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1. Smart city intersections: Intelligence nodes for future metropolises

   Z. Kostic, A. Angus (May 2022, Cornell University Library)

   Traffic intersections are the most suitable locations for the deployment of computing, communications, and intelligence services for smart cities of the future. The abundance of data to be collected and processed, in combination with privacy and security concerns, motivates the use of the edgecomputing paradigm which aligns well with physical intersections in metropolises. This paper focuses on high-bandwidth, lowlatency applications, and in that context it describes: (i) system design considerations for smart city intersection intelligence nodes; (ii) key technological components including sensors, networking, edge computing, low latency design, and AI-based intelligence; and (iii) applications such as privacy preservation, cloud-connected vehicles, a real-time ”radar-screen”, traffic management, and monitoring of pedestrian behavior during pandemics. The results of the experimental studies performed on the COSMOS testbed located in New York City are illustrated. Future challenges in designing human-centered smart city intersections are summarized. 

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2. Smart city intersections: Intelligence nodes for future metropolises

   Z. Kostic, A. Angus (January 2022, arXiv:2205.01686v2 [cs.CV], May 2022.)

   Traffic intersections are the most suitable locations for the deployment of computing, communications, and intelligence services for smart cities of the future. The abundance of data to be collected and processed, in combination with privacy and security concerns, motivates the use of the edgecomputing paradigm which aligns well with physical intersections in metropolises. This paper focuses on high-bandwidth, lowlatency applications, and in that context it describes: (i) system design considerations for smart city intersection intelligence nodes; (ii) key technological components including sensors, networking, edge computing, low latency design, and AI-based intelligence; and (iii) applications such as privacy preservation, cloud-connected vehicles, a real-time ”radar-screen”, traffic management, and monitoring of pedestrian behavior during pandemics. The results of the experimental studies performed on the COSMOS testbed located in New York City are illustrated. Future challenges in designing human-centered smart city intersections are summarized. 

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3. Smart city intersections: Intelligence nodes for future metropolises

   Z. Kostic, A. Angus (May 2022, ArXivorg)

   —Traffic intersections are the most suitable locations for the deployment of computing, communications, and intelligence services for smart cities of the future. The abundance of data to be collected and processed, in combination with privacy and security concerns, motivates the use of the edgecomputing paradigm which aligns well with physical intersections in metropolises. This paper focuses on high-bandwidth, lowlatency applications, and in that context it describes: (i) system design considerations for smart city intersection intelligence nodes; (ii) key technological components including sensors, networking, edge computing, low latency design, and AI-based intelligence; and (iii) applications such as privacy preservation, cloud-connected vehicles, a real-time ”radar-screen”, traffic management, and monitoring of pedestrian behavior during pandemics. The results of the experimental studies performed on the COSMOS testbed located in New York City are illustrated. Future challenges in designing human-centered smart city intersections are summarized. 

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4. Anomaly based Incident Detection in Large Scale Smart Transportation Systems

   https://doi.org/10.1109/ICCPS54341.2022.00026  

   Islam, Jaminur; Talusan, Jose Paolo; Bhattacharjee, Shameek; Tiausas, Francis; Vazirizade, Sayyed Mohsen; Dubey, Abhishek; Yasumoto, Keiichi; Das, Sajal K. (May 2022, 2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS))

   Modern smart cities are focusing on smart transportation solutions to detect and mitigate the effects of various traffic incidents in the city. To materialize this, roadside units and ambient trans-portation sensors are being deployed to collect vehicular data that provides real-time traffic monitoring. In this paper, we first propose a real-time data-driven anomaly-based traffic incident detection framework for a city-scale smart transportation system. Specifically, we propose an incremental region growing approximation algorithm for optimal Spatio-temporal clustering of road segments and their data; such that road segments are strategically divided into highly correlated clusters. The highly correlated clusters enable identifying a Pythagorean Mean-based invariant as an anomaly detection metric that is highly stable under no incidents but shows a deviation in the presence of incidents. We learn the bounds of the invariants in a robust manner such that anomaly detection can generalize to unseen events, even when learning from real noisy data. We perform extensive experimental validation using mobility data collected from the City of Nashville, Tennessee, and prove that the method can detect incidents within each cluster in real-time. 

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5. Smart city intersections: Intelligence nodes for future metropolises

   Z. Kostic, A. Angus (December 2022, IEEE Computer, Special Issue on Smart and Circular Cities)

   This article explores city intersections as intelligence nodes using high-bandwidth, low-latency services for providing privacy-preserving smart city applications. COSMOS testbed experiments using edge-computing-based artificialintelligence techniques are reported, for monitoring of pedestrians, cloud-connected vehicles, and traffic management. 

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