Sophie Pavia is a graduate student in the Department of Computer Science and Computer Engineering at Vanderbilt University, and works as a research assistant at the Institute for Software Integrated Systems. She completed her undergraduate studies in computer science at Florida State University in April 2022.

Sophie Pavia 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. Pavia, D. Rogers, A. Sivagnanam, M. Wilbur, D. Edirimanna, Y. Kim, P. Pugliese, S. Samaranayake, A. Laszka, A. Mukhopadhyay, and A. Dubey, Deploying mobility-on-demand for all by optimizing paratransit services, in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024.
   Bibtex Abstract DOI PDF

   @inproceedings{paviaIJCAI24AISG,
     author = {Pavia, Sophie and Rogers, David and Sivagnanam, Amutheezan and Wilbur, Michael and Edirimanna, Danushka and Kim, Youngseok and Pugliese, Philip and Samaranayake, Samitha and Laszka, Aron and Mukhopadhyay, Ayano and Dubey, Abhishek},
     booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence},
     title = {Deploying mobility-on-demand for all by optimizing paratransit services},
     year = {2024},
     series = {IJCAI '24},
     articleno = {822},
     contribution = {lead},
     acceptance = {15},
     doi = {10.24963/ijcai.2024/822},
     isbn = {978-1-956792-04-1},
     location = {Jeju, Korea},
     numpages = {8},
     url = {https://doi.org/10.24963/ijcai.2024/822}
   }
   

   While on-demand ride-sharing services have become popular in recent years, traditional on-demand transit services cannot be used by everyone, e.g., people who use wheelchairs. Paratransit services, operated by public transit agencies, are a critical infrastructure that offers door-to-door transportation assistance for individuals who face challenges in using standard transit routes. However, with declining ridership and mounting financial pressure, public transit agencies in the USA struggle to operate existing services. We collaborate with a public transit agency from the southern USA, highlight the specific nuances of paratransit optimization, and present a vehicle routing problem formulation for optimizing paratransit. We validate our approach using real-world data from the transit agency, present results from an actual pilot deployment of the proposed approach in the city, and show how the proposed approach comprehensively outperforms existing approaches used by the transit agency. To the best of our knowledge, this work presents one of the first examples of using open-source algorithmic approaches for paratransit optimization.

3. S. Pavia, D. Rogers, A. Sivagnanam, M. Wilbur, D. Edirimanna, Y. Kim, A. Mukhopadhyay, P. Pugliese, S. Samaranayake, A. Laszka, and A. Dubey, SmartTransit.AI: a dynamic paratransit and microtransit application, in Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024.
   Bibtex Abstract DOI PDF

   @inproceedings{paviaIJCAI24demo,
     author = {Pavia, Sophie and Rogers, David and Sivagnanam, Amutheezan and Wilbur, Michael and Edirimanna, Danushka and Kim, Youngseo and Mukhopadhyay, Ayan and Pugliese, Philip and Samaranayake, Samitha and Laszka, Aron and Dubey, Abhishek},
     booktitle = {Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence},
     title = {SmartTransit.AI: a dynamic paratransit and microtransit application},
     year = {2024},
     series = {IJCAI '24},
     articleno = {1028},
     contribution = {lead},
     acceptance = {15},
     doi = {10.24963/ijcai.2024/1028},
     isbn = {978-1-956792-04-1},
     location = {Jeju, Korea},
     numpages = {4},
     url = {https://doi.org/10.24963/ijcai.2024/1028}
   }
   

   New rideshare and shared mobility services have transformed urban mobility in recent years. Such services have the potential to improve efficiency and reduce costs by allowing users to share rides in high-capacity vehicles and vans. Most transit agencies already operate various ridepooling services, including microtransit and paratransit. However, the objectives and constraints for implementing these services vary greatly between agencies and can be challenging. First, off-the-shelf ridepooling formulations must be adapted for real-world conditions and constraints. Second, the lack of modular and reusable software makes it hard to implement and evaluate new ridepooling algorithms and approaches in real-world settings. We demonstrate a modular on-demand public transportation scheduling software for microtransit and paratransit services. The software is aimed at transit agencies looking to incorporate state-of-the-art rideshare and ridepooling algorithms in their everyday operations. We provide management software for dispatchers and mobile applications for drivers and users and conclude with results from the demonstration in Chattanooga, TN.

4. S. Pavia, J. C. M. Mori, A. Sharma, P. Pugliese, A. Dubey, S. Samaranayake, and A. Mukhopadhyay, Designing Equitable Transit Networks, ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO). 2023.
   Bibtex Abstract PREPRINT PDF

   @misc{pavia2023designing,
     author = {Pavia, Sophie and Mori, J. Carlos Martinez and Sharma, Aryaman and Pugliese, Philip and Dubey, Abhishek and Samaranayake, Samitha and Mukhopadhyay, Ayan},
     title = {Designing Equitable Transit Networks},
     year = {2023},
     category = {poster},
     acceptance = {16},
     contribution = {lead},
     journal = {ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO)},
     preprint = {https://arxiv.org/abs/2212.12007}
   }
   

   public transit is an essential infrastructure enabling access to employment, healthcare, education, and recreational facilities. While accessibility to transit is important in general, some sections of the population depend critically on transit. However, existing public transit is often not designed equitably, and often, equity is only considered as an additional objective post hoc, which hampers systemic changes. We present a formulation for transit network design that considers different notions of equity and welfare explicitly. We study the interaction between network design and various concepts of equity and present trade-offs and results based on real-world data from a large metropolitan area in the United States of America.