Michael Wilbur Publications

A. Dubey, M. Wilbur, A. Mukhopadhyay, and A. Laszka, Forecasting energy consumption in a mixed-vehicle fleet, US Patent App. 18/708,438. Jan-2025.
```
@misc{dubey2025forecasting,
  author = {Dubey, Abhishek and Wilbur, Michael and Mukhopadhyay, Ayan and Laszka, Aron},
  month = jan,
  title = {Forecasting energy consumption in a mixed-vehicle fleet},
  year = {2025},
  journal = {US Patent App. 18/708,438},
  url = {https://patents.google.com/patent/US20250030766A1/en}
}
```
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.
```
@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.
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.
```
@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.
M. Wilbur, A. Sivagnanam, A. Ayman, S. Samaranayeke, A. Dubey, and A. Laszka, Artificial Intelligence for Smart Transportation, in AI for Social Impact, 2024.
```
@inbook{wilbur2023artificialintelligencesmarttransportation,
  author = {Wilbur, Michael and Sivagnanam, Amutheezan and Ayman, Afiya and Samaranayeke, Samitha and Dubey, Abhishek and Laszka, Aron},
  title = {Artificial Intelligence for Smart Transportation},
  year = {2024},
  archiveprefix = {arXiv},
  contribution = {colab},
  eprint = {2308.07457},
  journal = {AI for Social Impact},
  primaryclass = {cs.AI},
  url = {https://arxiv.org/abs/2308.07457}
}
```
There are more than 7,000 public transit agencies in the U.S. (and many more private agencies), and together, they are responsible for serving 60 billion passenger miles each year. A well-functioning transit system fosters the growth and expansion of businesses, distributes social and economic benefits, and links the capabilities of community members, thereby enhancing what they can accomplish as a society. Since affordable public transit services are the backbones of many communities, this work investigates ways in which Artificial Intelligence (AI) can improve efficiency and increase utilization from the perspective of transit agencies. This book chapter discusses the primary requirements, objectives, and challenges related to the design of AI-driven smart transportation systems. We focus on three major topics. First, we discuss data sources and data. Second, we provide an overview of how AI can aid decision-making with a focus on transportation. Lastly, we discuss computational problems in the transportation domain and AI approaches to these problems.
M. Wilbur, A. Ayman, A. Sivagnanam, A. Ouyang, V. Poon, R. Kabir, A. Vadali, P. Pugliese, D. Freudberg, A. Laszka, and A. Dubey, Impact of COVID-19 on Public Transit Accessibility and Ridership, Transportation Research Record, vol. 2677, no. 4, pp. 531–546, 2023.
```
@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},
  journal = {Transportation Research Record},
  title = {Impact of COVID-19 on Public Transit Accessibility and Ridership},
  year = {2023},
  number = {4},
  pages = {531--546},
  volume = {2677},
  contribution = {minor},
  doi = {10.1177/03611981231160531},
  eprint = {https://doi.org/10.1177/03611981231160531},
  url = {https://doi.org/10.1177/03611981231160531}
}
```
COVID-19 has radically transformed urban travel behavior throughout the world. Agencies have had to provide adequate service while navigating a rapidly changing environment with reduced revenue. As COVID-19-related restrictions are lifted, transit agencies are concerned about their ability to adapt to changes in ridership behavior and public transit usage. To aid their becoming more adaptive to sudden or persistent shifts in ridership, we addressed three questions: To what degree has COVID-19 affected fixed-line public transit ridership and what is the relationship between reduced demand and -vehicle trips? How has COVID-19 changed ridership patterns and are they expected to persist after restrictions are lifted? Are there disparities in ridership changes across socioeconomic groups and mobility-impaired riders? Focusing on Nashville and Chattanooga, TN, ridership demand and vehicle trips were compared with anonymized mobile location data to study the relationship between mobility patterns and transit usage. Correlation analysis and multiple linear regression were used to investigate the relationship between socioeconomic indicators and changes in transit ridership, and an analysis of changes in paratransit demand before and during COVID-19. Ridership initially dropped by 66% and 65% over the first month of the pandemic for Nashville and Chattanooga, respectively. Cellular mobility patterns in Chattanooga indicated that foot traffic recovered to a greater degree than transit ridership between mid-April and the last week in June, 2020. Education-level had a statistically significant impact on changes in fixed-line bus transit, and the distribution of changes in demand for paratransit services were similar to those of fixed-line bus transit.
