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This article presents the value of further increase of the energy efficiency in railway transport in combination with transition to the non-standard sources of energy. This is done within the paradigm of sustainable development and decarbonization in the global economy and by also taking into account the objectives stated in the document “Global perspective of railway transport development until 2050.” Hydrogen fuel cells, which are being studied by the Technical and Scientific Council of the JSC “RZD”, have demonstrated to be the most beneficial. The global trends of commercial use of fuel cells for transport are based on the unique properties of hydrogen as a fuel: high energy efficiency, ecological safety, and capability to be used without electric grids. Taking into account technological and ecological value, the transition from diesel traction to hydrogen fuel cell traction is justified and can be evaluated as a step of evolution that is as valuable as the transition from steam traction to diesel traction. Empirical proof is available that hydrogen is not hazardous to handle with properly designed equipment. The potential of the Russian science allows fulfilling appropriate research and development. We have taken into account that the required scientific and technological competences are available in different scientific centers and their effective integration is required. The conclusion states that the required conditions exist for introduction of the hydrogen fuel cells to the rolling stock in Russia. Although manufacturing of the critical fuel cells is not currently available in the Russian Federation, it can be arranged if demand is sufficient. This will require the collaboration of customers and scientific investigators in the project development of electric rolling stock in which hydrogen fuel cells can be used.

Abstract Vehicular velocity prediction is of great significance to intelligent transportation system, as it provides a possible future velocity sequence for vehicle's decision‐making system. A velocity prediction method via meta learning is proposed, which provides an adaptive and generative framework for multiple‐driving cycles. The prediction model is devised using a deep neural network structure. The model's training is performed by the recently proposed meta‐supervised learning, which ensures that one trained model could meet the adaptability to multiple driving cycles. The complete framework consists of three parts: Pre‐training, fine‐tune‐training and real‐time prediction, which is tested to predict the hybrid electric city buses’ future velocity in a variable traffic scenario. The average prediction accuracy of 3, 5 and 10 s horizons is 0.51, 0.63 and 0.88 m s−1, which is 25.9%, 16.78% and 7.47% higher than that trained by the conventional supervised learning method. As suggested, the proposed prediction method is effective and could meet the requirement of energy‐saving control for hybrid electric city buses. With further study, potential application of this method may also exist in the field of driving behaviour prediction and transportation mode recognition.

The private motor vehicles are significantly important means of transportation in modern lifestyle, however, these also contribute to a large proportion of the total air pollution and primary energy consumption. In order to develop green transportation system, it becomes imperative to use integrated technologies to achieve reduced emissions and utilize renewable energy. Electric vehicles (EVs) have been considered as one of these technologies to transform the traditional vehicle mix. However, the uptake of EV has been debated on factors like cost, performance (autonomous mileage), charging point infrastructure construction, energy saving, policy and end users’ adaptation. Present study investigates the technology feasibility (which usually refer to EVs’ cost, EV charging, supplier’s customer services quality, EV travel performance) and users’ adaptation of EV in Beijing, which is a key driver for the EV uptake into the Beijing transportation system. The relevant data have been collected and analyzed in the form of questionnaire survey around all of these factors. While considering the user perception and satisfaction, safety of charging and energy bills have also been investigated. According to the data analysis, it has been found the policy of ‘No traffic restrictions for EVs’ (the traffic restrictions means for certain date, from Monday to Friday the motor vehicles with the last register number of 1 and 6, 2 and 7, 3 and 8, 4 and 9, 5 and 0, are restricted to travel, respectively), the availability of the charging infrastructure and technical support are the most significant factors affecting the users’ opinions on using EVs.

Abstract Reducing energy consumption, regardless of fuel or electricity, of ground vehicles is a paramount pursuit in academia and industry. Current research concentrates exclusively on the improvement of the vehicle, but accepts the road conditions as external influences. The innovation of this article is to consider the road conditions, particularly the grade angle, as design variables. It is assumed that the stochastic speed trajectories of all vehicles on the road can be modelled by a Markov chain. The expected value of the average energy consumptions of all vehicles running on the road is defined as the objective function. The optimisation problem is solved by dynamic programming with Markov model. Evaluations have been made on both simulated and measured speed trajectories. For the simulated speed trajectories, the optimal road grade profile designed by the method saves up to 22% energy compared with a flat road. For the measured speed trajectories, the optimal road grade profile saves up to 2.7% transportation energy compared with the actual road profile. Application of this method on building road could lead to considerable energy saving.

The objective of this paper is to present an integrated picture of the relationship between the waves and the modifications induced by them in the Romanian shoreline. Thus, the hydrodynamic processes at the Mangalia beaches, located in the southern side of the Romanian nearshore, are simulated using the modeling system Mike 21 SW (MIKE 21 Spectral Waves), developed by the Danish Hydration Institute (DHI). This is one of the newest spectral wave models, which can be used for regional- and local-scale simulations. The model has been calibrated and validated using buoy measurements. The analysis of the statistical parameters shows a good match between the model and the observed data. Furthermore, a model to compare the differences that occur on the beach profiles between the cold and warm seasons was developed. The results obtained indicate a reinforcement of the coastal erosion in the winter, when the waves are stronger (especially in January and February).

