Chirag Choudhary - Profile on Academia.edu (original) (raw)
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Papers by Chirag Choudhary
Transportation Research Procedia, 2020
The large vehicle movement traffic datasets offer a lot of great opportunities for the evolution ... more The large vehicle movement traffic datasets offer a lot of great opportunities for the evolution of new methodologies for the analysis of the transportation system. However, deriving relevant traffic patterns from such a vast amount of historical dataset is challenging. In this paper, several data mining techniques have been applied to obtain more understanding about urban traffic patterns by analyzing hourly and daily variation in urban traffic flow dataset. A model has been developed for the analysis of spatial and temporal patterns in urban traffic data. Model development involves the formulation of algorithms to be applied to the data and choice of various metrics to evaluate the clustering algorithm. Furthermore, these techniques have been applied to the traffic dataset of Aarhus, the second-largest city of Denmark. Finally, results are analyzed to determine the various factors that affect the traffic flow patterns in an urban area.
Author(s): Choudhary, Chirag | Advisor(s): Singh, Sameer | Abstract: A knowledge graph represents... more Author(s): Choudhary, Chirag | Advisor(s): Singh, Sameer | Abstract: A knowledge graph represents factual information in the form of graphs, where nodes repre- sent real-world entities such as people, places and movies, and edges represent the relation- ships between these entities. Existing knowledge graphs are far from complete. Knowledge graph completion or link prediction refers to the task of predicting new relations (links) between entities by deriving information from the existing relations. A number of link pre- diction model have been proposed, several of which make probabilistic predictions about new links. These models can be rule-based methods derived from observed edges, latent represen- tation based embedding methods, or a combination of both. These methods must capture different kinds of relational patterns in the data, such as symmetry or inversion patterns to fully model the data. Rule-based methods explicitly learn these patterns, and provide an interpretable appro...
Transportation Research Procedia, 2020
The large vehicle movement traffic datasets offer a lot of great opportunities for the evolution ... more The large vehicle movement traffic datasets offer a lot of great opportunities for the evolution of new methodologies for the analysis of the transportation system. However, deriving relevant traffic patterns from such a vast amount of historical dataset is challenging. In this paper, several data mining techniques have been applied to obtain more understanding about urban traffic patterns by analyzing hourly and daily variation in urban traffic flow dataset. A model has been developed for the analysis of spatial and temporal patterns in urban traffic data. Model development involves the formulation of algorithms to be applied to the data and choice of various metrics to evaluate the clustering algorithm. Furthermore, these techniques have been applied to the traffic dataset of Aarhus, the second-largest city of Denmark. Finally, results are analyzed to determine the various factors that affect the traffic flow patterns in an urban area.
Author(s): Choudhary, Chirag | Advisor(s): Singh, Sameer | Abstract: A knowledge graph represents... more Author(s): Choudhary, Chirag | Advisor(s): Singh, Sameer | Abstract: A knowledge graph represents factual information in the form of graphs, where nodes repre- sent real-world entities such as people, places and movies, and edges represent the relation- ships between these entities. Existing knowledge graphs are far from complete. Knowledge graph completion or link prediction refers to the task of predicting new relations (links) between entities by deriving information from the existing relations. A number of link pre- diction model have been proposed, several of which make probabilistic predictions about new links. These models can be rule-based methods derived from observed edges, latent represen- tation based embedding methods, or a combination of both. These methods must capture different kinds of relational patterns in the data, such as symmetry or inversion patterns to fully model the data. Rule-based methods explicitly learn these patterns, and provide an interpretable appro...