Smart Mobility

Transit Agencies struggle to maintain transit accessibility with reduced resources, changing ridership patterns, vehicle capacity constraints. We have been working for the last several years to design AI-based scheduling systems to solve the problem of allocating vehicles and drivers to transit services, scheduling vehicle maintenance, and electric-vehicle charging, proactive stationing, and dispatch of vehicles for fixed-line service to mitigate unscheduled maintenance and unmet transit demand, aggregating on-demand transit requests, and dispatching and routing on-demand vehicles. Similar to the incident response, the decision support systems face key challenges: environments are non-stationary and difficult to predict due to human factors and complex processes affecting transit demand and traffic as well as unscheduled maintenance and accidents; simulations are expensive and complex as city-scale simulations need to consider millions of individuals and vehicles.

Visit SmartTransit.ai for details on current projects in this area.

Publications in this area

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,
  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 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.
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},
  title = {Energy and Emission Prediction for Mixed-Vehicle Transit Fleets Using Multi-Task and Inductive Transfer Learning},
  booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  year = {2021},
  tag = {ai4cps,transit}
}
```
Public transit agencies are focused on making their fixed-line bus systems more energy efficient by introducing electric (EV) and hybrid (HV) vehicles to their eets. However, because of the high upfront cost of these vehicles, most agencies are tasked with managing a mixed-fleet of internal combustion vehicles (ICEVs), EVs, and HVs. In managing mixed-fleets, agencies require accurate predictions of energy use for optimizing the assignment of vehicles to transit routes, scheduling charging, and ensuring that emission standards are met. The current state-of-the-art is to develop separate neural network models to predict energy consumption for each vehicle class. Although different vehicle classes’ energy consumption depends on a varied set of covariates, we hypothesize that there are broader generalizable patterns that govern energy consumption and emissions. In this paper, we seek to extract these patterns to aid learning to address two problems faced by transit agencies. First, in the case of a transit agency which operates many ICEVs, HVs, and EVs, we use multi-task learning (MTL) to improve accuracy of forecasting energy consumption. Second, in the case where there is a significant variation in vehicles in each category, we use inductive transfer learning (ITL) to improve predictive accuracy for vehicle class models with insufficient data. As this work is to be deployed by our partner agency, we also provide an online pipeline for joining the various sensor streams for xed-line transit energy prediction. We find that our approach outperforms vehicle-specific baselines in both the MTL and ITL settings.
R. Sun, R. Gui, H. Neema, Y. Chen, J. Ugirumurera, J. Severino, P. Pugliese, A. Laszka, and A. Dubey, Transit-Gym: A Simulation and Evaluation Engine for Analysis of Bus Transit Systems, in Preprint at Arxiv. Accepted at IEEE SmartComp., 2021.
```
@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},
  tag = {transit},
  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.
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, 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.},
  title = {Data driven methods for effective micromobility parking},
  journal = {Transportation Research Interdisciplinary Perspectives},
  tag = {transit},
  year = {2021}
}
```
R. Sun, Y. Chen, A. Dubey, and P. Pugliese, Hybrid electric buses fuel consumption prediction based on real-world driving data, Transportation Research Part D: Transport and Environment, vol. 91, p. 102637, 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},
  tag = {transit},
  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.
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,
  title = {Efficient Data Management for Intelligent Urban Mobility Systems},
  tag = {ai4cps,transit},
  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.
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,
  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)},
  tag = {ai4cps,transit},
  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.
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,
  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},
  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 \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.
F. Tiausas, J. P. Talusan, Y. Ishimaki, H. Yamana, H. Yamaguchi, S. Bhattacharjee, A. Dubey, K. Yasumoto, and S. K. Das, User-centric Distributed Route Planning in Smart Cities based on Multi-objective Optimization, in 2021 IEEE International Conference on Smart Computing (SMARTCOMP), 2021, pp. 77–82.
```
@inproceedings{jp21,
  author = {Tiausas, Francis and Talusan, Jose Paolo and Ishimaki, Yu and Yamana, Hayato and Yamaguchi, Hirozumi and Bhattacharjee, Shameek and Dubey, Abhishek and Yasumoto, Keiichi and Das, Sajal K.},
  booktitle = {2021 IEEE International Conference on Smart Computing (SMARTCOMP)},
  title = {User-centric Distributed Route Planning in Smart Cities based on Multi-objective Optimization},
  year = {2021},
  tag = {transit},
  volume = {},
  number = {},
  pages = {77-82},
  doi = {10.1109/SMARTCOMP52413.2021.00031}
}
```
The realization of edge-based cyber-physical systems (CPS) poses important challenges in terms of performance, robustness, security, etc. This paper examines a novel approach to providing a user-centric adaptive route planning service over a network of Road Side Units (RSUs) in smart cities. The key idea is to adaptively select routing task parameters such as privacy-cloaked area sizes and number of retained intersections to balance processing time, privacy protection level, and route accuracy for privacy-augmented distributed route search while also handling per-query user preferences. This is formulated as an optimization problem with a set of parameters giving the best result for a set of queries given system constraints. Processing Throughput, Privacy Protection, and Travel Time Accuracy were developed as the objective functions to be balanced. A Multi-Objective Genetic Algorithm based technique (NSGA-II) is applied to recover a feasible solution. The performance of this approach was then evaluated using traffic data from Osaka, Japan. Results show good performance of the approach in balancing the aforementioned objectives based on user preferences.
