Abstract

The work reported here is a result of the EPSRC Hydrogen and Fuel Cell Research Hub research programme (Grant ref. EP/J016454/1). A core theme of the UK Government's new Industrial Strategy is exploiting opportunities for domestic supply chain development. This extends to a special ‘Automotive Sector Deal’ that focuses on the shift to low emissions vehicles (LEVs). Here attention is on electric vehicle and battery production and innovation. In this paper, we argue that a more straightforward gain in terms of framing policy around potential economic benefits may be made through supply chain activity to support refuelling of battery/hydrogen vehicles. We set this in the context of LEV refuelling supply chains potentially replicating the strength of domestic upstream linkages observed in the UK electricity and/or gas industries. We use input-output multiplier analysis to deconstruct and assess the structure of these supply chains relative to that of more import-intensive petrol and diesel supply. A crucial multiplier result is that for every £1million of spending on electricity (or gas), 8 full-time equivalent jobs are supported throughout the UK. This compares to less than 3 in the case of petrol/diesel supply. Moreover, the importance of service industries becomes apparent, with 67% of indirect and induced supply chain employment to support electricity generation being located in services industries. The comparable figure for GDP is 42%. Publisher PDF Peer reviewed Document type: Book

Abstract

Environmental impact assessment (EIA) is a systematic process designed to identify and predict the potential impacts of human activity on the biophysical and human environment. It also functions as an environmental management tool to identify measures to avoid, mitigate or compensate for those effects. EIA is intended to be an iterative process to follow-up to projects postimplementation to determine actual environmental outcomes, interpret and communicate information about those outcomes and investigate opportunities for improved project environmental performance. Originating from the United States’ National Environmental Policy Act of 1970, EIA is now amongst the most successful and widely practiced environmental management tools in the world. Key Concepts: EIA is an aid to decision making through which concerns about the potential environmental consequences of proposed projects are assessed before those projects become a reality. Screening in EIA ensures that assessments are done when needed and not done when not needed. Scope is essential to good EIA and the goal is to focus on a select set of environmental components that are deemed important from scientific, public or regulatory perspectives and are likely to be adversely affected by the project. The underlying intent of EIA is to allow project proponents, managers and decision makers to enhance the benefits of proposed development projects and to mitigate potentially adverse impacts to the point of acceptability. EIA must be applied early in the development planning processes if it is to be influential in project design and decision making. Information gained in EIA follow-up studies, after the project is implemented, provides feedback to improve predictions and mitigation and management programmes, and an opportunity to learn for subsequent project proposals. Keywords: environmental impact assessment; baseline studies; impact prediction; mitigation and management of impacts; cumulative environmental effects; follow-up studie

Abstract

The article looks at how taxes influence the tax payers' decision as to whether to own and use commercial passenger vehicles. Types of taxes and fees imposed on the purchase and operation of road passenger transport as a business are described in detail. Substantiation is provided for an idea that it might be useful to introduce a classification that divides taxes into fixed, variable and operation categories with regard to the intensity of commercial passenger vehicle use. The study examines the share of different taxes in the cost of owning and running a commercial passenger vehicle. Calculations are done for the amount of fixed transport taxes per day of owning a vehicle regardless of the intensity of use. A conclusion is made that the total amount of all taxes grows in absolute values for vehicles with a bigger seating capacity, but declines per passenger-kilometer of travel. A conclusion is made that the total amount of all taxes on the use of road passenger transport is much lower than the amount of taxes paid on transporting passengers by car. The goal of this study is to design a method of analyzing the fiscal value of taxes in the cost of owning and operating road passenger transport in Russia. © 2017 WIT Press.

Abstract

Climate change and global resource shortages have led to rethinking traditional individual mobility services based on combustion engines. As the consequence of technological improvements, the first electric vehicles are now being introduced and greater market penetration can be expected. But any wider implementation of battery-powered electrical propulsion systems in the future will give rise to new challenges for both the traditional automotive industry and other new players, e.g. battery manufacturers, the power supply industry and other service providers. Different application cases of electric vehicles are currently being discussed which means that numerous business models could emerge, leading to new shares in value creation and involving new players. Consequently, individual stakeholders are uncertain about which business models are really effective with regard to targeting a profitable overall concept. Therefore, this paper aims to define a holistic approach to developing business models for electric mobility, which analyzes the system as a whole on the one hand and provides decision support for affected enterprises on the other. To do so, the basic elements of electric mobility are considered and topical approaches to business models for various stakeholders are discussed. The paper concludes by presenting a systemic instrument for business models based on morphological methods.

