Content uploaded by David Jonietz
Author content
All content in this area was uploaded by David Jonietz on Jun 13, 2017
Content may be subject to copyright.
A preview of the PDF is not available
Towards an Energy Efficient and Climate Compatible Future Swiss Transportation System
Figures
Figures - uploaded by David Jonietz
Author content
All figure content in this area was uploaded by David Jonietz
Content may be subject to copyright.
... We should concentrate on the shared mobility role in reducing the number of private vehicles per family to highlight its contribution to the social aspect and on how the consumers abandon ownership of their vehicles and use shared transportation services for their convenience. However, in some studies (e.g., [69]), shared mobility has been regarded as an isolated system. In other words, the complexity of its interaction with other modes of transportation is ignored by shared mobility, which makes it very difficult to explore its impact on the transportation system. ...
Assessing the impact of new mobility systems (e.g., shared mobility services, mobility as a service (MaaS), and Mobihubs) in urban contexts remains a challenging endeavor due to the varying priorities (social, economic, and environmental) of different stakeholders and restricted and/or limited availability of data. In a broad sense, new mobility services (NMS) can be characterized as a way of optimizing the ownership and use of a variety of mobility resources, tailored to the needs of an entire (urban) community. In this context, providing an up-to-date and critical review on the impact of NMS is the main contribution and added value of this study. To this end, this study presents an in-depth review of NMS and their diverse features (e.g., car sharing, bike sharing, Mobihubs, etc.), as an alternative to privately-owned travel modes. By reviewing more than 100 relevant sources from academic journals (Google Scholar, Science Direct, and Web of Science) and media reports, this study explains the key elements on how to address the impact assessment of NMS in terms of social, environmental, and economic aspects of sustainable mobility services. This study concludes that the implementation of NMS offers the potential to promote efficiency, sustainability, social equity, and quality of life. The main findings of this study serve as a perfect starting point for mobility providers and policymakers who are concerned about the growing demands for clean and green cities.
... Long-term decarbonisation of transport will also require that people actively make an effort to contain demand and shift to lower emission transport modes [3]. Mobility pattern analysis [29] allows to evaluate people's transport behavior and mode choices-even in real time-and to predict future behavior and transport network states. ...
The constant rise of urban mobility and transport has led to a dramatic increase in greenhouse gas emissions. In order to ensure livable environments for future generations and counteract climate change, it will be necessary to reduce our future CO2 footprint. Spatial data science contributes to this effort in major ways, also fuelled by recent
progress regarding the availability of spatial big data, computational methods, and geospatial technologies. This paper demonstrates important contributions from spatial data science to mobility pattern analysis and prediction, context integration, and the employment
of geospatial technologies for changing people’s mobility behavior. Among the interdisciplinary research challenges that lie ahead of us are an enhanced public availability of mobility studies and their data sets, improved privacy protection strategies, spatially-aware
machine learning methods, and evaluating the potential for people’s long-term behavior change towards sustainable mobility
... The increased use of cogenerated heat would increase overall efficiency. In addition, in the transport sector, alternative renewable-based fuels will likely play an important role [93]. ...
In the energy sector, decisions and technology implementations often necessitate a mid- to long-term perspective. Thus, reliable assessments of future resource availability are needed to support the decision-making process. In Switzerland, similarly to other countries, only a limited part of the available wet biomass feedstock is currently used for anaerobic digestion. Understanding potential future trajectories of the available biomass amount is therefore essential to facilitate its deployment for energetic use and to establish adequate bioenergy strategies. Here, we utilized extensive government data, historical trends, and data from academic literature to identify relevant drivers and their trends. Starting with current biomass potential, the future availability and variation of resources was estimated by taking into account selected drivers and their projected future development. Our results indicated an increase of over 6% in available wet bioenergy resources by 2050 (from 43.4 petajoules (PJ) of primary energy currently to 44.3 PJ in 2035 and 45.4 PJ in 2050), where a Monte Carlo analysis showed that this projection is linked to high uncertainty. Manure remains by far the biomass with the largest additional potential. Possible consequences regarding the country’s pool of biogas facilities and their development are discussed.
