Demand For Vehicles Research Articles

Development of an acoustic fault diagnosis system for UAV propeller blades

With the rapid growth in demand for unmanned aerial vehicles (UAVs), novel maintenance technologies are essential for ensuring automatic, safe, and reliable operations. This study compares two fault detection systems that utilize the acoustic signature of UAV propeller blades for classifying their health state. By employing an acoustic camera with 112 microphones for spatial resolution of sound sources, datasets of acoustic images are generated in three differently reverberating environments for the third octave frequency bands of 6300 Hz, 8000 Hz, 10,000 Hz and 12,500 Hz. A convolutional neural network (CNN) is trained and evaluated with maximum F1-scores of 0.9962 and 0.9745 for two and three propeller health classes, respectively. Furthermore, we propose a second approach based on a linear classification (LC), which utilizes a rotating beamformer for comparison. This approach uses only two sound sources that are identified after the acoustic beamforming of a two-bladed propeller. In comparison, this algorithm detects propeller tip damages without applying a machine learning algorithm and reaches a slightly lower F1-score of 0.9441.

Bottom-up assessment of air quality management strategies in Vijayawada, India: Emissions inventory, atmospheric capacity, and policy scenarios

A bottom-up approach evaluates air quality management strategies in Vijayawada, India, a rapidly urbanizing city facing declining air quality. Detailed emission inventories and dispersion modeling assess pollutant concentrations (PM10, PM2.5, SO2, NOx) under various policy scenarios for 2025 and 2030. Business-as-usual (BAU) scenarios reveal the inadequacy of current regulations for PM, SO2, and NOx control. While BS-VI standards effectively reduced NOx emissions from vehicles, overall trends indicate a diminishing atmospheric capacity to assimilate pollutants. Alternative scenarios (ALTs) incorporating sector-specific controls project emission reductions (12–15 % PM10, 19–24 % PM2.5, 24–48 % SO2, 19–32 % NOx) and are expected to maintain pollutant levels below national ambient air quality standards. This integrated approach offers valuable insights for policymakers to develop sustainable air quality strategies aligned with SDGs. The study emphasises the need to consider potential trade-offs between environmental benefits and resource demands, such as increased electricity demand for electric vehicles (SDG 7) and land use competition from urban greening (SDG 11). This study aids in identifying strategies that maximise environmental benefits while minimising trade-offs, fostering sustainable urban development. The research's novelty lies in its integration of scenario-based analysis with atmospheric capacity evaluation, providing a robust framework for air quality management in rapidly urbanising regions.

Acceptance and demand of autonomous vehicles for long-distance recreational travel: An investigation based on a survey of visitors to US national parks

The study of anticipated changes in travel behavior that could be brought by autonomous vehicles (AVs) has been widespread for commuting and short-distance daily travel, yet little attention has been given to long-distance travel. To address this research gap, this paper presents an analytical model for estimating public preference towards the adoption and use (in terms of frequency and length of trips) of AVs, focusing on long-distance recreational travel (LDRT). The model considers socio-demographics, trip-specific characteristics, in-vehicle time use-related factors, and attitudinal latent variables (AV usefulness, AV concern, driving enjoyment) as potential influencers of AV acceptance and demand. To analyze the proposed model, data were collected from a survey of visitors to US national parks conducted in Summer 2022, and the structural equation modeling (SEM) technique was employed. The results indicate that the frequency and length of long-distance recreational trips will likely be higher in the AV era. This draws attention to tourism destination managers not only to manage the tourists’ demand at destinations but also to manage traffic on the roads leading to the destinations. Additionally, the results show that the LDRT demand will continue to rise with the increase in AV acceptance due to the usefulness and potential for several in-vehicle activities offered by vehicle automation, despite concerns about system safety, data privacy, and legal liability. The study also reveals that some travelers will likely miss manual driving enjoyment in AV driving, particularly in unique travel settings like tourism travel, and thus might opt for manual driving options. Based on these findings, we advocate for the timely consideration of induced LDRT, and tourism travel demand generated by AVs—such as provisioning sustainable public transport options to recreational and tourism destinations.

