Search Results (16)

E-bikes are emerging as a competitive alternative to private cars in both urban and suburban contexts, enhancing accessibility to daily amenities and aligning with the proximity-oriented principles of X-minute city development. However, empirical knowledge remains limited regarding e-bike adopter profiles, trip purposes, influencing factors, and modal substitution patterns, particularly in urban Global South contexts. This exploratory pilot study employs correlation analysis and exploratory factor analysis to examine the sociodemographic characteristics of e-bike users in Surabaya, identify trip behavior patterns, and uncover potential determinants associated with e-bike usage within the X-minute city framework. Based on a sample of 71 e-bike users, the preliminary findings reveal notable socioeconomic patterns in e-bike adoption, with lower-income inner-city residents, particularly women in informal employment, emerging as early adopters. Additionally, two potential influence dimensions are identified: utilitarian trip chaining and active mobility infrastructure. While these findings require validation through larger-scale studies, they suggest potential for e-bikes to expand feasible X-minute city catchments and support low-carbon mobility transitions in similar Global South contexts. Full article

Light Electric Vehicles (LEVs) offer a promising response to environmental and urban mobility challenges. This study is among the first to exploratorily examine their use, user characteristics, and owner evaluations. A qualitative pre-study with four LEV owners was conducted and informed a subsequent quantitative phase involving 23 owners. Over two weeks, participants recorded all LEV trips using GPS tracking and completed two questionnaires. Findings show that LEVs are regularly used for commuting, shopping, and work-related trips. Notably, many users live outside urban centers, indicating strong potential for short-distance travel in rural and small-town contexts for our sample. This challenges the view of LEVs as primarily urban or recreational vehicles. Within our sample, usage patterns were diverse, indicating that even among early adopters there is no single typical usage profile. While cars were perceived as slightly safer, no participant reported feeling unsafe in their LEV. User satisfaction was high: 24 of 27 respondents would choose the same vehicle again. Overall, LEVs emerge as a versatile and satisfying mobility option, relevant beyond city limits. Given their wide range of uses and positive user feedback, LEVs should be more strongly considered in transport policy to promote more sustainable and needs-based mobility. Full article

The in-wheel motor (IWM) powertrain layout offers greater design flexibility and higher efficiency of an electric vehicle but has limited commercial success mainly due to the concerns of increased unsprung mass. This paper proposes a semi-active suspension system for in-wheel motors that combines both a dynamic vibration-absorbing structure (DVAS) and a PID-controlled MR damper, in order to achieve optimised comfort, handling and IWM vibration for a small car application. Whilst PID control and DVAS are not entirely new concepts, the usage of both optimisation techniques in a semi-active in-wheel motor suspension has seen limited implementation, which makes the current work novel and significant. The semi-active suspension operating both in passive fail-safe mode and full feedback control was compared to a conventional in-wheel motor passive suspension in terms of sprung mass acceleration, displacement, stator acceleration, tyre deflection and suspension travel for three different road profile inputs using MATLAB/Simulink. The implementation of a PID-controlled MR damper improved road comfort and road holding performance and decreased in-wheel motor vibration over the DVAS passive suspension mainly in terms of a maximum peak amplitude decrease of 40%, 35% and 32% for the sprung mass acceleration, tyre deflection and stator acceleration, respectively. The results are significant since they show that the use of a simple, easily implemented control scheme like PID control was able to significantly improve IWM suspension performance when paired with a DVAS. This study provides further confidence to manufacturers to commercially develop and implement the IWM layout as its major disadvantage can be reasonably addressed using a simple readily available control approach. Full article

E-scooters have emerged as a popular micromobility option for short trips, with many cities embracing shared e-scooters to enhance convenience for travelers and reduce reliance on automobiles. Despite their rising popularity, there is a lack of clear understanding of how user preferences and adoption practices vary by location. This study aims to explore user and non-user attitudes towards e-scooter use in diverse urban settings. A meta-analysis of data from three surveys (N = 1197) conducted in Washington, D.C., Miami, FL, and Los Angeles, CA, was performed to compare e-scooter users and non-user profiles, mode choice factors, and attitudes and preferences towards e-scooter use. Additionally, machine learning (ML) and SHAP (SHapley Additive exPlanations) analysis were utilized to identify influential factors in predicting e-scooter use in each city. The results reveal that the majority of e-scooter users are 25 to 39 of age, male, with higher income and a bachelor’s degree, and 92% possess a driver’s license. Significant differences in attitudes between e-scooter users and non-users highlight the complexity of perceptions towards e-scooter usage. The ML model indicates that employment status negatively impacts the prediction of e-scooter users, while factors such as living without a car and using non-motorized modes positively influence e-scooter use. Educational background is a significant e-scooter mode choice factor in Washington, D.C. and Miami, whereas attitudinal questions on car and technology usage are influential in Los Angeles. These findings provide valuable insights into the factors shaping e-scooter adoption, informing urban transportation planning and policymaking and enhancing understanding of shared micromobility and its impact on urban mobility. Full article

