Dr. Sayyed Mohsen Vazirizade Publications

1. G. Pettet, H. Baxter, S. Vazirizade, H. Purohit, M. Ma, A. Mukhopadhyay, and A. Dubey, Designing Decision Support Systems for Emergency Response: Challenges and Opportunities, in 2022 Workshop on Cyber Physical Systems for Emergency Response (CPS-ER), Los Alamitos, CA, USA, 2022, pp. 30–35.
   Bibtex Abstract DOI PDF

   @inproceedings{pettet2022designing,
     author = {Pettet, G. and Baxter, H. and Vazirizade, S. and Purohit, H. and Ma, M. and Mukhopadhyay, A. and Dubey, A.},
     booktitle = {2022 Workshop on Cyber Physical Systems for Emergency Response (CPS-ER)},
     title = {Designing Decision Support Systems for Emergency Response: Challenges and Opportunities},
     year = {2022},
     volume = {},
     issn = {},
     pages = {30-35},
     keywords = {decision support systems;road accidents;uncertainty;decision making;medical services;emergency services;hazards},
     doi = {10.1109/CPS-ER56134.2022.00012},
     url = {https://doi.ieeecomputersociety.org/10.1109/CPS-ER56134.2022.00012},
     publisher = {IEEE Computer Society},
     address = {Los Alamitos, CA, USA},
     month = may
   }
   

   Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In addition to responding to frequent incidents each day (about 240 million emergency medical services calls and over 5 million road accidents in the US each year), these systems also support response during natural hazards. Recently, there has been a consistent interest in building decision support and optimization tools that can help emergency responders provide more efficient and effective response. This includes a number of principled subsystems that implement early incident detection, incident likelihood forecasting and strategic resource allocation and dispatch policies. In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with our community partners.

2. J. Islam, J. P. Talusan, S. Bhattacharjee, F. Tiausas, S. M. Vazirizade, A. Dubey, K. Yasumoto, and S. Das, Anomaly based Incident Detection in Large Scale Smart Transportation Systems, in ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS), 2022.
   Bibtex Abstract PDF

   @inproceedings{jp2022,
     title = {Anomaly based Incident Detection in Large Scale Smart Transportation Systems},
     booktitle = {ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)},
     publisher = {IEEE},
     author = {Islam, Jaminur and Talusan, Jose Paolo and Bhattacharjee, Shameek and Tiausas, Francis and Vazirizade, Sayyed Mohsen and Dubey, Abhishek and Yasumoto, Keiichi and Das, Sajal},
     year = {2022},
     month = apr
   }
   

   Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient transportation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. We perform extensive experimental validation using mobility data collected from the City of Nashville, Tennessee, and prove that the method can detect incidents within each cluster in real-time.

3. A. Mukhopadhyay, G. Pettet, S. M. Vazirizade, D. Lu, A. Jaimes, S. E. Said, H. Baroud, Y. Vorobeychik, M. Kochenderfer, and A. Dubey, A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management, Accident Analysis & Prevention, vol. 165, p. 106501, 2022.
   Bibtex Abstract DOI PREPRINT

   @article{mukhopadhyay2021review,
     title = {A Review of Incident Prediction, Resource Allocation, and Dispatch Models for Emergency Management},
     journal = {Accident Analysis & Prevention},
     volume = {165},
     pages = {106501},
     year = {2022},
     issn = {0001-4575},
     doi = {https://doi.org/10.1016/j.aap.2021.106501},
     url = {https://www.sciencedirect.com/science/article/pii/S0001457521005327},
     author = {Mukhopadhyay, Ayan and Pettet, Geoffrey and Vazirizade, Sayyed Mohsen and Lu, Di and Jaimes, Alejandro and Said, Said El and Baroud, Hiba and Vorobeychik, Yevgeniy and Kochenderfer, Mykel and Dubey, Abhishek},
     keywords = {Resource allocation for smart cities, Incident prediction, Computer aided dispatch, Decision making under uncertainty, Accident analysis, Emergency response},
     preprint = {https://arxiv.org/abs/2006.04200}
   }
   

   In the last fifty years, researchers have developed statistical, data-driven, analytical, and algorithmic approaches for designing and improving emergency response management (ERM) systems. The problem has been noted as inherently difficult and constitutes spatio-temporal decision making under uncertainty, which has been addressed in the literature with varying assumptions and approaches. This survey provides a detailed review of these approaches, focusing on the key challenges and issues regarding four sub-processes: (a) incident prediction, (b) incident detection, (c) resource allocation, and (c) computer-aided dispatch for emergency response. We highlight the strengths and weaknesses of prior work in this domain and explore the similarities and differences between different modeling paradigms. We conclude by illustrating open challenges and opportunities for future research in this complex domain.

4. H. Baroud, A. Dubey, S. M. Vazirizade, and others, Collaborative Research Project to Coordinate the Data from the CRASH Predictive Analytics Program Between TDOT and TDOSHS, Tennessee. Department of Transportation, 2021.
   Bibtex PDF

   @techreport{dot_61069_DS1,
     title = {Collaborative Research Project to Coordinate the Data from the CRASH Predictive Analytics Program Between TDOT and TDOSHS},
     author = {Baroud, Hiba and Dubey, Abhishek and Vazirizade, Sayyed Mohsen and others},
     year = {2021},
     institution = {Tennessee. Department of Transportation}
   }
   

5. Y. Senarath, A. Mukhopadhyay, S. Vazirizade, hemant Purohit, S. Nannapaneni, and A. Dubey, Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services, in 21st IEEE International Conference on Data Mining (ICDM 2021), 2021.
   Bibtex Abstract PDF