Experimental study on the hydrodynamic of foldable-wing unmanned aerial-underwater vehicles during water-exit process

The Foldable-Wing Unmanned Aerial-Underwater Vehicle (F-UAUV) demands swift movement over water surfaces at significant pitch angles during the water-exit procedure, a phase highly influenced by strong, non-linear, time-varying hydrodynamic effects. To tackle these complexities, we devised a model experiment scheme mirroring the actual water-exit process. Crafted through structural drainage comparisons, high-fidelity models were developed, complemented by a test platform featuring a double track for accurate evaluations. Subsequent water-exit tests, conducted at various speeds and angles, unveiled a consistent increase in water-exit resistance with speed, peaking at the moment of exit. Concurrently, resultant torque escalated inversely with both speed and angle. Notably, the heightened resistance due to structural drainage exhibited a tendency to stabilize during the latter half of the water-exit process. Utilizing the foundational formula for resistance in a single medium, we proposed an enhanced formula for predicting maximum water-exit resistance, a tool instrumental in facilitating F-UAUV design and powertrain selection. This paper offers an exhaustive analysis of hydrodynamic influences during the water-exit process, serving as a pivotal reference for Unmanned Aerial-Underwater Vehicles development.

Fuzzy logic-based estimation model of distribution transformers aging

Currently, we are moving towards more ecological modes of transport. As a result, the demand for electric vehicles is growing exponentially, and new loads, related to charging, can negatively affect the operation of electrical grids and their elements. Concurrent and uncontrolled charging of electric vehicles can cause a blackout. Considering the stochastic nature of the additional load from electric cars, it is difficult to predict such a load by analytical methods. The fuzzy logic approach can be used to formalize the tasks with ambiguity. In this paper, a fuzzy-based block diagram of the algorithm was developed in the MATLAB-Simulink environment for evaluating the impact of the charging load of electric vehicles on the aging of transformers, as well as a model was built and the effect of various penetrations of electric cars was evaluated. The fuzzy logic-based model includes the effects of ambient temperature, power quality, and overloads. It contains a diagnostic part that warns the dispatcher of the power distribution network about possible problems, providing up-to-date information about the state of the transformer. In addition, the efficiency of photovoltaic generators to increase the service life of distribution transformers was analyzed. The results show that photovoltaic power plants are not effective enough for high levels of electric vehicle penetration, and more advanced strategies should be used.

Polymer-Supported Graphene Sheet as a Vertically Conductive Anode of Lithium-Ion Battery.

The increasing demand for electric vehicles necessitates the development of cost-effective, mass-producible, long-lasting, and highly conductive batteries. Making this kind of battery is exceedingly tricky. This study introduces an innovative fabrication technique utilizing a laser-induced graphene (LIG) approach on commercial Kapton film to create hexagonal pores. These pores form vertical conduction paths for electron and ion transportation during lithiation and delithiation, significantly enhancing conductivity. The nongraphitized portion of the Kapton film makes it a binder-less, free-standing electrode, providing mechanical stability. Various analytical techniques, including scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), Raman spectroscopy, and atomic force microscopy (AFM) are utilized to confirm the transformation of a 3D porous graphene sheet from a commercial Kapton film. Cross-sectional SEM images verify the vertical connections. The specific capacity of 581 mAh g-1 is maintained until the end, with 99% coulombic efficiency at 0.1C. This simple manufacturing method paves the pathway for future LIG-based, cost-effective, lightweight, mass-producible, long-lasting, vertically conductive electrodes for lithium-ion batteries.

Research on optimization algorithms for localization and capacity determination of chargers considering the spatiotemporal distribution of electric vehicles

The optimized layout of electric vehicle (EV) chargers is not only crucial for users' convenience but also a key element in urban sustainable development, energy transition, and the promotion of new energy vehicles. In order to provide a basis for the problem of localization and capacity determination of chargers and compare the merits of several mainstream algorithms, this paper first establishes an optimization model with the objective of minimizing the total investment cost of all the chargers and the constraint of meeting the charging demands of all electric vehicles. Optimizations were performed using genetic algorithm (GA), surrogate optimization algorithm (SOA), and mixed integer linear programming (MILP) algorithm, respectively. In the case of using MILP, the original nonlinear optimization problem was transformed into a linear problem. In the planning of city-level EV chargers, MILP took 14182.57 s to calculate the minimum cost of 34.62 million yuan. After retaining only 10% of the original data amount, SOA took 87651.34 s to calculate the minimum cost of 3.01 million yuan. The results indicate that GA is prone to falling into local optima and is not suitable for large-scale optimization problems. SOA, on the other hand, requires significant memory consumption, so the issue of memory usage needs to be carefully considered when using it directly. Although MILP is only applicable to linear programming problems, it has the advantages of lower memory usage and higher reliability if the problem can be transformed into a linear one.