Y. Chen, G. Wu, R. Sun, A. Dubey, A. Laszka, and P. Pugliese, A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles, Society of Automotive Engineers (SAE) International Journal of Sustainable Transportation, Energy, Environment, & Policy, 2021.
```
@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},
  tag = {transit}
}
```
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.
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},
  tag = {decentralization,transit},
  volume = {8},
  number = {},
  pages = {176134-176148}
}
```
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.
Y. Chen, G. Wu, R. Sun, A. Dubey, A. Laszka, and P. Pugliese, A Review and Outlook of Energy Consumption Estimation Models for Electric Vehicles, in Preprint at Arxiv, 2020.
```
@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},
  tag = {transit},
  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.
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,
  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},
  tag = {decentralization,transit},
  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.
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},
  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},
  tag = {ai4cps,transit},
  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.
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},
  title = {Data-Driven Prediction and Optimization of Energy Use for Transit Fleets of Electric and ICE Vehicles},
  journal = {ACM Transations of Internet Technology},
  year = {2020},
  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.
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, in Preprint at Arxiv, 2020.
```
@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},
  tag = {ai4cps,transit},
  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.
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.},
  title = {Streaming computation algorithms for spatiotemporal micromobility service availability},
  booktitle = {2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION)},
  year = {2020},
  tag = {transit},
  pages = {32-38},
  doi = {https://doi.org/10.1109/DESTION50928.2020.00012}
}
```
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.
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},
  title = {On Decentralized Route Planning Using the Road Side Units as Computing Resources},
  booktitle = {2020 IEEE International Conference on Fog Computing (ICFC)},
  year = {2020},
  tag = {decentralization,transit},
  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.
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},
  title = {Data Driven Methods for Effective Micromobility Parking},
  booktitle = {Proceedings of the Transportation Research Board Annual Meeting},
  year = {2020},
  tag = {transit},
  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.
S. Shekhar, A. Chhokra, H. Sun, A. Gokhale, A. Dubey, X. Koutsoukos, and G. Karsai, URMILA: Dynamically Trading-off Fog and Edge Resources for Performance and Mobility-Aware IoT Services, Journal of Systems Architecture, 2020.
```
@article{SHEKHAR2020101710,
  author = {Shekhar, Shashank and Chhokra, Ajay and Sun, Hongyang and Gokhale, Aniruddha and Dubey, Abhishek and Koutsoukos, Xenofon and Karsai, Gabor},
  title = {URMILA: Dynamically Trading-off Fog and Edge Resources for Performance and Mobility-Aware IoT Services},
  journal = {Journal of Systems Architecture},
  year = {2020},
  issn = {1383-7621},
  tag = {platform,transit},
  doi = {https://doi.org/10.1016/j.sysarc.2020.101710},
  keywords = {Fog/Edge Computing, User Mobility, Latency-sensitive IoT Services, Resource Management, middleware, performance},
  project = {cps-middleware},
  url = {http://www.sciencedirect.com/science/article/pii/S1383762120300047}
}
```
The fog/edge computing paradigm is increasingly being adopted to support a range of latency-sensitive IoT services due to its ability to assure the latency requirements of these services while supporting the elastic properties of cloud computing. IoT services that cater to user mobility, however, face a number of challenges in this context. First, since user mobility can incur wireless connectivity issues, executing these services entirely on edge resources, such as smartphones, will result in a rapid drain in the battery charge. In contrast, executing these services entirely on fog resources, such as cloudlets or micro data centers, will incur higher communication costs and increased latencies in the face of fluctuating wireless connectivity and signal strength. Second, a high degree of multi-tenancy on fog resources involving different IoT services can lead to performance interference issues due to resource contention. In order to address these challenges, this paper describes URMILA, which makes dynamic resource management decisions to achieve effective trade-offs between using the fog and edge resources yet ensuring that the latency requirements of the IoT services are met. We evaluate URMILA’s capabilities in the context of a real-world use case on an emulated but realistic IoT testbed.
F. Sun, A. Dubey, J. White, and A. Gokhale, Transit-hub: a smart public transportation decision support system with multi-timescale analytical services, Cluster Computing, vol. 22, no. Suppl 1, pp. 2239–2254, Jan. 2019.
```
@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},
  tag = {transit},
  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.
S. Basak, A. Dubey, and B. P. Leao, Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks, in IEEE Big Data, Los Angeles, Ca, 2019.
```
@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},
  tag = {ai4cps,incident,transit},
  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.
S. Nannapaneni and A. Dubey, Towards demand-oriented flexible rerouting of public transit under uncertainty, in Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering, SCOPE@CPSIoTWeek 2019, Montreal, QC, Canada, 2019, pp. 35–40.
```
@inproceedings{Nannapaneni2019,
  author = {Nannapaneni, Saideep and Dubey, Abhishek},
  title = {Towards demand-oriented flexible rerouting of public transit under uncertainty},
  booktitle = {Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering, SCOPE@CPSIoTWeek 2019, Montreal, QC, Canada},
  year = {2019},
  tag = {transit},
  pages = {35--40},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/cpsweek/NannapaneniD19},
  category = {workshop},
  doi = {10.1145/3313237.3313302},
  file = {:Nannapaneni2019-Towards_demand-oriented_flexible_rerouting_of_public_transit_under_uncertainty.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities},
  timestamp = {Tue, 10 Sep 2019 13:47:28 +0200},
  url = {https://doi.org/10.1145/3313237.3313302}
}
```
