Transport Scenarios Research Articles

Improved Drug Traceability Code Detection Algorithm Based on YOLOv5

In order to enhance the storage efficiency of drug traceability code information on the blockchain and improve the extraction capability of drug traceability codes on drug packaging, a detection algorithm based on an enhanced version of YOLOv5 is proposed for the drug production and transportation scenario. The proposed algorithm introduces the SPD-Conv module into the backbone network, thereby enhancing the network’s ability to extract detailed feature information. Additionally, the CA attention mechanism is incorporated into the Neck of the network, providing the network with superior feature fusion capabilities. Furthermore, the activation function in the network is replaced with the LeakyReLU activation function, reducing computational requirements during training and inference. This replacement improves the model’s test accuracy and detection speed. Experimental evaluation, conducted on a self-built dataset, demonstrates the effectiveness of the improved YOLOv5 model. The results indicate a compression of model parameters from 6.14M to 2.71M, an increase in mAP@.5 from 82.4% to 93.7%, and a boost in detection speed from 32FPS to 51FPS. These findings establish the superior performance of the enhanced model compared to the original version.

Manual vs Mechanical Cardiopulmonary Resuscitation for Out-of-Hospital Cardiac Arrest on a Ski Slope: A Pilot Study.

The quality of cardiopulmonary resuscitation (CPR) is critical in increasing the probability of survival with a good neurologic outcome after out-of-hospital cardiac arrest. In an austere environment with a potentially salvageable patient, bystanders or first responders may need to provide chest compressions for a prolonged duration or during physically challenging transportation scenarios. Consequently, they may be at risk of fatigue or injury, and chest compression quality may deteriorate. The study sought to assess whether or not access to and utilization of a mechanical compression device (Lund University Cardiopulmonary Assist System) was feasible and not inferior to manual compressions while extricating and transporting a patient from a ski slope. Variable 3-person ski patrol teams responded to a simulated patient with out-of-hospital cardiac arrest in a nonshockable rhythm. Using a mannequin and CPR quality monitor, performance during manual CPR was compared with that of a mechanical compression device. This is a prospective, crossover analysis of CPR quality during extrication from a ski slope. Across 8 total runs, chest compression fraction, which is the proportion of time without spontaneous circulation during which compressions occurred, and high-quality CPR, as measured by appropriate rate and depth, were compared between the 2 groups. Extrication times between the 2 groups were also measured. There was no difference in compression fraction between the manual (91.4%; 95% CI [86.8-96.1]) and mechanical arms (92.8%; 95% CI [88.8-96.8]) (P=0.67). There was an increase in the time performing high-quality CPR in the mechanical group (58.5%; 95% CI [45.8-71.2]) vs that in the manual group (25.6%; 95% CI [13.5-37.8]) (P<0.001). There was a statistically significant difference in the extrication times between the 2 groups, 7.6 ± 0.5 min in the manual group vs 8.6 ± 0.4 min in the mechanical group (P=0.014). Mechanical CPR devices are noninferior for use in ski areas during initial resuscitation and transportation. Compared with manual CPR, mechanical CPR would likely improve the fraction of time performing high-quality CPR.

An Agent-Based Decision Support Framework for a Prospective Analysis of Transport and Heat Electrification in Urban Areas

One of the main pathways that cities are taking to reduce greenhouse gas emissions is the decarbonisation of the electricity supply in conjunction with the electrification of transport and heat services. Estimating these future electricity demands, greatly influenced by end-users’ behaviour, is key for planning energy systems. In this context, support tools can help decision-makers assess different scenarios and interventions during the design of new planning guidelines, policies, and operational procedures. This paper presents a novel bottom-up decision support framework using an agent-based modelling and simulation approach to evaluate, in an integrated way, transport and heat electrification scenarios in urban areas. In this work, an open-source tool named SmartCityModel is introduced, where agents represent energy users with diverse sociodemographic and technical attributes. Based on agents’ behavioural rules and daily activities, vehicle trips and building occupancy patterns are generated together with electric vehicle charging and building heating demands. A representative case study set in London, UK, is shown in detail, and a summary of more than ten other case studies is presented to highlight the flexibility of the framework to generate high-resolution spatiotemporal energy demand profiles in urban areas, supporting decision-makers in planning low-carbon and sustainable cities.