Shared e-scooters were introduced in urban public spaces as a way to promote a modal shift from cars in short-distance trips, as well as to improve sustainability, resilience, and equity in urban transport. However, the expansion of shared e-scooter services in 2019 proved that this mode of transport, without integrated planning strategies, can bring some problems to cities, which are related to the illegal parking of e-scooters, an increase in head injuries, and the lack of population diversity among users. Regarding the latest, this research work aims at conducting a case study in the city of Braga, Portugal to reveal who the actual and potential users of shared e-scooters are and how their socioeconomic profile (e.g., gender, age, income range, literacy, occupation) and usage patterns related to the ones found in other cities. For this, a revealed preference survey was deployed on the case study site, and the respondents’ profiles were statistically correlated with the socioeconomic characteristics of the city’s general population in order to assess if this mode of transport provides an equitable service. Results show that shared e-scooters are not equally used by people of different genders, ages, and income ranges. Information on e-scooter usage inequalities across the population is useful for the city to proceed with more socially equitable mobility policies. Full article

The emerging concept of integrated community energy systems (ICESs) proves its suitability for improving the operation of local grids—increasing self-consumption from local generation, enhancing the load factor, and reducing energy cost. In Ballen marina—located on the Danish island of Samsø—the battery energy storage system (BESS)’s action can be possibly complemented by the flexibility of boats and electric cars. With the greater involvement of energy consumers, the energy system’s performance may become more efficient—from both technical and economic perspectives. Within this framework, the optimal charging and discharging strategies of the marina’s electric cars were developed and evaluated. The car usage profile was generated, utilising a stochastic approach to resemble daily variations in the driving pattern. The optimal charging strategy was established, subsequently integrating this action with boat flexibility. As a future scenario, the benefits of vehicle-to-grid (V2G) technology implementation were examined, proving significant enhancements of the future marina’s grid—with increased photovoltaic (PV) generation capacity and the number of electric cars. The economic benefits of bidirectional charging were proven, with ample advantages for the marina and the rental company, leading to cost savings of up to 51.7% and minimising the energy export by 21.3%. Therefore, increasing the integration level of Ballen marina’s flexible units—electric cars and boats—was concluded to be an important goal for the coming years. Full article

Bike-sharing systems implemented in cities with good bike lane networks could potentiate a modal shift from short car trips, boosting sustainable mobility. Both passenger and last-mile goods transportation can benefit from such systems and, in fact, bike sharing (dockless or with docking stations) is increasing worldwide, especially in Europe. This research focused on a European city, Lisbon, and the e-bike sharing system GIRA, in its early deployment, in 2018, where it had about 409 bikes of which 30% were non-electric conventional bikes and 70% were e-bikes. The research aims at answering the main research questions: (1) What is the number of trips per day and travel time in conventional bikes and e-bikes?; (2) Do the daily usage peaks follow the trends of other modes of transport in terms of rush hours?; (3) Are there seasonality patterns in its use (weekdays and weekends, workdays and holiday periods)?; (4) How do climate conditions affect its use?; and finally, (5) What would be the impact on final energy consumption and GHG emissions? The dataset for 2018 regarding GIRA trips (distance, time, conventional or e-bike, docking station origin and destination) and weather (temperature, wind speed, relative humidity, precipitation) was available from Lisbon City Hall by means of the program “Lisboa aberta”. Data regarding the profile of the users (which trips GIRA replaces?) and data regarding electricity consumption were not available. The latter was estimated by means of literature e-bike data and electric motor specifications combined with powertrain efficiency. Greenhouse gas (GHG) emissions were estimated by using the latest Intergovernmental Panel on Climate Change (IPCC) CO₂ equivalents and a spreadsheet simulator for the Portuguese electricity GHG intensity, which was adaptable to other countries/locations. In a private car fleet dominated by fossil fuels and internal combustion engines, the e-bike sharing system is potentially avoiding 36 Ton GHG/year and reducing the energy consumption by 451 GJ/year. If the modal shift occurs from walking or urban bus to an e-bike sharing system, the impact will be detrimental for the environment. Full article