This paper proposes a flexible rerouting strategy for the public transit to accommodate the spatio-temporal variation in the travel demand. Transit routes are typically static in nature, i.e., the buses serve well-defined routes; this results in people living in away from the bus routes choose alternate transit modes such as private automotive vehicles resulting in ever-increasing traffic congestion. In the flex-transit mode, we reroute the buses to accommodate high travel demand areas away from the static routes considering its spatio-temporal variation. We perform clustering to identify several flex stops; these are stops not on the static routes, but with high travel demand around them. We divide the bus stops on the static routes into critical and non-critical bus stops; critical bus stops refer to transfer points, where people change bus routes to reach their destinations. In the existing static scheduling process, some slack time is provided at the end of each trip to account for any travel delays. Thus, the additional travel time incurred due to taking flexible routes is constrained to be less than the available slack time. We use the percent increase in travel demand to analyze the effectiveness of the rerouting process. The proposed methodology is demonstrated using real-world travel data for Route 7 operated by the Nashville Metropolitan Transit Authority (MTA).
S. Basak, F. Sun, S. Sengupta, and A. Dubey, Data-Driven Optimization of Public Transit Schedule, in Big Data Analytics - 7th International Conference, BDA 2019, Ahmedabad, India, 2019, pp. 265–284.
```
@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},
  tag = {ai4cps,transit},
  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.
S. Shekhar, A. Chhokra, H. Sun, A. Gokhale, A. Dubey, and X. D. Koutsoukos, Supporting fog/edge-based cognitive assistance IoT services for the visually impaired: poster abstract, in Proceedings of the International Conference on Internet of Things Design and Implementation, IoTDI 2019, Montreal, QC, Canada, 2019, pp. 275–276.
```
@inproceedings{Shekhar2019,
  author = {Shekhar, Shashank and Chhokra, Ajay and Sun, Hongyang and Gokhale, Aniruddha and Dubey, Abhishek and Koutsoukos, Xenofon D.},
  title = {Supporting fog/edge-based cognitive assistance IoT services for the visually impaired: poster abstract},
  booktitle = {Proceedings of the International Conference on Internet of Things Design and Implementation, IoTDI 2019, Montreal, QC, Canada},
  year = {2019},
  pages = {275--276},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/iotdi/ShekharCSGDK19},
  category = {poster},
  doi = {10.1145/3302505.3312592},
  file = {:Shekhar2019-Supporting_fog_edge-based_cognitive_assistance_IoT_services_for_the_visually_impaired_poster_abstract.pdf:PDF},
  keywords = {middleware},
  tag = {platform,transit},
  project = {cps-middleware,smart-cities},
  timestamp = {Fri, 29 Mar 2019 00:00:00 +0100},
  url = {https://doi.org/10.1145/3302505.3312592}
}
```
The fog/edge computing paradigm is increasingly being adopted to support a variety of latency-sensitive IoT services, such as cognitive assistance to the visually impaired, due to its ability to assure the latency requirements of these services while continuing to benefit from the elastic properties of cloud computing. However, user mobility in such applications imposes a new set of challenges that must be addressed before such applications can be deployed and benefit the society. This paper presents ongoing work on a dynamic resource management middleware called URMILA that addresses these concerns. URMILA ensures that the service remains available despite user mobility and ensuing wireless connectivity issues by opportunistically leveraging both fog and edge resources in such a way that the latency requirements of the service are met while ensuring longevity of the battery life on the edge devices. We present the design principles of URMILA’s capabilities and a real-world cognitive assistance application that we have built and are testing on an emulated but realistic IoT testbed.
G. Pettet, S. Sahoo, and A. Dubey, Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge, in IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019, 2019, pp. 511–516.
```
@inproceedings{Pettet2019a,
  author = {Pettet, Geoffrey and Sahoo, Saroj and Dubey, Abhishek},
  title = {Towards an Adaptive Multi-Modal Traffic Analytics Framework at the Edge},
  booktitle = {{IEEE} International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2019, Kyoto, Japan, March 11-15, 2019},
  year = {2019},
  pages = {511--516},
  tag = {platform,incident,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/percom/PettetSD19},
  category = {workshop},
  doi = {10.1109/PERCOMW.2019.8730577},
  file = {:Pettet2019a-Towards_an_Adaptive_Multi-Modal_Traffic_Analytics_Framework_at_the_Edge.pdf:PDF},
  keywords = {middleware, transit},
  project = {cps-middleware,smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/PERCOMW.2019.8730577}
}
```
The Internet of Things (IoT) requires distributed, large scale data collection via geographically distributed devices. While IoT devices typically send data to the cloud for processing, this is problematic for bandwidth constrained applications. Fog and edge computing (processing data near where it is gathered, and sending only results to the cloud) has become more popular, as it lowers network overhead and latency. Edge computing often uses devices with low computational capacity, therefore service frameworks and middleware are needed to efficiently compose services. While many frameworks use a top-down perspective, quality of service is an emergent property of the entire system and often requires a bottom up approach. We define services as multi-modal, allowing resource and performance tradeoffs. Different modes can be composed to meet an application’s high level goal, which is modeled as a function. We examine a case study for counting vehicle traffic through intersections in Nashville. We apply object detection and tracking to video of the intersection, which must be performed at the edge due to privacy and bandwidth constraints. We explore the hardware and software architectures, and identify the various modes. This paper lays the foundation to formulate the online optimization problem presented by the system which makes tradeoffs between the quantity of services and their quality constrained by available resources.
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},
  title = {Smart Transportation Delay and Resiliency Testbed Based on Information Flow of Things Middleware},
  booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA, June 12-15, 2019},
  year = {2019},
  pages = {13--18},
  tag = {platform,incident,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/TalusanTYWPDB19},
  category = {workshop},
  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},
  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.

