@article{paviaIJCAI24AISG,
  title = {Deploying Mobility-On-Demand for All by Optimizing Paratransit Services},
  author = {Pavia, Sophie and Rogers, David and Sivagnanam, Amutheezan and Wilbur, Michael and Edirimanna, Danushka and Kim, Youngseo and Pugliese, Philip and Samaranayake, Samitha and Laszka, Aron and Mukhopadhyay, Ayan and Dubey, Abhishek},
  year = {2024},
  journal = {International Joint Conference on Artificial Intelligence (IJCAI)}
}

@article{paviaIJCAI24demo,
  title = {SmartTransit.AI: A Dynamic Paratransit and Microtransit Application},
  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},
  year = {2024},
  journal = {International Joint Conference on Artificial Intelligence (IJCAI)}
}

@inproceedings{samir2024smartcomp,
  title = {A Graph Neural Network Framework for Imbalanced Bus Ridership Forecasting},
  author = {Gupta, Samir and Khanna, Agrima and Talusan, Jose Paolo and Said, Anwar and Freudberg, Dan and Mukhopadhyay, Ayan and Dubey, Abhishek},
  year = {2024},
  month = jun,
  booktitle = {2024 IEEE International Conference on Smart Computing (SMARTCOMP)},
  volume = {},
  number = {}
}

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.

@inproceedings{talusan2024ICCPS,
  title = {An Online Approach to Solving Public Transit Stationing and Dispatch Problem},
  author = {Talusan, Jose Paolo and Han, Chaeeun and Mukhopadhyay, Ayan and Laszka, Aron and Freudberg, Dan and Dubey, Abhishek},
  year = {2024},
  booktitle = {Proceedings of the ACM/IEEE 15th International Conference on Cyber-Physical Systems (ICCPS)},
  location = {Hong Kong, China},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {ICCPS '24},
  numpages = {10}
}

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 with the Metropolitan Transportation Authority for a mid-sized city in the USA 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 using 3 years of data from our partner agency. Our experiments show that the proposed framework serves 2% more passengers while reducing deadhead miles by 40%.

@inproceedings{talusan2024AAMAS,
  title = {Forecasting and Mitigating Disruptions in Public Bus Transit Services},
  author = {Han, Chaeeun and Talusan, Jose Paolo and Freudberg, Dan and Mukhopadhyay, Ayan and Dubey, Abhishek and Laszka, Aron},
  year = {2024},
  booktitle = {Proceedings of the 23rd Conference on Autonomous Agents and MultiAgent Systems, {AAMAS} 2024, Auckland, New Zealand},
  location = {Auckland, New Zealand},
  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
  address = {Richland, SC},
  series = {AAMAS '24},
  numpages = {9},
  keywords = {Public transportation, Data-driven optimization, Disruption forecasting, Simulation, Metaheuristic optimization}
}

Public transportation systems often suffer from unexpected fluctuations in demand and disruptions, such as mechanical failures and medical emergencies. These fluctuations and disruptions lead to delays and overcrowding, which are detrimental to the passengers’ experience and to the overall performance of the transit service. To proactively mitigate such events, many transit agencies station substitute (reserve) vehicles throughout their service areas, which they can dispatch to augment or replace vehicles on routes that suffer overcrowding or disruption. However, determining the optimal locations where substitute vehicles should be stationed is a challenging problem due to the inherent randomness of disruptions and due to the combinatorial nature of selecting locations across a city. In collaboration with the transit agency of a mid-size U.S. city, we address this problem by introducing data-driven statistical and machine-learning models for forecasting disruptions and an effective randomized local-search algorithm for selecting locations where substitute vehicles are to be stationed. Our research demonstrates promising results in proactive disruption management, offering a practical and easily implementable solution for transit agencies to enhance the reliability of their services. Our results resonate beyond mere operational efficiency—by advancing proactive strategies, our approach fosters more resilient and accessible public transportation, contributing to equitable urban mobility and ultimately benefiting the communities that rely on public transportation the most.

@article{shadi2024TRB,
  title = {Beyond spatial proximity: Understanding segregation and job accessibility among the racial and low-income population in Chattanooga city.},
  author = {Tehrani, Shadi and Ward, Savannah and Ward, Chandra and Speer, Paul and Crawford, Megan and Dubey, Abhishek and Pugliese, Philip},
  year = {2024},
  journal = {Transportation Research Board 104th Annual Meeting}
}

In many cities across the United States, racial minorities and low-income households predominantly reside within the urban core. This pattern, a legacy of historic segregative practices such as restrictive deeds and redlining, remains despite laws and regulations designed to eliminate racial residential segregation. Surprisingly, many transportation accessibility studies suggest that low-income and disproportionately black and brown communities, despite their marginalized status, are not necessarily disadvantaged in their ability to access job opportunities because their central urban locations often position them favorably in relation to the wide distribution of employment opportunities across metropolitan areas. However, methods of job accessibility diverge across different racial, ethnic, and socioeconomic, and understanding this complex issue requires more nuanced exploration. To provide clearer insight into this multifaceted issue, our research employed a blend of spatial and statistical analysis, visualization of segregation indices, and measuring accessibility to jobs by different modes of transportation such as walking, driving and public transit in Chattanooga, Tennessee using a gravity model approach. Our findings reveal that while a majority of racial minorities and low-income individuals possess an advantage in job accessibility due to their central locations, a substantial proportion remain seriously disadvantaged. Moreover, our analyses of various socioeconomic and housing variables further underscore the intricate dynamics at play. Therefore, it becomes apparent that while central urban locations may provide a degree of accessibility, the reality is multifaceted and deeply intertwined with historic and systemic disparities which necessitates a comprehensive understanding and remediation of these underlying issues.