Angular momentum transport by magnetic fields in main-sequence stars with Gamma Doradus pulsators

Context. Asteroseismic studies show that cores of post-main-sequence stars rotate more slowly than theoretically predicted by stellar models with purely hydrodynamical transport processes. Recent studies of main-sequence stars, particularly Gamma Doradus (γ Dor) stars, have revealed the internal rotation rates for hundreds of stars, offering a counterpart on the main sequence for studies of angular momentum transport. Aims. We investigate whether such a disagreement between observed and predicted internal rotation rates is present in main-sequence stars by studying angular momentum transport in γ Dor stars. Furthermore, we test whether models of rotating stars with internal magnetic fields can reproduce their rotational properties. Methods. We computed rotating models with the Geneva stellar evolution code taking into account meridional circulation and shear instability. We also computed models with internal magnetic fields using a general formalism for transport by the Tayler-Spruit dynamo. We then compared these models to observational constraints for γ Dor stars that we compiled from the literature, thus combining the core rotation rates, projected rotational velocities from spectroscopy, and constraints on their fundamental parameters. Results. We show that combining the different observational constraints available for γ Dor stars enable us to clearly distinguish the different scenarios for internal angular momentum transport. Stellar models with purely hydrodynamical processes are in disagreement with the data, whereas models with internal magnetic fields can reproduce both core and surface constraints simultaneously. Conclusions. Similarly to results obtained for subgiant and red giant stars, angular momentum transport in radiative regions of γ Dor stars is highly efficient, in good agreement with predictions of models with internal magnetic fields.

Subsidies and extra-tariff revenues in the Brazilian Public Transportation scenario and its relationship with urban mobility

Urban Passenger Transportation is an essential service of public utility and, therefore, seeks to implement the right to universal mobility for different social classes. It is evident that the model must be regulated to guarantee social equity also through sustainable mobility. The objective of this paper is to identify policies applied to Public Transportation that are directly related to the subsidy theme, to propose a method to ensure the value of the affordable fare, maintaining the economic-financial balance, without, however, conditioning it to the stipulated level of service by the public concession contract. Applied research was used with a descriptive objective, performing analysis into the Brazilian scenario. Since these are public resources intended for service providers for extra-contractual intervention, the challenges and complexity of interpreting the effectiveness of the subsidy to the detriment of the social tariff are considered. In addition to proposing the method itself, this paper shows how to use a subsidy combined with extra-tariff revenues, in order to equalize operating costs without interfering with the system's level of service.

Impact of Green Energy Transportation Systems on Urban Air Quality: A Predictive Analysis Using Spatiotemporal Deep Learning Techniques

Transitioning to green energy transport systems, notably electric vehicles, is crucial to both combat climate change and enhance urban air quality in developing nations. Urban air quality is pivotal, given its impact on health, necessitating accurate pollutant forecasting and emission reduction strategies to ensure overall well-being. This study forecasts the influence of green energy transport systems on the air quality in Lahore and Islamabad, Pakistan, while noting the projected surge in electric vehicle adoption from less than 1% to 10% within three years. Predicting the impact of this change involves analyzing data before, during, and after the COVID-19 pandemic. The lockdown led to minimal fossil fuel vehicle usage, resembling a green energy transportation scenario. The novelty of this work is twofold. Firstly, remote sensing data from the Sentinel-5P satellite were utilized to predict air quality index (AQI) trends before, during, and after COVID-19. Secondly, deep learning models, including long short-term memory (LSTM) and bidirectional LSTM, and machine learning models, including decision tree and random forest regression, were utilized to forecast the levels of NO2, SO2, and CO in the atmosphere. Our results demonstrate that implementing green energy transportation systems in urban centers of developing countries can enhance air quality by approximately 98%. Notably, the bidirectional LSTM model outperformed others in predicting NO2 and SO2 concentrations, while the LSTM model excelled in forecasting CO concentration. These results offer valuable insights into predicting air pollution levels and guiding green energy policies to mitigate the adverse health effects of air pollution.

