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The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

Higher education institutions (HEIs) in India were caught completely unawares by the Covid-19 pandemic and necessitated lockdown. Despite almost two decades of experimentation with online and distance learning by top-tier and private institutions, the vast majority were unprepared and looked for standalone solutions for different components of teaching and learning. Valuable lessons have been learned based on which a more comprehensive solution must be sought for the post-Covid-19 environment. The lockdown has provided the much-needed impetus to reshape higher education in India. Calls for the adoption of blended learning (BL) have been made on prior occasions; this chapter renews that call and stresses its urgency. It is imperative that educational institutions amplify the momentum gained during the lockdown and transition to a BL model supported by the adoption and use of learning management systems (LMSs). Government should support this initiative by providing a centralized LMS. Corporations must “adopt an HEI,” channeling their unused corporate social responsibility funds to support information and communication technology needs at educational institutions. All stakeholders must work together to transform the country to a digitally empowered society and knowledge economy as envisioned in the National Education Policy 2020.

The aim of this study is to use the guiding strategy, Melbourneâ™’s Scholarly Information Future, which is a ten-year strategy that identifies in its aspirations the importance of building effective access to the rich cultural, scholarly and research collections of the University of Melbourne and acknowledges the critical role that digitization plays in achieving this vision. The University of Melbourne has a rich, complex and ultimately voluminous array of cultural, scholarly and research material that is of great interest and value to the its community, scholarly researchers and the global community. Since the strategy endorsement in 2008, the authors have progressively moved from a digitization environment that was uncoordinated, ad hoc and lacked centralized expertise that led to a proliferation of isolated, under-resourced areas producing inconsistent and indifferent quality images to our goal of an exemplar digitization framework, program and enterprise capability for the University to leverage.

Case study of the journey taken by the University of Melbourne in building an enterprise digitization capability.

This article outlines the journey and the approach in building this capability in a challenging economic environment, the engagement strategies to gain support and funding, skills and equipment and the unique challenges of the digitization of a diverse array of University collections. Second, it also explores digitization as transformation and outlines some of the infinite and extraordinary possibilities created from digitized content of library collections.

This article will be of value to institutions that are considering taking similar steps.

THE Twenty‐fourth S.B.A.C. Flying Display and Exhibition to be held at Farnborough during the week Monday,September 7, to Sunday, September 13, promises to be the most interesting ever held. This is in part due to the decision taken in 1962 not to hold an S.B.A.C. Show in the summer of 1963, and partly the result of the effort now being concentrated upon the Concord supersonic airliner project, the TSR‐2 supersonic strike and reconnaissance bomber, the Hawker Siddeley P. 1154 V/STOL fighter, two new research aircraft and a whole range of new transport aircraft. Apart from the models and displays which will be mounted by the major airframe and engine manufacturers demonstrating their own involvement with these projects and programmes, the stands of the Associate Members of the Society of British Aerospace Companies will abound with examples of materials, techniques and equipment which these companies are producing in support of the latest programmes—notably TSR.2 and Concord.