A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts

The aim of the study is the development of methodology for accurate estimation of electric vehicle demand; which is paramount regarding various aspects of the firms decision-making such as optimal price, production level, and corresponding amounts of capital and labor; as well as supply chain, inventory control, capital financing, and operational expenses management. The forecasting methods utilized include statistical techniques (autoregressive integrated moving average [ARIMA], and polynomial regression), machine learning (nonlinear autoregressive neural network [NAR]), deep learning (long short-term memory [LSTM]), hybrid and combination forecasting. With regard to the latter method, our study experiments with four different combining model approaches, including the introduction of an original, novel combining method with the employment of a transcendental LASSO function, which is used to form combinations of forecasts generated by the NAR, ARIMA, and polynomial regression models. The LASSO-based combining model proved superior to all other models, for the majority of forecast error statistics; where the root mean square error (RMSE) and mean absolute percentage error (MAPE) values are 4.5% and 8% respectively lower than the average level of the component model forecasts. The major implications of our empirical findings are that greater accuracy in demand forecasting can be achieved with a combining model approach, rather than reliance on any particular, singular model. Furthermore, given its superior performance, the employment of the studys LASSO-based combining model to forecast electric vehicle demand may lead to optimal firm decision-making over a range of organizational facets, which is predicated on accurate demand function estimation.

Application of Model‐Based Systems Engineering Within the Automotive Industry —‐ a Current State

AbstractModel‐Based Systems Engineering (MBSE) has been utilized within the automotive industry for several years. Increasing complexity due to highly automated, connected vehicles demands methods of coping with this complexity. In most cases, currently, only specific partial aspects or single methods of MBSE are used, which even varies across different companies. This paper aims to examine the current implementation of MBSE based on samples collected from companies acting within the automotive industry. Various aspects are explored, including the scope of application throughout the product life cycle, the use of simulation methods and the collaboration with other disciplines within product development. In the end, an evaluation discusses the reasons for the current state, and recommendations are given.

Meeting the charging demand of Electric Vehicles in Greece: Enabling intercity trips

The increasing need for sustainable transportation has underscored the pivotal role of electric mobility in shaping the future of mobility. Electric Vehicles (EVs) have emerged as a promising solution, but their widespread adoption is contingent upon the availability of a reliable charging network. This challenge extends even to EV adoption pioneers like Norway and China. This study delves into the context of EV adoption in Greece, providing insights into the EV market, existing incentives, and the state of charging infrastructure. Reports indicate that Greece is at a critical juncture, with fast EV adoption but sluggish development of Charging Stations (CSs). To address this issue, a Monte Carlo simulation is employed to evaluate the feasibility of long-distance EV trips in Greece and propose an infrastructure development plan. For EVs, the study reveals a linear relationship between battery size and autonomy, with some exceptions emphasizing the importance of data acquisition and individual EV assessment. The study also provides insights into the relationship between trip length and energy consumption, indicating that longer trips exhibit greater fluctuation in energy consumption due to varying conditions, while energy usage patterns of shorter trips are more predictable. Additionally, the analysis presents average energy consumption values for each EV model in different temperature conditions, highlighting the impact of environmental factors on EV performance.

Mapping the charging demand for electric vehicles in 2050 from mobility habits

This paper proposes a method to spatially model and compare charging needs on the European scale, considering local disparities in population density, distance to city centres, car ownership and mobility habits. Mobility habits are modelled across Europe in terms of distance and time frame to elaborate scenarios of charging behaviour. The first step of the method is to calculate the density of electric vehicles with a resolution of 1km2, according to the progressive electrification of the fleet each year between 2020 and 2050. The second step is to quantify the mobility of commuters using their driving distance to work areas and mobility statistics. The model is then applied in a case study in Switzerland to plan the public charging infrastructure required to satisfy the charging needs of the local population. Despite lower motorization rates and driving distances, the results show a stronger need for charging in cities. With 50% of commuters charging at work and 20% at home during the workday, the demand in the evening can be reduced by 50% in the suburban areas compared to the baseline scenario in which all commuters are charging at home in the evening. This model can be used to quantify the energy needs of commuters, plan the deployment of the charging infrastructure, or simulate the effect of policies.