Life cycle assessment to address the environmental impacts of tourism in a Spanish tourist destination: The case of Rias Baixas (Galicia) holidays

Tourism has grown steadily in recent decades, becoming a strategic sector for the economy in many countries. However, the environmental impacts associated with tourism have also experienced an upward trend. In this sense, innovation is needed in the tourism sector, to move towards new models and strategies that integrate environmental sustainability with the social aspects of the sector. In this study, a holistic assessment of the environmental impact of tourism has been carried out using the Life Cycle Assessment (LCA) method, considering all stages of tourism activity: transportation from the place of origin to destination and back, accommodation, catering, and activities conducted. For this purpose, a case study has been carried out based on a typical trip made from Madrid to Rías Baixas (Galicia), considering a four-night stay and the performance of two activities (music festival and cultural museum) at the destination. Two alternative transportation scenarios (train or plane) have been defined to analyze the influence of the type of transportation on the overall impact. Other touristic activities such as visiting gardens or thermal baths instead of visiting a cultural museum or attending a music festival have been analyzed and it has been found that the thermal baths and the museum have the greatest environmental impacts.Transportation was the biggest contributor to most of the environmental impacts in the selected categories. On the other hand, the stay at the destination has stood out due to the impact of the consumption of food and energy used at the accommodation facility. The impact of the activities conducted at the destination is also worth highlighting. Finally, alternative scenarios for transportation have shown that the mode of transportation selected is key for lowering the overall environmental impact of the stay at the destination, highlighting the public transportation alternative, such as the train, as the most environmentally friendly option.

Micromobility Vehicles in India: Urban Commute Analysis with users’ Perspective

Since 1817 there has been using of the term micro-mobility from bicycles to modern-day electric-powered micro-mobility vehicles. The paper presents the current Indian urban commute, user behaviour, Indian urban scenario of last and first-mile transportation and scope of micro-mobility vehicles in Indian urban markets for small distance commutes. This also discusses the potential challenges in the growth of the micro-mobility ecosystem. A survey is conducted locally for urban cases and presented to take users' perspectives and preferences regarding daily commute and last-mile travel options.&#x0D; Keywords: Charging facilities, Electric Vehicles, e-PMV, Last Mile Transport, Micro mobility, Network, Sustainable transport, Startups, Traffic Congestion, Urban Areas

Non-intrusive model order reduction for parametric radiation transport simulations

We present a parametric, non-intrusive reduced-order model (ROM) for multigroup radiation transport, addressing the high computational cost and resource requirements associated with transport simulations in scenarios involving multiple queries. To achieve this, we employ Proper Orthogonal Decomposition (POD) to learn a groupwise reduced basis representation of the observed data (obtained from full-order simulations). We utilize Gaussian Process Regression and Multivariate Adaptive Regression Splines to learn the parametric, low-dimensional latent code (the reduced coordinates in the subspace spanned by the observed data). The approach is non-invasive, making it applicable to any software without the need for accessing the source code. We provide numerical results for various parametric problems, including a 3D multigroup transport case. Comparing the ROM-based surrogate to the full-order radiation transport model, solved using discrete ordinates in angle and discontinuous finite elements in space, we achieve a scalar flux solution that is 20,000 times faster with less than 1% relative error in a 3D transport scenario. Moreover, by utilizing the ROM, we demonstrate how a simple uncertainty quantification study can be completed within minutes, which would otherwise require significantly larger computational resources.

Multi-body dynamics model for spent nuclear fuel transportation system under normal transport test conditions

The transportation of spent nuclear fuel is an important process that involves road and sea transport from an interim storage facility to storage and final disposal sites. As spent nuclear fuel poses a significant risk, carefully evaluating its vibration and shock characteristics under normal transport conditions is essential. In this regard, full-scale multi-modal transport tests (MMTT) have been conducted domestically and internationally. In this paper, we discuss the process of developing a multi-body dynamics (MBD) model to analytically simulate conditions that cannot be considered in tests. The MBD model is based on the KORAD-21 transportation system was validated using the Korean MMTT results from 2020 to 2021. This paper summarizes the details of the development and verification of the MBD model for the KORAD-21 transportation system under normal transport test conditions. This approach can be applicable to various transportation scenarios and systems, and the results of this study will help to ensure that nuclear fuel transportation is conducted safely and effectively.

