Search Results (118)

Analyses of student mobility are typically conducted in an urban environment and are informed by socio-demographic or trip attributes. The prevailing focus is on individual modes of transport, different groups of commuters travelling to campus, students’ behavioural perceptions, and the totality of student trips. This paper starts with the identification of the determinants of student mobility that have received insufficient research attention. Utilising surveys, the study captures the mobility patterns of a sample of 1014 students and calculates their carbon footprint (CF; in kg/academic year) to assess whether the factors neglected in previous studies influence differences in the actual environmental load of student commuting. A regression analysis is employed to ascertain the significance of these factors as predictors of sustainable student mobility. This study exclusively focuses on the group of student commuters to campus and analyses the trips associated with compulsory activities at a suburban campus that is distant from the university centre and student facilities, which changes the mobility context in terms of commuting options. The under-researched factors identified in this research have not yet been quantified as CF. The findings confirm that only some of the factors neglected in previous research are statistically significant predictors of the local environmental load of student mobility. Specifically, variables such as student employment, frequency of class attendance, and propensity for ride-sharing could be utilised to forecast and regulate students’ mobility towards more sustainable patterns. However, all of the under-researched factors (including household size, region of origin (i.e., past experiences), residing at term-time accommodation while studying, and the availability of a family car) have an influence on the differences in CF magnitude in the studied campus. Full article

As shared micromobility grows quickly in metropolitan settings, e-scooter safety issues have become more urgent. This paper uses a Bayesian hierarchical model applied to census block groups in several Texas metropolitan areas to construct a spatial risk assessment methodology for e-scooter crashes. Based on crash statistics from 2018 to 2024, we develop a severity-weighted crash risk index and combine it with variables related to land use, transportation, demographics, economics, and other factors. The model comprises a geographically structured random effect based on a Conditional Autoregressive (CAR) model, which accounts for residual spatial clustering after capture. It also includes fixed effects for covariates such as car ownership and nightlife density, as well as regional random intercepts to account for city-level heterogeneity. Markov Chain Monte Carlo is used for model fitting; evaluation reveals robust spatial calibration and predictive ability. The following key predictors are statistically significant: a higher share of working-age residents shows a positive association with crash frequency (incidence rate ratio (IRR): ≈1.55 per +10% population aged 18–64), as does a greater proportion of car-free households (IRR ≈ 1.20). In the built environment, entertainment-related employment density is strongly linked to elevated risk (IRR ≈ 1.37), and high intersection density similarly increases crash risk (IRR ≈ 1.32). In contrast, higher residential housing density has a protective effect (IRR ≈ 0.78), correlating with fewer crashes. Additionally, a sensitivity study reveals that the risk index is responsive to policy scenarios, including reducing car ownership or increasing employment density, and is sensitive to varying crash intensity weights. Results show notable collision hotspots near entertainment venues and central areas, as well as increased baseline risk in car-oriented urban environments. The results provide practical information for targeted initiatives to lower e-scooter collision risk and safety planning. Full article

Commuting plays a critical role in shaping sustainable transport systems, yet understanding the diverse preferences of commuter groups remains a challenge for policymakers. As cities aim to promote sustainable transport, it is essential to better understand the factors influencing travel behaviors. This study investigates the commuting preferences and behaviors of urban employees in Italy, focusing on identifying distinct user profiles and their implications for policy development. Using a dataset of 2301 participants from Italian cities, the research analyzed transport mode choices, willingness to adopt sustainable transport options, and perceptions of public transport (PT) services, including factors such as travel time, proximity to PT stops, cost, and comfort, rated on a four-point Likert scale. K-modes clustering was employed to segment participants into three clusters based on their travel behaviors. The results revealed three distinct user profiles: (1) car-dependent users with negative perceptions of PT, driven by family obligations and dissatisfaction with PT services; (2) individuals who primarily use cars but are somewhat open to improvements in PT; (3) individuals willing to adopt alternative mobility options, including active and shared transport modes. Significant differences were found across clusters in terms of mode choices, willingness to use sustainable transport, and satisfaction with PT services. Notably, employees showed limited interest in alternative sustainable transport modes such as e-scooters and walking, with 73% and 66% of participants expressing little or no interest, respectively. Despite incentives such as company subsidies for purchasing bicycles or e-scooters, 58% of employees remained uninterested in adopting these alternatives. Additionally, employees’ perceptions of PT services revealed dissatisfaction with factors such as travel time, comfort, and punctuality, with over 70% rating these aspects as “Poor” or “Fair”. These findings suggest that improving the quality of PT services, particularly in terms of travel time, punctuality, comfort, and cost, should be a priority for enhancing user satisfaction. This research provides valuable insights for policymakers seeking to reduce car dependence and promote sustainable urban transport planning. Full article

