Search Results (392)

This research simulates Aswan City’s urban transportation dynamics utilizing the Multi-Agent Transport Simulation (MATSim) framework. As a fast-expanding urban center, Aswan has many transportation difficulties that require extensive modeling toward sustainable mobility solutions. MATSim, recognized for its agent-based methodology, offers a detailed portrayal and analysis of individual travel behaviors and their interactions within the metropolitan transportation system. This study compiled and combined many databases, including demographic data, road infrastructure, public transit plans, and travel demand trends. These data are altered to produce a realistic digital clone of Aswan’s transportation system. Simulated scenarios analyze the consequences of several actions, such as increased public transit scheduling, traffic flow management, and the adoption of alternative transport modes, on minimizing congestion and boosting accessibility. Pilot findings show that MATSim effectively captures the distinct features of Aswan’s transportation network and offers practical insights for decision-makers. The results identified some opportunities to improve mobility and promote sustainable urban growth in developing cities. This study emphasized the importance of agent-based simulations in designing future transportation systems and urban infrastructure. Full article

Higher education institutions face a critical methodological challenge in pursuing net-zero commitments: Within the amount ofhe emissions related to Scope 3, including indirect emissions from water consumption, waste disposal, business travel, and mobility, employees commuting represents 50–92% of campus carbon footprints, yet reliable quantification remains elusive due to fragmented data collection and governance silos. The present research investigates how purposeful integration of the Home-to-Work Commuting Plan (HtWCP)—mandatory under Italian Decree 179/2021—into the Climate Neutrality Plan (CNP) could constitute an innovative strategy to enhance emissions accounting rigor while strengthening institutional governance. Stemming from the University of Genoa case study, we show how leveraging mandatory HtWCP survey infrastructure to collect granular mobility behavioral data (transportation mode, commuting distance, and travel frequency) directly addresses the GHG Protocol-specified distance-based methodology for Scope 3 accounting. In turn, the CNP could support the HtWCP in framing mobility actions into a wider long-term perspective, as well as suggesting a compensation mechanism and paradigm for mobility actions that are currently not included. We therefore establish a replicable model that simultaneously advances three institutional dimensions, through the operationalization of the Avoid–Shift–Improve framework within an integrated workflow: (1) methodological rigor—replacing proxy methodologies with actual behavioral data to eliminate the notorious Scope 3 data gap; (2) governance coherence—aligning voluntary and regulatory instruments to reduce fragmentation and enhance cross-functional collaboration; and (3) adaptive management—embedding biennial feedback cycles that enable continuous validation and iterative refinement of emissions reduction strategies. This framework positions universities as institutional innovators capable of modeling integrated governance approaches with potential transferability to municipal, corporate, and public administration contexts. The findings contribute novel evidence to scholarly literature on institutional sustainability, policy integration, and climate governance, whilst establishing methodological standards relevant to international harmonization efforts in carbon accounting. Full article

Against the backdrop of rising global carbon emissions, promoting active transportation modes such as walking and cycling has become a key strategy for countries worldwide to meet carbon reduction targets and advance the goals of sustainable development. In China, the concept of low-carbon mobility has gained rapid traction, leading to a significant increase in public demand for non-motorized travel options like walking and cycling. From the perspective of inclusive urban development, gender imbalances in sample representation during design and evaluation processes have contributed to homogenization and a lack of diversity in urban slow-traffic environments. To address this issue, this study adopts a problem-oriented approach. First, we collect street scene images of slow-traffic environments through self-conducted field surveys. Concurrently, we gather satisfaction survey responses from 511 urban residents regarding existing slow-traffic streets, identifying three key environmental evaluation indicators: safety, liveliness, and beauty. Second, an experimental analysis is conducted to compare machine-generated assessments based on self-collected street view data with manual evaluations performed by 27 female participants. The findings reveal significant perceptual differences between genders in the assessment of slow-moving environments, particularly regarding attention to environmental elements, challenges in utilizing non-motorized lanes, and overall environmental satisfaction. Moreover, notable discrepancies are observed between machine scores and manual assessments performed by women. Based on these findings, this study investigates the underlying causes of such perceptual disparities and the mechanisms influencing them. Finally, it proposes female-inclusive strategies aimed at enhancing the quality of slow-traffic environments, thereby addressing the current absence of gender considerations in their design. This research seeks to provide a robust female perspective and empirical evidence to support improvements in the quality of slow-moving environments and to inform strategic advancements in their design. The findings of this study can provide a theoretical and empirical basis for the optimization of gender-inclusive non-motorized transportation environment design, policy formulation, and subsequent interdisciplinary research. Full article

