This study aims to assess the safety level of the Automatic Dependent Surveillance-Broadcast (ADS-B) signal quality during airplane departures at Sultan Mahmud Badaruddin II Airport. The Aero-track application was utilized to monitor commercial aircraft departures and collect observation data. The collected data underwent processing using data analysis algorithms and labeling processes, resulting in a comprehensive dataset for evaluating ADS-B signal quality. Signal quality was categorized into four levels, and a model was built using the Random Forest algorithm, achieving an accuracy of 99%. Comparative analysis with SVM and Naive Bayes algorithms showed accuracy values of 93% and 97% respectively. Consequently, the Random Forest Model was chosen for estimating ADS-B signal quality during commercial aircraft takeoff and landing.

The rapid urbanization accompanying the evolution into “smart” communities presents numerous challenges, not least of which is the significant increase in road vehicles. This proliferation exacerbates congestion and accident rates, posing major barriers to the successful implementation of innovative technologies such as Wireless Sensor Networks (WSNs), surveillance cameras, and the Internet of Things (IoT). Accurate traffic flow prediction, a crucial component of these technological initiatives, requires a reliable and efficient methodology. This research explores the implementation of an intelligent traffic control system that employs a Transferable Texture Convolutional Neural Network (TTCNN). The design of this system eschews the traditional pooling layer, instead incorporating three convolutional layers and a single Energy Layer (EL). This configuration facilitates the provision of real-time traffic updates, which can enhance the utility and efficiency of the smart city infrastructure. A model inspired by the Hybrid Fruit Fly (HFFO) optimizes the system's hyperparameters. The application of HFFO to the TTCNN showcases the potential for improved accuracy in traffic flow prediction. Simulation results suggest that the HFFO provides superior organizational boundaries for the TTCNN, enhancing the overall accuracy of the model's predictions. The hybrid forecasting method discussed herein demonstrates its potential to outperform other established techniques. This investigation sheds light on the potential benefits of applying deep learning algorithms and hybrid models in the context of traffic flow prediction and control, contributing to the ongoing development of smart urban communities.

This study delves into the crucial task of embedding climate change resilience within the sphere of railway infrastructure planning and design in India. As climate change continues to threaten global transportation systems, the creation of robust, sustainable infrastructure becomes indispensable for minimizing its impacts. Initial investigation entails assessing both existing and anticipated climate change scenarios in India, encompassing elements like temperature fluctuations, changes in precipitation, and severe weather phenomena. Following this, the study proceeds to pinpoint the specific risks and vulnerabilities that the Indian railway system stands to confront due to these climatic shifts. A thorough exploration of current adaptation policies and strategies provides a framework to merge these into railway infrastructure planning and design, using a mix of literary review, best practices, and international case studies as resources. The Indian railway network undergoes a meticulous analysis to evaluate its vulnerability, leading to the identification of key adaptation measures like devising new railway tracks, enhancing the existing infrastructure, adopting resilience-based technologies, and implementing nature-centric solutions. The research probes the economic, social, and environmental ramifications of these measures, underlining the long-term sustainability and beneficial impacts on the transportation industry. Expert interviews, stakeholder consultations, and policy analysis culminate in a set of recommendations for policymakers, urban planners, and transportation authorities. These recommendations aim to shape the progression of a climate-resilient railway infrastructure in the light of India’s distinct challenges. Such an integration of climate change adaptation strategies contributes towards a more robust and sustainable transportation system. This study enriches the existing body of knowledge on climate change adaptation in transportation, offering valuable perspectives for policymakers, practitioners, and researchers aiming for climate resilience in the railway sector.