@article{Pavia2024TransitDesign,
  title = {Transit Design: A Holistic Approach Considering Equity and Efficiency},
  author = {Pavia, Sophie and Omidvar Tehrani, Shadi and Edirimanna, Danushka and Sen, Rishav and Wilbur, Michael and Ward, Chandra and Speer, Paul and Pugliese, Philip and Mukhopadhyay, Ayan and Laszka, Aron and Samaranayake, Samitha and Dubey, Abhishek},
  year = {2024},
  address = {Washington, D.C.},
  journal = {Transportation Research Board 104th Annual Meeting}
}

@article{talusan2023tcps2,
  title = {HPRoP: Hierarchical Privacy-Preserving Route Planning for Smart Cities},
  author = {Tiausas, Francis and Yasumoto, Keiichi and Talusan, Jose Paolo and Yamana, Hayato and Yamaguchi, Hirozumi and Bhattacharjee, Shameek and Dubey, Abhishek and Das, Sajal K.},
  year = {2023},
  month = jun,
  journal = {ACM Trans. Cyber-Phys. Syst.},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3603381},
  issn = {2378-962X},
  url = {},
  note = {Just Accepted},
  keywords = {Security and privacy, Privacy-preserving protocols, Domain-specific security and privacy architectures}
}

Route Planning Systems (RPS) are a core component of autonomous personal transport systems essential for safe and efficient navigation of dynamic urban environments with the support of edge-based smart city infrastructure, but they also raise concerns about user route privacy in the context of both privately-owned and commercial vehicles. Numerous high profile data breaches in recent years have fortunately motivated research on privacy-preserving RPS, but most of them are rendered impractical by greatly increased communication and processing overhead. We address this by proposing an approach called Hierarchical Privacy-Preserving Route Planning (HPRoP) which divides and distributes the route planning task across multiple levels, and protects locations along the entire route. This is done by combining Inertial Flow partitioning, Private Information Retrieval (PIR), and Edge Computing techniques with our novel route planning heuristic algorithm. Normalized metrics were also formulated to quantify the privacy of the source/destination points (endpoint location privacy) and the route itself (route privacy). Evaluation on a simulated road network showed that HPRoP reliably produces routes differing only by <=20% in length from optimal shortest paths, with completion times within  25 seconds which is reasonable for a PIR-based approach. On top of this, more than half of the produced routes achieved near-optimal endpoint location privacy ( 1.0) and good route privacy (>= 0.8).

@article{talusan2023tcps1,
  title = {Scalable Pythagorean Mean Based Incident Detection in Smart Transportation Systems},
  author = {Islam, Md. Jaminur and Talusan, Jose Paolo and Bhattacharjee, Shameek and Tiausas, Francis and Dubey, Abhishek and Yasumoto, Keiichi and Das, Sajal K.},
  year = {2023},
  month = jun,
  journal = {ACM Trans. Cyber-Phys. Syst.},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  doi = {10.1145/3603381},
  issn = {2378-962X},
  url = {https://doi.org/10.1145/3603381},
  note = {Just Accepted},
  keywords = {Incident Detection, Weakly Unsupervised Learning, Graph Algorithms, Approximation Algorithm., Regression, Smart Transportation, Cluster Analysis, Anomaly Detection}
}

Modern smart cities need smart transportation solutions to quickly detect various traffic emergencies and incidents in the city to avoid cascading traffic disruptions. To materialize this, roadside units and ambient transportation sensors are being deployed to collect speed data that enables the monitoring of traffic conditions on each road segment. In this paper, we first propose a scalable 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. Second, using cluster-level detection, we propose a folded Gaussian classifier to pinpoint the particular segment in a cluster where the incident happened in an automated manner. We perform extensive experimental validation using mobility data collected from four cities in Tennessee, compare with the state-of-the-art ML methods, to prove that our method can detect incidents within each cluster in real-time and outperforms known ML methods.

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

@misc{pavia2023designing_abstract,
  title = {Designing Equitable Transit Networks},
  author = {Pavia, Sophie and Mori, J. Carlos Martinez and Sharma, Aryaman and Pugliese, Philip and Dubey, Abhishek and Samaranayake, Samitha and Mukhopadhyay, Ayan},
  year = {2023},
  journal = {INFORMS Transportation and Logistics Society Conference (extended abstract) (TSL)}
}

@inproceedings{wilbur2023mobility,
  title = {Mobility-On-Demand Transportation: A System for Microtransit and Paratransit Operations},
  author = {Wilbur, Michael and Coursey, Maxime and Koirala, Pravesh and Al-Quran, Zakariyya and Pugliese, Philip and Dubey, Abhishek},
  year = {2023},
  booktitle = {Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023)},
  location = {San Antonio, TX, USA},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  series = {ICCPS '23},
  pages = {260--261},
  doi = {10.1145/3576841.3589625},
  isbn = {9798400700361},
  url = {https://doi.org/10.1145/3576841.3589625},
  numpages = {2},
  keywords = {ridepooling, software, mobility-on-demand, transit operations}
}

New rideshare and shared-mobility services have transformed urban mobility in recent years. Therefore, transit agencies are looking for ways to adapt to this rapidly changing environment. In this space, ridepooling has 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. This brings multiple challenges. 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. Therefore, we propose an on-demand transportation scheduling software for microtransit and paratransit services. This 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. Lastly, we discuss the challenges in adapting state-of-the-art methods to real-world operations.

@inproceedings{youngseo2023,
  title = {Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows},
  author = {Kim, Youngseo and Edirimanna, Danushka and Wilbur, Michael and Pugliese, Philip and Laszka, Aron and Dubey, Abhishek and Samaranayake, Samitha},
  booktitle = {Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23)},
  tag = {ai4cps,transit},
  year = {2023}
}

The offline pickup and delivery problem with time windows (PDPTW) is a classical combinatorial optimization problem in the transportation community, which has proven to be very challenging computationally. Due to the complexity of the problem, practical problem instances can be solved only via heuristics, which trade-off solution quality for computational tractability. Among the various heuristics, a common strategy is problem decomposition, that is, the reduction of a largescale problem into a collection of smaller sub-problems, with spatial and temporal decompositions being two natural approaches. While spatial decomposition has been successful in certain settings, effective temporal decomposition has been challenging due to the difficulty of stitching together the sub-problem solutions across the decomposition boundaries. In this work, we introduce a novel temporal decomposition scheme for solving a class of PDPTWs that have narrow time windows, for which it is able to provide both fast and high quality solutions. We utilize techniques that have been popularized recently in the context of online dial-a-ride problems along with the general idea of rolling horizon optimization. To the best of our knowledge, this is the first attempt to solve offline PDPTWs using such an approach. To show the performance and scalability of our framework, we use the optimization of paratransit services as a motivating example. Due to the lack of benchmark solvers similar to ours (i.e., temporal decomposition with an online solver), we compare our results with an offline heuristic algorithm using Google OR-Tools. In smaller problem instances (with an average of 129 requests per instance), the baseline approach is as competitive as our framework. However, in larger problem instances (approximately 2,500 requests per instance), our framework is more scalable and can provide good solutions to problem instances of varying degrees of difficulty, while the baseline algorithm often fails to find a feasible solution within comparable compute times.

