Abstract Fiber Bragg Grating (FBG) strain sensors represent a cutting-edge technology in the field of optical sensing, providing precise measurements of strain, temperature, and other physical parameters in various environments. This article explores the fundamental principles, applications, and advantages of FBG strain sensors, highlighting their scientific and industrial significance.

Abstract Livable cities involve such quality-of-life factors as transportation, convenience of daily life, education, and a safe and stable built and natural environment. The livability of a city also has social and psychological dimensions, such as emotion and perception. This paper explores how the advantages of new transportation technology can be realized under mixed-traffic conditions, while meeting the requirements for safety, convenience, and high educational standards. One of the key challenges to realizing the advantages of a new technology, such as autonomous shuttle buses, under mixed-traffic conditions and under adverse weather conditions is how to strike a balance between innovation and adherence to essential urban demands. Ensuring that the introduction of advanced technologies like autonomous shuttle buses does not compromise safety is of the utmost importance. Under mixed-traffic conditions, autonomous shuttle buses must negotiate interactions with human-driven vehicles, pedestrians, and bicyclists. Predictable and safe driving behavior is vital to gaining the confidence and acceptance of all road users. Additionally, adopting vehicle-to-everything (V2X) communication enables real-time information exchange with other vehicles and infrastructure, which enhance safety. Autonomous shuttle buses also face crucial challenges in adverse weather conditions. Robust sensor systems are required to accurately perceive the environment despite rain, snow, fog, and other weather-related constraints. Continual learning and software updates ensure the system’s ability to adapt to changing weather conditions. To overcome these challenges, collaboration with regulatory bodies and traffic management authorities is essential. Furthermore, public awareness campaigns and educational initiatives can inform the public about the safety measures and benefits of autonomous shuttle buses, fostering acceptance and reducing concerns during the transition.

Abstract This paper presents a distortion compensation algorithm for multi-camera systems that does not require specialized calibration targets. A review of classical distortion models as well as modern calibration approaches (such as OpenCV) is provided. A modification of the distortion compensation algorithm is proposed, and an experimental comparison with the classical Brown–Conrady model is conducted using data from cameras equipped with the Sony IMX462 sensor. The results show that the proposed algorithm achieves projection rectification comparable to target-based calibration while preserving a wide field of view. This opens up opportunities for applying the method in wearable monitoring systems, panoramic image creation, and pipeline inspection, where the use of calibration targets is challenging.

Abstract Globally, over a billion people live with disabilities, facing significant challenges in accessing public transport, which impacts their autonomy, social participation, and economic status. Current research indicates that common problems include inadequate service in terms of destination, timing, and travel duration, as well as physical barriers at stops and the behaviour and proficiency of transit staff. These issues are exacerbated for those with visual impairments or mobility challenges, such as wheelchair users, who face even greater obstacles. This research emphasizes the necessity of an “enabling transport” environment that considers all aspects of travel for those with limited mobility. This includes the physical layout of pedestrian routes, the design of buildings, and the functionality of public transportation systems. Practical measures like aligning bus floors with pavements, as mandated in the European Union, and optimizing the deployment of accessibility equipment like pallets are discussed as essential for improving access. The authors propose a research methodology that employs a graph-based approach in combination with recurrent neural networks models to suggest most accessible pathways considering fleet availability, vehicle capacity and road quality of sidewalks. The approach includes a comprehensive case study in Riga, Latvia, utilizing data from local transport operators and crowdsourced information to assess and address physical barriers. This innovative application of deep learning on graphs aims to significantly improve the inclusivity and efficiency of public transport for people with disabilities. The study emphasizes the broader benefits of creating accessible environments that improve usability for all citizens, not just those with disabilities.

Abstract This paper addresses the problem of determining bandwidth allocation and traffic routes in fixed microwave networks such that overall bandwidth cost is minimized while traffic demands are satisfied with a required reliability level. These networks exhibit high variability in link throughput as modulations schemes are adapted dynamically to ensure acceptable bit-error rate at the receivers according to external conditions such as the weather. First, we formulate an optimal optimization approach based on mixed-integer linear programming, which is subsequently reinforced by inserting problem-specific valid inequalities based on global network capacity to reduce the search space and eliminate the unfeasible bandwidth/modulation combinations, thus reducing the number of decision variables. Then, we introduce a Lagrangian-based heuristic that provides near optimal solutions while reducing drastically the computation time. In comparison to previous work, our experimental results show that our approaches are capable to solve large real-world network instances in an effective manner. Furthermore, the results evaluate the impact of reliability and transported traffic demands on bandwidth cost.