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},
  title = {A Decentralized Approach for Real Time Anomaly Detection in Transportation Networks},
  booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA},
  year = {2019},
  pages = {274--282},
  month = jun,
  tag = {ai4cps,platform,decentralization,incident,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/WilburDLB19},
  category = {selectiveconference},
  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},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2019.00063}
}

C. Samal, A. Dubey, and L. J. Ratliff, Mobilytics-Gym: A Simulation Framework for Analyzing Urban Mobility Decision Strategies, in IEEE International Conference on Smart Computing, SMARTCOMP 2019, Washington, DC, USA, 2019, pp. 283–291.
```
@inproceedings{Samal2019,
  author = {Samal, Chinmaya and Dubey, Abhishek and Ratliff, Lillian J.},
  title = {Mobilytics-Gym: {A} Simulation Framework for Analyzing Urban Mobility Decision Strategies},
  booktitle = {{IEEE} International Conference on Smart Computing, {SMARTCOMP} 2019, Washington, DC, USA},
  year = {2019},
  pages = {283--291},
  month = jun,
  tag = {transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/SamalDR19},
  category = {selectiveconference},
  doi = {10.1109/SMARTCOMP.2019.00064},
  file = {:Samal2019-Mobilytics-Gym_A_Simulation_Framework_for_Analyzing_Urban_Mobility_Decision_Strategies.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2019.00064}
}
```
The rise in deep learning models in recent years has led to various innovative solutions for intelligent transportation technologies. Use of personal and on-demand mobility services puts a strain on the existing road network in a city. To mitigate this problem, city planners need a simulation framework to evaluate the effect of any incentive policy in nudging commuters towards alternate modes of travel, such as bike and car-share options. In this paper, we leverage MATSim, an agent-based simulation framework, to integrate agent preference models that capture the altruistic behavior of an agent in addition to their disutility proportional to the travel time and cost. These models are learned in a data-driven approach and can be used to evaluate the sensitivity of an agent to system-level disutility and monetary incentives given, e.g., by the transportation authority. This framework provides a standardized environment to evaluate the effectiveness of any particular incentive policy of a city, in nudging its residents towards alternate modes of transportation. We show the effectiveness of the approach and provide analysis using a case study from the Metropolitan Nashville area.
S. Basak, A. Aman, A. Laszka, A. Dubey, and B. Leao, Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks, in Proceedings of the 11th Annual Conference of the Prognostics and Health Management Society (PHM), 2019.
```
@inproceedings{Basak2019b,
  author = {Basak, Sanchita and Aman, Afiya and Laszka, Aron and Dubey, Abhishek and Leao, Bruno},
  title = {Data-Driven Detection of Anomalies and Cascading Failures in Traffic Networks},
  booktitle = {Proceedings of the 11th Annual Conference of the Prognostics and Health Management Society (PHM)},
  year = {2019},
  month = oct,
  tag = {ai4cps,transit},
  attachments = {https://www.isis.vanderbilt.edu/sites/default/files/PHM_traffic_cascades_paper.pdf},
  category = {conference},
  doi = {https://doi.org/10.36001/phmconf.2019.v11i1.861},
  file = {:Basak2019b-Data_Driven_Detection_of_Anomalies_and_Cascading_Failures_in_Traffic_Networks.pdf:PDF},
  keywords = {transit, reliability},
  project = {smart-transit,smart-cities,cps-reliability}
}
```
Traffic networks are one of the most critical infrastructures for any community. The increasing integration of smart and connected sensors in traffic networks provides researchers with unique opportunities to study the dynamics of this critical community infrastructure. Our focus in this paper is on the failure dynamics of traffic networks. By failure, we mean in this domain the hindrance of the normal operation of a traffic network due to cyber anomalies or physical incidents that cause cascaded congestion throughout the network. We are specifically interested in analyzing the cascade effects of traffic congestion caused by physical incidents, focusing on developing mechanisms to isolate and identify the source of a congestion. To analyze failure propagation, it is crucial to develop (a) monitors that can identify an anomaly and (b) a model to capture the dynamics of anomaly propagation. In this paper, we use real traffic data from Nashville, TN to demonstrate a novel anomaly detector and a Timed Failure Propagation Graph based diagnostics mechanism. Our novelty lies in the ability to capture the the spatial information and the interconnections of the traffic network as well as the use of recurrent neural network architectures to learn and predict the operation of a graph edge as a function of its immediate peers, including both incoming and outgoing branches. Our results show that our LSTM-based traffic-speed predictors attain an average mean squared error of 6.55\times10^-4 on predicting normalized traffic speed, while Gaussian Process Regression based predictors attain a much higher average mean squared error of 1.78\times10^-2. We are also able to detect anomalies with high precision and recall, resulting in an AUC (Area Under Curve) of 0.8507 for the precision-recall curve. To study physical traffic incidents, we augment the real data with simulated data generated using SUMO, a traffic simulator. Finally, we analyzed the cascading effect of the congestion propagation by formulating the problem as a Timed Failure Propagation Graph, which led us in identifying the source of a failure/congestion accurately.
A. Oruganti, S. Basak, F. Sun, H. Baroud, and A. Dubey, Modeling and Predicting the Cascading Effects of Delay in Transit Systems, in Transportation Research Board Annual Meeting, 2019.
```
@inproceedings{Oruganti2019,
  author = {Oruganti, Aparna and Basak, Sanchita and Sun, Fangzhou and Baroud, Hiba and Dubey, Abhishek},
  title = {Modeling and Predicting the Cascading Effects of Delay in Transit Systems},
  booktitle = {Transportation Research Board Annual Meeting},
  year = {2019},
  tag = {transit},
  attachments = {https://www.isis.vanderbilt.edu/sites/default/files/final poster.pdf},
  category = {selectiveconference},
  file = {:Oruganti2019-Modeling_and_Predicting_the_Cascading_Effects_of_Delay_in_Transit_Systems.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities}
}
```
