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. A. Khanna, F. Liu, S. Gupta, S. Pavia, A. Mukhopadhyay, and A. Dubey, PDPTW-DB: MILP-Based Offline Route Planning for PDPTW with Driver Breaks, in Proceedings of the 26th International Conference on Distributed Computing and Networking, New York, NY, USA, 2025, pp. 73–83.
   Bibtex Abstract DOI PDF

   @inproceedings{khanna2025driverbreaks,
     author = {Khanna, Agrima and Liu, Fangqi and Gupta, Samir and Pavia, Sophie and Mukhopadhyay, Ayan and Dubey, Abhishek},
     booktitle = {Proceedings of the 26th International Conference on Distributed Computing and Networking},
     title = {PDPTW-DB: MILP-Based Offline Route Planning for PDPTW with Driver Breaks},
     year = {2025},
     address = {New York, NY, USA},
     pages = {73--83},
     acceptance = {32},
     publisher = {Association for Computing Machinery},
     series = {ICDCN '25},
     category = {other},
     contribution = {lead},
     doi = {10.1145/3700838.3700854},
     isbn = {9798400710629},
     keywords = {Microtransit, Vehicular Networks, Pickup and Delivery Problem with Time Windows, Driver Breaks, MILP, Optimization, Microtransit, Vehicular Networks, Pickup and Delivery Problem with Time Windows, Driver Breaks, MILP, Optimization},
     numpages = {11},
     url = {https://doi.org/10.1145/3700838.3700854}
   }
   

   The Pickup and Delivery Problem with Time Windows (PDPTW) involves optimizing routes for vehicles to meet pickup and delivery requests within specific time constraints, a challenge commonly faced in logistics and transportation. Microtransit, a flexible and demand-responsive service using smaller vehicles within defined zones, can be effectively modeled as a PDPTW. Yet, the need for driver breaks—a key human constraint—is frequently overlooked in PDPTW solutions, despite being necessary for regulatory compliance. This study presents a novel mixed-integer linear programming formulation for the Pickup and Delivery Problem with Time Windows and Driver Breaks (PDPTW-DB). To the best of our knowledge this formulation is the first to consider mandatory periodic driver breaks within optimized Microtransit routes. The proposed model incorporates regulatory compliant break scheduling directly within the vehicle routing optimization framework. By considering driver break requirements as an integral component of the optimization process, rather than as a post-processing step, the model enables the generation of routes that respect hours of service regulations while minimizing operational costs. This integrated approach facilitates the generation of schedules that are operationally efficient and prioritize driver welfare through driver breaks. We work with a public transit agency from the southern USA, and highlight the specific nuances of driver break optimization, and present a Pickup and Delivery Problem with Time Windows formulation for optimizing Microtransit operations and scheduling driver breaks. We validate our approach using real-world data from the transit agency. Our results validate our formulation in producing cost-effective, and regulation-compliant solutions.

2. 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,
     author = {Gupta, Samir and Khanna, Agrima and Talusan, Jose Paolo and Said, Anwar and Freudberg, Dan and Mukhopadhyay, Ayan and Dubey, Abhishek},
     booktitle = {2024 IEEE International Conference on Smart Computing (SMARTCOMP)},
     title = {A Graph Neural Network Framework for Imbalanced Bus Ridership Forecasting},
     year = {2024},
     acceptance = {32.9},
     month = jun,
     contribution = {lead}
   }
   

   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.