Spatiotemporal periodical pattern mining in traffic data

Jindal, Tanvi; Giridhar, Prasanna; Tang, Lu-An; Li, Jun

doi:10.1145/2505821.2505837

Cited by 28 publications

(8 citation statements)

References 16 publications

(17 reference statements)

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“…Mining periodic patterns from spatio-temporal trajectories reveals information about people's regular and recurrent movements and behaviors. In [12], the authors proposed a method to mine spatio-temporal periodic patterns in the traffic data and use these periodic behaviors to summarize the huge road network by clustering. In our work, we do not interest in periodicity but in evolution of extracted patterns over time (between two studied time windows, are they emerging, jumping, lost, etc.?).…”

Section: Related Workmentioning

confidence: 99%

Sequential Patterns for Spatio-Temporal Traffic Prediction

Almuhisen

Durand

Brenner

et al. 2021

IEEE/WIC/ACM International Conference on Web Intelligence

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This paper proposes a method for predicting the traffic status of a city within time windows. The method takes advantage of spacepartitioning, closed sequential pattern extraction, emerging pattern detection, and Markov chain modeling. From trajectories, we identify active regions in which moving objects mostly visit. The traffic status of each region is detected based on continuous tracking of closed sequential patterns evolution over time. Based on the proposed Markov model, the near-future status of traffic is then predicted. The traffic status is reported on maps and can be used to enhance future city transportation. The experiments on realworld data sets show that the proposed method provides promising results.

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Section: Related Workmentioning

confidence: 99%

Sequential Patterns for Spatio-Temporal Traffic Prediction

Almuhisen

Durand

Brenner

et al. 2021

IEEE/WIC/ACM International Conference on Web Intelligence

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“…Regarding the application domain, there are numerous studies for finding patterns in traffic and weather data, with the following goals: to study the impact of precipitation on likelihood or severity of accidents [7,16,28]; to explore the impact of weather on traffic intensity [5,31]; to reveal the effect of climate change and weather condition on road safety [1,11,29]; to characterize road accidents locations [18]; or, to discover frequent spatiotemporal patterns in traffic data [15,17,19]. The scale of data in most of these studies is limited to one or at most a few cities.…”

Section: Related Workmentioning

confidence: 99%

Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data

Moosavi

Samavatian

Nandi

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Pattern discovery in geo-spatiotemporal data (such as traffic and weather data) is about finding patterns of collocation, co-occurrence, cascading, or cause and effect between geospatial entities. Using simplistic definitions of spatiotemporal neighborhood (a common characteristic of the existing general-purpose frameworks) is not semantically representative of geo-spatiotemporal data. We therefore introduce a new geo-spatiotemporal pattern discovery framework which defines a semantically correct definition of neighborhood; and then provides two capabilities, one to explore propagation patterns and the other to explore influential patterns. Propagation patterns reveal common cascading forms of geospatial entities in a region. Influential patterns demonstrate the impact of temporally long-term geospatial entities on their neighborhood. We apply this framework on a large dataset of traffic and weather data at countrywide scale, collected for the contiguous United States over two years. Our important findings include the identification of 90 common propagation patterns of traffic and weather entities (e.g., rain → accident → conдestion), which results in identification of four categories of states within the US; and interesting influential patterns with respect to the "location", "duration", and "type" of longterm entities (e.g., a major construction → more traffic incidents). These patterns and the categorization of the states provide useful insights on the driving habits and infrastructure characteristics of different regions in the US, and could be of significant value for applications such as urban planning and personalized insurance.

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“…J. Geo-Inf. 2020, 9, 441 2 of 15 behaviors of a moving object and can be used to predict future events [2][3][4]. However, the existing research of trajectory periodic pattern mining mainly focuses on a simple trajectory with a linear structure, such as people, animals, vehicles and so on (Figure 1a).…”

Section: Introductionmentioning

confidence: 99%

“…The periodic pattern of a trajectory could be defined as cycle of the behaviors of an object that is moving at a regular time interval at specific locations [1]. The periodic patterns can show the repetitive behaviors of a moving object and can be used to predict future events [2][3][4]. However, the existing research of trajectory periodic pattern mining mainly focuses on a simple trajectory with a linear structure, such as people, animals, vehicles and so on (Figure 1a).…”

Section: Introductionmentioning

confidence: 99%

“…The second category, the region of interest-based periodic pattern mining method breaks a trajectory into a sequence of segments within the regions of interest and uses the frequent sequence pattern mining method to find the yearly, monthly, and/or weekly periodic patterns [13,14]. The third category assumes objects frequently move along the same or similar paths with a fixed time interval and the periodic patterns are then identified from the spatiotemporal characteristics of the trajectories [2,3]. For example, Cao et al [2] addressed the problems of circular pattern mining from spatiotemporal data and proposed a density clustering algorithm (DBSCAN) for periodic pattern mining.…”

Section: Introductionmentioning

confidence: 99%

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Mining Evolution Patterns from Complex Trajectory Structures—A Case Study of Mesoscale Eddies in the South China Sea

Wang

et al. 2020

IJGI

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Real-word phenomena, such as ocean eddies and clouds, tend to split and merge while they are moving around within a space. Their trajectories usually bear one or more branches and are accordingly defined as complex trajectories in this study. The trajectories may show significant spatiotemporal variations in terms of their structures and some of them may be more prominent than the others. The identification of prominent structures in the complex trajectories of such real-world phenomena could better reveal their evolution processes and even shed new light on the driving factors behind them. Methods have been proposed for the extraction of periodic patterns from simple trajectories (i.e., those with linear structure and without any branches) with a focus on mining the related temporal, spatial or semantic information. Unfortunately, it is not appropriate to directly use such methods to examine complex trajectories. This study proposes a novel method to study the periodic patterns of complex trajectories by considering the inherent spatial, temporal and topological information. First, we use a sequence of symbols to represent the various structures of a complex trajectory over its lifespan. We then, on the basis of the PrefixSpan algorithm, propose a periodic pattern mining of structural evolution (PPSE) algorithm and use it to identify the largest and most frequent patterns (LFPs) from the symbol sequence. We also identify potential periodic behaviors. The PPSE method is then used to examine the complex trajectories of the mesoscale eddy in the South China Sea (SCS) from 1993 to 2016. The complex trajectories of ocean eddies in the southeast of Vietnam show are different from other regions in the SCS in terms of their structural evolution processes, as indicated by the LFPs with the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. The LFP in the southeast of Vietnam has the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. Across the SCS, we found seven migration channels. The LFPs of the eddies that migrate through these channels have a temporal cycle of 17–24 years. These channels are also the regions where eddies frequently emerge, as revealed by flow field data.

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Spatiotemporal periodical pattern mining in traffic data

Cited by 28 publications

References 16 publications

Sequential Patterns for Spatio-Temporal Traffic Prediction

Sequential Patterns for Spatio-Temporal Traffic Prediction

Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data

Mining Evolution Patterns from Complex Trajectory Structures—A Case Study of Mesoscale Eddies in the South China Sea

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