Recent improvements in machine vision algorithms have led to closed-circuit television (CCTV) cameras emerging as an important data source for determining of the state of traffic congestion. In this study we used two different deep learning techniques, you only look once (YOLO) and deep convolution neural network (DCNN), to detect traffic congestion from camera images. The support vector machine (SVM), a shallow algorithm, was also used as a comparison to determine the improvements obtained using deep learning algorithms. Occupancy data from nearby radar sensors were used to label congested images in the dataset and for training the models. YOLO and DCCN achieved 91.5% and 90.2% accuracy, respectively, whereas SVM's accuracy was 85.2%. Receiver operating characteristic curves were used to determine the sensitivity of the models with regard to different camera configurations, light conditions, and so forth. Although poor camera conditions at night affected the accuracy of the models, the areas under the curve from the deep models were found to be greater than 0.9 for all conditions. This shows that the models can perform well in challenging conditions as well. Disciplines Disciplines Transportation Engineering Comments Comments This is a manuscript of an article published as Chakraborty,

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Probe-based speed data provide great value to agencies; especially, in areas which are not feasibly covered by traffic sensors. However, as with sensors, probe data are not without nuance and issues like latency prevent alignment between calculated metrics by data source. In recent years, there has been a strong impetus on using data-driven decision making. Data-driven insights have become critical for smart mobility. To support data-driven decision making, Federal Highway Administration has procured probe data feeds and provides free access to state and local agencies as National Performance Measures Research dataset (NPRMDS). In addition to the NPRMDS, several state agencies subscribe to a paid probe data provider for obtaining real-time streams of high-resolution probe data. These datasets are used to generate nationwide urban mobility reports as well as reports focusing on certain jurisdiction. These mobility reports are integrated in several Transportation System Management and Operations (TSMO) plans which are often used to drive several resource allocation projects. This paper examines accuracy of methodology used to derive two frequently used performance measures in the mobility reports; namely, number of congested hours and number of congestion events. An improve methodology is then proposed to find accurate estimates for number of congested hours and number of congested incidents.

In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for the wireless access to the metaverse. Then it goes through the privacy and security issues inside the metaverse from data-centric, learning-centric, and human-centric points-of-view. The authors address private and secure mechanisms for privatizing sensitive data attributes and securing machine learning algorithms running in a distributed manner within the metaverse platforms. Novel visions and less-investigated methods are reviewed to help mobile network operators and metaverse service providers facilitate the realization of secure and private MaaS through different layers of the metaverse, ranging from the access layer to the social interactions among clients. Later in the article, it has been explained how the paradigm of edge computing can strengthen different aspects of the metaverse. Along with that, the challenges of using edge computing in the metaverse have been comprehensively investigated. Additionally, the paper has comprehensively investigated and analyzed 10 main challenges of MaaS platforms and thoroughly discussed how blockchain technology provides solutions for these constraints. At the final, future vision and directions, such as content-centric security and zero-trust metaverse, some blockchain's unsolved challenges are also discussed to bring further insights for the network designers in the metaverse era.

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