An effective real-time estimation of the travel time for vehicles, using AVL(Automatic Vehicle Locators) has added a new dimension to the smart city planning. In this paper, we used data collected over several months from a transit agency and show how this data can be potentially used to learn patterns of travel time during specially planned events like NFL (National Football League) games and music award ceremonies. The impact of NFL games along with consideration of other factors like weather, traffic condition, distance is discussed with their relative importance to the prediction of travel time. Statistical learning models are used to predict travel time and subsequently assess the cascading effects of delay. The model performance is determined based on its predictive accuracy according to the out-of-sample error. In addition, the models help identify the most significant variables that influence the delay in the transit system. In order to compare the actual and predicted travel time for days having special events, heat maps are generated showing the delay impacts in different time windows between two timepoint-segments in comparison to a non-game day. This work focuses on the prediction and visualization of the delay in the public transit system and the analysis of its cascading effects on the entire transportation network. According to the study results, we are able to explain more than 80% of the variance in the bus travel time at each segment and can make future travel predictions during planned events with an out-of-sample error of 2.0 minutes using information on the bus schedule, traffic, weather, and scheduled events. According to the variable importance analysis, traffic information is most significant in predicting the delay in the transit system.
W. Barbour, C. Samal, S. Kuppa, A. Dubey, and D. B. Work, On the Data-Driven Prediction of Arrival Times for Freight Trains on U.S. Railroads, in 21st International Conference on Intelligent Transportation Systems, ITSC 2018, Maui, HI, USA, November 4-7, 2018, 2018, pp. 2289–2296.
```
@inproceedings{Barbour2018,
  author = {Barbour, William and Samal, Chinmaya and Kuppa, Shankara and Dubey, Abhishek and Work, Daniel B.},
  title = {On the Data-Driven Prediction of Arrival Times for Freight Trains on {U.S.} Railroads},
  booktitle = {21st International Conference on Intelligent Transportation Systems, {ITSC} 2018, Maui, HI, USA, November 4-7, 2018},
  year = {2018},
  pages = {2289--2296},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/itsc/BarbourSKDW18},
  category = {selectiveconference},
  doi = {10.1109/ITSC.2018.8569406},
  file = {:Barbour2018-On_the_Data-Driven_Prediction_of_Arrival_Times_for_Freight_Trains_on_U.S._Railroads.pdf:PDF},
  keywords = {transit},
  tag = {transit},
  project = {smart-transit,cps-reliability,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:57 +0200},
  url = {https://doi.org/10.1109/ITSC.2018.8569406}
}
```
The high capacity utilization and the pre-dominantly single-track network topology of freight railroads in the United States causes large variability and unpredictability of train arrival times. Predicting accurate estimated times of arrival (ETAs) is an important step for railroads to increase efficiency and automation, reduce costs, and enhance customer service. We propose using machine learning algorithms trained on historical railroad operational data to generate ETAs in real time. The machine learning framework is able to utilize the many data points produced by individual trains traversing a network track segment and generate periodic ETA predictions with a single model. In this work we compare the predictive performance of linear and non-linear support vector regression, random forest regression, and deep neural network models, tested on a section of the railroad in Tennessee, USA using over two years of historical data. Support vector regression and deep neural network models show similar results with maximum ETA error reduction of 26% over a statistical baseline predictor. The random forest models show over 60% error reduction compared to baseline at some points and average error reduction of 42%.
F. Sun, A. Dubey, C. Samal, H. Baroud, and C. Kulkarni, Short-Term Transit Decision Support System Using Multi-task Deep Neural Networks, in 2018 IEEE International Conference on Smart Computing, SMARTCOMP 2018, Taormina, Sicily, Italy, June 18-20, 2018, 2018, pp. 155–162.
```
@inproceedings{Sun2018,
  author = {Sun, Fangzhou and Dubey, Abhishek and Samal, Chinmaya and Baroud, Hiba and Kulkarni, Chetan},
  title = {Short-Term Transit Decision Support System Using Multi-task Deep Neural Networks},
  booktitle = {2018 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2018, Taormina, Sicily, Italy, June 18-20, 2018},
  year = {2018},
  pages = {155--162},
  tag = {ai4cps,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/SunDSBK18},
  category = {selectiveconference},
  doi = {10.1109/SMARTCOMP.2018.00086},
  file = {:Sun2018-Short-Term_Transit_Decision_Support_System_Using_Multi-task_Deep_Neural_Networks.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,cps-reliability,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2018.00086}
}
```
Unpredictability is one of the top reasons that prevent people from using public transportation. To improve the on-time performance of transit systems, prior work focuses on updating schedule periodically in the long-term and providing arrival delay prediction in real-time. But when no real-time transit and traffic feed is available (e.g., one day ahead), there is a lack of effective contextual prediction mechanism that can give alerts of possible delay to commuters. In this paper, we propose a generic tool-chain that takes standard General Transit Feed Specification (GTFS) transit feeds and contextual information (recurring delay patterns before and after big events in the city and the contextual information such as scheduled events and forecasted weather conditions) as inputs and provides service alerts as output. Particularly, we utilize shared route segment networks and multi-task deep neural networks to solve the data sparsity and generalization issues. Experimental evaluation shows that the proposed toolchain is effective at predicting severe delay with a relatively high recall of 76% and F1 score of 55%.
C. Samal, A. Dubey, and L. J. Ratliff, Mobilytics- An Extensible, Modular and Resilient Mobility Platform, in 2018 IEEE International Conference on Smart Computing, SMARTCOMP 2018, Taormina, Sicily, Italy, June 18-20, 2018, 2018, pp. 356–361.
```
@inproceedings{Samal2018,
  author = {Samal, Chinmaya and Dubey, Abhishek and Ratliff, Lillian J.},
  title = {Mobilytics- An Extensible, Modular and Resilient Mobility Platform},
  booktitle = {2018 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2018, Taormina, Sicily, Italy, June 18-20, 2018},
  year = {2018},
  pages = {356--361},
  tag = {platform,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/SamalDR18},
  category = {selectiveconference},
  doi = {10.1109/SMARTCOMP.2018.00029},
  file = {:Samal2018-Mobilytics-An_Extensible_Modular_and_Resilient_Mobility_Platform.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2018.00029}
}
```
Transportation management platforms provide communities the ability to integrate the available mobility options and localized transportation demand management policies. A central component of a transportation management platform is the mobility planning application. Given the societal relevance of these platforms, it is necessary to ensure that they operate resiliently. Modularity and extensibility are also critical properties that are required for manageability. Modularity allows to isolate faults easily. Extensibility enables update of policies and integration of new mobility modes or new routing algorithms. However, state of the art mobility planning applications like open trip planner, are monolithic applications, which makes it difficult to scale and modify them dynamically. This paper describes a microservices based modular multi-modal mobility platform Mobilytics, that integrates mobility providers, commuters, and community stakeholders. We describe our requirements, architecture, and discuss the resilience challenges, and how our platform functions properly in presence of failure. Conceivably, the patterns and principles manifested in our system can serve as guidelines for current and future practitioners in this field.