Accurate car-sharing demand prediction is a key factor in enhancing the operational efficiency of shared mobility systems. However, mobility data often exhibit temporal, spatial, and spatio-temporal interdependencies that pose significant challenges for conventional models. These models typically struggle to capture nonlinear and high-dimensional patterns. Existing methods struggle to model entangled relationships across these modalities and lack scalability in dynamic urban environments. This paper presents the Quantum-Inspired Spatio-Temporal Inference Network (QSTIN), an enhanced approach that builds upon our previously proposed Explainable Spatio-Temporal Inference Network (eX-STIN). QSTIN integrates a Quantum-Inspired Neural Network (QINN) into the fusion module, generating complex-valued feature representations. This enables the model to capture intricate, nonlinear dependencies across heterogeneous mobility features. Additionally, Quantum Particle Swarm Optimization (QPSO) is applied at the final prediction stage to optimize output parameters and improve convergence stability. Experimental results indicate that QSTIN consistently outperforms both conventional baseline models and the earlier eX-STIN in predictive accuracy. By enhancing demand prediction, QSTIN supports efficient vehicle allocation and planning, reducing energy use and emissions and promoting sustainable urban mobility from both environmental and economic perspectives. Full article

Efficient resource allocation in car-sharing systems relies on precise predictions of demand. Predicting vehicle demand is challenging due to the interconnections of temporal, spatial, and spatio-temporal features. This paper presents the Explainable Spatio-Temporal Inference Network (eX-STIN), a new approach that improves upon our prior Unified Spatio-Temporal Inference Prediction Network (USTIN) model. It offers a comprehensive framework for the integration of various data. The eX-STIN model enhances the previous one by utilizing Ensemble Empirical Mode Decomposition (EEMD), which results in refined feature extraction. It uses Minimum Redundancy Maximum Relevance (mRMR) to find features that are relevant and not redundant, and Shapley Additive Explanations (SHAP) to show how each feature affects the model’s predictions. We conducted extensive experiments that use real car-sharing data to thoroughly evaluate the efficacy of the eX-STIN model. The studies revealed the model’s ability to accurately represent the relationships among temporal, spatial, and spatio-temporal features, outperforming the state-of-the-art models. Moreover, the experiments revealed that eX-STIN exhibits enhanced predictive accuracy compared to the USTIN model. This proposed approach enhances both the accuracy of demand prediction and the transparency of resource allocation decisions in car-sharing services. Full article

Air pollution, largely driven by car traffic, poses significant challenges in many cities, including Košice, Slovakia. As the city explores micromobility as a part of its smart city initiatives and sustainable alternative to individual car use, understanding its spatial dynamics becomes essential. Despite the growing adoption of shared micromobility systems, research on their spatial patterns in Central Europe is still limited. This study analyzes over 900,000 trips made between 2019 and 2022 using bicycles, e-bikes, e-scooters, and e-mopeds in Košice’s dockless system. Using spatial analysis, we identified key hubs near public transport stops, pedestrian zones, and universities, highlighting how micromobility addresses the first/last mile transport challenge. A notable shift from bicycles to e-scooters was observed, enabling wider adoption in areas with fragmented terrain and neighborhoods farther from the city center. Our findings show a significant demand for shared micromobility, indicating its potential to reduce urban car dependency and support smart and sustainable urban transport. However, winter months remain a challenge, with high smog levels but near-zero demand for shared micromobility. Full article

