Search Results (12)

Shared e-scooter systems are frequently characterized as zero-emission mobility solutions; however, lifecycle greenhouse gas (GHG) emissions depend on manufacturing, electricity generation, and operational logistics. While conventional life cycle assessment (LCA) studies quantify environmental impacts using static average parameters, they rarely integrate lifecycle emissions into real-time fleet decision-making. This study proposes a formally defined carbon-aware operational framework that integrates ride-level telemetry, time-varying electricity grid carbon intensity, amortized production emissions, and dynamically allocated logistics impacts into a unified optimization architecture. Lifecycle emissions are computed at ride-level granularity and incorporated into charging and rebalancing decisions through a constrained optimization framework. A multi-objective extension is introduced to account for environmental–economic trade-offs. An illustrative simulation of 1000 rides was conducted to evaluate the operational performance of the framework. Under the assumed baseline scenario, the illustrative carbon-aware simulation indicated a potential reduction of up to 24.5% relative to conventional scheduling. Sensitivity analysis across variations in grid carbon intensity, scooter lifetime, energy consumption, and logistics emissions demonstrated reduction outcomes ranging between 18% and 29%, indicating robustness to parameter uncertainty. The study does not present large-scale empirical validation but provides a mathematically formalized decision-support architecture that operationalizes lifecycle assessment within shared micro-mobility fleet management. The results suggest that integrating carbon metrics into operational control may substantially enhance the environmental performance of shared e-scooter systems. Future research should validate the framework using real-world fleet data and incorporate a comprehensive economic assessment. The proposed framework provides a scalable methodological basis for integrating environmental metrics into real-time micro-mobility management and urban sustainability planning. Full article

This paper showcases how electric scooter data can be used to visually explore and interpret urban dynamics, offering a perspective on city structure and mobility patterns. The goal of the study is to investigate how visual analysis of micromobility data can reveal spatial and temporal patterns that support urban planning and operational decision-making. Through a series of visual analyses, the article identifies high-demand areas and popular travel routes, with areas of particularly strong traffic—insights valuable for infrastructure planning and operational optimization. Temporal visualizations reveal distinct peaks in e-scooter activity during lunch hours and late evenings, highlighting behavior patterns that may inform service adjustments. Clustering techniques are used to delineate functional zones within the city, which are then visualized to reflect how users interact with urban space. These visuals help uncover mobility-based boundaries and support a deeper understanding of the city’s layout. Additionally, the approach highlights key locations that may be attractive for business development, such as new commercial spots, based on user behavior. By focusing on visual storytelling rather than predictive modeling, this work proposes analyses suitable for urban planners, mobility providers, and other stakeholders with actionable insights into urban movement and structure. Full article

The rapid growth of dockless electric scooter (e-scooter) sharing services has transformed short-distance urban mobility, offering convenience and sustainability benefits while amplifying challenges related to demand imbalance, fleet rebalancing, and spatial inequity. Accurate spatiotemporal demand prediction is therefore essential for optimizing resource allocation and supporting data-driven policy interventions. This study proposes a hybrid deep learning framework that integrates a Graph Convolutional Network (GCN) with a Gated Recurrent Unit (GRU) and community detection to enhance short-term prediction of e-scooter pick-up and drop-off demands. The Louvain algorithm is employed to partition urban areas into mobility-based communities, enabling the model to capture functional connectivity rather than relying solely on geographic proximity. Using real-world e-scooter trip data from Calgary, Canada, the model’s performance is evaluated against established baselines, including a Masked Fully Convolutional Network (MFCN) and conventional GRU architectures. Results show that the proposed approach achieves up to 11.8% improvement in mean absolute error (MAE) compared with the MFCN baseline and more robust generalization across temporal horizons. The findings demonstrate that integrating community structures into graph-based learning effectively captures complex urban dynamics, providing practical insights for sustainable micromobility operation and service deployment. Full article