@inproceedings{rishav2022eEnergy,
  author = {Sen, Rishav and Bharati, Alok Kumar and Khaleghian, Seyedmehdi and Ghosal, Malini and Wilbur, Michael and Tran, Toan and Pugliese, Philip and Sartipi, Mina and Neema, Himanshu and Dubey, Abhishek},
  title = {E-Transit-Bench: Simulation Platform for Analyzing Electric Public Transit Bus Fleet Operations},
  year = {2022},
  isbn = {9781450393973},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3538637.3539586},
  doi = {10.1145/3538637.3539586},
  booktitle = {Proceedings of the Thirteenth ACM International Conference on Future Energy Systems},
  pages = {532–541},
  numpages = {10},
  keywords = {model-integration, cyber-physical systems, co-simulation, powergrid simulation, traffic simulation},
  location = {Virtual Event},
  series = {e-Energy '22}
}

When electrified transit systems make grid aware choices, improved social welfare is achieved by reducing grid stress, reducing system loss, and minimizing power quality issues. Electrifying transit fleet has numerous challenges like non availability of buses during charging, varying charging costs and so on, that are related the electric grid behavior. However, transit systems do not have access to the information about the co-evolution of the grid’s power flow and therefore cannot account for the power grid’s needs in its day-to-day operation. In this paper we propose a framework of transportation-grid co-simulation, analyzing the spatio-temporal interaction between the transit operations with electric buses and the power distribution grid. Real-world data for a day’s traffic from Chattanooga city’s transit system is simulated in SUMO and integrated with a realistic distribution grid simulation (using GridLAB-D) to understand the grid impact due to transit electrification. Charging information is obtained from the transportation simulation to feed into grid simulation to assess the impact of charging. We also discuss the impact to the grid with higher degree of transit electrification that further necessitates such an integrated transportation-grid co-simulation to operate the integrated system optimally. Our future work includes extending the platform for optimizing the charging and trip assignment operations.

@inproceedings{kang2022generative,
  author = {Kang, Zhuangwei and Mukhopadhyay, Ayan and Gokhale, Aniruddha and Wen, Shijie and Dubey, Abhishek},
  booktitle = {2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)},
  title = {Traffic Anomaly Detection Via Conditional Normalizing Flow},
  year = {2022},
  volume = {},
  number = {},
  pages = {2563-2570},
  doi = {10.1109/ITSC55140.2022.9922061}
}

Traffic congestion anomaly detection is of paramount importance in intelligent traffic systems. The goals of transportation agencies are two-fold: to monitor the general traffic conditions in the area of interest and to locate road segments under abnormal congestion states. Modeling congestion patterns can achieve these goals for citywide roadways, which amounts to learning the distribution of multivariate time series (MTS). However, existing works are either not scalable or unable to capture the spatial-temporal information in MTS simultaneously. To this end, we propose a principled and comprehensive framework consisting of a data-driven generative approach that can perform tractable density estimation for detecting traffic anomalies. Our approach first clusters segments in the feature space and then uses conditional normalizing flow to identify anomalous temporal snapshots at the cluster level in an unsupervised setting. Then, we identify anomalies at the segment level by using a kernel density estimator on the anomalous cluster. Extensive experiments on synthetic datasets show that our approach significantly outperforms several state-of-the-art congestion anomaly detection and diagnosis methods in terms of Recall and F1-Score. We also use the generative model to sample labeled data, which can train classifiers in a supervised setting, alleviating the lack of labeled data for anomaly detection in sparse settings.

@misc{sophiefairtransit2022arxiv,
  doi = {10.48550/ARXIV.2212.12007},
  url = {https://arxiv.org/abs/2212.12007},
  author = {Pavia, Sophie and Mori, J. Carlos Martinez and Sharma, Aryaman and Pugliese, Philip and Dubey, Abhishek and Samaranayake, Samitha and Mukhopadhyay, Ayan},
  keywords = {Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Designing Equitable Transit Networks},
  publisher = {arXiv},
  year = {2022},
  preprint = {https://arxiv.org/abs/2212.12007},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

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.

@article{wilbur2022_trr,
  author = {Wilbur, Michael and Ayman, Afiya and Sivagnanam, Amutheezan and Ouyang, Anna and Poon, Vincent and Kabir, Riyan and Vadali, Abhiram and Pugliese, Philip and Freudberg, Daniel and Laszka, Aron and Dubey, Abhishek},
  title = {Impact of COVID-19 on Public Transit Accessibility and Ridership},
  journal = {Transportation Research Record},
  year = {2022},
  doi = {}
}

The novel coronavirus COVID-19 has radically transformed travel behavior in urban areas throughout the world. Foremost, agencies must determine how to provide adequate service while navigating a rapidly changing environment with reduced revenues. Even as COVID-19 related restrictions are lifted, transit agencies are increasingly concerned with their ability to adapt to fundamental changes in ridership behavior and public transit usage. To aid transit agencies in becoming more adaptive to sudden or persistent shifts in ridership patterns, we aim to address three questions. First, to what degree has the COVID-19 pandemic affected ridership of fixed-line public transit and what is the relationship between reduced demand and reduced vehicle trips? Second, how has COVID-19 changed ridership patterns and are these changes expected to persist after restrictions are lifted? Lastly, are there disparities in ridership changes across socio-economic groups and the mobility impaired? We focus on Nashville and Chattanooga, TN where we compare ridership demand and reduced vehicle trips imposed by the two cities. These patterns are compared to anonymized mobile location data to study the relationship between mobility patterns and transit usage. Additionally, we provide a correlation analysis and explanatory multiple variable linear model to investigate the relationship between socio-economic indicators and changes in transit ridership. Lastly, we include an analysis of changes in paratransit demand before and during COVID-19. We find that ridership initially dropped by 66% and 65% over the first month of the pandemic for Nashville and Chattanooga respectively before starting a moderate recovery. Additionally, cellular mobility patterns in Chattanooga indicate that foot traffic recovered to a greater degree between mid-April, 2020 and the last week in June, 2020 than transit ridership. Our models show that education level had a statistically significant impact on change in fixed-line bus transit. Lastly, we found that the distribution of changes in demand for paratransit services are similar to to our findings from fixed-line bus transit.