... These shared transportation options are broadly discussed as ways to develop a more sustainable transportation system and reduce the number of privately owned cars. Numerous simulation studies assume that shared AVs (SAVs) or autonomous taxis (auto-taxis) have the potential to become relevant market players in the transportation system (e.g., Kockelman, 2014, 2015;Spieser et al., 2014;International Transport Forum, 2015;Raubal et al., 2017;Zhang et al., 2015). These studies demonstrate the potential of AVs to substantially reduce fleet size and required parking spaces. ...
Autonomous vehicles, understood as vehicles that do not require manual steering, will cause disruptive changes in the transportation sector. Many studies on autonomous vehicles address the sustainability potential of this technology, and they assume that vehicles will no longer be privately owned and will be used with pooling options (multiple riders on a trip). However, there is currently little evidence to indicate whether this assumption is supported by user preference. To address this gap, an online choice experiment including 709 participants was conducted. It assumed the full-market penetration of autonomous vehicles and explored future mode choices, considering both short-term and long-term mobility decisions. The experiment tested the influence of 15 short-term and 13 long-term decision instruments to encourage the adoption of shared and pooled use of autonomous vehicles, like autonomous taxis and autonomous public transport. Our findings partly support the assumption in the existing literature that vehicles are likely to be used in a pooled mode. In the control condition, 61% of Swiss respondents preferred pooled autonomous vehicles over private autonomous cars. Moreover, stated preferences indicated that combined instruments influencing comfort, cost, and time are likely to increase the proportion of pooled uses of autonomous vehicles.
... Technological advances, infrastructural limits and the demands for more ecologically sustainable transport lead to a rapid change in the ways we perceive and use mobility. A potential pillar for future mobility is Mobility as a Service (MaaS) [1], where multiple modes of transport are integrated and made available as an easily accessible, packaged offer. With Green Class, the Swiss Federal Railways (SBB CFF FFS) offered such a service as part of a pilot study in 2017 to a limited number of people. ...
Technological advances, infrastructural limits and the demands for more ecologically sustainable transport lead to a rapid change in the ways we perceive and use mobility. A potential pillar for future mobility is Mobility as a Service (MaaS), where multiple modes of transport are integrated and made available as an easily accessible, packaged offer. With Green Class, the Swiss Federal Railways (SBB CFF FFS) offered such a service as part of a pilot study in 2017 to a limited number of people. We here present the geographic information system (GIS)-based framework used to analyze the effects of this offer on the mobility behavior and greenhouse gas (GHG) emissions of the involved users. The results show that people change their mobility behavior depending on the transport modes available to them before their use of MaaS. Additionally, the electric car (part of the MaaS offer) led to a noticeable reduction in GHG emissions.
... In the last few decades, the importance of shared mobility has grown, as well as the need to understand how to integrate it into urban transportation systems, and make it more efficient from a social, environmental, and economic perspective. However, in most studies, shared mobility has been considered as an isolated system, disregarding the complexity of its interactions with other transportation modes, which makes it extremely difficult to estimate its impact on the transportation system [13]. For this reason, this paper discusses the main aspects of manifold forms of shared mobility solutions, in order to provide a revision of them and to give a theoretical background that can be useful to promote this integration. ...
In a wider understanding, shared mobility can be defined as trip alternatives that aim to maximize the utilization of the mobility resources that a society can pragmatically afford, disconnecting their usage from ownership. Then, shared mobility is the short-term access to shared vehicles according to the user’s needs and convenience. The contributions and added value of this paper are to provide an up-to-date and well-structured review on the area of shared mobility to researchers and practitioners of the transport sector. Hence, this paper presents a bibliographical review of shared mobility and its diverse modalities, as an alternative to individual transportation, especially in cases of individual automobiles or short trips restricted to an urban city. The present literature review on shared modes of transportation has discovered that the introduction of these modes alone will not solve transportation problems in large cities, with elevated and growing motorization rates. However, it can among the strategies employed to help alleviate the problems caused by traffic jams and pollution by reducing the number of vehicles in circulation, congestions, and the urban emission of polluting gases. Thus, the implementation of shared mobility schemes offers the potential to enhance the efficiency, competitiveness, social equity, and quality of life in cities. This paper covers the fundamental aspects of vehicle and/or ride sharing in urban centers, and provides an overview of current shared mobility systems.