Ethyl alcohol is a known additive to automotive gasoline. In commercially available gasolines, its concentration is between 5 and 10%. Since ethyl alcohol can be considered as a renewable fuel, efforts are being made to further increase its content in gasoline. This article describes the results of comparison experiments on a Euro 5 direct injection spark-ignition car engine fueled with conventional gasoline and gasoline with 30% v/v ethyl alcohol content (E30). The test results showed that a significant share of ethanol in the fuel did not affect most of the regulated emissions of gaseous components (namely: CO, HC, NO), i.e., a three-way catalyst effectively removed these components, regardless of the fuel composition. Slightly lower CO₂ emissions with the E30 fuel were noticeable. A significant difference, however, in lower particulate number emissions for the fuel with high-ethanol content was seen. At high engine load, the use of the E30 fuel resulted in a tenfold reduction in particulate number emissions. This might be considered as a very valuable effect of ethanol since direct injection spark-ignition engines are typically characterized by higher particulate emissions compared to engines equipped with other types of injection systems. Full article

We investigate the construction of a partial absorbing continuous-time Markov chain (CTMC) using a heuristic algorithm aimed at approximate transient analysis. The accuracy of transient state probabilities is indicated by the probability of absorbing state(s) at the specified time moment. A key challenge is the construction of a partial CTMC that minimizes the probability of reaching the absorbing state(s). The generation of all possible partial CTMCs is too computationally demanding, in general. Thus, we turn to investigation of heuristic algorithms that chose to include one state at a time based on limited information (i.e., the partial chain that is already constructed) and without any assumptions about the structure of the underlying CTMC. We consider three groups of such algorithms: naive, based on state characterization by the shortest path (obtained by Dijkstra method) and based on exact/approximate state probabilities. After introducing the algorithms, we discuss the problem of optimal partial CTMC construction and provide several examples. Then we compare the algorithm performance by constructing the partial CTMCs for two models: car sharing system and a randomly generated CTMC. Our obtained numerical results suggest that heuristic algorithms using state characterization via the shortest path offer a balance between accuracy and computational effort. Full article

The possibility of charging and possibly discharging electric cars can influence not only the balancing of power demand profiles in the grid and the stabilization of voltage profiles but also the appropriate management of electricity within the grid of an industrial plant equipped with its own RES resources. For this purpose, the concept of “power supply modes” can be introduced, which involves intelligent demand-side management. Each technological process in an industrial plant should be assigned a specific level of importance and priority. These priorities can be numbered according to their importance (weights) and marked with appropriate colors. One thus obtains a qualitative assessment of energy consumption within the plant (demand side) through the lens of power modes. With respect to the ability to charge electric vehicles within the plant grid, such priorities can also be assigned to individual charging options. If a given RES has sufficient generation capacity during a particular time period, the cost of charging is low. However, if the RESs are not operational during a given period (e.g., nighttime in the case of photovoltaics or during calm weather in the case of wind turbines), vehicles can still be charged but according to a different priority, which, of course, involves higher costs. By having access to data on the generation capacity of distributed RESs and knowing the preferences of employees, including the number of electric cars and the expected periods of vehicle charging, it is possible to predict the degree of use of available green energy and manage it efficiently. The analyses presented in the article represent an original approach to the flexibility of operation not only of the electricity grid but also of the internal energy system of industrial plants. It offers a novel perspective aimed at maximizing the share of RESs in the overall energy balance and minimizing the costs associated with the operation of RESs. The theoretical opportunity of sustainable sharing with employees a dedicated charging mode named “free charging”, powered by RESs, could represent an appropriate solution for CO₂ emission reduction within Scope 3, Category 3, “employee commuting”, according to the GHG Protocol requirements. The original methodology proposed in the article aligns with activities related to the energy transition. Full article