NdFeB Magnets Recycling via High-Pressure Selective Leaching and the Impurities Behaviors

Global concerns about climate change are driving increased demand of electric vehicles for sustainable transportation and turbines in emerging energy solutions, where permanent magnets (PMs) and rare earth elements (REEs) play a critical role. However, global REEs recycling rates are only 3% and 8% for light and heavy REEs, respectively. This work proposes an effective approach to separate the REEs and iron via high-pressure selective leaching by low-concentrated nitric acid from the end-of-life NdFeB magnet and investigates the impurities behavior during the leaching and precipitation steps. The results from the optimized leaching conditions demonstrated over 95% REEs leaching efficiency with less than 0.3% Fe dissolution. Approximately 70% of Al and B were leached as well, while other elements (Co, Ni, Cu) had leaching efficiencies below 40%, leaving a hematite rich residue. Adjusting the pH removes Al and Fe in leachate but minimally affects Cu, Co, and Ni. Na2S addition is more effective against transition metals, but both methods result in around 10% REEs loss. Direct oxalate precipitation is suggested for the obtained leachate, which can yield over 97.5% REEs oxides with approximately 1.0% alumina, which is acceptable for magnet remanufacturing due to the aluminum content commonly found in magnets. The technology developed in this study offers opportunities for closed-loop recycling and remanufacturing of PMs, benefiting the environment, economy, and supply chain security.Graphical

ECONOMIC MECHANISMS FOR THE DEVELOPMENT OF URBAN TRANSPORT AND LOGISTICS SYSTEMS

The article proposes the definition and substantiation of economic mechanisms for the development of urban transport and logistics systems that are appropriate in Ukraine, acceptable and implemented for use within "smart" cities on the basis of sustainability, reliability and safety, based on certain signs and principles of understanding sustainable transport and logistics systems that should be built on: understanding the use of means of transport with a low level of impact on the environment, include both the industrial component of the system and public and private traffic, transport-oriented development, ecological means of transport, car sharing, as well as construction and/ or the protection of urban transport and logistics systems that are energy efficient, take into account measures to save space and promote a healthy lifestyle. The proposed approaches provide for obtaining an economic effect by creating a greater added value from each unit of used resource compared to traditional methods and linear business models. Such economic incentives should contribute to the market demand for energy-efficient vehicles in two ways: - through the gravity effect, by increasing the demand for such environmentally attractive vehicles from consumers, users and the population of the urban agglomeration; - due to the "push-out" effect, which increases the attractiveness of new vehicle developments for manufacturers. These methods can be most effective when combined. In the first case, when the demand for vehicles that will meet the established criteria of ecological efficiency increases, in the second case, in the case of creating a critical market mass with sufficient leverage to justify specific developments and/or equipment on the part of manufacturers, where the development and implementation of environmentally friendly and of energy-efficient vehicles is considered as one of the tools for reducing the impact of transport and logistics systems on the environment of "smart" cities that strive to increase innovation and obtain competitive advantages.

MoSSe-graphene based sandwiched nanolayer hybrid as high-performance lithium sulfur-selenium (LiSSe) battery cathodes

Rechargeable lithium batteries have demonstrated highly promising storage of energy systems because of their advantageous characteristics such as high energy density, extended cycle life, increased output power, and improved safety. To meet the ever-increasing high energy demands for powering hybrid and electric vehicles, it is necessary to develop electrode materials that possess high capacity and excellent rate capability. Hence, this work focuses on a MoSSe-Graphene based sandwiched nanolayer hybrid electrode material (MoSSe-Gr) for efficient Lithium-sulfur-selenium batteries applications. The MoSSe-Gr was synthesized through the solvothermal route, and subsequent calcination of the material at 800 °C under an inert atmosphere. Scanning electron microscopy (SEM), X-aray diffratometry (XRD), high-resolution-transmission electron microscopy (HR-TEM), X-ray photoelectron spectroscopy (XPS), Raman shifts examinations reveal that graphitization of organic solvent from the precursor solution entrapped in the MoSSe interlayer spacing took place during calcination process, forming MoSSe-Graphene hybrid sandwich nanolayers. When employed as a cathodic material in a LiSSe battery, the hybrid sandwiched layers exhibited high-speed charging capability and extended cycle life. Specifically, the MoSSe-Gr material demonstrated a high-rate capability, delivering high capacities of 806.58/100, 668.42/500, 585.83/1000, 409.25/5000, and 284.16/10000 mAh/g/mA/g.