C. Samal, L. Zheng, F. Sun, L. J. Ratliff, and A. Dubey, Towards a Socially Optimal Multi-Modal Routing Platform, CoRR, vol. abs/1802.10140, 2018.
```
@article{Samal2018a,
  author = {Samal, Chinmaya and Zheng, Liyuan and Sun, Fangzhou and Ratliff, Lillian J. and Dubey, Abhishek},
  title = {Towards a Socially Optimal Multi-Modal Routing Platform},
  journal = {CoRR},
  tag = {transit},
  year = {2018},
  volume = {abs/1802.10140},
  archiveprefix = {arXiv},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/journals/corr/abs-1802-10140},
  eprint = {1802.10140},
  file = {:Samal2018a-Towards_a_Socially_Optimal_Multi-Modal_Routing_Platform.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities},
  timestamp = {Mon, 13 Aug 2018 01:00:00 +0200},
  url = {http://arxiv.org/abs/1802.10140}
}
```
The increasing rate of urbanization has added pressure on the already constrained transportation networks in our communities. Ride-sharing platforms such as Uber and Lyft are becoming a more commonplace, particularly in urban environments. While such services may be deemed more convenient than riding public transit due to their on-demand nature, reports show that they do not necessarily decrease the congestion in major cities. One of the key problems is that typically mobility decision support systems focus on individual utility and react only after congestion appears. In this paper, we propose socially considerate multi-modal routing algorithms that are proactive and consider, via predictions, the shared effect of riders on the overall efficacy of mobility services. We have adapted the MATSim simulator framework to incorporate the proposed algorithms present a simulation analysis of a case study in Nashville, Tennessee that assesses the effects of our routing models on the traffic congestion for different levels of penetration and adoption of socially considerate routes. Our results indicate that even at a low penetration (social ratio), we are able to achieve an improvement in system-level performance.
F. Sun, A. Dubey, and J. White, DxNAT - Deep neural networks for explaining non-recurring traffic congestion, in 2017 IEEE International Conference on Big Data, BigData 2017, Boston, MA, USA, December 11-14, 2017, 2017, pp. 2141–2150.
```
@inproceedings{Sun2017,
  author = {Sun, Fangzhou and Dubey, Abhishek and White, Jules},
  title = {DxNAT - Deep neural networks for explaining non-recurring traffic congestion},
  booktitle = {2017 {IEEE} International Conference on Big Data, BigData 2017, Boston, MA, USA, December 11-14, 2017},
  year = {2017},
  pages = {2141--2150},
  tag = {ai4cps,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/bigdataconf/SunDW17},
  category = {selectiveconference},
  doi = {10.1109/BigData.2017.8258162},
  file = {:Sun2017-DxNAT-Deep_neural_networks_for_explaining_non-recurring_traffic_congestion.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities,cps-reliability},
  timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
  url = {https://doi.org/10.1109/BigData.2017.8258162}
}
```
Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately.
A. Ghafouri, A. Laszka, A. Dubey, and X. D. Koutsoukos, Optimal detection of faulty traffic sensors used in route planning, in Proceedings of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, SCOPE@CPSWeek 2017, Pittsburgh, PA, USA, April 21, 2017, 2017, pp. 1–6.
```
@inproceedings{Ghafouri2017,
  author = {Ghafouri, Amin and Laszka, Aron and Dubey, Abhishek and Koutsoukos, Xenofon D.},
  title = {Optimal detection of faulty traffic sensors used in route planning},
  booktitle = {Proceedings of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, SCOPE@CPSWeek 2017, Pittsburgh, PA, USA, April 21, 2017},
  year = {2017},
  pages = {1--6},
  tag = {ai4cps,platform,incident,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/cpsweek/GhafouriLDK17},
  category = {workshop},
  doi = {10.1145/3063386.3063767},
  file = {:Ghafouri2017-Optimal_detection_of_faulty_traffic_sensors_used_in_route_planning.pdf:PDF},
  keywords = {transit},
  project = {cps-reliability,smart-transit,smart-cities},
  timestamp = {Tue, 06 Nov 2018 16:59:05 +0100},
  url = {https://doi.org/10.1145/3063386.3063767}
}
```
In a smart city, real-time traffic sensors may be deployed for various applications, such as route planning. Unfortunately, sensors are prone to failures, which result in erroneous traffic data. Erroneous data can adversely affect applications such as route planning, and can cause increased travel time. To minimize the impact of sensor failures, we must detect them promptly and accurately. However, typical detection algorithms may lead to a large number of false positives (i.e., false alarms) and false negatives (i.e., missed detections), which can result in suboptimal route planning. In this paper, we devise an effective detector for identifying faulty traffic sensors using a prediction model based on Gaussian Processes. Further, we present an approach for computing the optimal parameters of the detector which minimize losses due to false-positive and false-negative errors. We also characterize critical sensors, whose failure can have high impact on the route planning application. Finally, we implement our method and evaluate it numerically using a real- world dataset and the route planning platform OpenTripPlanner.
S. P. Khare, J. Sallai, A. Dubey, and A. S. Gokhale, Short Paper: Towards Low-Cost Indoor Localization Using Edge Computing Resources, in 20th IEEE International Symposium on Real-Time Distributed Computing, ISORC 2017, Toronto, ON, Canada, May 16-18, 2017, 2017, pp. 28–31.
```
@inproceedings{Khare2017,
  author = {Khare, Shweta Prabhat and Sallai, J{\'{a}}nos and Dubey, Abhishek and Gokhale, Aniruddha S.},
  title = {Short Paper: Towards Low-Cost Indoor Localization Using Edge Computing Resources},
  booktitle = {20th {IEEE} International Symposium on Real-Time Distributed Computing, {ISORC} 2017, Toronto, ON, Canada, May 16-18, 2017},
  year = {2017},
  tag = {transit},
  pages = {28--31},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/isorc/KhareSDG17},
  category = {selectiveconference},
  doi = {10.1109/ISORC.2017.23},
  file = {:Khare2017-Short_Paper_Towards_Low-Cost_Indoor_Localization_Using_Edge_Computing_Resources.pdf:PDF},
  keywords = {performance, middleware},
  project = {cps-middleware},
  timestamp = {Wed, 16 Oct 2019 14:14:53 +0200},
  url = {https://doi.org/10.1109/ISORC.2017.23}
}
```