@article{btesim2022,
  author = {Sen, Rishav and Tran, Toan and Khaleghian, Seyedmehdi and Sartipi, Mina and Neema, Himanshu and Dubey, Abhishek},
  title = {BTE-Sim: Fast simulation environment for public transportation},
  year = {2022},
  isbn = {},
  publisher = {IEEE},
  address = {},
  url = {},
  booktitle = {2022 IEEE International Conference on Big Data},
  pages = {},
  numpages = {8},
  keywords = {public transit, fast traffic simulation, model integration, data processing, road speed calibration},
  location = {},
  series = {}
}

@misc{talusan2022apc,
  doi = {10.48550/ARXIV.2210.04989},
  url = {https://arxiv.org/abs/2210.04989},
  author = {Talusan, Jose Paolo and Mukhopadhyay, Ayan and Freudberg, Dan and Dubey, Abhishek},
  keywords = {Machine Learning (cs.LG), Computers and Society (cs.CY), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {On Designing Day Ahead and Same Day Ridership Level Prediction Models for City-Scale Transit Networks Using Noisy APC Data},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{sivagnanam2022offline,
  author = {Sivagnanam, Amutheezan and Kadir, Salah Uddin and Mukhopadhyay, Ayan and Pugliese, Philip and Dubey, Abhishek and Samaranayake, Samitha and Laszka, Aron},
  title = {Offline Vehicle Routing Problem with Online Bookings: A Novel Problem Formulation with Applications to Paratransit},
  booktitle = {31st International Joint Conference on Artificial Intelligence (IJCAI)},
  year = {2022},
  month = jul
}

@inproceedings{ayman2022neural,
  author = {Ayman, Afiya and Martinez, Juan and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
  title = {Neural Architecture and Feature Search for Predicting the Ridership of Public Transportation Routes},
  booktitle = {8th IEEE International Conference on Smart Computing (SMARTCOMP)},
  year = {2022},
  month = jun
}

@inproceedings{ijcai22Ayan,
  title = {ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria},
  author = {Nair, Vineet and Prakash, Kritika and Wilbur, Michael and Taneja, Aparna and Namblard, Corinne and Adeyemo, Oyindamola and Dubey, Abhishek and Adereni, Abiodun and Tambe, Milind and Mukhopadhyay, Ayan},
  doi = {https://doi.org/10.48550/ARXIV.2204.13663},
  url = {https://arxiv.org/abs/2204.13663},
  booktitle = {31st International Joint Conference on Artificial Intelligence (IJCAI)},
  year = {2022},
  month = jul
}

More than 5 million children under five years die from largely preventable or treatable medical conditions every year, with an overwhelmingly large proportion of deaths occurring in under-developed countries with low vaccination uptake. One of the United Nations’ sustainable development goals (SDG 3) aims to end preventable deaths of newborns and children under five years of age. We focus on Nigeria, where the rate of infant mortality is appalling. We collaborate with HelpMum, a large non-profit organization in Nigeria to design and optimize the allocation of heterogeneous health interventions under uncertainty to increase vaccination uptake, the first such collaboration in Nigeria. Our framework, ADVISER: AI-Driven Vaccination Intervention Optimiser, is based on an integer linear program that seeks to maximize the cumulative probability of successful vaccination. Our optimization formulation is intractable in practice. We present a heuristic approach that enables us to solve the problem for real-world use-cases. We also present theoretical bounds for the heuristic method. Finally, we show that the proposed approach outperforms baseline methods in terms of vaccination uptake through experimental evaluation. HelpMum is currently planning a pilot program based on our approach to be deployed in the largest city of Nigeria, which would be the first deployment of an AIdriven vaccination uptake program in the country and hopefully, pave the way for other data-driven programs to improve health outcomes in Nigeria.

@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.

@inproceedings{wilbur2022,
  title = {An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services},
  booktitle = {ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)},
  publisher = {IEEE},
  author = {Wilbur, Michael and Kadir, Salah and Kim, Youngseo and Pettet, Geoffrey and Mukhopadhyay, Ayan and Pugliese, Philip and Samaranayake, Samitha and Laszka, Aron and Dubey, Abhishek},
  year = {2022},
  month = apr
}

Many transit agencies operating paratransit and microtransit services have to respond to trip requests that arrive in real-time, which entails solving hard combinatorial and sequential decision-making problems under uncertainty. To avoid decisions that lead to significant inefficiency in the long term, vehicles should be allocated to requests by optimizing a non-myopic utility function or by batching requests together and optimizing a myopic utility function. While the former approach is typically offline, the latter can be performed online. We point out two major issues with such approaches when applied to paratransit services in practice. First, it is difficult to batch paratransit requests together as they are temporally sparse. Second, the environment in which transit agencies operate changes dynamically (e.g., traffic conditions can change over time), causing the estimates that are learned offline to become stale. To address these challenges, we propose a fully online approach to solve the dynamic vehicle routing problem (DVRP) with time windows and stochastic trip requests that is robust to changing environmental dynamics by construction. We focus on scenarios where requests are relatively sparse—our problem is motivated by applications to paratransit services. We formulate DVRP as a Markov decision process and use Monte Carlo tree search to compute near-optimal actions for any given state. Accounting for stochastic requests while optimizing a non-myopic utility function is computationally challenging; indeed, the action space for such a problem is intractably large in practice. To tackle the large action space, we leverage the structure of the problem to design heuristics that can sample promising actions for the tree search. Our experiments using real-world data from our partner agency show that the proposed approach outperforms existing state-of-the-art approaches both in terms of performance and robustness.

@article{aymantoit2020,
  author = {Ayman, Afiya and Sivagnanam, Amutheezan and Wilbur, Michael and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
  title = {Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles},
  journal = {ACM Transactions on Internet Technology},
  volume = {22},
  number = {1},
  pages = {7:1--7:29},
  year = {2022},
  month = feb
}

Due to the high upfront cost of electric vehicles, many public transit agencies can afford only mixed fleets of internal combustion and electric vehicles. Optimizing the operation of such mixed fleets is challenging because it requires accurate trip-level predictions of electricity and fuel use as well as efficient algorithms for assigning vehicles to transit routes. We present a novel framework for the data-driven prediction of trip-level energy use for mixed-vehicle transit fleets and for the optimization of vehicle assignments, which we evaluate using data collected from the bus fleet of CARTA, the public transit agency of Chattanooga, TN. We first introduce a data collection, storage, and processing framework for system-level and high-frequency vehicle-level transit data, including domain-specific data cleansing methods. We train and evaluate machine learning models for energy prediction, demonstrating that deep neural networks attain the highest accuracy. Based on these predictions, we formulate the problem of minimizing energy use through assigning vehicles to fixed-route transit trips. We propose an optimal integer program as well as efficient heuristic and meta-heuristic algorithms, demonstrating the scalability and performance of these algorithms numerically using the transit network of CARTA.

@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}
}

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.

