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Goal: The United States (US) is currently one of the countries hardest-hit by the novel SARS-CoV-19 virus. One key difficulty in managing the outbreak at the national level is that due to the US' diversity, geographic spread, and economic inequality, the COVID-19 pandemic in the US acts more as a series of diverse regional outbreaks rather than a synchronized homogeneous one. Method: In order to determine how to assess regional risk related to COVID-19, a two-phase modeling approach is developed while considering demographic and economic criteria. First, an unsupervised clustering technique, specifically k-means, is employed to group US counties based on demographic and economic similarities. Then, time series series forecasting of each cluster of counties is developed to assess the short-run viral transmissibility risk. Results: To this end, we test ARIMA and Seasonal Trend Random Walk forecasts to determine which is more appropriate for modeling the spread and lethality of COVID-19. From our analysis, we then utilize the superior ARIMA models to forecast future COVID-19 trends in the clusters, and present the areas in the US which have the highest COVID-19 related risk heading into the winter of 2020. Conclusion: Including sub-national socioeconomic characteristics to data-driven COVID-19 infection and fatality forecasts may play a key role in assessing the risk associated with changes in infection patterns at the national level.
Michael C. Lucic; Hakim Ghazzai; Carlo Lipizzi; Yehia Massoud. Integrating County-Level Socioeconomic Data for COVID-19 Forecasting in the United States. IEEE Open Journal of Engineering in Medicine and Biology 2021, 2, 235 -248.
AMA StyleMichael C. Lucic, Hakim Ghazzai, Carlo Lipizzi, Yehia Massoud. Integrating County-Level Socioeconomic Data for COVID-19 Forecasting in the United States. IEEE Open Journal of Engineering in Medicine and Biology. 2021; 2 (99):235-248.
Chicago/Turabian StyleMichael C. Lucic; Hakim Ghazzai; Carlo Lipizzi; Yehia Massoud. 2021. "Integrating County-Level Socioeconomic Data for COVID-19 Forecasting in the United States." IEEE Open Journal of Engineering in Medicine and Biology 2, no. 99: 235-248.
The current and expected future proliferation of mobile and embedded technology provides unique opportunities for crowdsourcing platforms to gather more user data for making data-driven decisions at the system level. Intelligent Transportation Systems (ITS) and Vehicular Social Networks (VSN) can be leveraged by mobile, spatial, and passive sensing crowdsourcing techniques due to improved connectivity, higher throughput, smart vehicles containing many embedded systems and sensors, and novel distributed processing techniques. These crowdsourcing systems have the capability of profoundly transforming transportation systems for the better by providing more data regarding (but not limited to) infrastructure health, navigation pathways, and congestion management. In this paper, we review and discuss the architecture and types of ITS crowdsourcing. Then, we delve into the techniques and technologies that serve as the foundation for these systems to function while providing some simulation results to show benefits from the implementation of these techniques and technologies on specific crowdsourcing-based ITS systems. Afterward, we provide an overview of cutting edge work associated with ITS crowdsourcing challenges. Finally, we propose various use-cases and applications for ITS crowdsourcing, and suggest some open research directions.
Michael C. Lucic; Xiangpeng Wan; Hakim Ghazzai; Yehia Massoud. Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview. Smart Cities 2020, 3, 341 -361.
AMA StyleMichael C. Lucic, Xiangpeng Wan, Hakim Ghazzai, Yehia Massoud. Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview. Smart Cities. 2020; 3 (2):341-361.
Chicago/Turabian StyleMichael C. Lucic; Xiangpeng Wan; Hakim Ghazzai; Yehia Massoud. 2020. "Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview." Smart Cities 3, no. 2: 341-361.
Roadside unit (RSU) planning is vital for the operation of an intelligent transportation system (ITS). RSUs provide ground coverage limited by obstacles. Unmanned aerial vehicles (UAVs) can complement RSU coverage by providing flexible connectivity capable of adapting coverage for traffic fluctuations, energy consumption, and budgetary constraints that all have effects on ITS operations. This article proposes a general RSU/UAV joint planning solution, where complex dynamic parameters are investigated. The objective is to maximize the effective coverage of placed RSUs and UAV docks given: a budget comprised of periodic operating expenses and capital expenditures, limitations of the ground transceivers and UAVs, and use of renewable energy to offset the on-grid electricity cost. We formulate a mixed-integer quadratically constrained problem that can determine the optimal placement of RSUs and UAV stations, RSU activation schedules, if solar panels are attached, and their coverage during each time period. Due to NP-hard complexity of such a planning problem, we design a heuristic algorithm that produces suboptimal solutions in less time. Afterward, we perform a sensitivity analysis and show that changes to the parameters lead to logical shifts in infrastructure coverage. Additionally, we visualize the algorithm’s performance on a large setting—Manhattan Island.
Michael C. Lucic; Hakim Ghazzai; Yehia Massoud. A Generalized Dynamic Planning Framework for Green UAV-Assisted Intelligent Transportation System Infrastructure. IEEE Systems Journal 2020, 14, 4786 -4797.
AMA StyleMichael C. Lucic, Hakim Ghazzai, Yehia Massoud. A Generalized Dynamic Planning Framework for Green UAV-Assisted Intelligent Transportation System Infrastructure. IEEE Systems Journal. 2020; 14 (4):4786-4797.