Emerging smart services, such as indoor smart parking or patient monitoring and tracking in hospitals, incur a significant technical roadblock stemming primarily from a lack of cost-effective and easily deployable localization framework that impedes their widespread deployment. To address this concern, in this paper we present a low-cost, indoor localization and navigation system, which performs continuous and real-time processing of Bluetooth Low Energy (BLE) and IEEE 802.15.4a compliant Ultra-wideband (UWB) sensor data to localize and navigate the concerned entity to its desired location. Our approach depends upon fusing the two feature sets, using the UWB to calibrate the BLE localization mechanism.
C. Samal, F. Sun, and A. Dubey, SpeedPro: A Predictive Multi-Model Approach for Urban Traffic Speed Estimation, in 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017, Hong Kong, China, May 29-31, 2017, 2017, pp. 1–6.
```
@inproceedings{Samal2017,
  author = {Samal, Chinmaya and Sun, Fangzhou and Dubey, Abhishek},
  title = {SpeedPro: {A} Predictive Multi-Model Approach for Urban Traffic Speed Estimation},
  booktitle = {2017 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2017, Hong Kong, China, May 29-31, 2017},
  year = {2017},
  pages = {1--6},
  tag = {ai4cps,transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/SamalSD17},
  category = {workshop},
  doi = {10.1109/SMARTCOMP.2017.7947048},
  file = {:Samal2017-SpeedPro_A_Predictive_Multi-Model_Approach_for_Urban_Traffic_Speed_Estimation.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2017.7947048}
}
```
Data generated by GPS-equipped probe vehicles, especially public transit vehicles can be a reliable source for traffic speed estimation. Traditionally, this estimation is done by learning the parameters of a model that describes the relationship between the speed of the probe vehicle and the actual traffic speed. However, such approaches typically suffer from data sparsity issues. Furthermore, most state of the art approaches does not consider the effect of weather and the driver of the probe vehicle on the parameters of the learned model. In this paper, we describe a multivariate predictive multi-model approach called SpeedPro that (a) first identifies similar clusters of operation from the historic data that includes the real-time position of the probe vehicle, the weather data, and anonymized driver identifier, and then (b) uses these different models to estimate the traffic speed in real-time as a function of current weather, driver and probe vehicle speed. When the real-time information is not available our approach uses a different model that uses the historical weather and traffic information for estimation. Our results show that the purely historical data is less accurate than the model that uses the real-time information.
F. Sun, C. Samal, J. White, and A. Dubey, Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles, in 2017 IEEE International Conference on Smart Computing, SMARTCOMP 2017, Hong Kong, China, May 29-31, 2017, 2017, pp. 1–8.
```
@inproceedings{Sun2017a,
  author = {Sun, Fangzhou and Samal, Chinmaya and White, Jules and Dubey, Abhishek},
  title = {Unsupervised Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles},
  booktitle = {2017 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2017, Hong Kong, China, May 29-31, 2017},
  year = {2017},
  tag = {ai4cps,transit},
  pages = {1--8},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/SunSWD17},
  category = {selectiveconference},
  doi = {10.1109/SMARTCOMP.2017.7947057},
  file = {:Sun2017a-Unsupervised_Mechanisms_for_Optimizing_On-Time_Performance_of_Fixed_Schedule_Transit_Vehicles.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2017.7947057}
}
```
The on-time arrival performance of vehicles at stops is a critical metric for both riders and city planners to evaluate the reliability of a transit system. However, it is a non-trivial task for transit agencies to adjust the existing bus schedule to optimize the on-time performance for the future. For example, severe weather conditions and special events in the city could slow down traffic and cause bus delay. Furthermore, the delay of previous trips may affect the initial departure time of consecutive trips and generate accumulated delay. In this paper, we formulate the problem as a single-objective optimization task with constraints and propose a greedy algorithm and a genetic algorithm to generate bus schedules at timepoints that improve the bus on-time performance at timepoints which is indicated by whether the arrival delay is within the desired range. We use the Nashville bus system as a case study and simulate the optimization performance using historical data. The comparative analysis of the results identifies that delay patterns change over time and reveals the efficiency of the greedy and genetic algorithms.
A. Oruganti, F. Sun, H. Baroud, and A. Dubey, DelayRadar: A multivariate predictive model for transit systems, in 2016 IEEE International Conference on Big Data, BigData 2016, Washington DC, USA, December 5-8, 2016, 2016, pp. 1799–1806.
```
@inproceedings{Oruganti2016,
  author = {Oruganti, Aparna and Sun, Fangzhou and Baroud, Hiba and Dubey, Abhishek},
  title = {DelayRadar: {A} multivariate predictive model for transit systems},
  booktitle = {2016 {IEEE} International Conference on Big Data, BigData 2016, Washington DC, USA, December 5-8, 2016},
  year = {2016},
  pages = {1799--1806},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/bigdataconf/OrugantiSBD16},
  category = {selectiveconference},
  doi = {10.1109/BigData.2016.7840797},
  file = {:Oruganti2016-DelayRadar_A_multivariate_predictive_model_for_transit_systems.pdf:PDF},
  keywords = {transit},
  tag = {transit},
  project = {smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:51 +0200},
  url = {https://doi.org/10.1109/BigData.2016.7840797}
}
```
Effective public transit operations are one of the fundamental requirements for a modern community. Recently, a number of transit agencies have started integrating automated vehicle locators in their fleet, which provides a real-time estimate of the time of arrival. In this paper, we use the data collected over several months from one such transit system and show how this data can be potentially used to learn long term patterns of travel time. More specifically, we study the effect of weather and other factors such as traffic on the transit system delay. These models can later be used to understand the seasonal variations and to design adaptive and transient transit schedules. Towards this goal, we also propose an online architecture called DelayRadar. The novelty of DelayRadar lies in three aspects: (1) a data store that collects and integrates real-time and static data from multiple data sources, (2) a predictive statistical model that analyzes the data to make predictions on transit travel time, and (3) a decision making framework to develop an optimal transit schedule based on variable forecasts related to traffic, weather, and other impactful factors. This paper focuses on identifying the model with the best predictive accuracy to be used in DelayRadar. According to the preliminary study results, we are able to explain more than 70% of the variance in the bus travel time and we can make future travel predictions with an out-of-sample error of 4.8 minutes with information on the bus schedule, traffic, and weather.