Application and Efficiency of a Series-Hybrid Drive for Agricultural Use Based on a Modified Version of the World Harmonized Transient Cycle

Off-road vehicles and transportation are vital for agricultural economics, yet the transition to green energies is challenging. To make this transition easier, a tool that enables the testing of heavy-duty off-road vehicles in various scenarios was created. Based on the methods of the World Harmonized Transient Cycle (WHTC), a new Hybrid Operational Cycle (HOC) that reflects the features of agricultural work was created and applied in a graphical model simulation. This was a newly developed methodology. The cycle and the model were based on gathered research data. A numerical model of a medium-power tractor with an internal combustion engine and a series-hybrid setup was created, and simulations were performed in Matlab and AVL Cruise. Both diesel and hybrid vehicles were compared in terms of their power production, fuel consumption, and efficiency in fieldwork and transportation scenarios. The results showed that a series-hybrid transmission can achieve an efficiency similar to that of a tractor with a continuously variable transmission (CVT), but because it uses an electric powertrain, it still provides the opportunity to exploit energy regeneration during transportation and under low-load conditions. The designed model may also be used to develop control algorithms for hybrid drives and improve their efficiency.

Climate change mitigation potentials of on grid-connected Power-to-X fuels and advanced biofuels for the European maritime transport

This study proposes a country-based life-cycle assessment (LCA) of several conversion pathways related to both on grid-connected Power-to-X (PtX) fuels and advanced biofuel production for maritime transport in Europe. We estimate the biomass resource availability (both agricultural and forest residues and second-generation energy crops from abandoned cropland), electricity mix, and a future-oriented prospective LCA to assess how future climate change mitigation policies influence the results. Our results indicate that the potential of PtX fuels to achieve well-to-wake greenhouse gas intensities lower than those of fossil fuels is limited to countries with a carbon intensity of the electricity mix below 100 gCO2eq kWh−1. The more ambitious FuelEU Maritime goal could be achieved with PtX only if connected to electricity sources below ca. 17 gCO2eq kWh−1 which can become possible for most of the national electricity mixes in Europe by 2050 if renewable energy sources will become deployed at large scales. For drop-in and hydrogen-based biofuels, biomass residues have a higher potential to reduce emissions than dedicated energy crops. In Europe, the potentials of energy supply from all renewable and low-carbon fuels (RLFs) range from 32 to 149% of the current annual fuel consumption in European maritime transport. The full deployment of RLFs with carbon capture and storage technologies could mitigate up to 184% of the current well-to-wake shipping emissions in Europe. Overall, our study highlights how the strategic use of both hydrogen-based biofuels and PtX fuels can contribute to the climate mitigation targets for present and future scenarios of European maritime transport.

Hydrogen Energy Storage and Its Applications

The combination of electrolysis for hydrogen production, storage, and utilization can be effectively achieved through the use of hydrogen energy storage systems, which are designed for long-distance transportation and storage. This is due to hydrogen being an environmentally friendly energy source. In order to effectively address the current issue of "wind," this article will delve into four facets: electrolysis of hydrogen, storage techniques, fuel cells, and the utilization of hydrogen energy storage. This will enable foster efficient and sophisticated clean energy development and utilization models, and ensure the continual advancement and maturity of hydrogen energy storage. Renewable energy utilization, with its utilization of light waste, is being encouraged to be further developed and utilized. In the context of global carbon neutrality, the energy properties of hydrogen are expected to gradually emerge. As major economies around the world have successively proposed long-term carbon neutrality goals in recent years, it is expected that the energy properties of hydrogen will gradually emerge, and the application fields will gradually expand to power, transportation, construction and other scenarios. In the long run, it is expected to become an excellent form of electric energy storage. Whether in the temporal or spatial dimensions, the power system will be more diverse in the future, and the forms of energy storage will also be more diverse.

Obtaining the optimal origin-destination multimodal freight transportation network for the City of Bogotá

Bogotá’s logistics performance as the principal hub for shipping cargo to other destinations in Colombia has not been optimal. Excessive truck traffic, low density of railways in service, and no accessible river routes seem to be major determinant factors. This article proposes a balanced linear transshipment (BLT) model to estimate the optimal origin-destination (OD) multimodal freight transportation network for the mentioned city to seven main destinations in the country. This research also provides an appropriate framework for assessing several hypothetical transportation scenarios. Thus, based on national transport statistics, this study evaluates three main criteria: freight transportation cost, number of nodes, and number of routes. The results of analysis suggest that if alternative modes of transportation are implemented in Bogotá, the three mentioned criteria will improve substantially. The analysis is limited by the size of the OD transportation network considered, the assumptions associated with the research methodology, and the scope and diversity of data sources used as reference. To the authors' knowledge, this study is the first of its kind to examine the optimal OD transportation network in Bogotá by using a BLT model.