The research objective of this paper is to analyse what is behind the self-driving offer implemented in Phoenix (Arizona) by Waymo and a normal rent-a-car company by modelling both in e³value. A gap analysis proposes a new model of the rent-a-car business with the integration of a shared autonomous vehicle ride-hailing service. The goal is to encourage the growth of additional global shared autonomous vehicle trials and their incorporation into conventional businesses. The primary objective is to enhance shared autonomous mobility options, resulting in increased road safety, decreased traffic, and decreased emissions in urban areas. As a result, modelling Waymo can serve as a foundation for expanding the use of shared autonomous vehicles by other businesses in different geographic areas. Full article

Background/Objectives: Biogenic volatile organic compounds (BVOCs), extensively studied in terrestrial plants with global emissions around 1 PgC yr⁻¹, are also produced by marine organisms. However, benthic species, especially seagrasses, are understudied despite their global distribution (177,000–600,000 km²). This study aims to examine BVOC emissions from key Mediterranean seagrass species (Cymodocea nodosa, Posidonia oceanica, Zostera noltei, and Zostera marina) in marine and coastal lagoon environments. Methods: BVOCs were collected using headspace solid-phase microextraction (HS-SPME) using divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fibers and analyzed by gas chromatography–mass spectrometry (GC-MS). Results: An important chemical diversity was found with a total of 92 volatile compounds (61 for Z. noltei, 59 for C. nodosa, 55 for P. oceanica, and 51 for Z. marina), from different biosynthetic pathways (e.g., terpenoids, benzenoids, and fatty acid derivatives) and with several types of chemical functions (e.g., alkanes, esters, aldehydes, and ketones) or heteroatoms (e.g., sulfur). No differences in chemical richness or diversity of compounds were observed between species. The four species shared 29 compounds enabling us to establish a specific chemical footprint for Mediterranean marine plants, including compounds like benzaldehyde, benzeneacetaldehyde, 8-heptadecene, heneicosane, heptadecane, nonadecane, octadecane, pentadecane, tetradecane, and tridecanal. PLS-DA and Heatmap show that the four species presented significantly different chemical profiles. The major compounds per species in relative abundance were isopropyl myristate for C. nodosa (25.6%), DMS for P. oceanica (39.3%), pentadecane for Z. marina (42.9%), and heptadecane for Z. noltei (46%). Conclusions: These results highlight the potential of BVOCs’ emission from seagrass ecosystems and reveal species-specific chemical markers. Full article

The urban landscape of Kinshasa, Democratic Republic of Congo, faces significant mobility challenges, primarily stemming from rapid urbanization, overpopulation, and outdated infrastructure. These challenges necessitate the exploration of modern smart mobility concepts to improve traffic flow, road safety, and sustainability. This study investigates the potential of solutions such as Mobility-as-a-Service, car sharing, micro-mobility, Vehicle-as-a-Service, and electric vehicles in addressing these challenges. Through a comparative analysis of global implementations, this research identifies key success factors and barriers that inform the feasibility of integrating these solutions into Kinshasa’s unique socio-political and infrastructural context. The study presents a conceptual framework, supported by stakeholder analysis, for adapting these solutions locally. A detailed feasibility analysis considers technological, economic, social, environmental, and regulatory factors, offering a clear roadmap for implementation. Drawing on lessons from cities facing similar urban mobility challenges, the paper concludes with actionable recommendations and insights for policymakers and urban planners in Kinshasa. This research not only highlights the viability of smart mobility solutions in Kinshasa but also contributes to the broader discourse on sustainable urban development in rapidly growing cities. While smart mobility studies have largely focused on cities with developed infrastructure, there is a gap in understanding how these solutions apply to cities like Kinshasa with different infrastructural and socio-political contexts. Previous research has often overlooked the challenges of integrating smart mobility in rapidly urbanizing cities with underdeveloped transportation systems and financial constraints. This study fills that gap by offering a feasibility analysis tailored to Kinshasa, assessing smart mobility solutions for its traffic congestion and road safety issues. The smart mobility solutions studied—Mobility-as-a-Service (MaaS), car sharing, electric vehicles (EVs), and micro-mobility—were chosen for their ability to address Kinshasa’s key mobility challenges. MaaS reduces reliance on private vehicles, easing congestion and improving public transport. Car sharing offers affordable alternatives to vehicle ownership, essential in a city with income inequality. EVs align with sustainability goals by reducing emissions, while micro-mobility (bikes and e-scooters) improves last-mile connectivity, addressing public transit gaps. These solutions are adaptable to Kinshasa’s context and offer scalable, sustainable improvements for urban mobility. Full article