M. Wilbur, M. Coursey, P. Koirala, Z. Al-Quran, P. Pugliese, and A. Dubey, Mobility-On-Demand Transportation: A System for Microtransit and Paratransit Operations, in Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023), New York, NY, USA, 2023, pp. 260–261.
```
@inproceedings{wilbur2023mobility,
  author = {Wilbur, Michael and Coursey, Maxime and Koirala, Pravesh and Al-Quran, Zakariyya and Pugliese, Philip and Dubey, Abhishek},
  booktitle = {Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023)},
  title = {Mobility-On-Demand Transportation: A System for Microtransit and Paratransit Operations},
  year = {2023},
  address = {New York, NY, USA},
  note = {demonstration},
  pages = {260--261},
  publisher = {Association for Computing Machinery},
  series = {ICCPS '23},
  contribution = {lead},
  doi = {10.1145/3576841.3589625},
  isbn = {9798400700361},
  keywords = {ridepooling, software, mobility-on-demand, transit operations},
  location = {San Antonio, TX, USA},
  numpages = {2},
  url = {https://doi.org/10.1145/3576841.3589625}
}
```
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.
Y. Kim, D. Edirimanna, M. Wilbur, P. Pugliese, A. Laszka, A. Dubey, and S. Samaranayake, Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows, in Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI-23), 2023.
```
@inproceedings{youngseo2023,
  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)},
  title = {Rolling Horizon based Temporal Decomposition for the Offline Pickup and Delivery Problem with Time Windows},
  year = {2023},
  contribution = {colab},
  acceptance = {19.6},
  tag = {ai4cps,transit}
}
```
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 highquality 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.
S. Eisele, M. Wilbur, T. Eghtesad, K. Silvergold, F. Eisele, A. Mukhopadhyay, A. Laszka, and A. Dubey, Decentralized Computation Market for Stream Processing Applications, in 2022 IEEE International Conference on Cloud Engineering (IC2E), Pacific Grove, CA, USA, 2022.
```
@inproceedings{eisele2022Decentralized,
  author = {Eisele, Scott and Wilbur, Michael and Eghtesad, Taha and Silvergold, Kevin and Eisele, Fred and Mukhopadhyay, Ayan and Laszka, Aron and Dubey, Abhishek},
  booktitle = {2022 IEEE International Conference on Cloud Engineering (IC2E)},
  title = {Decentralized Computation Market for Stream Processing Applications},
  year = {2022},
  address = {Pacific Grove, CA, USA},
  month = oct,
  acceptance = {32.6},
  publisher = {IEEE Computer Society},
  contribution = {lead}
}
```
While cloud computing is the current standard for outsourcing computation, it can be prohibitively expensive for cities and infrastructure operators to deploy services. At the same time, there are underutilized computing resources within cities and local edge-computing deployments. Using these slack resources may enable significantly lower pricing than comparable cloud computing; such resources would incur minimal marginal expenditure since their deployment and operation are mostly sunk costs. However, there are challenges associated with using these resources. First, they are not effectively aggregated or provisioned. Second, there is a lack of trust between customers and suppliers of computing resources, given that they are distinct stakeholders and behave according to their own interests. Third, delays in processing inputs may diminish the value of the applications. To resolve these challenges, we introduce an architecture combining a distributed trusted computing mechanism, such as a blockchain, with an efficient messaging system like Apache Pulsar. Using this architecture, we design a decentralized computation market where customers and suppliers make offers to deploy and host applications. The proposed architecture can be realized using any trusted computing mechanism that supports smart contracts, and any messaging framework with the necessary features. This combination ensures that the market is robust without incurring the input processing delays that limit other blockchain based solutions. We evaluate the market protocol using game-theoretic analysis to show that deviation from the protocol is discouraged. Finally, we assess the performance of a prototype implementation based on experiments with a streaming computer-vision application.
V. Nair, K. Prakash, M. Wilbur, A. Taneja, C. Namblard, O. Adeyemo, A. Dubey, A. Adereni, M. Tambe, and A. Mukhopadhyay, ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria, in 31st International Joint Conference on Artificial Intelligence (IJCAI), 2022.
```
@inproceedings{ijcai22Ayan,
  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},
  booktitle = {31st International Joint Conference on Artificial Intelligence (IJCAI)},
  title = {ADVISER: AI-Driven Vaccination Intervention Optimiser for Increasing Vaccine Uptake in Nigeria},
  year = {2022},
  month = jul,
  acceptance = {15},
  contribution = {minor},
  doi = {https://doi.org/10.48550/ARXIV.2204.13663},
  url = {https://arxiv.org/abs/2204.13663}
}
```
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.
