REVA University

This study examines the Public Bicycle Sharing System (PBSS) in Ahmedabad, the patterns of ridership, user behavior, and spatial distribution in various density zones based on the data provided by MYBYK. In 2023, 577,228 rides were registered, and high ridership areas were concentered around educational and recreational locations, which recorded 350,000 rides in 132 stations. In the high-density zones, the analysis indicates that most of the preference is for the short-distance ride, 55 percent of the total number of trips are made by zero-displacement rides, which is a good indicator that the trend is shifting towards leisure cycling. The monthly ridership trends show some significant variations, with the highest number of 68,529 rides in March, indicating that significant operation policies should be implemented when demand is very high. The dominance of short-term subscriptions (87.3%) indicates the users' preference for flexibility, whereas the number of rides longer than 5 km (424,893 trips) shows a growing tendency towards longer trips. Furthermore, a multinomial logistic regression model was used to identify factors affecting the use of PBSS, with emphasis on the relationships between the type of subscription, length of trip, time of use, and weather conditions. The model findings indicate that short-term users tend to have shorter rides, and the length of the trip is highly influenced by climatic conditions. The results can be useful to policymakers and urban planners to optimize the PBSS operations and improve sustainable mobility in Ahmedabad and other urban settings.

Abstract The facilitation of a high speed 6G network will boost the performance of data transmission in order to meet its dynamic communication demand with massive generation of data in Internet-of-Vehicle (IoV) environment. Adoption of existing methods towards 6G in IoV is significantly witnessed with loopholes and limitation related to lack of structurization of massively generated data considering practical grounds. This challenge is addressed in proposed study by introducing a novel baseline architecture where a layer-wise operation is carried out towards developing a sustainable communication model in IoV. The monitoring and sensing is carried out by sensing layer while computing layer facilitates short range communication system using Request For Comment (RFC) 7625 compliant of 6G communication. The study outcome shows that proposed scheme is capable of structuring traffic data from IoV environment and offers better control on delay and stabilized links on multiple testcases of channel capacities.

• Introduces a novel method using Hilbert-Huang Transform (HHT) with LSTM networks for fault detection in DC microgrids. • Targets marine and satellite DC microgrids, addressing challenges with no zero-crossing points in traditional methods. • Demonstrates superior performance, outperforming SVM and Decision Tree techniques in fault detection time and accuracy. • Validates the method using MATLAB/Simulink, effectively detecting faults with resistance up to 1.3 ohms. • Ensures compliance with IEEE 8570 standards, enhancing reliability and applicability in real-world microgrid systems. Fault detection in DC microgrids is a major challenge because traditional protection methods like wavelet and Fourier transforms rely on zero-crossing points, which the DC signal lacks. This paper introduces a new approach combining the Hilbert-Huang Transform (HHT) and a long short-term memory (LSTM) neural network to improve fault detection and classification in DC microgrids. The proposed method first uses Empirical Mode Decomposition (EMD) to break down the fault current signal into multiple intrinsic mode function (IMFs). These IMFs capture important fault-related information at different frequency levels. Next, the Hilbert Transform is applied to these IMFs to obtain amplitude and frequency features over time. These features are used to create a set of numerical descriptors that summarize the fault characteristics. These extracted features are then fed into a two-layer LSTM neural network that can learn patterns in time-series data. The LSTM model is trained to classify the input into three categories: no fault, Pole-Ground (P-G) Fault, or Pole-Pole (P-P) Fault. Simulation tests were carried out using a MATLAB Simulink model of a 20kWatt DC microgrid under different fault conditions, noise levels, and load transients. Results show that the proposed HHT-LSTM model detects faults faster and more accurately than conventional techniques like Support Vector Machines (SVM) and Decision Trees (DT). It also meets the fault detection time requirements set by IEEE8570 and aligns with IEC standards. The model remains robust even under noisy conditions, making it reliable for real-time fault protection in marine, satellite, and an effective solution for enhancing fault detection in modern DC microgrids.