@article{yucheTRB21,
  author = {Zhang, Yunteng and Chen, Yuche and Sun, Ruixiao and Dubey, Abhishek and Pugliese, Philip},
  title = {A Data Partitioning-based Artificial Neural Network Model to Estimate Real-driving  Energy Consumption of Electric Buses},
  journal = {Transportation Research Board 100th Annual Meeting},
  year = {2021}
}

Reliable and accurate estimation of electric bus energy consumption is critical for electric bus operation and planning. But energy prediction for electric buses is challenging because of diversified driving cycles of transit services. We propose to establish a data-partition based artificial neural network model to predict energy consumption of electric buses at microscopic level and link level. The purpose of data partitioning is to separate data into charging and discharging modes and then develop most efficient prediction for each mode. We utilize a long-term transit operation and energy consumption monitoring dataset from Chattanooga, SC to train and test our neural network models. The microscopic model estimates energy consumption of electric bus at 1Hz frequency based on instantaneous driving and road environment data. The prediction errors of micro model ranges between 8% and 15% on various instantaneous power demand, vehicle specific power, bins. The link-level model is to predict average energy consumption rate based on aggregated traffic pattern parameters derived from instantaneous driving data at second level. The prediction errors of link-level model are around 15% on various average speed, temperature and road grade conditions. The validation results demonstrate our models’ capability to capture impacts of driving, meteorology and road grade on electric bus energy consumption at different temporal and spatial resolution.

@article{perivier2021real,
  title = {Real-time Approximate Routing for Smart Transit Systems},
  author = {Banerjee, Siddhartha and Hssaine, Chamsi and P{\'e}rivier, No{\'e}mie and Samaranayake, Samitha},
  journal = {Proceedings of the ACM on Measurement and Analysis of Computing Systems},
  volume = {5},
  number = {2},
  pages = {1--30},
  year = {2021},
  publisher = {ACM New York, NY, USA}
}

@inproceedings{sun2021transitgym,
  author = {Sun, Ruixiao and Gui, Rongze and Neema, Himanshu and Chen, Yuche and Ugirumurera, Juliette and Severino, Joseph and Pugliese, Philip and Laszka, Aron and Dubey, Abhishek},
  title = {Transit-Gym: A Simulation and Evaluation Engine for Analysis of Bus Transit Systems},
  booktitle = {Preprint at Arxiv. Accepted at IEEE SmartComp.},
  year = {2021},
  archiveprefix = {arXiv},
  eprint = {2107.00105},
  preprint = {https://arxiv.org/abs/2107.00105},
  primaryclass = {eess.SY}
}

Public transit is central to cultivating equitable communities. Meanwhile, the novel coronavirus disease COVID-19 and associated social restrictions has radically transformed ridership behavior in urban areas. Perhaps the most concerning aspect of the COVID-19 pandemic is that low-income and historically marginalized groups are not only the most susceptible to economic shifts but are also most reliant on public transportation. As revenue decreases, transit agencies are tasked with providing adequate public transportation services in an increasingly hostile economic environment. Transit agencies therefore have two primary concerns. First, how has COVID-19 impacted ridership and what is the new post-COVID normal? Second, how has ridership varied spatio-temporally and between socio-economic groups? In this work we provide a data-driven analysis of COVID-19’s affect on public transit operations and identify temporal variation in ridership change. We then combine spatial distributions of ridership decline with local economic data to identify variation between socio-economic groups. We find that in Nashville and Chattanooga, TN, fixed-line bus ridership dropped by 66.9% and 65.1% from 2019 baselines before stabilizing at 48.4% and 42.8% declines respectively. The largest declines were during morning and evening commute time. Additionally, there was a significant difference in ridership decline between the highest-income areas and lowest-income areas (77% vs 58%) in Nashville.

@article{sandoval2021data,
  author = {Sandoval, Ricardo and Van Geffen, Caleb and Wilbur, Michael and Hall, Brandon and Dubey, Abhishek and Barbour, William and Work, Daniel B.},
  title = {Data driven methods for effective micromobility parking},
  journal = {Transportation Research Interdisciplinary Perspectives},
  year = {2021}
}

@article{SUN2021102637,
  title = {Hybrid electric buses fuel consumption prediction based on real-world driving data},
  journal = {Transportation Research Part D: Transport and Environment},
  volume = {91},
  pages = {102637},
  year = {2021},
  issn = {1361-9209},
  doi = {https://doi.org/10.1016/j.trd.2020.102637},
  url = {https://www.sciencedirect.com/science/article/pii/S1361920920308221},
  author = {Sun, Ruixiao and Chen, Yuche and Dubey, Abhishek and Pugliese, Philip},
  keywords = {Hybrid diesel transit bus, Artificial neural network, Fuel consumption prediction}
}

Estimating fuel consumption by hybrid diesel buses is challenging due to its diversified operations and driving cycles. In this study, long-term transit bus monitoring data were utilized to empirically compare fuel consumption of diesel and hybrid buses under various driving conditions. Artificial neural network (ANN) based high-fidelity microscopic (1 Hz) and mesoscopic (5–60 min) fuel consumption models were developed for hybrid buses. The microscopic model contained 1 Hz driving, grade, and environment variables. The mesoscopic model aggregated 1 Hz data into 5 to 60-minute traffic pattern factors and predicted average fuel consumption over its duration. The prediction results show mean absolute percentage errors of 1–2% for microscopic models and 5–8% for mesoscopic models. The data were partitioned by different driving speeds, vehicle engine demand, and road grade to investigate their impacts on prediction performance.

@inproceedings{juan21,
  title = {Predicting Public Transportation Load to Estimate the Probability of Social Distancing Violations},
  author = {Martinez, Juan and Ayman, Ayan Mukhopadhyay Afiya and Wilbur, Michael and Pugliese, Philip and Freudberg, Dan and Laszka, Aron and Dubey, Abhishek},
  booktitle = {Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence (AAAI-21)},
  year = {2021}
}

Public transit agencies struggle to maintain transit accessibility with reduced resources, unreliable ridership data, reduced vehicle capacities due to social distancing, and reduced services due to driver unavailability. In collaboration with transit agencies from two large metropolitan areas in the USA, we are designing novel approaches for addressing the afore-mentioned challenges by collecting accurate real-time ridership data, providing guidance to commuters, and performing operational optimization for public transit. We estimate rider-ship data using historical automated passenger counting data, conditional on a set of relevant determinants. Accurate ridership forecasting is essential to optimize the public transit schedule, which is necessary to improve current fixed lines with on-demand transit. Also, passenger crowding has been a problem for public transportation since it deteriorates passengers’ wellbeing and satisfaction. During the COVID-19 pandemic, passenger crowding has gained importance since it represents a risk for social distancing violations. Therefore, we are creating optimization models to ensure that social distancing norms can be adequately followed while ensuring that the total demand for transit is met. We will then use accurate forecasts for operational optimization that includes \textit(a) proactive fixed-line schedule optimization based on predicted demand, \textit(b) dispatch of on-demand micro-transit, prioritizing at-risk populations, and \textit(c) allocation of vehicles to transit and cargo trips, considering exigent vehicle maintenance requirements (\textiti.e., disinfection). Finally, this paper presents some initial results from our project regarding the estimation of ridership in public transit.

