A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cameras

Muppalla, RoopTeja; Lalithsena, Sarasi; Banerjee, Tanvi; Sheth, Amit P.

doi:10.1145/3091478.3162384

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…The developed KG conceptualises the US National Airspace System by incorporating entities describing, airspace infrastructure, flights, and flight operating conditions. Detecting traffic events by employing KG was proposed in [193]. The authors built a KG named ITSKG (Imagerybased Traffic Sensing Knowledge Graph) that was used to comprehend traffic patterns based on stationary traffic camera imagery data.…”

Section: Travelmentioning

confidence: 99%

“…In fact, KG evaluation has been indicated as one of the most indicated weaknesses amongst the examined studies. For example, some studies carried out a superficial and subjective evaluation to the KG construction with no incorporation to concrete evaluation metrics [131,141,193]. Another thread of efforts attempted to involve theoretically proven evaluation metrics to systematically measure KG completion and KG correctness approaches.…”

Section: E) Kg Evaluationmentioning

confidence: 99%

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Domain-specific knowledge graphs: A survey

Abu-Salih

2021

Journal of Network and Computer Applications

213

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Section: Travelmentioning

confidence: 99%

Section: E) Kg Evaluationmentioning

confidence: 99%

Domain-specific knowledge graphs: A survey

Abu-Salih

2021

Journal of Network and Computer Applications

213

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“…Building a traffic knowledge graph can help solve traffic problems by using the traffic information contained in the data. Muppalla et al [9] used traffic surveillance video data to build a video information graph to help understand traffic patterns. Zhang et al [10] proposed an urban knowledge graph neural network model (Urban Knowledge Graph Neural Network, UKG-NN) to combine urban knowledge graph and neural network to solve the problem of traffic accident reasoning and optimizing cargo storage.…”

Section: Related Workmentioning

confidence: 99%

Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network

Wang

Yuan

et al. 2022

Journal of Advanced Transportation

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Existing traffic flow prediction methods generally only consider the spatiotemporal characteristics of traffic flow. However, in addition to the spatiotemporal characteristics, the interference of various external factors needs to be considered in traffic flow prediction, including severe weather, major events, traffic control, and metro failures. The current research still cannot fully use the information contained in these external factors. To address this issue, we propose a novel metro traffic flow prediction method (KGR-STGNN) based on knowledge graph representation learning. We construct a knowledge graph that stores factors related to metro traffic networks. Through the knowledge graph representation learning technology, we can learn the influence representation of external factors from the traffic knowledge graph, which can better incorporate the influence of external factors into the prediction model based on the spatiotemporal graph neural network. Experimental results demonstrate the effectiveness of our proposed model.

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“…Knowledge graphs are also applied in industries such as e-commerce [31]. They also play roles in transportation, such as in site selection [32] and traffic accidents [33], [34].…”

Section: B Relation Mining In Multisource Datamentioning

confidence: 99%

KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

Zhu,

Han,

Deng

et al. 2020

Preprint

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When considering the spatial and temporal features of traffic, capturing the impacts of various external factors on travel is an important step towards achieving accurate traffic forecasting. The impacts of external factors on the traffic flow have complex correlations. However, existing studies seldom consider external factors, or neglecting the effect of the complex correlations among external factors on traffic. Intuitively, knowledge graphs can naturally describe these correlations, but knowledge graphs and traffic networks are essentially heterogeneous networks; thus, it is a challenging problem to integrate the information in both networks. We propose a knowledge representation-driven traffic forecasting method based on spatiotemporal graph convolutional networks. We first construct a city knowledge graph for traffic forecasting, then use KS-Cells to combine the information from the knowledge graph and the traffic network, and finally, capture the temporal changes of the traffic state with GRU. Testing on real-world datasets shows that the KST-GCN has higher accuracy than the baseline traffic forecasting methods at various prediction horizons. We provide a new way to integrate knowledge and the spatiotemporal features of data for traffic forecasting tasks. Without any loss of generality, the proposed method can also be extended to other spatiotemporal forecasting tasks.

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A Knowledge Graph Framework for Detecting Traffic Events Using Stationary Cameras

Cited by 12 publications

References 15 publications

Domain-specific knowledge graphs: A survey

Domain-specific knowledge graphs: A survey

Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network

KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

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