Urban environments are undergoing significant transformations, with pedestrian areas emerging as complex hubs of diverse mobility modes. This shift demands a more nuanced approach to urban planning and navigation technologies, highlighting the limitations of traditional, road-centric datasets in capturing the detailed dynamics of pedestrian spaces. In response, we introduce the DELTA dataset, designed to improve the analysis and mapping of pedestrian zones, thereby filling the critical need for sidewalk-centric multimodal datasets. The DELTA dataset was collected in a single urban setting using a custom-designed modular multi-sensing e-scooter platform encompassing high-resolution and synchronized audio, visual, LiDAR, and GNSS/IMU data. This assembly provides a detailed, contextually varied view of urban pedestrian environments. We developed three distinct pedestrian route segmentation models for various sensors—the 4K camera, stereocamera, and LiDAR—each optimized to capitalize on the unique strengths and characteristics of the respective sensor. These models have demonstrated strong performance, with Mean Intersection over Union (IoU) values of 0.84 for the reflectivity channel, 0.96 for the 4K camera, and 0.92 for the stereocamera, underscoring their effectiveness in ensuring precise pedestrian route identification across different resolutions and sensor types. Further, we explored audio event-based classification to connect unique soundscapes with specific geolocations, enriching the spatial understanding of urban environments by associating distinctive auditory signatures with their precise geographical origins. We also discuss potential use cases for the DELTA dataset and the limitations and future possibilities of our research, aiming to expand our understanding of pedestrian environments. Full article

E-scooter vibrations are a problem recently studied. Theoretical models based on dynamic simulations and also real measurements have confirmed the high impact of e-scooter vibrations on driver comfort and health. Some authors recommend improving e-scooter damping systems, including tyres. However, it has not been suggested nor has any research been published studying how to improve e-scooter frame design for reducing driver vibrations and improving comfort. In this paper, we have modelled a real e-scooter to have a reference. Then, we have developed a multibody dynamic model for running dynamic simulations studying the influence of mass geometry parameters of the e-scooter frame (mass, centre of gravity and inertia moment). Acceleration results have been analysed based on the UNE-2631 standard for obtaining comfort values. Based on results, a qualitative e-scooter frame design guide for mitigating vibrations and increasing the comfort of e-scooter driver has been developed. Some application cases have been running on the multibody dynamic simulation model, finding improvements of comfort levels higher than 9% in comparison with the e-scooter reference model. The dynamic model has been qualitatively validated from real measurements. In addition, a basic sensor proposal and comfort colour scale is proposed for giving feedback to e-scooter drivers. Full article

Given the escalating issue of climate change, environmental protection is of growing importance. A rising proportion of battery-powered scooters are becoming available. However, their range is limited, and they require a long charging time. The fuel cell–battery-powered electric scooter appears to be a promising alternative. Further development of the active hybrid is the passive hybrid, in which the fuel cell is directly coupled to the battery, eliminating the need for a DC/DC converter. The passive hybrid promises the possibility of a reduction in the installation volume and cost. A simulation model is created MATLAB/Simulink for the passive fuel cell–battery hybrid electric scooter. It specifically focuses on how the power split between the fuel cell and battery occurs under dynamic load requirements. The scooter is powered by two air–hydrogen Proton Exchange Membrane Fuel Cell (PEMFC) systems with a nominal power of 250 W each and a Li-ion battery (48 V, 12 Ah). The validation is performed following an ECE-R47 driving cycle. The maximum relative deviation of the fuel cell is 2.82% for the current value. The results of the simulation show a high level of agreement with the test data. This study provides a method allowing for an efficient assessment of the passive fuel cell–battery hybrid electric scooter. Full article

The research objective is to approach the dynamic and consumed electrical energy of an electric scooter by varying the key input parameters, including rider mass, electric scooter mass, wind speed, wheel radius, and slope grade. A simulation model of an electric scooter was applied in a MATLAB-Simulink environment to investigate the scooter velocity, required power, battery voltage, and propulsion torque of the e-scooter. It was established by employing mathematical equations during the of electric scooters. The study found that the scooter velocity and electricity consumption were optimized by 3.9% and 0.08%, respectively, when the scooter weight decreased from 26 to 10 kg. The scooter velocity, electricity consumption, and required power decreased by 23.2%, 0.55%, and 8.56%, respectively, when the slope grade decreased from 1.15% to 0%. Following a wind speed reduction from 4 to 0 m/s, the consumed electricity and required power were optimized by 0.2% and 5.5%, respectively. The consumed electricity increased by 0.2% and the scooter velocity and required power significantly increased by 36.5% and 34.3% when the wheel radius increased from 0.105 to 0.185 m. Furthermore, the e-scooter could achieve an effective performance with a weight of 10 kg, wheel radius of 0.185 m, wind speed of 0 km/h, slope grade of 0%, and minimal rider weight. The simulation results showed that the scooter’s effective performance range and consumed electrical energy could be optimized by suitably adjusting the key structures and operating parameters. To support this research, a concurrent experiment investigated the dynamic characteristics and electricity consumption of the electric scooter during operation. The experimental and simulated results had the same patterns in similar initial conditions. Full article