@inproceedings{wilbur21,
  title = {Efficient Data Management for Intelligent Urban Mobility Systems},
  author = {Wilbur, Michael and Pugliese, Philip and Laszka, Aron and Dubey, Abhishek},
  booktitle = {Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence (AAAI-21)},
  year = {2021}
}

Modern intelligent urban mobility applications are underpinned by large-scale, multivariate, spatiotemporal data streams. Working with this data presents unique challenges of data management, processing and presentation that is often overlooked by researchers. Therefore, in this work we present an integrated data management and processing framework for intelligent urban mobility systems currently in use by our partner transit agencies. We discuss the available data sources and outline our cloud-centric data management and stream processing architecture built upon open-source publish-subscribe and NoSQL data stores. We then describe our data-integrity monitoring methods. We then present a set of visualization dashboards designed for our transit agency partners. Lastly, we discuss how these tools are currently being used for AI-driven urban mobility applications that use these tools.

@inproceedings{aaai21,
  title = {Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service},
  author = {Sivagnanam, Amutheezan and Ayman, Afiya and Wilbur, Michael and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
  booktitle = {Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21)},
  year = {2021}
}

Affordable public transit services are crucial for communities since they enable residents to access employment, education, and other services. Unfortunately, transit services that provide wide coverage tend to suffer from relatively low utilization, which results in high fuel usage per passenger per mile, leading to high operating costs and environmental impact. Electric vehicles (EVs) can reduce energy costs and environmental impact, but most public transit agencies have to employ them in combination with conventional, internal-combustion engine vehicles due to the high upfront costs of EVs. To make the best use of such a mixed fleet of vehicles, transit agencies need to optimize route assignments and charging schedules, which presents a challenging problem for large transit networks. We introduce a novel problem formulation to minimize fuel and electricity use by assigning vehicles to transit trips and scheduling them for charging, while serving an existing fixed-route transit schedule. We present an integer program for optimal assignment and scheduling, and we propose polynomial-time heuristic and meta-heuristic algorithms for larger networks. We evaluate our algorithms on the public transit service of Chattanooga, TN using operational data collected from transit vehicles. Our results show that the proposed algorithms are scalable and can reduce energy use and, hence, environmental impact and operational costs. For Chattanooga, the proposed algorithms can save $145,635 in energy costs and 576.7 metric tons of CO2 emission annually.

@article{yuchesae2021,
  author = {Chen, Yuche and Wu, Guoyuan and Sun, Ruixiao and Dubey, Abhishek and Laszka, Aron and Pugliese, Philip},
  title = {A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles},
  journal = {Society of Automotive Engineers (SAE) International Journal of Sustainable Transportation, Energy, Environment, \& Policy},
  year = {2021}
}

Electric vehicles (EVs) are critical to the transition to a low-carbon transportation system. The successful adoption of EVs heavily depends on energy consumption models that can accurately and reliably estimate electricity consumption. This paper reviews the state of the art of EV energy consumption models, aiming to provide guidance for future development of EV applications. We summarize influential variables of EV energy consumption in four categories: vehicle component, vehicle dynamics, traffic, and environment-related factors. We classify and discuss EV energy consumption models in terms of modeling scale (microscopic vs. macroscopic) and methodology (data-driven vs. rule-based). Our review shows trends of increasing macroscopic models that can be used to estimate trip-level EV energy consumption and increasing data-driven models that utilize machine learning technologies to estimate EV energy consumption based on a large volume of real-world data. We identify research gaps for EV energy consumption models, including the development of energy estimation models for modes other than personal vehicles (e.g., electric buses, trucks, and nonroad vehicles), energy estimation models that are suitable for applications related to vehicle-to-grid integration, and multiscale energy estimation models as a holistic modeling approach.

@inproceedings{rsuicfc2020,
  author = {Talusan, Jose Paolo and Wilbur, Michael and Dubey, Abhishek and Yasumoto, Keiichi},
  title = {On Decentralized Route Planning Using the Road Side Units as Computing Resources},
  booktitle = {2020 IEEE International Conference on Fog Computing (ICFC)},
  year = {2020},
  organization = {IEEE},
  category = {selectiveconference},
  keywords = {transit, middleware}
}

Residents in cities typically use third-party platforms such as Google Maps for route planning services. While providing near real-time processing, these state of the art centralized deployments are limited to multiprocessing environments in data centers. This raises privacy concerns, increases risk for critical data and causes vulnerability to network failure. In this paper, we propose to use decentralized road side units (RSU) (owned by the city) to perform route planning. We divide the city road network into grids, each assigned an RSU where traffic data is kept locally, increasing security and resiliency such that the system can perform even if some RSUs fail. Route generation is done in two steps. First, an optimal grid sequence is generated, prioritizing shortest path calculation accuracy but not RSU load. Second, we assign route planning tasks to the grids in the sequence. Keeping in mind RSU load and constraints, tasks can be allocated and executed in any non-optimal grid but with lower accuracy. We evaluate this system using Metropolitan Nashville road traffic data. We divided the area into 500 grids, configuring load and neighborhood sizes to meet delay constraints while maximizing model accuracy. The results show that there is a 30 percent decrease in processing time with a decrease in model accuracy of 99 percent to 92.3 percent, by simply increasing the search area to the optimal grid’s immediate neighborhood.

@inproceedings{wilbur2020decentralized,
  title = {Time-dependent Decentralized Routing using Federated Learning},
  author = {Wilbur, Michael and Samal, Chinmaya and Talusan, Jose Paolo and Yasumoto, Keiichi and Dubey, Abhishek},
  booktitle = {2020 IEEE 23nd International Symposium on Real-Time Distributed Computing (ISORC)},
  year = {2020},
  organization = {IEEE}
}

Recent advancements in cloud computing have driven rapid development in data-intensive smart city applications by providing near real time processing and storage scalability. This has resulted in efficient centralized route planning services such as Google Maps, upon which millions of users rely. Route planning algorithms have progressed in line with the cloud environments in which they run. Current state of the art solutions assume a shared memory model, hence deployment is limited to multiprocessing environments in data centers. By centralizing these services, latency has become the limiting parameter in the technologies of the future, such as autonomous cars. Additionally, these services require access to outside networks, raising availability concerns in disaster scenarios. Therefore, this paper provides a decentralized route planning approach for private fog networks. We leverage recent advances in federated learning to collaboratively learn shared prediction models online and investigate our approach with a simulated case study from a mid-size U.S. city.