A Novel UAV-Assisted Multi-Mobility Channel Model for Urban Transportation Emergency Communications

With the increasing requirements for unmanned aerial vehicle (UAV) communication in various application scenarios, the UAV-assisted emergency communication in urban transportation scenario has received great attention. In this paper, a novel UAV-assisted UAV-to-vehicle (U2V) geometry-based stochastic model (GBSM) for the urban traffic communication scenario is proposed. The three-dimensional (3D) multi-mobilities of the transmitter (Tx), receiver (Rx), and clusters are considered by introducing the time-variant acceleration and velocity correspondingly. The velocity variation of the clusters is used to simulate the motion of vehicles around the Rx. Moreover, to describe the vehicles’ moving states, Markov chain is adopted to analyze the changes in cluster motion states, including survival, death, dynamic, and static states. By adjusting the scenario-specific parameters, such as the vehicle density (ρ) and dynamic–static ratio (Ω), the model can support various urban traffic scenarios. Based on the proposed model, several key statistical properties, namely the root mean square (RMS) delay spread, temporal autocorrelation function (ACF), level-crossing rate (LCR), power delay profile (PDP), and stationary interval, under different clusters and antenna accelerations are obtained and analyzed. The accuracy of the proposed model is verified by the measured data. The results demonstrate the usability of our model, which can be provided as a reference for the design, evaluation, and optimization of future communication networks between UAV and vehicles in urban transportation emergency communications.

Non-linear Wire Rope Isolator Model to Enhance Transportation Simulation of Fragile Equipment

IntroductionWire rope isolators are often used as means of vibration isolation for the transportation of fragile machine equipment. When deployed as suspension systems in a transportation scenario, a simple spring–damper model is often used to predict the dynamic loads that act on the structure being transported. However, this simple model has proved to be too simplistic to be used in the development of an accurate numerical model that can predict the vibration levels experienced by the inner components of fragile equipment during its transportation. This paper describes the experimental tests conducted on a wire rope isolator used for the transportation of the prototype SSR1 cryomodule.Materials and methodsA hysteretic Bouc–Wen model has been used to analytically describe the force–deformation relationship of the wire rope isolators. The developed model of the isolator has been implemented in a larger model to simulate the actual transportation of the prototype SSR1 cryomodule, a section of the new PIP-II linear accelerator under construction at Fermilab. A series of multibody dynamic simulations with rigid and flexible components was used to numerically determine the acceleration of some critical components.Results and conclusionsAn actual experimental transportation was simulated using two numerical models: the developed Bouc–Wen model and a conventional spring–damper model. It is shown how the Bouc–Wen formulation of the isolator characteristics drastically improves the correspondence between experimental and simulated results if compared to a spring–damper model, especially in the range of 0–30 Hz which is the most critical for transportation problems.

A Trust-Based Reliable Confident Information Coverage Model of Wireless Sensor Networks for Intelligent Transportation

Wireless sensor nodes are vulnerable to interference or intrusion, especially in the tunnel scenario of intelligent transportation, which leads to potential risks to network security and reliability. Reliable coverage can ensure data perception and transmission, which helps to strengthen the reliability and quality of service (QoS) of wireless sensor networks (WSNs). Existing coverage models, such as the disc model, do not consider malicious intrusion, which still has a great gap with the realistic application scenarios. Therefore, it is urgent to study a coverage model considering network intrusion and node reliability. In this paper, a novel trust-based reliable confident information coverage (T-RCIC) model is proposed based on the confident information coverage (CIC) model and trust management. Trust management is adopted to determine whether a node is trusted, critical trusted, or malicious. If a node is identified as malicious, all its functions will be blocked. For critical trusted nodes, the trust value is utilized to determine the trust feature distance to reduce its proportion of coverage estimation. Furthermore, the reliable coverage degree (RCD) is defined to evaluate coverage reliability from node trust value and reliable coverage percentage. Simulation results indicate that the proposed model inherits the advantages of the CIC model in the spatial correlation and can effectively identify malicious nodes. The proposed T-RCIC model can significantly enhance coverage reliability.

