Neetesh Kumar
Biographic Data
| ID | 10054097 |
|---|---|
| NAME | Neetesh Kumar |
| GIVEN NAMES | Neetesh |
| FAMILY NAME | Kumar |
| SIGNATURE | KUMAR N |
| AFFILIATIONS | Indian Institute of Technology Roorkee |
| ORCID | 0000-0002-0516-1095 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
STLTEformer
Traffic flow prediction is crucial for efficient transportation systems, allowing better traffic management and decision-making. However, it’s a complex challenge due to its intricate spatial and temporal dependencies and fine-grained variations. Various network architectures, particularly graph neural networks (GNN) and transformers, have been proposed to handle these complexities. Nevertheless, as network architectures advanced, their performan…
Selective State-Focused Graph Attention Networks for Multihorizon Traffic Forecasting
Traffic forecasting plays an important role in minimizing travel delays and enhancing road safety by using historical and real-time data to predict future traffic conditions. However, current state-of-the-art methods using graph representational learning fail to model localized complex and dynamic spatio–temporal relationships. Also, they struggle with segmenting long, context-aware traffic sequences, which are crucial for accurate forecasting. T…
Exploring Spatial–Temporal Correlations With Dual Stream Conv-GRU and Fuzzy-Inspired Attention Mechanism for Traffic Flow Prediction
Novel Confined Attention-Enabled Hierarchical Bi-LSTM and Bi-GRU Fusion
Traffic flow prediction is essential for alleviating congestion and enhancing traffic management. While previous efforts have mainly concentrated on road-level predictions, the advancement of technology and the growing presence of automated vehicles have encouraged lane-level forecasting for more efficient driving and routing decisions. Therefore, this work presents a hierarchical model using recurrent neural networks (RNNs) that takes the lane-l…
No prominent works on this page.
Novel Confined Attention-Enabled Hierarchical Bi-LSTM and Bi-GRU Fusion
Traffic flow prediction is essential for alleviating congestion and enhancing traffic management. While previous efforts have mainly concentrated on road-level predictions, the advancement of technology and the growing presence of automated vehicles have encouraged lane-level forecasting for more efficient driving and routing decisions. Therefore, this work presents a hierarchical model using recurrent neural networks (RNNs) that takes the lane-l…
STLTEformer
Traffic flow prediction is crucial for efficient transportation systems, allowing better traffic management and decision-making. However, it’s a complex challenge due to its intricate spatial and temporal dependencies and fine-grained variations. Various network architectures, particularly graph neural networks (GNN) and transformers, have been proposed to handle these complexities. Nevertheless, as network architectures advanced, their performan…
Selective State-Focused Graph Attention Networks for Multihorizon Traffic Forecasting
Traffic forecasting plays an important role in minimizing travel delays and enhancing road safety by using historical and real-time data to predict future traffic conditions. However, current state-of-the-art methods using graph representational learning fail to model localized complex and dynamic spatio–temporal relationships. Also, they struggle with segmenting long, context-aware traffic sequences, which are crucial for accurate forecasting. T…
Exploring Spatial–Temporal Correlations With Dual Stream Conv-GRU and Fuzzy-Inspired Attention Mechanism for Traffic Flow Prediction
Neural Networks and Applications (3 works) · Traffic Prediction and Management Techniques (3 works) · Artificial Intelligence (2 works) · Computer Science (2 works) · Attention network (1 work) · Data modeling (1 work) · Electrical engineering (1 work) · Embedding (1 work) · Engineering (1 work) · Fusion (1 work)