@inproceedings{chen2020review,
  author = {Chen, Yuche and Wu, Guoyuan and Sun, Ruixiao and Dubey, Abhishek and Laszka, Aron and Pugliese, Philip},
  title = {A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles},
  booktitle = {Preprint at Arxiv},
  year = {2020},
  archiveprefix = {arXiv},
  eprint = {2003.12873},
  preprint = {https://arxiv.org/abs/2003.12873},
  primaryclass = {eess.SY}
}

Electric vehicles (EVs) are critical to the transition to a low-carbon transportation system. The successful adoption of EVs heavily depends on energy consumption models that can accurately and reliably estimate electricity consumption. This paper reviews the state-of-the-art of EV energy consumption models, aiming to provide guidance for future development of EV applications. We summarize influential variables of EV energy consumption into four categories: vehicle component, vehicle dynamics, traffic and environment related factors. We classify and discuss EV energy consumption models in terms of modeling scale (microscopic vs. macroscopic) and methodology (data-driven vs. rule-based). Our review shows trends of increasing macroscopic models that can be used to estimate trip-level EV energy consumption and increasing data-driven models that utilized machine learning technologies to estimate EV energy consumption based on large volume real-world data. We identify research gaps for EV energy consumption models, including the development of energy estimation models for modes other than personal vehicles (e.g., electric buses, electric trucks, and electric non-road vehicles); the development of energy estimation models that are suitable for applications related to vehicle-to-grid integration; and the development of multi-scale energy estimation models as a holistic modeling approach.

@inproceedings{wilbur2020impact,
  author = {Wilbur, Michael and Ayman, Afiya and Ouyang, Anna and Poon, Vincent and Kabir, Riyan and Vadali, Abhiram and Pugliese, Philip and Freudberg, Daniel and Laszka, Aron and Dubey, Abhishek},
  title = {Impact of COVID-19 on Public Transit Accessibility and Ridership},
  booktitle = {Preprint at Arxiv},
  year = {2020},
  archiveprefix = {arXiv},
  eprint = {2008.02413},
  preprint = {https://arxiv.org/abs/2008.02413},
  primaryclass = {physics.soc-ph}
}

Public transit is central to cultivating equitable communities. Meanwhile, the novel coronavirus disease COVID-19 and associated social restrictions has radically transformed ridership behavior in urban areas. Perhaps the most concerning aspect of the COVID-19 pandemic is that low-income and historically marginalized groups are not only the most susceptible to economic shifts but are also most reliant on public transportation. As revenue decreases, transit agencies are tasked with providing adequate public transportation services in an increasingly hostile economic environment. Transit agencies therefore have two primary concerns. First, how has COVID-19 impacted ridership and what is the new post-COVID normal? Second, how has ridership varied spatio-temporally and between socio-economic groups? In this work we provide a data-driven analysis of COVID-19’s affect on public transit operations and identify temporal variation in ridership change. We then combine spatial distributions of ridership decline with local economic data to identify variation between socio-economic groups. We find that in Nashville and Chattanooga, TN, fixed-line bus ridership dropped by 66.9% and 65.1% from 2019 baselines before stabilizing at 48.4% and 42.8% declines respectively. The largest declines were during morning and evening commute time. Additionally, there was a significant difference in ridership decline between the highest-income areas and lowest-income areas (77% vs 58%) in Nashville.

@inproceedings{micromobility2020,
  author = {Barbour, William and Wilbur, Michael and Sandoval, Ricardo and Geffen, Caleb Van and Hall, Brandon and Dubey, Abhishek and Work, Dan},
  title = {Data Driven Methods for Effective Micromobility Parking},
  booktitle = {Proceedings of the Transportation Research Board Annual Meeting},
  year = {2020},
  category = {selectiveconference},
  keywords = {transit}
}

Proliferation of shared urban mobility devices (SUMDs), particularly dockless e-scooters, has created opportunities for users with efficient, short trips, but raised management challenges for cities and regulators in terms of safety, infrastructure, and parking. There is a need in some high-demand areas for dedicated parking locations for dockless e-scooters and other devices. We propose the use of data generated by SUMD trips for establishing locations of parking facilities and assessing their required capacity and anticipated utilization. The problem objective is: find locations for a given number of parking facilities that maximize the number of trips that could reasonably be ended and parked at these facilities. Posed another way, what is the minimum number and best locations of parking facilities needed to cover a desired portion of trips at these facilities? In order to determine parking locations, areas of high-density trip destination points are found using unsupervised machine learning algorithms. The dwell time of each device is used to estimate the number of devices parked in a location over time and the necessary capacity of the parking facility. The methodology is tested on a dataset of approximately 100,000 e-scooter trips at Vanderbilt University in Nashville, Tennessee, USA. We find DBSCAN to be the most effective algorithm at determining high-performing parking locations. A selection of 19 parking locations, is enough to capture roughly 25 percent of all trips in the dataset. The vast majority of parking facilities found require a mean capacity of 6 scooters when sized for the 98th percentile observed demand.

@inproceedings{Lasz2006Data,
  author = {Ayman, Afiya and Wilbur, Michael and Sivagnanam, Amutheezan and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
  title = {{Data-Driven} Prediction of {Route-Level} Energy Use for {Mixed-Vehicle}
                 Transit Fleets},
  booktitle = {2020 IEEE International Conference on Smart Computing (SMARTCOMP)
                 (SMARTCOMP 2020)},
  address = {Bologna, Italy},
  days = {21},
  month = jun,
  year = {2020},
  keywords = {data-driven prediction; electric vehicle; public transit; on-board
                 diagnostics data; deep learning; traffic data}
}

Due to increasing concerns about environmental impact, operating costs, and energy security, public transit agencies are seeking to reduce their fuel use by employing electric vehicles (EVs). However, because of the high upfront cost of EVs, most agencies can afford only mixed fleets of internal-combustion and electric vehicles. Making the best use of these mixed fleets presents a challenge for agencies since optimizing the assignment of vehicles to transit routes, scheduling charging, etc. require accurate predictions of electricity and fuel use. Recent advances in sensor-based technologies, data analytics, and machine learning enable remedying this situation; however, to the best of our knowledge, there exists no framework that would integrate all relevant data into a route-level prediction model for public transit. In this paper, we present a novel framework for the data-driven prediction of route-level energy use for mixed-vehicle transit fleets, which we evaluate using data collected from the bus fleet of CARTA, the public transit authority of Chattanooga, TN. We present a data collection and storage framework, which we use to capture system-level data, including traffic and weather conditions, and high-frequency vehicle-level data, including location traces, fuel or electricity use, etc. We present domain-specific methods and algorithms for integrating and cleansing data from various sources, including street and elevation maps. Finally, we train and evaluate machine learning models, including deep neural networks, decision trees, and linear regression, on our integrated dataset. Our results show that neural networks provide accurate estimates, while other models can help us discover relations between energy use and factors such as road and weather conditions.