Pricing Strategies for Distribution Network Electric Vehicle Operators Considering the Uncertainty of Renewable Energy

In the future, the active load of the distribution network side will be dominated by electric vehicles (EVs), showing that the charging power demand of electric vehicles will change with the change in charging electricity price. With the popularity of electric vehicles in the distribution network, their aggregation operators will play a more prominent role in pricing management and charging behavior, and setting an appropriate charging price can achieve a win–win situation for operators and electric vehicle users. At the same time, the proportion of scenery in the distribution network is relatively high, and the uncertainty of self-output has a certain impact on the pricing strategy of operators and the charging behavior of electric vehicle users, which has become an important research topic. Based on the above background, an EV operator pricing strategy considering the landscape uncertainty is proposed, a Stackelberg game model is established to maximize the respective benefits of operators and EV users, and the two-layer model is further transformed into a single-layer model through the Karush–Kuhn–Tucker (KKT) condition and duality theorem. Finally, the IEEE 33 system is simulated with the CPLEX solver, and the global optimal pricing strategy is obtained. Simulation results prove that electric vehicle operators experience a maximum profit increase of 2.6% due to the impact of maximum capacity of energy storage equipment and the uncertainty of renewable energy output can result in electric vehicle operators losing approximately 20% of their profits at most.

Recent Progress of Deep Learning Methods for Health Monitoring of Lithium-Ion Batteries

In recent years, the rapid evolution of transportation electrification has been propelled by the widespread adoption of lithium-ion batteries (LIBs) as the primary energy storage solution. The critical need to ensure the safe and efficient operation of these LIBs has positioned battery management systems (BMS) as pivotal components in this landscape. Among the various BMS functions, state and temperature monitoring emerge as paramount for intelligent LIB management. This review focuses on two key aspects of LIB health management: the accurate prediction of the state of health (SOH) and the estimation of remaining useful life (RUL). Achieving precise SOH predictions not only extends the lifespan of LIBs but also offers invaluable insights for optimizing battery usage. Additionally, accurate RUL estimation is essential for efficient battery management and state estimation, especially as the demand for electric vehicles continues to surge. The review highlights the significance of machine learning (ML) techniques in enhancing LIB state predictions while simultaneously reducing computational complexity. By delving into the current state of research in this field, the review aims to elucidate promising future avenues for leveraging ML in the context of LIBs. Notably, it underscores the increasing necessity for advanced RUL prediction techniques and their role in addressing the challenges associated with the burgeoning demand for electric vehicles. This comprehensive review identifies existing challenges and proposes a structured framework to overcome these obstacles, emphasizing the development of machine-learning applications tailored specifically for rechargeable LIBs. The integration of artificial intelligence (AI) technologies in this endeavor is pivotal, as researchers aspire to expedite advancements in battery performance and overcome present limitations associated with LIBs. In adopting a symmetrical approach, ML harmonizes with battery management, contributing significantly to the sustainable progress of transportation electrification. This study provides a concise overview of the literature, offering insights into the current state, future prospects, and challenges in utilizing ML techniques for lithium-ion battery health monitoring.

Malicious mode attack on electric vehicle coordinated charging and its defense strategy