The development of green production types such as personalized production and shared manufacturing, which use additive technologies in city multifloor manufacturing clusters (CMFMCs), has led to an increase in last-mile parcel delivery (LMPD) activity. This study investigates the integration of electric vehicles and crowdshipping systems into smart CMMCs to improve urban logistics operations related to the distribution of products to consumers. The aim of this study is to improve the LMPD performance of these integrated systems and to provide alternative solutions for sustainable city logistics using the potential of crowdshipping and vehicle sharing fleets (VSFs) in the city logistics nodes (CLNs) of CMFMCs. The issues presented by the loading–unloading operations and sustainable crowdshipping scenarios for LMPD in CMFMCs are considered. This paper presents a new performance evaluation model for crowdshipping LMPD in CMFMCs using VSFs. The case study shows that the proposed model enables the analysis of LMPD performance in CMFMCs, taking into account their finite production capacity, and that it facilitates the planning of cargo turnover and the structure of VSFs consisting of e-bicycles, e-cars, and e-light commercial vehicles (e-LCVs). The model is verified based on a case study for sustainable LMPD scenarios using VSFs. The proposed model enables the planning of both short- and long-term logistics operations with the specified performance indicator of VSF usage in CMFMCs. The validity of using the integrated potential of crowdshipping and vehicle sharing services for LMPD under demand uncertainty in CMFMCs is discussed. This study should prove useful for decision-making and planning processes related to LMPD in CMFMCs and large cities. Full article

This research presents a system dynamics model to study the interaction among demand and supply evolutions, government regulations, sustainable adoption trends, investments in different decarbonization technologies, and environmental requirements for the European Aluminum Rolled Product Supply Chain (ARPSC). It allows stakeholders to assess the quantitative impact of investing in decarbonization technologies on supply chain sustainability. Investing in decarbonization technologies reduces greenhouse gas (GHG) emissions. The most substantial GHG emission reductions can be achieved if upstream ARPSC actors invest according to an aggressive investment strategy between 2031 and 2040. However, even with an aggressive investment strategy, investing in decarbonization technologies alone is likely to be insufficient to achieve the European Green Deal goals. Furthermore, barriers to investment in decarbonization technologies and a low rate of progress in doubling the European Union’s circularity rate may put extra stress on achieving the European Green Deal goals for the European ARPSC. Instead, ARPSC actors will additionally need to optimize the recycling of aluminum rolled products and adopt strategies for resource sufficiency, e.g., by sharing cars and using packaging multiple times. Full article

Any sharing mobility service aims to make urban mobility sustainable to help reduce environmental impacts and improve the quality of life for all in cities. Many transport services are not currently self-sustainable. The Life for Silver Coast (LifeSC) opened its mobility services on 22 May 2021 and offered electric mobility services during the summer for a few cities in Tuscany. E-bikes and e-scooters can be financially neutral, and even profitable, thanks to the low costs of the vehicles, but they only see a high utilization rate in winter. Shared electric cars, meanwhile, are not profitable. A new shared service that is viable must be profitable to become widely adopted and significantly contribute to sustainability. A few key characteristics have been identified, and one has been tested with a new business model that combines ride-sharing and car-sharing. The innovative Ride Sharing Algorithm (RSA) has been tested based on data from a potential city, Monterondo, where many commuters travel daily to Rome by train. The Italian census and local survey data allowed for the simulation of the scheduling of vehicle rides and an evaluation of the economic results, which could be positive if enough interest for such a system exists among the people, as at least 400 commuters from Monterotondo go to the train station daily in the morning and return in the afternoon. Such a transport demand would justify a new commercial sharing service by using the model tested with the RSA algorithm. Full article