This concept study extends the power-to-gas approach to small combined heat and power devices in buildings that alternately operate fuel cells and electrolysis. While the heat is used to replace existing fossil heaters on-site, the power is either fed into the grid or consumed via heat-coupled electrolysis to balance the grid power at the nearest grid node. In detail, the power demand of Germany is simulated as a snapshot for 2030 with 100% renewable sourcing. The standard load profile is supplemented with additional loads from 100% electric heat pumps, 100% electric cars, and a fully electrified industry. The renewable power is then scaled up to match this demand with historic hourly yield data from 2018/2019. An optimal mix of photovoltaics, wind, biomass and hydropower is calculated in respect to estimated costs in 2030. Hydrogen has recently entered a large number of national energy roadmaps worldwide. However, most of them address the demands of heavy industry and heavy transport, which are more difficult to electrify. Hydrogen is understood to be a substitute for fossil fuels, which would be continuously imported from non-industrialized countries. This paper focuses on hydrogen as a storage technology in an all-electric system. The target is to model the most cost-effective end-to-end use of local renewable energies, including excess hydrogen for the industry. The on-site heat coupling will be the principal argument for decentralisation. Essentially, it flattens the future peak from massive usage of electric heat pumps during cold periods. However, transition speed will either push the industry or the prosumer approach in front. Batteries are tried out as supplementary components for short-term storage, due to their higher round trip efficiencies. Switching the gas net to hydrogen is considered as an alternative to overcome the slow power grid expansions. Further decentral measures are examined in respect to system costs. Full article

Car sharing services have expanded in order to meet the new necessities of mobility worldwide in an innovative way. Before the COVID-19 pandemic, car sharing was a very popular mode of transportation among young adults in big cities. However, during this ongoing pandemic and with public transportation considered a super-spreading transmitter, the usage of car sharing is unclear. Therefore, the aim of this study, which is explorative in nature, is to investigate the usage, advantages, drivers, and barriers to car sharing during this ongoing pandemic era. To this end, 66 interviews were conducted among users of car sharing during the COVID-19 pandemic. The findings provide key information for the planning of car sharing operations and public transportation in the context of avoiding COVID-19 infection and respecting the recommendations of local governments. In addition, new emerging profiles of car sharing users in the ongoing pandemic are identified. This research provides relevant insights for both business practice and policy makers. Full article

The development and the diffusion of the electromobility is crucial for reducing air pollution and increase sustainable transport. In particular, electrification of private mobility has a significantly role in the energy transition within urban areas, since the progressive substitution of conventional passenger cars by electric vehicles (EVs) leads to the decarbonisation of transport sector without direct emissions. However, increasing EV penetration in the market forces an expansion of the existing charging infrastructure with potential negative impacts on the distribution grid. In this context, a simplified approach is proposed to estimate the energy and power demand owing to the recharge of electric passenger cars within the city of Turin in Italy. This novel approach is based on the usage of floating car data (FCD) to identify the travel behaviour and parking habits of a non-EV passenger car in the city. Mobility data were then used to evaluate EVs energy consumption and charging needs considering different charging options (public or domestic) and range anxiety in different scenarios of EV diffusion. Aggregated load profiles and demand were finally evaluated both for the whole and for each zone of the city as possible resource for city planner or distribution system operators (DSO). Full article

Shared e-scooters refer to a micro-mobility service that enables the short rentals of e-scooters. The rapid growth of e-scooter sharing has sparked a heated discussion about its role in the urban mobility sector. This article presents a systematic review of the current knowledge on its uses and users, health and environmental impacts, and policy issues. The analysis is based on academic literature, identified with Google Scholar by using keywords and publication years from 2017, and relevant gray literature. Firstly, we highlight that the profiles of e-scooter renters seem to highly match the characteristics of other micro-mobility services users. Secondly, e-scooters are often associated with a high perception of risk from the public and an increasing occurrence of related road accidents. Thirdly, even if promoted as a green mobility option, the true environmental impact of shared e-scooters has only started to be investigated. Early studies point out negative impacts around their production, usage, and maintenance. Fourthly, the integration of shared e-scooters into the existing transport systems requires policy changes, both at the local and national level, including traffic regulations, safety rules, and physical infrastructure. Finally, this paper reveals the ambiguity of the term “e-scooter” and stresses the need for more research, as the future of cities is tied to the development of low-car lifestyles. Full article