M. Wilbur, S. Kadir, Y. Kim, G. Pettet, A. Mukhopadhyay, P. Pugliese, S. Samaranayake, A. Laszka, and A. Dubey, An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services, in ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS), 2022.
```
@inproceedings{wilbur2022,
  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},
  booktitle = {ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)},
  title = {An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services},
  year = {2022},
  month = apr,
  acceptance = {28},
  publisher = {IEEE},
  contribution = {lead}
}
```
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.
R. Sen, A. K. Bharati, S. Khaleghian, M. Ghosal, M. Wilbur, T. Tran, P. Pugliese, M. Sartipi, H. Neema, and A. Dubey, E-Transit-Bench: Simulation Platform for Analyzing Electric Public Transit Bus Fleet Operations, in Proceedings of the Thirteenth ACM International Conference on Future Energy Systems, New York, NY, USA, 2022, pp. 532–541.
```
@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},
  booktitle = {Proceedings of the Thirteenth ACM International Conference on Future Energy Systems},
  title = {E-Transit-Bench: Simulation Platform for Analyzing Electric Public Transit Bus Fleet Operations},
  year = {2022},
  address = {New York, NY, USA},
  pages = {532–541},
  publisher = {Association for Computing Machinery},
  series = {e-Energy '22},
  contribution = {lead},
  doi = {10.1145/3538637.3539586},
  isbn = {9781450393973},
  keywords = {model-integration, cyber-physical systems, co-simulation, powergrid simulation, traffic simulation},
  location = {Virtual Event},
  numpages = {10},
  url = {https://doi.org/10.1145/3538637.3539586}
}
```
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.
A. Sivagnanam, A. Ayman, M. Wilbur, P. Pugliese, A. Dubey, and A. Laszka, Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service, in Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI-21), 2021.
```
@inproceedings{aaai21,
  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)},
  title = {Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service},
  year = {2021},
  acceptance = {21.4},
  contribution = {colab},
  tag = {ai4cps,transit}
}
```
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.
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.
```
@inproceedings{ecml2021,
  author = {Wilbur, Michael and Mukhopadhyay, Ayan and Vazirizade, Sayyed and Pugliese, Philip and Laszka, Aron and Dubey, Abhishek},
  booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  title = {Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-Task and Inductive Transfer Learning},
  year = {2021},
  acceptance = {29},
  contribution = {lead},
  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 fixed-line transit energy prediction. We find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.
J. Martinez, A. M. A. Ayman, M. Wilbur, P. Pugliese, D. Freudberg, A. Laszka, and A. Dubey, Predicting Public Transportation Load to Estimate the Probability of Social Distancing Violations, in Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence (AAAI-21), 2021.
```
@inproceedings{juan21,
  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)},
  title = {Predicting Public Transportation Load to Estimate the Probability of Social Distancing Violations},
  year = {2021},
  contribution = {minor},
  tag = {transit}
}
```
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 (a) proactive fixed-line schedule optimization based on predicted demand, (b) dispatch of on-demand micro-transit, prioritizing at-risk populations, and (c) allocation of vehicles to transit and cargo trips, considering exigent vehicle maintenance requirements (i.e., disinfection). Finally, this paper presents some initial results from our project regarding the estimation of ridership in public transit.
R. Sandoval, C. Van Geffen, M. Wilbur, B. Hall, A. Dubey, W. Barbour, and D. B. Work, Data driven methods for effective micromobility parking, Transportation Research Interdisciplinary Perspectives, vol. 10, p. 100368, 2021.
```
@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.},
  journal = {Transportation Research Interdisciplinary Perspectives},
  title = {Data driven methods for effective micromobility parking},
  year = {2021},
  issn = {2590-1982},
  pages = {100368},
  volume = {10},
  contribution = {minor},
  doi = {https://doi.org/10.1016/j.trip.2021.100368},
  keywords = {micromobility, Clustering, Dockless scooters, Urban planning},
  tag = {transit},
  url = {https://www.sciencedirect.com/science/article/pii/S2590198221000750}
}
```
In this work, we propose a data-driven method to use proven clustering algorithms for establishing shared electric scooter (SES) parking locations and assessing their anticipated utilization. We first address the problem of finding locations for a given number of parking facilities, based pur0ely on demand, that maximize the number of trips that would likely be parked at these facilities. We then formulate an enhanced version of the SES parking facility problem in which exogenous environmental factors are considered, such as sidewalk width. Parking SESs on narrow sidewalks raises accessibility concerns for other users of this infrastructure and capturing these trips in dedicated parking facilities is a valid priority to trade off with pure demand maximization. These methods are demonstrated in two case studies, which use a large SES dataset from Nashville, Tennessee, USA. We provide empirical results on how many facilities are needed to serve demand of SESs and necessary capacity allocation of the facilities. When the methodology considers sidewalk width in facility placement, the refined parking locations can address 300% more problematic trips parked along narrow sidewalks, with only a nominal sacrifice, around 13%, in the overall number of trips served.
