

Cited by 15 publications

(8 citation statements)

References 47 publications

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“…The spatiotemporal association rule, as a significant algorithm in spatiotemporal data mining, represents an advancement of the association rule algorithm, specifically designed to extract association relationships within spatiotemporal data. This algorithm has been extensively applied in various domains, including traffic congestion research [20], air pollution analysis [21], ecology studies [22], climate investigations [23], marine environment assessments [24], among others. In many studies, spatiotemporal attributes are employed to classify geographic elements within a specific spatiotemporal range, and the correlations between these elements are explored based on classification results.…”

Section: Geographic Elements and Their Spatiotemporal Correlation Bas...mentioning

confidence: 99%

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: Geographic Elements and Their Spatiotemporal Correlation Bas...mentioning

confidence: 99%

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

Wang

Cao

2023

IJGI

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“…Another line of research investigates the evolution of RC patterns. Current approaches typically analyse the propagation of RC within a spatial grid [3,4,22] or a road network graph [23][24][25].…”

Section: Congestion Analysismentioning

confidence: 99%

“…Algorithm 1 presents an incremental greedy approach to merge spatially overlapping affected subgraphs. The algorithm consist of a main loop (line 6-24) where the individual steps include candidate generation (line 9-11), similarity computation (line 12-14) and merging (line [15][16][17][18][19][20][21][22][23][24]. For the candidate generation, we consider all subgraph pairs that share at least one unit as candidates (line 13).…”

Section: Spatial Merging Of Affected Subgraphsmentioning

confidence: 99%

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Mining Topological Dependencies of Recurrent Congestion in Road Networks

Tempelmeier

Feuerhake

Wage

et al. 2021

IJGI

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The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.

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“…These rules have been successfully applied in many real‐world applications. For instance, among other examples, they have been used to represent the dynamic characteristics of regional traffic congestion (Xie, Wang, & Zhao, 2020), and to generate rules from transaction databases in complex dynamical systems of financial markets making use of closed intertransaction itemsets (Hsieh, Yang, Wu, & Chen, 2016).…”

Section: Considering Time As An Implied Component In the Mining Processmentioning

confidence: 99%

Temporal association rule mining: An overview considering the time variable as an integral or implied component

Segura‐Delgado

Gacto

Alcalá

et al. 2020

WIREs Data Min & Knowl

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Association rules are commonly used to provide decision‐makers with knowledge that helps them to make good decisions. Most of the published proposals mine association rules without paying particular attention to temporal information. However, in real‐life applications data usually change over time or presenting different temporal situations. Therefore, the extracted knowledge may not be useful, since we may not know whether the rules are currently applicable or whether they will be applicable in the future. For this reason, in recent years, many methods have been proposed in the literature for mining temporal association rules, which introduce a greater predictive and descriptive power providing an additional degree of interestingness. One of the main problems in this research field is the lack of visibility most works suffer since there is no standard terminology to refer to it, making it difficult to find and compare proposals and studies in the field. This contribution attempts to offer a well‐defined framework that allows researchers both to easily locate the previous proposals and to propose well‐grounded methods in the future. To accomplish both objectives, a two‐level taxonomy is proposed according to whether the time variable is considered to provide order to the data collection and to locate some temporal constraints, or whether it is considered as an attribute within the learning process. Some recent applications, available software tools, and a bibliographical analysis in accordance with the Web of Science are also shown. Finally, some critical considerations and potential further directions are discussed. This article is categorized under: Technologies > Association Rules Algorithmic Development > Association Rules

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A Spatiotemporal Apriori Approach to Capture Dynamic Associations of Regional Traffic Congestion

Cited by 15 publications

References 47 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

Mining Topological Dependencies of Recurrent Congestion in Road Networks

Temporal association rule mining: An overview considering the time variable as an integral or implied component

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