David Rogers is a Research Engineer at the Institute for Software Integrated Systems. He graduated from Georgia Tech with a BS in Electrical Engineering and received his MS in Electrical and Computer Engineering in 2025. Prior to Vanderbilt, David was a civilian Software Engineer for the US Air Force, maintaining and developing mission critical software.

David Rogers Publications

1. J. P. Talusan, C. Han, D. Rogers, A. Mukhopadhyay, A. Laszka, D. Freudberg, and A. Dubey, An End-to-End Solution for Public Transit Stationing and Dispatch Problem, ACM Trans. Cyber-Phys. Syst., Jul. 2025.
   Bibtex Abstract DOI PDF

   @article{talusanTCPS2025,
     title = {An End-to-End Solution for Public Transit Stationing and Dispatch Problem},
     author = {Talusan, Jose Paolo and Han, Chaeeun and Rogers, David and Mukhopadhyay, Ayan and Laszka, Aron and Freudberg, Dan and Dubey, Abhishek},
     year = {2025},
     month = jul,
     journal = {ACM Trans. Cyber-Phys. Syst.},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     doi = {10.1145/3754454},
     issn = {2378-962X},
     url = {https://doi.org/10.1145/3754454},
     note = {Just Accepted},
     keywords = {Public transit, Monte Carlo, Optimization}
   }
   

   Public bus transit systems provide critical transportation services for large sections of modern communities. On-time performance and maintaining the reliable quality of service is therefore very important. Unfortunately, disruptions caused by overcrowding, vehicular failures, and road accidents often lead to service performance degradation. Though transit agencies keep a limited number of vehicles in reserve and dispatch them to relieve the affected routes during disruptions, the procedure is often ad-hoc and has to rely on human experience and intuition to allocate resources (vehicles) to affected trips under uncertainty. In this paper, we describe a principled approach using non-myopic sequential decision procedures to solve the problem and decide (a) if it is advantageous to anticipate problems and proactively station transit buses near areas with high-likelihood of disruptions and (b) decide if and which vehicle to dispatch to a particular problem. Our approach was developed in partnership WeGo Public Transit, a public transportation agency based in Nashville, Tennessee and models the system as a semi-Markov decision problem (solved as a Monte-Carlo tree search procedure) and shows that it is possible to obtain an answer to these two coupled decision problems in a way that maximizes the overall reward (number of people served). We sample many possible futures from generative models, each is assigned to a tree and processed using root parallelization. We validate our approach with both real-world and scaled-up data from two agencies in Tennessee. Our experiments show that the proposed framework serves 2% more passengers while reducing deadhead miles by 40%. Finally, we introduce Vectura, a dashboard providing transit dispatchers a complete view of the transit system at a glance along with access to our developed tools.

2. D. Rogers, S. Gupta, J. P. Talusan, M. Baig, A. Ramesh, N. Takahashi, N. Kojo, and A. Dubey, AVATAR: Autonomy Aware Routing for On-demand Transit Applications, in 2025 IEEE International Conference on Smart Computing (SMARTCOMP), 2025.
   Bibtex Abstract PDF

   @inproceedings{rogers2025,
     author = {Rogers, David and Gupta, Samir and Talusan, Jose Paolo and Baig, Mirza and Ramesh, Arti and Takahashi, Natsu and Kojo, Naoki and Dubey, Abhishek},
     booktitle = {2025 IEEE International Conference on Smart Computing (SMARTCOMP)},
     title = {AVATAR: Autonomy Aware Routing for On-demand Transit Applications},
     year = {2025},
     month = jun,
     contribution = {lead}
   }
   

   Autonomous vehicles (AVs) are becoming integral to on-demand micro transit, offering the potential for safer, efficient, and sustainable transportation. However, AV deploy- ment faces several challenges, including the lack of suitable roadways, varying travel conditions. Traditional routers prioritize speed and not reliability, leading to unpredictable operations and complications in planning. To address these, we introduce AVATAR, an autonomy-aware routing framework that prioritizes dependable, low-variance routes. Our approach encodes mul- tiple objectives including road speed, speed variability, zoning areas, pedestrian encounters, and operator preferred roadways into edge-level routing engines. Objective optimized routes are generated, then scored using a multi-criteria decision-making process. User-configurable preference profiles, allow operators to define a balance between reliability and speed. AVATAR is a data- driven framework that supports both real-time AV operations and offline analysis, enabling transit operators to assess and refine routing strategies. Our experiments using real-world data from Silicon Valley, California, and Yokohama, Japan show that our approach significantly improves AV reliability and performance and advances the sustainable and scalable integration of AVs into future transportation networks.

3. 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.

4. 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.