Abstract This paper introduces a modular simulation testbed developed for decision support in logistics and urban planning within the tourism sector, specifically applied to Şanlıurfa, Turkey, as part of the GeostaloVR project. The testbed integrates tourism value chains, urban development processes, and mobility flows to facilitate scenario testing and evaluation. By incorporating real-world data, the simulation framework enables the analysis of key performance indicators for sustainable urban tourism development. This approach aims to advance the application of modeling techniques to address complex challenges in urban tourism in order to provide valuable insights for stakeholders involved in urban and logistics planning.

Abstract This work presents the employing of LoRaWAN (Long Range Wide Area Network) for location applications through a network simulation to determine a mobile node position. We rely on FLoRa (Framework for LoRa) and OMNeT++ (Objective Modular Network Testbed in C++) simulator, which uses Python feature tools, following the calculation of node placement using the trilateration technique. Our method differs from others in that we calculate the FLoRa power loss and determine different simulation settings using the shadowing feature of the log-distance path loss model. We approached RSSI (Received Signal Strength Indicator) to measure the distance between the LoRa gateways and the nodes, establishing a link between these parameters. Our work aims to promote the integration of open-source tools for verifying signal intensity values based on node distance from gateways. We consider it useful for engineers in predicting signal behaviors according to topology and settings variations. During the experimentation, the network underwent different performances according to the transmission parameters considered during the simulation. This was critical when increasing the number of mobile nodes, leading to consuming computer capacity and resources. Through repetition of tests, we confirmed the lower intensity of the received signal as the node moves to farther positions, reaching consistent power indicators and positioning accuracy. Overall, the results show that LoRaWAN integrated with trilateration techniques can be practical in providing adequate performance for node positioning accuracy and long-distance communication with low power consumption.

Abstract Aviation is one of the most global industries, and if we can model and predict a country’s air transportation flow and indicators ahead of time, we may be able to use it as a key decision-making tool for the management and operation process. This study proposes a new modeling, and prediction method that employs both fractional calculus and Multi Deep Assessment Methodology (MDAM) techniques. For the application, air passengers carried, air freight, available seat kilometers, number of flights, destination points, international travelers, international destination points, and international flight data between 2011 and 2019 for eight countries with the busiest airports were chosen. As a result, the highest modeling error was discovered to be Germany’s air transport freight factor expressed as a percentage of 1,59E-02. The percentage of predictions with errors less than 10% was 90.278. We also compared the performance of two different MDAM methodologies. The novel MDAM wd methodology proposed in this paper has a higher accuracy in aviation factors prediction and modeling.

Abstract The adoption of the Voice over Internet Protocol (VoIP) system is growing due to several factors, including its meagre rate and the numerous contours that can be joined with VoIP systems. However, the wasteful utilisation of the computer network is an inevitable problem that limits the rapid growth of VoIP systems. The essential explanation behind this wasteful utilisation of the computer network bandwidth (BW) is the considerable preamble length of the VoIP packet. In this study, we invent a technique that addresses the considerable preamble length of the VoIP packet. The designed technique is known as the manikin voice frame (MVF). The primary idea of the MVF technique is to utilise the VoIP packet preamble tuples that are not essential to the voice calls, particularly client-to-client calls (voice calls between only two users). Specifically, these tuples will be utilised for reserving the data of the VoIP packet. In certain instances, this will make the VoIP packet data manikin or even make it empty. The performance assessment of the introduced MVF technique demonstrated that the utilisation of the computer network BW has enhanced by 33%. Along these lines, the MVF technique indicates potential progress in resolving the inefficient usage of the computer network BW.

Abstract This paper aims to provide a PESTLE (Political, Economic, Social, Technological, Legal and Environmental) analysis of current trends and changes in the urban goods transport market. The goal is to assess the macro-environmental factors that are affecting innovative solutions for last-mile delivery, and to use this analysis to support decision making and planning for horizontal schemes of collaboration and policy interventions that may be necessary for introducing these solutions successfully and becoming more competitive in practice. By carefully prioritizing these factors, relevant stakeholders can make informed decisions and take effective actions to improve the competitiveness of their operations in the urban goods transport industry.