@inproceedings{pettetisgt2020,
  title = {A Decision Support Framework for Grid-Aware Electric Bus Charge Scheduling
                    },
  author = {Pettet, Geoffrey and Ghosal, Malini and Mahserejian, Shant and Davis, Sarah and Sridhar, Siddharth and Dubey, Abhishek and Meyer, Michael},
  booktitle = {2020 IEEE Power \& Energy Society Innovative Smart Grid Technologies Conference (ISGT)},
  year = {2020},
  organization = {IEEE}
}

While there are many advantages to electric public transit vehicles, they also pose new challenges for fleet operators. One key challenge is defining a charge scheduling policy that minimizes operating costs and power grid disruptions while maintaining schedule adherence. An uncoordinated policy could result in buses running out of charge before completing their trip, while a grid agnostic policy might incur higher energy costs or cause an adverse impact on the grid’s distribution system. We present a grid aware decision-theoretic framework for electric bus charge scheduling that accounts for energy price and grid load. The framework co-simulates models for traffic (Simulation of Urban Mobility) and the electric grid (GridLAB-D), which are used by a decision-theoretic planner to evaluate charging decisions with regard to their long-term effect on grid reliability and cost. We evaluated the framework on a simulation of Richland, WA’s bus and grid network, and found that it could save over $100k per year on operating costs for the city compared to greedy methods.

@article{Sun2019,
  author = {Sun, Fangzhou and Dubey, Abhishek and White, Jules and Gokhale, Aniruddha},
  title = {Transit-hub: a smart public transportation decision support system with multi-timescale analytical services},
  journal = {Cluster Computing},
  year = {2019},
  volume = {22},
  number = {Suppl 1},
  pages = {2239--2254},
  month = jan,
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/journals/cluster/SunDWG19},
  doi = {10.1007/s10586-018-1708-z},
  file = {:Sun2019-Transit-hub_a_smart_public_transportation_decision_support_system_with_multi-timescale_analytical_services.pdf:PDF},
  keywords = {transit},
  project = {smart-cities,smart-transit},
  timestamp = {Wed, 21 Aug 2019 01:00:00 +0200},
  url = {https://doi.org/10.1007/s10586-018-1708-z}
}

Public transit is a critical component of a smart and connected community. As such, citizens expect and require accurate information about real-time arrival/departures of transportation assets. As transit agencies enable large-scale integration of real-time sensors and support back-end data-driven decision support systems, the dynamic data-driven applications systems (DDDAS) paradigm becomes a promising approach to make the system smarter by providing online model learning and multi-time scale analytics as part of the decision support system that is used in the DDDAS feedback loop. In this paper, we describe a system in use in Nashville and illustrate the analytic methods developed by our team. These methods use both historical as well as real-time streaming data for online bus arrival prediction. The historical data is used to build classifiers that enable us to create expected performance models as well as identify anomalies. These classifiers can be used to provide schedule adjustment feedback to the metro transit authority. We also show how these analytics services can be packaged into modular, distributed and resilient micro-services that can be deployed on both cloud back ends as well as edge computing resources.

@inproceedings{Basak2019,
  author = {Basak, Sanchita and Sun, Fangzhou and Sengupta, Saptarshi and Dubey, Abhishek},
  title = {Data-Driven Optimization of Public Transit Schedule},
  booktitle = {Big Data Analytics - 7th International Conference, {BDA} 2019, Ahmedabad, India},
  year = {2019},
  pages = {265--284},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/bigda/BasakSSD19},
  category = {selectiveconference},
  doi = {10.1007/978-3-030-37188-3\_16},
  file = {:Basak2019-Data_Driven_Optimization_of_Public_Transit_Schedule.pdf:PDF},
  keywords = {transit},
  project = {smart-cities,smart-transit},
  timestamp = {Fri, 13 Dec 2019 12:44:00 +0100},
  url = {https://doi.org/10.1007/978-3-030-37188-3\_16}
}

Bus transit systems are the backbone of public transportation in the United States. An important indicator of the quality of service in such infrastructures is on-time performance at stops, with published transit schedules playing an integral role governing the level of success of the service. However there are relatively few optimization architectures leveraging stochastic search that focus on optimizing bus timetables with the objective of maximizing probability of bus arrivals at timepoints with delays within desired on-time ranges. In addition to this, there is a lack of substantial research considering monthly and seasonal variations of delay patterns integrated with such optimization strategies. To address these, this paper makes the following contributions to the corpus of studies on transit on-time performance optimization: (a) an unsupervised clustering mechanism is presented which groups months with similar seasonal delay patterns, (b) the problem is formulated as a single-objective optimization task and a greedy algorithm, a genetic algorithm (GA) as well as a particle swarm optimization (PSO) algorithm are employed to solve it, (c) a detailed discussion on empirical results comparing the algorithms are provided and sensitivity analysis on hyper-parameters of the heuristics are presented along with execution times, which will help practitioners looking at similar problems. The analyses conducted are insightful in the local context of improving public transit scheduling in the Nashville metro region as well as informative from a global perspective as an elaborate case study which builds upon the growing corpus of empirical studies using nature-inspired approaches to transit schedule optimization.

@inproceedings{basak2019bigdata,
  author = {Basak, Sanchita and Dubey, Abhishek and Leao, Bruno P.},
  title = {Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks},
  booktitle = {IEEE Big Data},
  year = {2019},
  address = {Los Angeles, Ca},
  category = {selectiveconference},
  keywords = {reliability, transit}
}

This paper presents a data-driven approach for predicting the propagation of traffic congestion at road seg-ments as a function of the congestion in their neighboring segments. In the past, this problem has mostly been addressed by modelling the traffic congestion over some standard physical phenomenon through which it is difficult to capture all the modalities of such a dynamic and complex system. While other recent works have focused on applying a generalized data-driven technique on the whole network at once, they often ignore intersection characteristics. On the contrary, we propose a city-wide ensemble of intersection level connected LSTM models and propose mechanisms for identifying congestion events using the predictions from the networks. To reduce the search space of likely congestion sinks we use the likelihood of congestion propagation in neighboring road segments of a congestion source that we learn from the past historical data. We validated our congestion forecasting framework on the real world traffic data of Nashville, USA and identified the onset of congestion in each of the neighboring segments of any congestion source with an average precision of 0.9269 and an average recall of 0.9118 tested over ten congestion events.