M. Wilbur, P. Pugliese, A. Laszka, and A. Dubey, Efficient Data Management for Intelligent Urban Mobility Systems, in Proceedings of the Workshop on AI for Urban Mobility at the 35th AAAI Conference on Artificial Intelligence (AAAI-21), 2021.
```
@inproceedings{wilbur21,
  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)},
  title = {Efficient Data Management for Intelligent Urban Mobility Systems},
  year = {2021},
  contribution = {colab},
  tag = {ai4cps,transit}
}
```
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.
S. Singla, A. Mukhopadhyay, M. Wilbur, T. Diao, V. Gajjewar, A. Eldawy, M. Kochenderfer, R. Shachter, and A. Dubey, WildfireDB: An Open-Source Dataset ConnectingWildfire Spread with Relevant Determinants, in 35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks, 2021.
```
@inproceedings{wildfiredb2021,
  author = {Singla, Samriddhi and Mukhopadhyay, Ayan and Wilbur, Michael and Diao, Tina and Gajjewar, Vinayak and Eldawy, Ahmed and Kochenderfer, Mykel and Shachter, Ross and Dubey, Abhishek},
  booktitle = {35th Conference on Neural Information Processing Systems (NeurIPS 2021) Track on Datasets and Benchmarks},
  title = {WildfireDB: An Open-Source Dataset ConnectingWildfire Spread with Relevant Determinants},
  year = {2021},
  acceptance = {26},
  contribution = {minor},
  tag = {ai4cps,incident}
}
```
Modeling fire spread is critical in fire risk management. Creating data-driven models to forecast spread remains challenging due to the lack of comprehensive data sources that relate fires with relevant covariates. We present the first comprehensive and open-source dataset that relates historical fire data with relevant covariates such as weather, vegetation, and topography. Our dataset, named WildfireDB, contains over 17 million data points that capture how fires spread in the continental USA in the last decade. In this paper, we describe the algorithmic approach used to create and integrate the data, describe the dataset, and present benchmark results regarding data-driven models that can be learned to forecast the spread of wildfires.
A. Ayman, M. Wilbur, A. Sivagnanam, P. Pugliese, A. Dubey, and A. Laszka, Data-Driven Prediction of Route-Level Energy Use for Mixed-Vehicle Transit Fleets, in 2020 IEEE International Conference on Smart Computing (SMARTCOMP) (SMARTCOMP 2020), Bologna, Italy, 2020.
```
@inproceedings{Lasz2006Data,
  author = {Ayman, Afiya and Wilbur, Michael and Sivagnanam, Amutheezan and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
  booktitle = {2020 IEEE International Conference on Smart Computing (SMARTCOMP)
                    (SMARTCOMP 2020)},
  title = {{Data-Driven} Prediction of {Route-Level} Energy Use for {Mixed-Vehicle}
                    Transit Fleets},
  year = {2020},
  address = {Bologna, Italy},
  month = jun,
  acceptance = {32},
  contribution = {colab},
  days = {21},
  keywords = {data-driven prediction; electric vehicle; public transit; on-board
                    diagnostics data; deep learning; traffic data},
  tag = {ai4cps,transit}
}
```
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.
A. Ayman, A. Sivagnanam, M. Wilbur, P. Pugliese, A. Dubey, and A. Laszka, Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles, ACM Transations of Internet Technology, 2020.
```
@article{aymantoit2020,
  author = {Ayman, Afiya and Sivagnanam, Amutheezan and Wilbur, Michael and Pugliese, Philip and Dubey, Abhishek and Laszka, Aron},
  journal = {ACM Transations of Internet Technology},
  title = {Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles},
  year = {2020},
  contribution = {colab},
  tag = {ai4cps,transit}
}
```
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.
W. Barbour, M. Wilbur, R. Sandoval, A. Dubey, and D. B. Work, Streaming computation algorithms for spatiotemporal micromobility service availability, in 2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION), 2020, pp. 32–38.
