Emergency-Oriented Spatiotemporal Trajectory Pattern Recognition by Intelligent Sensor Devices

Zhang, Ping; Deng, Qing; Liu, Xiaodong; Yang, Rui; Zhang, Hui

doi:10.1109/access.2017.2678471

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…It is essential to have a functioning event detection system for disaster management in order to detect any undesirable events. Any incident may be detected within the first few seconds of happening thanks to event detection driven by IoT-based sensor data [42]. The capacity to recognize patterns in textual or geographical data sets, which is essential for disaster management, is provided by pattern recognition mechanisms [43].…”

Section: Data Analytics and Management Layermentioning

confidence: 99%

Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review

Ali¹,

Naeem²,

Adam³

et al. 2023

Preprint

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A modern-day society demands resilient, reliable, and smart urban infrastructure for effective and intelligent operations and deployment. However, unexpected, high-impact, and low-probability events such as earthquakes, tsunamis, tornadoes, and hurricanes make the design of such robust infrastructure more complex. As a result of such events, a power system infrastructure can be severely affected, leading to unprecedented events, such as blackouts. Nevertheless, the integration of smart grids into the existing framework of smart cities adds to their resilience. Therefore, designing a resilient and reliable power system network is an inevitable requirement of modern smart city infrastructure. With the deployment of the Internet of Things (IoT), smart cities' infrastructures have taken a transformational turn towards introducing technologies that do not only provide ease and comfort to the citizens but are also feasible in terms of sustainability and dependability. This paper presents a holistic view of a resilient and sustainable smart city architecture that utilizes IoT, big data analytics, unmanned aerial vehicles, and smart grids through intelligent integration of renewable energy resources. In addition, the impact of disasters on the power system infrastructure is investigated and different types of optimization techniques that can be used to sustain the power flow in the network during disturbances are compared and analyzed. Furthermore, a comparative review analysis of different data-driven machine learning techniques for sustainable smart cities is performed along with the discussion on open research issues and challenges.

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Section: Data Analytics and Management Layermentioning

confidence: 99%

Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review

Ali¹,

Naeem²,

Adam³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Event detection is very critical in disaster management and needs to be operational to identify any disastrous event that occurs. Event detection backed by IoT sensor data and social media streams can detect any incident within the first few seconds of its occurrence [47]. Pattern recognition mechanism offers the machine learning ability to detect the useful patterns of information from textual or spatial data sets crucial for disaster management [48].…”

Section: ) Data Analytics and Management Layermentioning

confidence: 99%

Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?

et al. 2019

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Disasters (natural or man-made) can be lethal to human life, the environment, and infrastructure. The recent advancements in the Internet of Things (IoT) and the evolution in big data analytics (BDA) technologies have provided an open opportunity to develop highly needed disaster resilient smart city environments. In this paper, we propose and discuss the novel reference architecture and philosophy of a disaster resilient smart city (DRSC) through the integration of the IoT and BDA technologies. The proposed architecture offers a generic solution for disaster management activities in smart city incentives. A combination of the Hadoop Ecosystem and Spark are reviewed to develop an efficient DRSC environment that supports both real-time and offline analysis. The implementation model of the environment consists of data harvesting, data aggregation, data pre-processing, and big data analytics and service platform. A variety of datasets (i.e., smart buildings, city pollution, traffic simulator, and twitter) are utilized for the validation and evaluation of the system to detect and generate alerts for a fire in a building, pollution level in the city, emergency evacuation path, and the collection of information about natural disasters (i.e., earthquakes and tsunamis). The evaluation of the system efficiency is measured in terms of processing time and throughput that demonstrates the performance superiority of the proposed architecture. Moreover, the key challenges faced are identified and briefly discussed. INDEX TERMS Big data analytics, Internet of Things, smart city, disaster management, Hadoop, spark, smart data analytics, geo-social media analytics, disaster resilient smart city.

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“…Abdelpakey et al [37] introduced a method to more accurately track targets rather than trajectory analysis. Fu et al [38] and Zhang et al [39] used the method of measuring the similarity of trajectory distance or trajectory structure to compare and analyze trajectories. The former uses feature learning based on dynamic time planning and a space-time collaboration algorithm to analyze the orbit, focusing on detecting abnormal trajectories; the latter uses a clustering algorithm to divide the trajectories, but focuses on predicting the potential location of the crowd.…”

Section: Related Researchmentioning

confidence: 99%

A Top–Bottom Clustering Algorithm Based on Crowd Trajectories for Small Group Classification

Li¹,

Liu²,

Zheng³

et al. 2019

IEEE Access

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The study of crowd movement has recently become a popular research topic due to the increasing frequency of public safety issues. Compared with human evacuation experiments and drills, which may have personal safety risks and require a large number of volunteers, a simple and convenient computer simulation has become the mainstream research method. The computer simulation first needs to characterize small groups in the crowd to model the motion state of crowds for more accurate crowd modeling. In this paper, a top-bottom hierarchical clustering algorithm based on off-line crowd trajectories is proposed to provide small group information for crowd motion simulation. First, an unmanned aerial vehicle and a tracking technology are used to capture the pedestrian flow and extract the pedestrian trajectory. Second, a top-bottom hierarchical clustering strategy is proposed to divide the crowd into groups, which solves the problem of the difficulty of ascertaining small groups. This method solves the problem of automatically determining cluster centers by using the improved density peak clustering algorithm combined with a greedy algorithm. One factor of distinguishing small groups is improved by replacing the angle of the direction of motion with the distance difference, thus reducing the computational complexity. Specifically, the mean distance between trajectories based on the Euclidean distance is used for the top-level coarsegrained clustering; then, the improved Hausdorff mean distance is determined in the bottom-level finegrained clustering. Third, the proposed algorithm is validated by classifying the groups of pedestrians in real videos. The experiments show that the proposed method is applicable and effective. INDEX TERMS Clustering, crowd simulation, small groups, trajectory analysis.

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Emergency-Oriented Spatiotemporal Trajectory Pattern Recognition by Intelligent Sensor Devices

Cited by 9 publications

References 31 publications

Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review

Integration of Data Driven Technologies in Smart Grids for Resilient and Sustainable Smart Cities: A Comprehensive Review

Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?

A Top–Bottom Clustering Algorithm Based on Crowd Trajectories for Small Group Classification

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