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1. A Decentralized Approach For Real Time Anomaly Detection In Transportation Networks

   https://doi.org/10.1109/SMARTCOMP.2019.00063  

   Wilbur, Michael; Dubey, Abhishek; Bruno, Leao; Bhattacharjee, Shameek. (June 2019, 2019 IEEE International Conference on Smart Computing (SMARTCOMP))

   Internet of Things (IoT), edge/fog computing, and the cloud are fueling rapid development in smart connected cities. Given the increasing rate of urbanization, the advancement of these technologies is a critical component of mitigating demand on already constrained transportation resources. Smart transportation systems are most effectively implemented as a decentralized network, in which traffic sensors send data to small low-powered devices called Roadside Units (RSUs). These RSUs host various computation and networking services. Data driven applications such as optimal routing require precise real-time data, however, data-driven approaches are susceptible to data integrity attacks. Therefore we propose a multi-tiered anomaly detection framework which utilizes spare processing capabilities of the distributed RSU network in combination with the cloud for fast, real-time detection. In this paper we present a novel real time anomaly detection framework. Additionally, we focus on implementation of our framework in smart-city transportation systems by providing a constrained clustering algorithm for RSU placement throughout the network. Extensive experimental validation using traffic data from Nashville, TN demonstrates that the proposed methods significantly reduce computation requirements while maintaining similar performance to current state of the art anomaly detection methods. 

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2. Pilot project purgatory? Assessing automated vehicle pilot projects in U.S. cities

   https://doi.org/10.1057/s41599-021-01006-2  

   McAslan, Devon; Najar Arevalo, Farah; King, David A.; Miller, Thaddeus R. (December 2021, Humanities and Social Sciences Communications)

   Abstract Pilot projects have emerged in cities globally as a way to experiment with the utilization of a suite of smart mobility and emerging transportation technologies. Automated vehicles (AVs) have become central tools for such projects as city governments and industry explore the use and impact of this emerging technology. This paper presents a large-scale assessment of AV pilot projects in U.S. cities to understand how pilot projects are being used to examine the risks and benefits of AVs, how cities integrate these potentially transformative technologies into conventional policy and planning, and how and what they are learning about this technology and its future opportunities and risks. Through interviews with planning practitioners and document analysis, we demonstrate that the approaches cities take for AVs differ significantly, and often lack coherent policy goals. Key findings from this research include: (1) a disconnect between the goals of the pilot projects and a city’s transportation goals; (2) cities generally lack a long-term vision for how AVs fit into future mobility systems and how they might help address transportation goals; (3) an overemphasis of non-transportation benefits of AV pilots projects; (4) AV pilot projects exhibit a lack of policy learning and iteration; and (5) cities are not leveraging pilot projects for public benefits. Overall, urban and transportation planners and decision makers show a clear interest to discover how AVs can be used to address transportation challenges in their communities, but our research shows that while AV pilot projects purport to do this, while having numerous outcomes, they have limited value for informing transportation policy and planning questions around AVs. We also find that AV pilot projects, as presently structured, may constrain planners’ ability to re-think transportation systems within the context of rapid technological change. 

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3. Simulation of Real-time Routing for UAS traffic Management with Communication and Airspace Safety Considerations

   https://doi.org/10.1109/DASC43569.2019.9081675  

   Jin, Zhao; Zhao, Ziyi; Luo, Chen; Basti, Franco; Solomon, Adrian; Gursoy, M. Cenk; Caicedo, Carlos; Qiu, Qinru (September 2019, Proceedings of 38th Digital Avionics Systems Conference)

   Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related applications, such as commercial delivery and land surveying, is expected to grow rapidly in next few years. In general, sUAS traffic routing and management functions are needed to coordinate the launching of sUAS from different launch sites and determine their trajectories to avoid conflict while considering several other constraints such as expected arrival time, minimum flight energy, and availability of communication resources. However, as the airborne sUAS density grows in a certain area, it is difficult to foresee the potential airspace and communications resource conflicts and make immediate decisions to avoid them. To address this challenge, we present a temporal and spatial routing algorithm and simulation platform for sUAS trajectory management in a high density urban area that plans sUAS movements in a spatial and temporal maze taking into account obstacles that are either static or dynamic in time. The routing allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other in-flight sUAS and areas that have congested communication resources (i.e. dynamic obstacles). The algorithm is evaluated using an agent-based simulation platform. The simulation results show that the proposed algorithm outperforms other route management algorithms in many areas, especially in processing speed and memory efficiency. Detailed comparisons are provided for the sUAS flight time, the overall throughput, conflict rate and communication resource utilization. The results demonstrate that our proposed algorithm can be used to address the airspace and communication resource utilization needs for a next generation smart city and smart transportation. 

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4. Data-driven Bus Crowding Prediction Models Using Context-specific Features

   https://doi.org/10.1145/3406962  

   Arabghalizi, Tahereh; Labrinidis, Alexandros (October 2020, ACM/IMS Transactions on Data Science)

   null (Ed.)

   Public transit is one of the first things that come to mind when someone talks about “smart cities.” As a result, many technologies, applications, and infrastructure have already been deployed to bring the promise of the smart city to public transportation. Most of these have focused on answering the question, “When will my bus arrive?”; little has been done to answer the question, “How full will my next bus be?” which also dramatically affects commuters’ quality of life. In this article, we consider the bus fullness problem. In particular, we propose two different formulations of the problem, develop multiple predictive models, and evaluate their accuracy using data from the Pittsburgh region. Our predictive models consistently outperform the baselines (by up to 8 times). 

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5. A stochastic optimization framework for planning of waste collection and value recovery operations in smart and sustainable cities

   https://doi.org/https://doi.org/10.1016/j.wasman.2018.05.019  

   Jatinkumar Shah, Parth; Anagnostopoulos, Theodoros; Zaslavsky, Arkady; Behdad, Sara (August 2018, Waste management)

   The concept of City 2.0 or smart city is offering new opportunities for handling waste management practices. The existing studies have started addressing waste management problems in smart cities mainly by focusing on the design of new sensor-based Internet of Things (IoT) technologies, and optimizing the routes for waste collection trucks with the aim of minimizing operational costs, energy consumption and transportation pollution emissions. In this study, the importance of value recovery from trash bins is highlighted. A stochastic optimization model based on chance-constrained programming is developed to optimize the planning of waste collection operations. The objective of the proposed optimization model is to minimize the total transportation cost while maximizing the recovery of value still embedded in waste bins. The value of collected waste is modeled as an uncertain parameter to reflect the uncertain value that can be recovered from each trash bin due to the uncertain condition and quality of waste. The application of the proposed model is shown by using a numerical example. The study opens new venues for incorporating the value recovery aspect into waste collection planning and development of new data acquisition technologies that enable municipalities to monitor the mix of recyclables embedded in individual trash bins. 

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