```
@inproceedings{barbour2020,
  author = {{Barbour}, W. and {Wilbur}, M. and {Sandoval}, R. and {Dubey}, A. and {Work}, D. B.},
  booktitle = {2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION)},
  title = {Streaming computation algorithms for spatiotemporal micromobility service availability},
  year = {2020},
  pages = {32-38},
  contribution = {colab},
  doi = {https://doi.org/10.1109/DESTION50928.2020.00012},
  tag = {transit}
}
```
Location-based services and fleet management are important components of modern smart cities. However, statistical analysis with large-scale spatiotemporal data in real-time is computationally challenging and can necessitate compromise in accuracy or problem simplification. The main contribution of this work is the presentation of a stream processing approach for real-time monitoring of resource equity in spatially-aware micromobility fleets. The approach makes localized updates to resource availability as needed, instead of batch computation of availability at regular update intervals. We find that the stream processing approach can compute, on average, 62 resource availability updates in the same execution time as a single batch computation. This advantage in processing time makes continuous real-time stream processing equivalent to a batch computation performed every 15 minutes, in terms of algorithm execution time. Since the stream processing approach considers every update to the fleet in real-time, resource availability is always up-to-date and there is no compromise in terms of accuracy.
W. Barbour, M. Wilbur, R. Sandoval, C. V. Geffen, B. Hall, A. Dubey, and D. Work, Data Driven Methods for Effective Micromobility Parking, in Proceedings of the Transportation Research Board Annual Meeting, 2020.
```
@inproceedings{micromobility2020,
  author = {Barbour, William and Wilbur, Michael and Sandoval, Ricardo and Geffen, Caleb Van and Hall, Brandon and Dubey, Abhishek and Work, Dan},
  booktitle = {Proceedings of the Transportation Research Board Annual Meeting},
  title = {Data Driven Methods for Effective Micromobility Parking},
  year = {2020},
  category = {selectiveconference},
  contribution = {minor},
  keywords = {transit},
  tag = {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.
J. P. Talusan, M. Wilbur, A. Dubey, and K. Yasumoto, On Decentralized Route Planning Using the Road Side Units as Computing Resources, in 2020 IEEE International Conference on Fog Computing (ICFC), 2020.
```
@inproceedings{rsuicfc2020,
  author = {Talusan, Jose Paolo and Wilbur, Michael and Dubey, Abhishek and Yasumoto, Keiichi},
  booktitle = {2020 IEEE International Conference on Fog Computing (ICFC)},
  title = {On Decentralized Route Planning Using the Road Side Units as Computing Resources},
  year = {2020},
  organization = {IEEE},
  category = {selectiveconference},
  contribution = {colab},
  keywords = {transit, middleware},
  tag = {decentralization,transit}
}
```
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.
M. Wilbur, C. Samal, J. P. Talusan, K. Yasumoto, and A. Dubey, Time-dependent Decentralized Routing using Federated Learning, in 2020 IEEE 23nd International Symposium on Real-Time Distributed Computing (ISORC), 2020.
```
@inproceedings{wilbur2020decentralized,
  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)},
  title = {Time-dependent Decentralized Routing using Federated Learning},
  year = {2020},
  organization = {IEEE},
  contribution = {lead},
  tag = {decentralization,transit}
}
```
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.
M. Wilbur, A. Ayman, A. Ouyang, V. Poon, R. Kabir, A. Vadali, P. Pugliese, D. Freudberg, A. Laszka, and A. Dubey, Impact of COVID-19 on Public Transit Accessibility and Ridership, Preprint at Arxiv. 2020.
```
@misc{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},
  year = {2020},
  archiveprefix = {arXiv},
  booktitle = {Preprint at Arxiv},
  contribution = {lead},
  eprint = {2008.02413},
  preprint = {https://arxiv.org/abs/2008.02413},
  primaryclass = {physics.soc-ph},
  tag = {ai4cps,transit}
}
```
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.
J. P. V. Talusan, M. Wilbur, A. Dubey, and K. Yasumoto, Route Planning Through Distributed Computing by Road Side Units, IEEE Access, vol. 8, pp. 176134–176148, 2020.
