Vehicular Cloud Computing (VCC) introduces the challenge of resource volatility, as vehicles unpredictably depart, impacting system stability and performance. To address this, we present a Stochastic Petri Net (SPN) model with a container allocation strategy to evaluate Static Vehicular Clouds (SVCs) in a car rental setting. Two key performance metrics are analyzed: Queue Discard Probability (QDP) and Utilization of Available Resources (UAR). A sensitivity analysis reveals that mean rental time (MRT), mean return time (MRRT), number of tasks per vehicle (NTV), and mean service time (MTST) are the primary factors influencing system behavior. Two case studies, varying NTV and MTST, demonstrate their impact on QDP and UAR. These findings offer valuable guidance to infrastructure planners aiming to optimize resource allocation, minimize task loss, and improve SVC efficiency.

As urban centers increasingly grapple with the challenges of population growth, traffic congestion, and environmental degradation, the need for sustainable mobility solutions has become imperative. This study presents a performance evaluation of a public bicycle-sharing system through the application of Stochastic Petri Nets (SPNs), which are employed to model and simulate user behavior under dynamic conditions. Drawing on recent advances in urban mobility and shared transportation research, the proposed stochastic framework effectively captures key operational metrics, such as bicycles in transit, station availability, waiting probability, system utilization, and the occupancy of docking stations. A case study analyzes three high-demand stations across varying demand scenarios (baseline, 25%, and +25%). Findings reveal that, while the system performs adequately under current demand levels, it becomes vulnerable to saturation with increased usage and demonstrates operational inefficiencies when demand decreases. The model offers valuable insights to inform operational strategies, guide infrastructure development, and support public policy aimed at fostering integrated and sustainable urban mobility.

This paper presents a stochastic performance model for evaluating battery swapping station (BSS) systems for electric vehicles (EV). The approach uses Stochastic Petri Nets (SPN) and discrete event simulation to model interactions among vehicles, battery swapping infrastructure, and demand distribution. A modular framework simulates a real-world case study in Recife, Brazil. The Average Probability of Not Finding a Battery (APNFB) is evaluated under various operational scenarios. A sensitivity analysis highlights key parameters affecting system performance: battery discharge time, charge time, and the number of motorcycles. These parameters significantly impact battery availability and reliability, emphasizing the need for informed infrastructure planning and design. The study illustrates the value of stochastic modeling in understanding BSS system performance under uncertainty, due to the aleatory behavior of the system, and supports strategic decision-making for future deployments.

The blockchain shared ledger emerged as an alternative to the bureaucratic banking system that may take days to confirm a payment or a transfer between clients. The blockchain concept evolved and became viable for various applications beyond the domain of financial transactions. Blockchains become a way to reach better relationships through contract validation, documents transfer, and personal and business data security. Recently, the blockchain-as-a-service has debuted on Microsoft Data Centers, and now many share an infrastructure that can change and improve their security routines. This paper evaluates the feasibility of a blockchain-as-a-service infrastructure and helps those who plan to deploy or sell blockchains. A modeling methodology based on Dynamical Reliability Block Diagrams (DRBD) is adopted to evaluate two dependability attributes: system's reliability and availability. The proposed infrastructure contains the minimum requirements to deploy the Hyperledger Cello, a platform to create and manage blockchains. The availability results pointed out a system downtime of 121 hours per year and reliability issues that must be addressed when building blockchain-as-a-service infrastructures.

Large cities face growing mobility problems, due to the major traffic jams that result from high numbers of vehicles on the roads. In response, city and national governments have invested in alternative means of urban passenger transit, such as subways, trains, as well as Bus Rapid Transit (BRT). This article aims to analyze the BRT system, by attempting to calculate the probability of reaching a destination at a specific time, thereby providing a tool that can be employed to improve the system and increase passenger confidence in it. To this end, a Continuous Time Markov Chain (CTMC) model is proposed to represent the bus stations and compute the probability metric for arrival at the destination within the specified time frame. The model allows a mathematical function to calculate the probabilities for the corresponding architecture. Two case studies were conducted in order to verify the model and illustrate its potential value in the planning of BRT systems.