Abstract

Within the renewable transport transition, a number of alternative technologies have emerged creating competing visions of how to reduce fossil fuel dependence. This paper examines the dynamics of competing fuels in two Swedish municipalities where electric buses have emerged, threatening incumbent biogas-based bus systems. While in Linkö ping, actors are resistant to the promise of electrification, in Malmö the shift to electrify urban buses has already begun. Here, the theoretical perspectives of obduracy and sociotechnical imaginaries are used to analyze obduracy and change in Linkö ping and Malmö , showing how the local contexts of these two municipalities influence obduracy or willingness to change. In Linkö ping, perceived connections between the biogas-based bus system and local infrastructures of renewable waste management and organic food production cause actors to place biogas buses at the center of a sustainable future region, while in Malmö linkages to the gas network (which also distributes natural gas) cause actors to question the sustainability of the fuel in use and opens up the city to welcome new electric vehicle tests. These examples show how fuel alternatives interact with each other in the wider renewable energy transition. Document type: Article

Abstract

This paper is an extension of work originally reported in SESAR Innovation Days 2018 : Zhengyi Wang, Man Liang, Daniel Delahaye. Automated Data-Driven Prediction on Aircraft Estimated Time of Arrival. SID 2018, 8th Sesar Innovations Days, Dec 2018, Salzburg, Austria. ⟨hal-01944608⟩; International audience; 4D trajectory prediction is the core element of the future air transportation system. It aims to improve the operational ability and the predictability of air traffic. In this paper, a novel automated data-driven framework to deal with the prediction of Estimated Time of Arrival (ETA) on the runway at the entry point of Terminal Manoeuvring Area (TMA) is introduced. The proposed framework mainly consists of data preprocessing and machine learning models. Firstly, the dataset is divided, analyzed, cleaned, and estimated. Then, the flights are clustered into partitions according to different runway-in-use (QFU). Several candidate machine learning models are trained and selected on the corresponding dataset of each QFU. Feature engineering is conducted to transform raw data into features. After that, the experiments are performed on real ADS-B data in Beijing TMA with nested cross validation. By comparing the prediction performance on the preprocessed and un-preprocessed datasets, the results demonstrate that the proposed data preprocessing is able to improve the data quality. It is also robust to outliers, missing data, and noise. Finally, an ensemble learning strategy named stacking is introduced. Compared to other individual models, the stacked model has a more complex structure and performs best in ETA prediction. This fact reveals that the framework proposed in this study could make accurate and reliable ETA predictions.

Abstract

The role of habits in influencing transportation behaviour is acknowledged in many studies. However, most of these analyses draw on a narrow perspective of habits. In this paper, we adopt a reversed perspective regarding the interplay between habits and rationality. The insightfulness of this perspective is illustrated with the findings of two empirical studies on urban transportation. This paper shows that the underlying structure (i.e. the "genotype") behind the phenotypic manifestation of a habit is essential to grasp for getting a better understanding of the role played by habits in explaining car use. The framework of habitual practices is then put forward in discussing the results. Its rationale is to provide a characterisation of the interconnected elements that make and shape the transportation practices, together with important aspects regarding how they are formed and sustained over time. Adequately picturing both constituent elements as well as dynamic aspects is crucial for explaining the strength of habitual practices and thus car dependence. In doing so, the framework of habitual practices could thus well be of help for policy-makers in reflecting on the design of efficient and innovative interventions for the transition towards more sustainable transportation behaviours. SCOPUS: ar.j info:eu-repo/semantics/published Document type: Article

Abstract

the number of electric vehicles increases, the impact of their charging on distribution networks is being investigated using different load profiles. Due to the lack of real charging data, the majority of these load impact studies are making assumptions for the electric vehicle charging demand profiles. In this paper a two-step modelling framework was developed to extract the useful information hidden in real EVs charging event data. Real EVs charging demand data were obtained from Plugged-in Midlands (PiM) project, one of the eight ‘Plugged-in Places’ projects supported by the UK Office for Low Emission Vehicles (OLEV). A data mining model was developed to investigate the characteristics of electric vehicle charging demand in a geographical area. A Fuzzy-Based model aggregates these characteristics and estimates the potential relative risk level of EVs charging demand among different geographical areas independently to their actual corresponding distribution networks. A case study with real charging and weather data from three counties in UK is presented to demonstrate the modelling framework. Document type: Article

Abstract

Many different definitions are currently used to define a design `peak’ hour at airports, such that the majority of passengers receive adequate service levels and only an acceptably small proportion experience the impact of congestion. The evaluation of level of service provision depends on this definition. Previous definitions have either used a nominal hour below the absolute peak or have taken a representative hour from the schedule of flights. This paper endeavours to define this level of traffic empirically. Data for 48 Brazilian airports covering a five-year period are analysed to develop a methodology for and determine the appropriate peak hour for design standards. This empirically derived traffic level is regressed against annual traffic levels to establish a relationship that should enable the forecast of design peak hour demand with changing annual throughputs.

Abstract

Shared mobility or mobility in the sharing economy is characterised by the sharing of a vehicle instead of ownership, and the use of technology to connect users and providers. Based on a literature review, the following four emerging models are identified: (1) peer to peer provision with a company as a broker, providing a platform where individuals can rent their cars when not in use; (2) short term rental of vehicles managed and owned by a provider; (3) companies that own no cars themselves but sign up ordinary car owners as drivers; and (4) on demand private cars, vans, or buses, and other vehicles, such as big taxis, shared by passengers going in the same direction. The first three models can yield profits to private parties, but they do not seem to have potential to reduce congestion and CO2 emissions substantially. The fourth model, which entails individuals not only sharing a vehicle, but actually travelling together at the same time, is promising in terms of congestion and CO2 emissions reductions. It is also the least attractive to individuals, given the disbenefits in terms of waiting time, travel time, comfort, and convenience, in comparison with the private car. Potential incentives to encourage shared mobility are also discussed, and research needs are outlined. Document type: Article