```
@article{wilburaccess2020,
  author = {{Talusan}, J. P. V. and {Wilbur}, M. and {Dubey}, A. and {Yasumoto}, K.},
  journal = {IEEE Access},
  title = {Route Planning Through Distributed Computing by Road Side Units},
  year = {2020},
  pages = {176134-176148},
  volume = {8},
  contribution = {minor},
  tag = {decentralization,transit}
}
```
Cities are embracing data-intensive applications to maximize their constrained transportation networks. Platforms such as Google offer route planning services to mitigate the effect of traffic congestion. These use remote servers that require an Internet connection, which exposes data to increased risk of network failures and latency issues. Edge computing, an alternative to centralized architectures, offers computational power at the edge that could be used for similar services. Road side units (RSU), Internet of Things (IoT) devices within a city, offer an opportunity to offload computation to the edge. To provide an environment for processing on RSUs, we introduce RSU-Edge, a distributed edge computing system for RSUs. We design and develop a decentralized route planning service over RSU-Edge. In the service, the city is divided into grids and assigned an RSU. Users send trip queries to the service and obtain routes. For maximum accuracy, tasks must be allocated to optimal RSUs. However, this overloads RSUs, increasing delay. To reduce delays, tasks may be reallocated from overloaded RSUs to its neighbors. The distance between the optimal and actual allocation causes accuracy loss due to stale data. The problem is identifying the most efficient allocation of tasks such that response constraints are met while maintaining acceptable accuracy. We created the system and present an analysis of a case study in Nashville, Tennessee that shows the effect of our algorithm on route accuracy and query response, given varying neighbor levels. We find that our system can respond to 1000 queries up to 57.17% faster, with only a model accuracy loss of 5.57% to 7.25% compared to using only optimal grid allocation.

M. Wilbur, A. Dubey, B. Leão, and S. Bhattacharjee, A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, 2019, pp. 274–282.

@inproceedings{Wilbur2019,
  author = {Wilbur, Michael and Dubey, Abhishek and Le{\~{a}}o, Bruno and Bhattacharjee, Shameek},
  booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA},
  title = {A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks},
  year = {2019},
  month = jun,
  acceptance = {29},
  pages = {274--282},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/WilburDLB19},
  category = {selectiveconference},
  contribution = {lead},
  doi = {10.1109/SMARTCOMP.2019.00063},
  file = {:Wilbur2019-A_Decentralized_Approach_for_Real_Time_Anomaly_Detection_in_Transportation_Networks.pdf:PDF},
  keywords = {transit, reliability},
  project = {cps-reliability,smart-transit,smart-cities},
  tag = {ai4cps,platform,decentralization,incident,transit},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2019.00063}
}

J. P. Talusan, F. Tiausas, K. Yasumoto, M. Wilbur, G. Pettet, A. Dubey, and S. Bhattacharjee, Smart Transportation Delay and Resiliency Testbed Based on Information Flow of Things Middleware, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, June 12-15, 2019, 2019, pp. 13–18.
```
@inproceedings{Talusan2019,
  author = {Talusan, Jose Paolo and Tiausas, Francis and Yasumoto, Keiichi and Wilbur, Michael and Pettet, Geoffrey and Dubey, Abhishek and Bhattacharjee, Shameek},
  booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA, June 12-15, 2019},
  title = {Smart Transportation Delay and Resiliency Testbed Based on Information Flow of Things Middleware},
  year = {2019},
  pages = {13--18},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/TalusanTYWPDB19},
  category = {workshop},
  contribution = {colab},
  acceptance = {29},
  doi = {10.1109/SMARTCOMP.2019.00022},
  file = {:Talusan2019-Smart_Transportation_Delay_and_Resiliency_Testbed_Based_on_Information_Flow_of_Things_Middleware.pdf:PDF},
  keywords = {middleware, transit},
  project = {cps-middleware,smart-transit},
  tag = {platform,incident,transit},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2019.00022}
}
```
Edge and Fog computing paradigms are used to process big data generated by the increasing number of IoT devices. These paradigms have enabled cities to become smarter in various aspects via real-time data-driven applications. While these have addressed some flaws of cloud computing some challenges remain particularly in terms of privacy and security. We create a testbed based on a distributed processing platform called the Information flow of Things (IFoT) middleware. We briefly describe a decentralized traffic speed query and routing service implemented on this framework testbed. We configure the testbed to test countermeasure systems that aim to address the security challenges faced by prior paradigms. Using this testbed, we investigate a novel decentralized anomaly detection approach for time-sensitive distributed smart transportation systems.

ScopeLab - Design, Operation and Optimization of Smart Cyber-Physical Systems

Michael Wilbur Publications

ScopeLab
- Design, Operation and Optimization of Smart Cyber-Physical Systems