Urban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.
Electric vehicles (EV) are critical to fulfilling global climate goals. Despite their environmental and societal benefits, only 2.2% of cars sold worldwide in 2018 were electric. To understand the reasons for the low level of EV purchasing and help define measures for more effectively promoting their sales, the vehicle purchase process should be understood. For this purpose, we studied consumer behavior literature and conducted an online survey of 553 Swiss car owners. This resulted in the generation of a novel conceptual framework of the vehicle purchase process. This consists of five stages that are underlined by differentiated decision-making strategies. Second, the results show that car dealers play a critical role at all stages of the process, but remain a barrier to EV sales. Finally, the importance of a plurality of specific information sources and of the existence of charging options is significantly correlated to EV consideration. Based on these findings, touchpoints for electric mobility at relevant stages of the vehicle purchase process are identified, and policy interventions for more effectively promoting EV sales in Switzerland are suggested.
The effects of hydrogen addition to internal combustion engines operated by natural gas/methane has been widely demonstrated experimentally in the literature. Already small hydrogen contents in the fuel show promising benefits with respect to increased engine efficiency, lower CO 2 emissions, extended lean operating limits and a higher exhaust gas recirculation tolerance while maintaining the knock resistance of methane. In this article, the influence of hydrogen addition to methane on a spark ignited single cylinder engine is investigated. This article proposes a modelling approach to consider hydrogen addition within three-dimensional reactive computational fluid dynamics in order to establish a framework to gain further insights into the involved processes. Experiments have been performed on a single-cylinder spark-ignition engine situated at a test bed and cater as reference data for validating the proposed reactive computational fluid dynamics modelling approach based around the G-Equation combustion model. Within the course of the first part, crucial aspects relevant to the modelling of the mean engine cycle are highlighted. In this article, a simplified early combustion phase model which considers the transition towards a fully developed turbulent flame following ignition is introduced, along with a second submodel considering combined effects of the walls. The sensitivity of the combustion process towards the modelling approach is presented. The submodels were calibrated for a reference operating point, and a sweep in hydrogen content in the fuel as well as stoichiometric and lean operation has been considered. It is shown that the flame speed coefficient A appearing in the used turbulent flame speed closure, weighting the influence of the turbulent fluctuating speed [Formula: see text], has to be adjusted for different hydrogen contents. The introduced submodels allowed for significant improvement of the in-cylinder pressure and heat release rate evolution throughout all considered operating conditions.
Current popular multi-modal routing systems often do not move beyond combining regularly scheduled public transportation with walking, cycling or car driving. Seldom included are other travel options such as carpooling, carsharing, or bikesharing, as well as the possibility to compute personalized results tailored to the specific needs and preferences of the individual user. Partially, this is due to the fact that the inclusion of various modes of transportation and user requirements quickly leads to complex, semantically enriched graph structures, which to a certain degree impede downstream procedures such as dynamic graph updates or route queries. In this paper, we aim to reduce the computational effort and specification complexity of personalized multi-modal routing by use of a preceding heuristic, which, based on information stored in a user profile, derives a set of feasible candidate travel options, which can then be evaluated by a traditional routing algorithm. We demonstrate the applicability of the proposed system with two practical examples.