S. Shekhar, F. Sun, A. Dubey, A. Gokhale, H. Neema, M. Lehofer, and D. Freudberg, A Smart Decision Support System for Public Transit Operations, in Internet of Things and Data Analytics Handbook, 2016.

@inbook{Shekhar2016,
  title = {A Smart Decision Support System for Public Transit Operations},
  year = {2016},
  tag = {transit},
  author = {Shekhar, Shashank and Sun, Fangzhou and Dubey, Abhishek and Gokhale, Aniruddha and Neema, Himanshu and Lehofer, Martin and Freudberg, Dan},
  booktitle = {Internet of Things and Data Analytics Handbook},
  file = {:Shekhar2016-Transit_Hub_A_Smart_Decision_Support_System_for_Public_Transit_Operations.pdf:PDF},
  keywords = {transit}
}

F. Sun, Y. Pan, J. White, and A. Dubey, Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System, in 2016 IEEE International Conference on Smart Computing, SMARTCOMP 2016, St Louis, MO, USA, May 18-20, 2016, 2016, pp. 1–8.
```
@inproceedings{Sun2016,
  author = {Sun, Fangzhou and Pan, Yao and White, Jules and Dubey, Abhishek},
  title = {Real-Time and Predictive Analytics for Smart Public Transportation Decision Support System},
  booktitle = {2016 {IEEE} International Conference on Smart Computing, {SMARTCOMP} 2016, St Louis, MO, USA, May 18-20, 2016},
  year = {2016},
  pages = {1--8},
  tag = {transit},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  biburl = {https://dblp.org/rec/bib/conf/smartcomp/SunPWD16},
  category = {selectiveconference},
  doi = {10.1109/SMARTCOMP.2016.7501714},
  file = {:Sun2016-Real-Time_and_Predictive_Analytics_for_Smart_Public_Transportation_Decision_Support_System.pdf:PDF},
  keywords = {transit},
  project = {smart-transit,smart-cities},
  timestamp = {Wed, 16 Oct 2019 14:14:54 +0200},
  url = {https://doi.org/10.1109/SMARTCOMP.2016.7501714}
}
```
Public bus transit plays an important role in city transportation infrastructure. However, public bus transit is often difficult to use because of lack of real- time information about bus locations and delay time, which in the presence of operational delays and service alerts makes it difficult for riders to predict when buses will arrive and plan trips. Precisely tracking vehicle and informing riders of estimated times of arrival is challenging due to a number of factors, such as traffic congestion, operational delays, varying times taken to load passengers at each stop. In this paper, we introduce a public transportation decision support system for both short-term as well as long-term prediction of arrival bus times. The system uses streaming real-time bus position data, which is updated once every minute, and historical arrival and departure data - available for select stops to predict bus arrival times. Our approach combines clustering analysis and Kalman filters with a shared route segment model in order to produce more accurate arrival time predictions. Experiments show that compared to the basic arrival time prediction model that is currently being used by the city, our system reduces arrival time prediction errors by 25 percent on average when predicting the arrival delay an hour ahead and 47 percent when predicting within a 15 minute future time window.

A. Dubey, M. Sturm, M. Lehofer, and J. Sztipanovits, Smart City Hubs: Opportunities for Integrating and Studying Human CPS at Scale, in Workshop on Big Data Analytics in CPS: Enabling the Move from IoT to Real-Time Control, 2015.

@inproceedings{Dubey2015,
  author = {Dubey, Abhishek and Sturm, Monika and Lehofer, Martin and Sztipanovits, Janos},
  title = {Smart City Hubs: Opportunities for Integrating and Studying Human CPS at Scale},
  booktitle = {Workshop on Big Data Analytics in CPS: Enabling the Move from IoT to Real-Time Control},
  year = {2015},
  category = {workshop},
  tag = {transit},
  file = {:Dubey2015-Smart_city_hubs_Opportunities_for_integrating_and_studying_human_cps_at_scale.pdf:PDF},
  keywords = {transit},
  url = {http://www.isis.vanderbilt.edu/sites/default/files/extendedAbstract.pdf}
}

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

Smart Mobility

Publications in this area

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