Electric vehicles (EVs) parking lots are representing significant charging loads for relatively a long period of time. Therefore, the aggregated charging load of EVs may coincide with the peak demand of the distribution power system and can greatly stress the power grid. The stress on the power grid can be characterized by the additional electricity demand and the introduction of a new peak load that may overwhelm both the substations and transmission systems. In order to avoid the stress on the power grid, the parking lot operators are required to limit the penetration level of EVs and optimally distribute the available power among them. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE is represents the satisfaction level of EV owners; whereas, the QoP is a measurement representing the ratio of EVs with QoE to the total number of EVs. This study proposes a fuzzy logic weight-based charging scheme (FLWCS) to optimally distribute the charging power among the most appropriate EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The developed fuzzy inference mechanism resolves the uncertainties and correlates the independent inputs such as state-of-charge, the remaining parking duration and the available power into weighted values for the EVs in each time slot. Once the weight values for all EVs are known, their charging operations are controlled such that the operational constraints of the power grid are respected in each time slot. The proposed FLWCS is applied to a parking lot with different capacities. The simulation results reveal an improved QoP comparing to the conventional first-come-first-served (FCFS) based scheme.

Abstract Background Personal transportation in urban areas is characterised by different transportation technologies with significantly varying properties regarding usability, infrastructural requirements and environmental impacts. This characterisation motivates the objective evaluation of mobility solutions, based on different criteria. State of the art evaluations in the scientific literature mainly focus on one specific criterion at a time. The most common criteria investigated are found in energy demand or equivalent fuel consumption. Other parameters include the traffic space demand or mean velocity as a reference for the user-related criterion “travel time”. Since different modes of transport show various potentials in different criteria, an interesting point for scientific research is consideration of the different criteria in a more comprehensive evaluation approach. To address this issue, the aim of this study is to present a new approach for an objective evaluation and comparison of different transport technologies under consideration of pre-defined range of criteria and defined boundary conditions and requirements for personal mobility in cities. Besides technical-oriented aspects like driving range, transport capability and life cycle-related consumption of resources, additional factors influencing user-behaviour and traffic density are reflected. The evaluation method is presented, based on a generated exemplary data collection regarding technical and in-use characteristics of different modes of transport, mainly investigated in the city of Graz, Austria. Methods The study focuses on different means of transport, in particular walking, bicycling, the use of powered two-wheelers, passenger cars with different propulsion systems and public transport systems. It is based on the determination of selected criteria, considering ecologic, infrastructural and user-related aspects. With respect to ecologic criteria, the study considers resources and energy consumption as also the resulting CO2 equivalent emissions. The mean velocity and transport capacity are considered in the context of user-related criteria. Traffic space demand is an important and limited resource, especially in urban areas. The present study thus includes the determination and comparison of the relative traffic and parking space demands for the different modes of transport. The evaluation is based on a specifically developed evaluation methodology, considering weighted traffic performance indices, which are also proposed and discussed. Results Within the present study, a database providing specific mobility-related criteria and parameters has been generated, representing technical characteristics and the effects of the use of different vehicles and means of transportation in urban areas. The illustrated results allow an objective evaluation of a broad range of different means of transportation and vehicles, based on introduced “weighted traffic performance indices” (WTPI). Conclusions The study contributes to a discussion of transportation technologies and allows a derivation of measures for further research topics to face future intra-urban mobility demands. This represents a basis for decision making on the priorisation of the most suitable transport systems for urban areas. It is shown that the motorised individual passenger transport as dominated by the use of private passenger cars in particular represents today the most inefficient form of inner-urban mobility.

Coastal hazards result from erosion of the shore, or flooding of low-elevation land when storm surges combine with high tides and/or large waves. Future sea-level rise will greatly increase the frequency and depth of coastal flooding and will exacerbate erosion and raise groundwater levels, forcing vulnerable communities to adapt. Communities, local councils and infrastructure operators will need to decide when and how to adapt. The process of decision making using adaptive pathways approaches, is now being applied internationally to plan for adaptation over time by anticipating tipping points in the future when planning objectives are no longer being met. This process requires risk and uncertainty considerations to be transparent in the scenarios used in adaptive planning. We outline a framework for uncertainty identification and management within coastal hazard assessments. The framework provides a logical flow from the land use situation, to the related level of uncertainty as determined by the situation, to which hazard scenarios to model, to the complexity level of hazard modeling required, and to the possible decision type. Traditionally, coastal flood hazard maps show inundated areas only. We present enhanced maps of flooding depth and frequency which clearly show the degree of hazard exposure, where that exposure occurs, and how the exposure changes with sea-level rise, to better inform adaptive planning processes. The new uncertainty framework and mapping techniques can better inform identification of trigger points for adaptation pathways planning and their expected time range, compared to traditional coastal flooding hazard assessments.