Generating Function Reallocation to Handle Contingencies in Human–Robot Teaming Missions: The Cases in Lunar Surface Transportation

On lunar missions, efficient and safe transportation of human–robot systems is essential for the success of human exploration and scientific endeavors. Given the fact that transportation constructs bottlenecks for numerous typical lunar missions, it is appealing to investigate what function allocation strategies can generate optimal task implementation paths for robots with low-human workloads when the situation changes. Thus, this paper presents a novel approach to dynamic human–robot function allocation explicitly designed for team transportation in lunar missions. The proposed dynamic allocation framework aims to optimize human–robot collaboration by responding to existing and potential contingencies. First, a fitness concept model is designed to quantify the factors that motivate the functional adaptation of each agent in dynamic lunar mission scenarios. A hierarchical reinforcement learning (HRL) algorithm with two layers is then employed for decision-making and optimization of human–robot function allocation. Finally, the validity of the framework and algorithm proposed is validated by a series of human–robot function allocation experiments on a simulated environment that mimics lunar transportation scenarios, and is compared with the performance of other algorithms. In the future, path-planning algorithms can be incorporated into the proposed framework to improve the adaptability and efficiency of the human–robot function allocation in lunar missions.

Shifting car travel to active modes to improve population health and achieve transport goals: A simulation study

IntroductionBeing physically active has multiple health benefits and contributes to the reduction of co-morbidities and mortality from chronic diseases. Active transport (walking and cycling) contributes to population health by enabling physical activity. We developed a simulation model to measure health impacts of transport scenarios for Melbourne, Australia. Our aim was to demonstrate active transport health impacts and support the materialization of policies for healthy cities and people. The model measures health impacts of increased physical activity from replacing short car trips for any purpose or for commuting under 5 km by walking and cycling. MethodsWe developed a micro-simulation model of physical activity and disease risk in combination with the well-established proportional multi-state life table model. We quantified life course health including health adjusted-life years, life years, new cases of diseases prevented, and deaths prevented for 14 chronic diseases associated with physical inactivity for the adult population of people from Melbourne, Australia in 2017. ResultsOver the life course of the Melbourne adult population of 3.6 million people in 2017, gains in health-adjusted life years ranged from 5,100 (95% Uncertainty Interval (UI) 3,700 to 6,500) for the scenario replacing commute trips by car under 1 km with walking up to 738,800 (95% UI 546,000 to 935,000) when replacing car trips under 2 km with walking and between 2 km and 5 km with cycling. We also estimated benefits in terms of reductions of new cases of diseases and deaths prevented, with the greatest gains for ischemic heart disease, stroke, Alzheimer's and other dementias and type 2 diabetes. ConclusionsWe found that shifting car travel to active modes would accrue important health benefits for the 2017 Melbourne population. Our results support policies and strategies for sustainable transport planning to contribute to reduce the burden from chronic diseases and environmental impact of car-oriented cities.

TRANSPORT EFFICIENCY OF DELIVERY TRUCKS: A STUDY OF COUPLING VEHICLE DESIGN AND TRANSPORT SYSTEM THROUGH FUNCTIONAL MODELLING AND OPTIMISATION

AbstractTo enable the emergence of new and efficient vehicle design from the transport system perspective, a formulation of a multifunctional vehicle-transport design optimisation problem is presented. System- wide measures of transportation and vehicle efficiency measures are conceptually considered in an integrative design approach to drive the transport solutions towards more resource-efficient and eventually more sustainable solutions. Considered efficiency measures associated with the system-wide aspect are namely, productivity and service efficiency, and measures associated with vehicle attributes are namely mass and shape efficiency. The conflicting nature of these measures is balanced using optimisation methodologies through multiple transportation scenarios with varying transport demand and deterministic drive cycles. The obtained optimisation results demonstrated that there is a strong interconnectivity between the vehicle's overall configuration and transportation aspects. Thus, conceptually demonstrating how the inclusion of various transport-vehicle efficiency measures simultaneously in an integrative design approach during the early vehicle design may yield a more efficient and better overall system performance.