Power systems exploit Internet of Things (IoT) to coordinate the huge charging demands of numerous electric vehicles (EVs) in intelligent transportation systems (ITSs). However, since connected with public networks, the coordinated charging control system is in a low-level cyber security and greatly vulnerable to malicious attacks. The malicious mode attack (MMA), a new cyber-attack pattern, generates high amplitude forced oscillations, which seriously threats the stability of power systems and the power supply of charging stations. However, the MMA model and attack process is not clear. Moreover, it is difficult to actively eliminate the MMA risk through the existing cyber defense technologies. Therefore, this paper analyzes the characteristics of MMA and provides a multi-information active distribution rejection control (MIADRC) method for the physical smart grid to defense MMA. First, the potential threat of MMA is clarified by investigating the vulnerability of the IoT-based coordinated charging load control system. And an MMA process like Mirai is given as an example, which affects the electricity charging system by penetration, infection and attack. Then, an MMA model is established for impact analysis, where expressions of the mean response (i.e. expected value) and stochastic response (i.e. standard deviation) of the MMA forced oscillation are derived to discover main impact factors. Further, to mitigate the impact of MMA, a defense strategy based on multi-index information active disturbance rejection control is proposed to improve the stability and anti-disturbance ability. Finally, simulations are conducted to verify the existence and characteristics of MMA threats. And it is verified that the proposed MIADRC can suppress 91.8 % of the MMA oscillation amplitude. As far as the authors know, it is the first time that the characteristics of MMA are investigated and multi-information-based defensive strategy for MMA is proposed, which enhances system damping through compensating for the attack disturbance.

Identification of Collision Simplified Model Parameters for Lithium‐Ion Batteries

The crash analysis of complete electric vehicles demands high accuracy, speed, and modeling flexibility of the crash finite element analysis of single battery and battery packs. Herein, the crash analysis process is optimized using an artificial neural network (ANN) and a genetic algorithm (GA), and according to experimental conditions, working characteristic parameters of a single 18650 lithium‐ion battery, such as state of charge value and discharge mode are examined. It is combined with the beam elements of a simplified model of a five‐layer single 18650 battery, and the mechanical characteristic parameters are identified. The process comprises two parts: prediction of the mechanical properties of the battery cell from the operating characteristics of the single 18650 battery and the rapid solution of the simplified mechanical parameters of the beam elements from the mechanical properties of the battery. The accuracy of the prediction results of the ANN model reaches over 97%, and the fit between the simulation results of the GA identification parameters and the experimental results reaches over 95%. The identification parameters can make quick responses to the experimental results under different working conditions, which ground the application of the simplified beam element model in the battery packs.

Estimating personal electric vehicle demand and its adoption timeframe: A study on consumer perception in Indian metropolitan cities

India’s transition to electric vehicles has entered its second decade. The government has set a target of having EV sales accounting for 30 % of private cars and 80 % for two-wheelers by 2030. However, despite several efforts of government and industry, the penetration of electric vehicles till-date has not been as per the set targets. This study aims to estimate the end-user demand and adoption timeframe of electric 4-wheelers (e-4 W) and 2-wheelers (e-2 W) in India’s four large metropolitan areas. Binary logit choice models are developed based on a discrete choice experiment carried out by utilizing 2,400 face-to-face interview responses. In addition, ordered logit models are developed to assess the adoption timeframe of the EVs. The study results show a significant geographic variation in demand for e-4Ws and e-2Ws within India. This demand is also driven by vehicle attributes, demographics, infrastructural elements, and user attitudes. Existing vehicle owners are more likely to purchase an EV in the future, and are also likely to drive/ride it more. In addition, consumers who are young and wealthy, and living in homes with dedicated parking spaces are more likely to be early adopters of EVs. These findings would assist policymakers in designing a tailormade and phased EV implementation scheme in India.

Urbanization inequality: evidence from vehicle ownership in Chinese cities

Unequal outcomes resulting from urbanization can pose a significant challenge to sustainable development. Vehicles are an important urbanization dimension as a critical component of urban infrastructure by providing mobility and accessibility to social services. China’s vehicle ownership (referred to as in-use vehicle stocks) has been growing quickly since 2000, but its per capita stocks are still much lower than that in developed economies. This raises the question of whether and when China’s vehicle stocks will reach a peak level close to that in the developed countries. By analyzing vehicle stocks in 283 Chinese cities during 2001–2018, we have the following findings: (1) vehicle stocks are predominantly distributed in northern and eastern coastal cities and provincial capital cities; (2) inequality in vehicle ownership rates between cities shows a declining trend at both national and region scales; (3) the growth of vehicle ownership rates follows an S-shape curve and most cities are still at the early stage of motorization; (4) China is likely to have a lower saturation level of vehicle ownership rate. These results could help to accurately forecast future vehicle demand in China, estimate the resulting environmental impacts, and explore strategies to achieve carbon neutrality in transportation.