The automotive sector is currently shifting its focus from traditional fossil fuels to electrification. The deployment of a Battery Management System (BMS) unit is the key point to oversee the battery state of the electric vehicle (EV) to ensure safety and performances. The development and assessment of electric vehicle models in turn lays the groundwork of the BMS design as it provides a quick and cheap solution to test battery optimal control logics in a Software-in-the-Loop environment. Despite the various contribution to the literature in battery and vehicle modeling, electric scooters are mostly disregarded together with a reliable estimation of their performance and electric range. The present paper hence aims at filling the gap of knowledge through the development of a numerical model for considering a two-wheeler. The latter model relies on the conservation energy based-longitudinal dynamic approach and is coupled to a Li-Ion Battery second-order RC equivalent circuit model for the electric range prediction. More specifically, the presented work assesses the performance and electric range of a two-wheeler pure electric scooter in a real-world driving cycle. The e-powertrain system embeds an Electrical Energy Storage System (EESS) Li-Ion Battery pack. On-road tests were initially conducted to retrieve the main model parameters and to perform its validation. A global battery-to-wheels efficiency was also calibrated to account for the percentual amount of available net power for the vehicle onset. The model proved to properly match the experimental data in terms of total distance traveled over a validation driving mission. Full article

Due to their small size and low energy demand, light electric vehicles (LEVs), such as electric moped scooters, are considered as a space efficient and eco-friendly alternative for mobility in cities. However, the growth of electric moped scooter sharing services raises the question of how environmentally friendly this business model is, considering the entire lifecycle. Due to the dynamic market and insufficient availability of public data on the business processes of sharing services only a few studies on the impact of shared electric mopeds are available. Especially there is a lack of research on the impacts of key operational logistic parameters of the sharing system. This paper aims to fill this gap by conducting a life cycle assessment using the example of an electric moped scooter manufactured and used in sharing services in Germany, based on different operating scenarios. The results show that e-moped sharing has a similar environmental impact on global warming potential, in terms of passenger kilometers, as public transport, especially if long product lifetimes as well as efficient operation logistics are realized. Full article

Though new technologies have been applied in all industries, electric mobility technology using eco-friendly energy is drawing a great deal of attention. This research focuses on a personal electric mobility system for urban tourism. Some tourism sites such as Gyeongju, Korea, have broad spaces for tourists to walk around, but the public transportation system has been insufficiently developed due to economic reasons. Therefore, personal mobility technology such as electric scooters can be regarded as efficient alternatives. For the operation of electric scooters, a charging infrastructure is necessary. Generally, scooters can be charged via wires, but this research suggests an advanced electric personal mobility system based on wireless electric charging technology that can accommodate user convenience. A mathematical model-based optimization was adopted to derive an efficient design for a wireless charging infrastructure while minimizing total investment costs. By considering the type of tourists and their tour features, optimal locations and lengths of the static and dynamic wireless charging infrastructure are derived. By referring to this research, urban tourism can handle transportation issues from a sustainable point of view. Moreover, urban tourism will have a better chance of attracting tourists by conserving heritage sites and by facilitating outdoor activities with electric personal mobility. Full article

This paper addresses diagnosis and prognosis problems for an electric scooter subjected to parameter uncertainties and compound faults (i.e., permanent fault and intermittent fault with non-monotonic degradation). First, the diagnostic bond graph in linear fractional transformation form is used to model the uncertain electric scooter and derive the analytical redundancy relations incorporating the nominal part and uncertain part, based on which the adaptive thresholds for robust fault detection and the fault signature matrix for fault isolation can be obtained. Second, an adaptive enhanced unscented Kalman filter is proposed to identify the fault magnitudes and distinguish the fault types where an auxiliary detector is introduced to capture the appearing and disappearing moments of intermittent fault. Third, a dynamic model with usage dependent degradation coefficient is developed to describe the degradation process of intermittent fault under various usage conditions. Due to the variation of degradation coefficient and the presence of non-monotonic degradation characteristic under some usage conditions, a sequential prognosis method is proposed where the reactivation of the prognoser is governed by the reactivation events. Finally, the proposed methods are validated by experiment results. Full article

Personal light electric vehicles (PLEVs) are a phenomenon that can currently be observed in cities, intended to be an ecological form of transport. The authors of the paper make an attempt to determine electricity consumption by PLEVs in the context of managing a large city in accordance with the concept of sustainable development. The article is of a cognitive nature. Research questions posed against the background of the goal formulated are as follows: how strong will the demand for PLEVs be (in the example of e-motor scooters, taking into consideration the number of vehicles) and for the electricity consumed by PLEVs. The method used is a simulation model. The conducted analyses demonstrate that a dynamic growth of PLEVs will result in an increased energy demand, which must be taken into account by the cities, developing according to the sustainable development conception. Full article