Ammar Bin Zulqarnain is a graduate student in the Department of Computer Science at Vanderbilt University and works as a research assistant at the Institute for Software Integrated Systems. He received his Bachelor’s degree in Engineering Science and Economics from Vanderbilt University in May 2022.

Ammar Zulqarnain Publications

1. A. Zulqarnain, J. Buckelew, J. P. Talusan, A. Mukhopadhyay, and A. Dubey, TRACE: Traffic Response Anomaly Capture Engine for Localization of Traffic Incidents, in 2025 IEEE International Conference on Smart Computing (SMARTCOMP), 2025.
   Bibtex Abstract PDF

   @inproceedings{zulqarnain2025,
     author = {Zulqarnain, Ammar and Buckelew, Jacob and Talusan, Jose Paolo and Mukhopadhyay, Ayan and Dubey, Abhishek},
     booktitle = {2025 IEEE International Conference on Smart Computing (SMARTCOMP)},
     title = {TRACE: Traffic Response Anomaly Capture Engine for Localization of Traffic Incidents},
     year = {2025},
     month = jun,
     contribution = {lead}
   }
   

   Effective traffic incident management is critical for road safety and operational efficiency. Yet, many transportation agencies rely on reactionary methods, where incidents are reported by human agents and managed through rule- based frameworks like traditional Traffic Incident Management (TIM) systems. However, these are vulnerable to human error, oversight, and delays during high-stress conditions. Although recent initiatives incorporating real-time sensor data for cor- ridor monitoring and enhanced roadway information systems represent strides toward modernization, these systems often still require substantial human intervention. Recent advancements in graph-based deep learning models offer promising potential for addressing the limitations of traditional methods. While state- of-the-art models exist, the complexities of incident localization within dynamic and interconnected road networks, along with limited availability of high-quality labeled data and variability in real-time traffic measurements, are still open challenges. To address these, we propose the Traffic Response Anomaly Capture Engine (TRACE), a novel approach that combines graph neural networks, transformers, and probabilistic normalizing flows to accurately detect and localize traffic anomalies in real time. TRACE captures spatial-temporal dependencies, manages data uncertainty, and enhances automation, supporting more precise and timely incident localization. Our approach is validated on real-world traffic data and improved incident localization by 0.6 miles (17%) than SOTA methods while maintaining similar incident detection accuracy and mean detection delay.

2. A. Zulqarnain, S. Gupta, J. P. Talusan, P. Pugliese, A. Mukhopadhyay, and A. Dubey, Addressing APC Data Sparsity in Predicting Occupancy and Delay of Transit Buses: A Multitask Learning Approach, in 2023 IEEE International Conference on Smart Computing (SMARTCOMP), 2023.
   Bibtex Abstract PDF

   @inproceedings{Zulqarnain2023,
     author = {Zulqarnain, Ammar and Gupta, Samir and Talusan, Jose Paolo and Pugliese, Philip and Mukhopadhyay, Ayan and Dubey, Abhishek},
     booktitle = {2023 IEEE International Conference on Smart Computing (SMARTCOMP)},
     title = {Addressing APC Data Sparsity in Predicting Occupancy and Delay of Transit Buses: A Multitask Learning Approach},
     year = {2023},
     acceptance = {31},
     contribution = {lead}
   }
   

   Public transit is a vital mode of transportation in urban areas, and its efficiency is crucial for the daily commute of millions of people. To improve the reliability and predictability of transit systems, researchers have developed separate single-task learning models to predict the occupancy and delay of buses at the stop or route level. However, these models provide a narrow view of delay and occupancy at each stop and do not account for the correlation between the two. We propose a novel approach that leverages broader generalizable patterns governing delay and occupancy for improved prediction. We introduce a multitask learning toolchain that takes into account General Transit Feed Specification feeds, Automatic Passenger Counter data, and contextual information temporal and spatial information. The toolchain predicts transit delay and occupancy at the stop level, improving the accuracy of the predictions of these two features of a trip given sparse and noisy data. We also show that our toolchain can adapt to fewer samples of new transit data once it has been trained on previous routes/trips as compared to state-of-the-art methods. Finally, we use actual data from Chattanooga, Tennessee, to validate our approach. We compare our approach against the state-of-the-art methods and we show that treating occupancy and delay as related problems improves the accuracy of the predictions. We show that our approach improves delay prediction significantly by as much as 6% in F1 scores while producing equivalent or better results for occupancy.