The large interest in analyzing one's own fitness led to the development of more and more powerful smartphone applications. Most are capable of tracking a user's position and mode of locomotion, data that do not only reflect personal health, but also mobility choices. A large field of research is concerned with mobility analysis and planning for a variety of reasons, including sustainable transport. Collecting data on mobility behavior using fitness tracker apps is a tempting choice, because they include many of the desired functions, most people own a smartphone and installing a fitness tracker is quick and convenient. However, as their original focus is on measuring fitness behavior, there are a number of difficulties in their usage for mobility tracking. In this paper we denote the various challenges we faced when deploying GoEco! Tracker (an app using the Moves R fitness tracker to collect mobility measurements), and provide an analysis on how to best overcome them. Finally, we summarize findings after one month of large scale testing with a few hundred users within the GoEco! living lab performed in Switzerland.
The transition towards electric mobility is increasingly acknowledged as one of the most beneficial strategies for the reduction of air pollution and noise in urban areas, for climate protection at the worldwide level and for direct integration with smart electric grids. Moreover, it may act as a leverage to promote a wider transition towards more sustainable mobility patterns.
Automated vehicles (AVs) promise many benefits for future mobility. One of them is a reduction of the required total vehicle fleet size, especially if AVs are used predominantly as shared vehicles. This paper presents research on this potential reduction for the greater Zurich, Switzerland, region. Fleets of shared AVs serving a predefined demand were simulated with a simulation framework introduced in the paper. Scenarios combining levels of demand for AVs with levels of supply (i.e., AV fleet sizes) were created. An important contribution of this study is the use of travel demand at highly detailed spatial and temporal resolutions that goes beyond the simplifications used in previous studies on the topic. This detailed travel demand provides a more solid basis for the ongoing discussion about the future fleet size. It was found that for a given fleet performance target (here, the target was for 95% of all transport requests to be served within 5 min), the relationship between served demand and required fleet size was nonlinear and the ratio increased as demand increased. A scale effect was detected. This effect has the important implication that for different levels of demand the fleet is used more or less efficiently. This study also found that if waiting times of up to 10 min were accepted, a reduction of up to 90% of the total vehicle fleet could be possible even without active fleet management, like vehicle redistribution. Such effects require, however, that a large enough share of the car demand be served by AVs.
Electric vehicles are seen as a future mobility option to respond to long term energy and environmental problems. The 2050 Swiss energy strategy envisages 30–75% introduction of electric cars by 2050, which is designed to support the goal of decarbonising the energy sector. While the Swiss government has decided to phase out nuclear electricity, deployment of electric cars can affect electricity supply and emission trajectories. Therefore, potential interactions between the electricity and transport sectors must be considered in assessing the future role of electric mobility. We analyse a set of scenarios using the Swiss TIMES energy system model with high temporal resolution. We generate insights into cross-sectoral trade-offs between electricity supply and electrification/decarbonisation of car fleets. E-mobility supports decarbonisation of car fleet even if electricity is supplied from large gas power plants or relatively low cost sources of imported electricity. However, domestic renewable based electricity generation is expected to be too limited to support e-mobility. Stringent abatement targets without centralised gas power plants render e-mobility less attractive, with natural gas hybrids becoming cost effective. Thus the cost effectiveness of electric mobility depends on policy decisions in the electricity sector. The substitution of fossil fuels with electricity in transport has the potential to reduce revenues from fuel taxation. Therefore it is necessary to ensure consistency between electricity sector and transport energy policies.
AUTONOMOUS CARS PROMISE to give us back the time we spend in traffic, improve the flow of traffic, reduce accidents, deaths, and injuries, and make personal car travel possible for everyone regardless of their abilities or condition. But despite impressive demonstrations and technical advances, many obstacles remain on the road to fully autonomous cars.20 Overcoming the challenges to enabling autonomous cars to safely operate in highly complex driving situations may take some time. Manufacturers already produce partially automated cars, and a spirited competition to deliver the most sophisticated ones is under way. Cars that provide high levels of automation in some circumstances (such as highway driving) have already arrived in the marketplace and promise to be in the hands of a large number of car owners in the next few years.
The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations.The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.