This study focuses on driver-behavior identification and its application to finding embedded solutions in a connected car environment. We present a lightweight, end-to-end deep-learning framework for performing driver-behavior identification using in-vehicle controller area network (CAN-BUS) sensor data. The proposed method outperforms the state-of-the-art driver-behavior profiling models. Particularly, it exhibits significantly reduced computations (i.e., reduced numbers both of floating-point operations and parameters), more efficient memory usage (compact model size), and less inference time. The proposed architecture features depth-wise convolution, along with augmented recurrent neural networks (long short-term memory or gated recurrent unit), for time-series classification. The minimum time-step length (window size) required in the proposed method is significantly lower than that required by recent algorithms. We compared our results with compressed versions of existing models by applying efficient channel pruning on several layers of current models. Furthermore, our network can adapt to new classes using sparse-learning techniques, that is, by freezing relatively strong nodes at the fully connected layer for the existing classes and improving the weaker nodes by retraining them using data regarding the new classes. We successfully deploy the proposed method in a container environment using NVIDIA Docker in an embedded system (Xavier, TX2, and Nano) and comprehensively evaluate it with regard to numerous performance metrics. Full article

Free-Floating Car Sharing (FFCS) services are currently available in tens of cities and countries spread all over the worlds. Depending on citizens’ habits, service policies, and road conditions, car usage profiles are rather variable and often hardly predictable. Even within the same city, different usage trends emerge in different districts and in various time slots and weekdays. Therefore, modeling car availability in FFCS systems is particularly challenging. For these reasons, the research community has started to investigate the applicability of Machine Learning models to analyze FFCS usage data. This paper addresses the problem of predicting the short-term level of availability of the FFCS service in the short term. Specifically, it investigates the applicability of Machine Learning models to forecast the number of available car within a restricted urban area. It seeks the spatial and temporal contexts in which nonlinear ML models, trained on past usage data, are necessary to accurately predict car availability. Leveraging ML has shown to be particularly effective while considering highly dynamic urban contexts, where FFCS service usage is likely to suddenly and unexpectedly change. To tailor predictive models to the real FFCS data, we study also the influence of ML algorithm, prediction horizon, and characteristics of the neighborhood of the target area. The empirical outcomes allow us to provide system managers with practical guidelines to setup and tune ML models. Full article

To counteract climate change, electric vehicles are replacing vehicles with internal combustion engine on the automotive market. Therefore, electric vehicles must be accepted and used like conventional vehicles. This study aims to investigate to which extent electric vehicles are already being used like conventional vehicles. To do this, we present a supervised method where we combine usage data from conventional vehicles (from car use model based on survey data) and electric vehicles (from sensor data) in Germany and California. Based on conventional vehicles, eight car usage profiles were defined by hierarchical clustering in a previous study. Using a softmax regression, we estimate for each electric vehicle a probability of assignment for every car usage profile. Comparison of conventional and electric vehicles with a high probability reveals that electric vehicles are used similar for long-distance travel (>100 km) and different for short-distance travel (<10 km) to conventional vehicles. This implies that electric vehicles are indeed used for long-distance travel but are still not entirely used for everyday mobility. This could be because electric vehicles are not yet suitable for all trip purposes (e.g., transport of larger items). Full article

As an important aspect of today’s efforts to reduce greenhouse gas emissions, the energy demand of passenger cars is a subject of research. Different drivetrain concepts like plug-in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV) are introduced into the market in addition to conventional internal combustion engine vehicles (ICEV) to address this issue. However, the consumption highly depends on individual usage profiles and external operating conditions, especially when considering secondary energy demands like heating, ventilation and air conditioning (HVAC). The approach presented in this work aims to estimate vehicle consumptions based on real world driving profiles and weather data under consideration of secondary demands. For this purpose, a primary and a secondary consumption model are developed that interact with each other to estimate realistic vehicle consumptions for different drivetrain concepts. The models are parametrized by referring to state of the art contributions and the results are made plausible by comparison to literature. The sensitivities of the consumptions are then analysed as a function of trip distance and ambient temperature to assess the influence of the operating conditions on the consumption. The results show that especially in the case of the BEV and PHEV, the trip distance and the ambient temperature are a first-order influencing factor on the total vehicle energy demand. Thus, it is not sufficient to evaluate new vehicle concepts solely on one-dimensional driving cycles to assess their energy demand. Instead, the external conditions must be taken into account for a proper assessment of the vehicle’s real world consumption. Full article