   @inproceedings{ICDM_2021,
     author = {Senarath, Yasas and Mukhopadhyay, Ayan and Vazirizade, Sayyed and hemant Purohit and Nannapaneni, Saideep and Dubey, Abhishek},
     booktitle = {21st IEEE International Conference on Data Mining (ICDM 2021)},
     tag = {ai4cps,incident},
     title = {Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services},
     year = {2021}
   }
   

   Emergency response is highly dependent on the time of incident reporting. Unfortunately, the traditional approach to receiving incident reports (e.g., calling 911 in the USA) has time delays. Crowdsourcing platforms such as Waze provide an opportunity for early identification of incidents. However, detecting incidents from crowdsourced data streams is difficult due to the challenges of noise and uncertainty associated with such data. Further, simply optimizing over detection accuracy can compromise spatial-temporal localization of the inference, thereby making such approaches infeasible for real-world deployment. This paper presents a novel problem formulation and solution approach for practitioner-centered incident detection using crowdsourced data by using emergency response management as a case-study. The proposed approach CROME (Crowdsourced Multi-objective Event Detection) quantifies the relationship between the performance metrics of incident classification (e.g., F1 score) and the requirements of model practitioners (e.g., 1 km. radius for incident detection). First, we show how crowdsourced reports, ground-truth historical data, and other relevant determinants such as traffic and weather can be used together in a Convolutional Neural Network (CNN) architecture for early detection of emergency incidents. Then, we use a Pareto optimization-based approach to optimize the output of the CNN in tandem with practitioner-centric parameters to balance detection accuracy and spatial-temporal localization. Finally, we demonstrate the applicability of this approach using crowdsourced data from Waze and traffic accident reports from Nashville, TN, USA. Our experiments demonstrate that the proposed approach outperforms existing approaches in incident detection while simultaneously optimizing the needs for realworld deployment and usability.

6. M. Wilbur, A. Mukhopadhyay, S. Vazirizade, P. Pugliese, A. Laszka, and A. Dubey, Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-Task and Inductive Transfer Learning, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2021.
   Bibtex Abstract PDF

   @inproceedings{ecml2021,
     author = {Wilbur, Michael and Mukhopadhyay, Ayan and Vazirizade, Sayyed and Pugliese, Philip and Laszka, Aron and Dubey, Abhishek},
     title = {Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-Task and Inductive Transfer Learning},
     booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
     year = {2021},
     tag = {ai4cps,transit}
   }
   

   Public transit agencies are focused on making their fixed-line bus systems more energy efficient by introducing electric (EV) and hybrid (HV) vehicles to their eets. However, because of the high upfront cost of these vehicles, most agencies are tasked with managing a mixed-fleet of internal combustion vehicles (ICEVs), EVs, and HVs. In managing mixed-fleets, agencies require accurate predictions of energy use for optimizing the assignment of vehicles to transit routes, scheduling charging, and ensuring that emission standards are met. The current state-of-the-art is to develop separate neural network models to predict energy consumption for each vehicle class. Although different vehicle classes’ energy consumption depends on a varied set of covariates, we hypothesize that there are broader generalizable patterns that govern energy consumption and emissions. In this paper, we seek to extract these patterns to aid learning to address two problems faced by transit agencies. First, in the case of a transit agency which operates many ICEVs, HVs, and EVs, we use multi-task learning (MTL) to improve accuracy of forecasting energy consumption. Second, in the case where there is a significant variation in vehicles in each category, we use inductive transfer learning (ITL) to improve predictive accuracy for vehicle class models with insufficient data. As this work is to be deployed by our partner agency, we also provide an online pipeline for joining the various sensor streams for xed-line transit energy prediction. We find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.

7. S. M. Vazirizade, A. Mukhopadhyay, G. Pettet, S. E. Said, H. Baroud, and A. Dubey, Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems, 2021.
   Bibtex Abstract PREPRINT

   @inproceedings{vazirizade2021learning,
     title = {Learning Incident Prediction Models Over Large Geographical Areas for Emergency Response Systems},
     author = {Vazirizade, Sayyed Mohsen and Mukhopadhyay, Ayan and Pettet, Geoffrey and Said, Said El and Baroud, Hiba and Dubey, Abhishek},
     year = {2021},
     eprint = {2106.08307},
     archiveprefix = {arXiv},
     tag = {ai4cps,incident},
     preprint = {https://arxiv.org/abs/2106.08307},
     primaryclass = {cs.LG}
   }
   

   Principled decision making in emergency response management necessitates the use of statistical models that predict the spatial-temporal likelihood of incident occurrence. These statistical models are then used for proactive stationing which allocates first responders across the spatial area in order to reduce overall response time. Traditional methods that simply aggregate past incidents over space and time fail to make useful short-term predictions when the spatial region is large and focused on fine-grained spatial entities like interstate highway networks. This is partially due to the sparsity of incidents with respect to the area in consideration. Further, accidents are affected by several covariates, and collecting, cleaning, and managing multiple streams of data from various sources is challenging for large spatial areas. In this paper, we highlight how this problem is being solved for the state of Tennessee, a state in the USA with a total area of over 100,000 sq. km. Our pipeline, based on a combination of synthetic resampling, non-spatial clustering, and learning from data can efficiently forecast the spatial and temporal dynamics of accident occurrence, even under sparse conditions. In the paper, we describe our pipeline that uses data related to roadway geometry, weather, historical accidents, and real-time traffic congestion to aid accident forecasting. To understand how our forecasting model can affect allocation and dispatch, we improve upon a classical resource allocation approach. Experimental results show that our approach can significantly reduce response times in the field in comparison with current approaches followed by first responders.