Agrima Khanna is a graduate student in the Department of Electrical Engineering and Computer Science at Vanderbilt University and works as a research assistant at the Institute for Software Integrated Systems. She received her bachelor’s degree in computer science from Netaji Subhash University of Technology, New Delhi in May 2023.

Agrima Khanna Publications

1. S. Gupta, A. Khanna, J. P. Talusan, A. Said, D. Freudberg, A. Mukhopadhyay, and A. Dubey, A Graph Neural Network Framework for Imbalanced Bus Ridership Forecasting, in 2024 IEEE International Conference on Smart Computing (SMARTCOMP), 2024.
   Bibtex Abstract PDF

   @inproceedings{samir2024smartcomp,
     title = {A Graph Neural Network Framework for Imbalanced Bus Ridership Forecasting},
     author = {Gupta, Samir and Khanna, Agrima and Talusan, Jose Paolo and Said, Anwar and Freudberg, Dan and Mukhopadhyay, Ayan and Dubey, Abhishek},
     year = {2024},
     month = jun,
     booktitle = {2024 IEEE International Conference on Smart Computing (SMARTCOMP)},
     volume = {},
     number = {}
   }
   

   Public transit systems are paramount in lowering carbon emissions and reducing urban congestion for environmental sustainability. However, overcrowding has adverse effects on the quality of service, passenger experience, and overall efficiency of public transit causing a decline in the usage of public transit systems. Therefore, it is crucial to identify and forecast potential windows of overcrowding to improve passenger experience and encourage higher ridership. Predicting ridership is a complex task, due to the inherent noise of collected data and the sparsity of overcrowding events. Existing studies in predicting public transit ridership consider only a static depiction of bus networks. We address these issues by first applying a data processing pipeline that cleans noisy data and engineers several features for training. Then, we address sparsity by converting the network to a dynamic graph and using a graph convolutional network, incorporating temporal, spatial, and auto-regressive features, to learn generalizable patterns for each route. Finally, since conventional loss functions like categorical cross-entropy have limitations in addressing class imbalance inherent in ridership data, our proposed approach uses focal loss to refine the prediction focus on less frequent yet task-critical overcrowding instances. Our experiments, using real-world data from our partner agency, show that the proposed approach outperforms existing state-of-the-art baselines in terms of accuracy and robustness.