Chicago/Turabian StyleMichael C. Lucic; Hakim Ghazzai; Yehia Massoud. 2020. "A Generalized Dynamic Planning Framework for Green UAV-Assisted Intelligent Transportation System Infrastructure." IEEE Systems Journal 14, no. 4: 4786-4797.
Roadside Unit (RSU) planning and scheduling is necessary in order to ensure that future Intelligent Transportation Systems receive the best possible wireless coverage given many different factors. On top of prior planning and scheduling, RSU networks must be capable of adapting to rapidly changing environments due to unexpected events, such as vehicle collisions. In this paper, we develop a novel RSU coverage adjustment algorithm that will utilize the RSU requiring the least amount of transmission power to cover an event, while reducing the coverage of other RSUs to keep costs within the scheduled amortized budget. We find that for the small-scale setting of a few Lower Manhattan neighborhoods, the system is capable of dynamically adjusting to cover events in varying locations, while remaining within the budget.
Michael Lucic; Hakim Ghazzai; Ahmed Khattab; Yehia Massoud. Rapid Management of Unexpected Events in Urban V2I Communications Systems. 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2019, 1 -6.
AMA StyleMichael Lucic, Hakim Ghazzai, Ahmed Khattab, Yehia Massoud. Rapid Management of Unexpected Events in Urban V2I Communications Systems. 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES). 2019; ():1-6.
Chicago/Turabian StyleMichael Lucic; Hakim Ghazzai; Ahmed Khattab; Yehia Massoud. 2019. "Rapid Management of Unexpected Events in Urban V2I Communications Systems." 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES) , no. : 1-6.
Roadside Unit (RSU) planning and management is not a straightforward task. Usually, the problem is modeled as an NP-hard mixed-integer combinatorial optimization problem especially when the planner ought to incorporate various problem components. For large-scale problems, heuristic approaches that achieve a trade-off between execution time and planning performance, can be good enough for making planning decisions. In this paper, we design an iterative reduction heuristic algorithm to maximize the coverage efficiency of a network of RSUs in an urban setting, given a daily amortized budget and other planning constraints. The framework also incorporates capture-and-use solar panels to offset operational electricity costs. We perform a sensitivity analysis, to study the model response to variations. The heuristic shows that variations in both financial as well as communication-related parameters have expected model solution response. We find that in 10% to 50% of the convergence time of the optimal solution, the heuristic found solutions that had a coverage efficiency around 10% of the optimal solution.
Michael Lucic; Hakim Ghazzai; Yehia Massoud. A Low Complexity Space-Time Algorithm for Green ITS-Roadside Unit Planning. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) 2019, 570 -573.
AMA StyleMichael Lucic, Hakim Ghazzai, Yehia Massoud. A Low Complexity Space-Time Algorithm for Green ITS-Roadside Unit Planning. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS). 2019; ():570-573.
Chicago/Turabian StyleMichael Lucic; Hakim Ghazzai; Yehia Massoud. 2019. "A Low Complexity Space-Time Algorithm for Green ITS-Roadside Unit Planning." 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS) , no. : 570-573.
Roadside Unit (RSU) planning is a key step for the development of a robust Intelligent Transportation System (ITS). Many factors, including traffic flow variation, energy consumption, and budgetary constraints, all affect the daily operation and performance of the ITS. Therefore, there is an urgent need to effectively incorporate all these factors in designing a planning program that addresses this complex and dynamic problem. In this paper, we propose a general RSU planning solution, where complex and dynamic parameters are investigated. The objective is to maximize the effective coverage area of the placed RSUs, given: i) a planning budget comprised of periodic operating expenses (OPEX) and capital expenditures (CAPEX), ii) the physical limitations of the transceivers, and iii) the potential use of renewable energy to offset the on-grid electricity cost. We formulate a Mixed-Integer Quadratically Constrained Programming (MIQCP) problem that can simultaneously determine the optimal placement and daily activation/deactivation schedules of each RSU, whether or not they have a solar panel attached, and their ranges during each period of time. We performed a sensitivity analysis over a realistic map, and results show that as the budget increases, no matter the CAPEX/OPEX, there is an increase in coverage efficiency with a diminishing-returns behavior, a positive correlation between maximum transmission power ratings on the RSUs and coverage efficiency, and a negative correlation between minimum required data transfer rate and coverage efficiency.
Michael Lucic; Hakim Ghazzai; Yehia Massoud. A Generalized and Dynamic Framework for Solar-Powered Roadside Transmitter Unit Planning. 2019 IEEE International Systems Conference (SysCon) 2019, 1 -7.
AMA StyleMichael Lucic, Hakim Ghazzai, Yehia Massoud. A Generalized and Dynamic Framework for Solar-Powered Roadside Transmitter Unit Planning. 2019 IEEE International Systems Conference (SysCon). 2019; ():1-7.
Chicago/Turabian StyleMichael Lucic; Hakim Ghazzai; Yehia Massoud. 2019. "A Generalized and Dynamic Framework for Solar-Powered Roadside Transmitter Unit Planning." 2019 IEEE International Systems Conference (SysCon) , no. : 1-7.