Conference Papers
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Item Delay Variability Analysis at Intersections Using Public Transit GPS Data(Springer Science and Business Media Deutschland GmbH, 2023) Lal, A.; Mulangi, R.H.; Harsha, M.M.India is competing with the fastest-growing countries in the world in terms of urbanization and development. Rapid urbanization and motorization have led to congestion in urban roads in India. Delay forms a significant part of congestion. It is necessary to analyze variability in delay for mitigation of traffic congestion. In recent years, GPS data has emerged as a novel data source for traffic state monitoring and analysis due to its better accuracy, coverage, and accessibility. However, little work has been done for control delay estimation especially in Indian traffic condition. In this paper, an attempt has been made to estimate control delay at selected intersections in Mysore city using GPS data from transit buses. A vehicle trajectory-based formulation is adopted for the estimation of delay. The results are fitted to statistical distributions to analyze variability in delay. Kolmogorov- Smirnov (K-S) test for goodness of fit is used to estimate best fitting distribution. Generalized extreme value (GEV) distribution is found to best-fit delay in terms of fitting performance, robustness, and accuracy. The performance analysis indicates greater variability in delay during morning and evening peak hours. Successful estimation of delay variability allows for the analysis of traffic state at various intersections, thus paving the way for effective congestion mitigation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Trustworthiness of COVID-19 News and Guidelines(Springer, 2023) Singh, S.; Nagar, L.; Lal, A.; Chandavarkar, B.R.COVID-19 pandemic is a serious health concern issue over the past couple of years. It spreads mostly due to bio-contacts, which leads people to follow social distancing and stay away from social gatherings. It leads the people to bound themselves to stay with their family members at their home only, being at home, staying idle, or following work from home schedule by working online through the Internet over the electronic gadgets such as mobiles, laptops, desktops, etc. It leads the people to attach to online activities more for spending their time at their home, which enormously increases people interest in social media platforms such as Twitter, Facebook, etc. As it was a major pandemic period, it created panic and a fearful situation in society. It makes the people believe any news and guidelines spreading through social media platforms irrespective of checking their trustworthiness and truthiness of it. This pandemic period created a seriously bad impact on society’s emotional, physical, and mental health that is a great loss to a country even all over the world. Under this, many unwanted messages are spreading for one’s interest or a group to polarize their interest. In a panic situation, it is highly required of a solution that prevents the spread of these negative vibes to maintain the overall health of society. This chapter tries to implement an optimal solution using various kinds of layers and different optimization functions. It particularly gives better performance in the case of sequential data using machine learning (ML) and deep learning (DL) frameworks trained with the dataset for identifying the fake news and guidelines spread over on COVID-19. To train the model, a dataset was taken from the Twitter Application Programming Interface (API). Finally, the truthiness detection technique with social interaction is completed using Twitter dataset. The efficacy of the suggested method is demonstrated by the obtained results on a Twitter dataset. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.