Iron powder represents a promising carbon-free, sustainable fuel, yet its practical utilisation in combustion has not yet been realised. Achieving stable, efficient iron-only flames is challenging, and the environmental impact of hybrid iron-hydrocarbon combustion, including particle emissions, is not fully understood. This study investigates hybrid methane–iron powder flames to assess iron’s role in modifying gas and particle phase emissions and its potential as a sustainable energy carrier. The combustion of iron was investigated at both the single particle and powder flow scales. Experimental diagnostics combined high-speed and microscopic imaging, ex situ particle sizing, in situ gas analysis, and aerosol measurements using an Aerodynamic Particle Sizer (APS™) and a Scanning Mobility Particle Sizer (SMPS™). For single particle combustion, high-speed imaging revealed rapid particle heating, oxide shell growth, cavity formation, micro-explosions, and nanoparticle release. For powder combustion, at 0.5 g/min and 1.26 g/min, the experiment yielded oxidation fractions of 15.15% and 23.43%, respectively, and increased CO₂ emissions by 0.22–0.35 vol% relative to methane–air flames, while NOx changes were negligible. Aerosol analysis showed a supermicron mode at ~2 µm and submicron ultrafine particles of 89% <100 nm with a modal diameter of ~56 nm. The observed ultrafine particle emissions highlight the need to evaluate health, material-loss, and fuel-recycling implications. Burner optimisation or premixed strategies could reduce CO₂ emissions while enhancing iron oxidation efficiency. Full article

This study introduces the Sustainable Urban Mobility Maturity Model (SUM-MM) to assess and enhance the maturity of sustainable urban mobility in cities. The SUM-MM comprises 3 main dimensions (enablers, sustainability, and transport modes) and 11 sub-dimensions (strategic and spatial planning, organization and human resources, information and communication technologies, environment, economy, social, walking, micromobility, public transport, paratransit systems, and multimodal integration), evaluated at 5 levels (beginner, initial, integrated, managed, and mature). Developed through a literature review and validated using a questionnaire-based expert opinion method, the model was tested in Konya, Türkiye. The results show that Konya’s overall maturity falls between integrated and managed, with significant variability across sub-dimensions. The enablers dimension demonstrated the highest maturity, driven by strong organizational and technological capabilities, whereas the transport modes dimension had the lowest—particularly in paratransit systems. The SUM-MM serves as both a benchmarking tool and a policy guidance framework, facilitating targeted strategies for sustainable urban mobility improvements. Unlike existing smart city or transport maturity models, the SUM-MM specifically focuses on sustainable urban mobility, offering a structured, operational, and decision-oriented framework for policy-makers and city administrations. The results can be used by local and national authorities to support comparative benchmarking, strategic planning, and the prioritization of sustainable urban mobility investments. Full article

Efficient metadata and page management are essential for sustaining database performance on modern flash-based storage. However, conventional SQLite configurations—rollback journal and WAL—often trigger excessive small writes and frequent synchronization events, leading to high write amplification and degraded tail latency, particularly on UFS and NVMe devices. This study introduces ALEX (Adaptive Log-Embedded Extent Layer), a lightweight VFS-level extension that coalesces scattered 4 KB page updates into sequential, page-aligned extents while embedding compact log records for recovery. The proposed design reduces redundant writes through in-memory page deduplication, minimizes fdatasync()frequency by flushing multi-page extents, and preserves full SQLite compatibility. We evaluate ALEX on both Linux NVMe SSDs and Android UFS storage under controlled workloads. Results show that ALEX significantly lowers write amplification, reduces sync counts, and improves p95–p99 write latency compared with baseline SQLite modes. The approach consistently achieves near-sequential write patterns without modifying SQLite internals. These findings demonstrate that lightweight extent-based coalescing can provide substantial efficiency gains for embedded and mobile database systems, offering a practical direction for enhancing SQLite performance on flash devices. Full article

In medium-sized cities, daily travel often follows routine patterns, which may lead to suboptimal route choices. This study examines such trips and evaluates them to assess the influence of travel information. The research is motivated by the growing importance of sustainable urban mobility and the need to address traffic congestion, environmental concerns, and inefficient transportation choices in the city of Volos, Greece. To achieve that, a survey of two phases was performed. First, self-reported and GPS data of an examined group of 96 participants from the University of Thessaly, Volos, Greece, were collected. The data were used to evaluate the daily trips in terms of travel time, cost, and environmental friendliness. Second, a stated preference survey was designed, targeting motorized vehicle users of the examined group. The survey investigated the extent to which shared information on social media can be used to recommend a different route than the usual one or convince them to shift to a sustainable way of transportation. The analysis shows that travelers are more inclined to accept the recommended route after receiving travel information; however, this effect does not translate into choosing a sustainable mode of transport. We also found that women are more likely to change routes than men. Full article

The increasing demand for active and light mobility (including bicycles, e-bikes and e-scooters) has become a key driver of sustainable urban transport, calling for a renewed approach to urban planning. A central challenge is redesigning infrastructure around users’ needs, inspired by the “15-min city” concept developed by Carlos Moreno. However, the existing literature on user preferences in this domain remains fragmented, both methodologically and thematically, and often lacks integration of user behaviour analysis. This paper presents a structured review of recent international studies on factors influencing route and infrastructure choices in active and light mobility. The findings are organized into an analytical framework based on five macro-criteria: external and infrastructural factors, transport mode, user typology, experimental methodology and infrastructure attributes. The synthesis tables aim to summarize the findings to guide planners, researchers and decision-makers towards more inclusive, adaptable and effective mobility systems, through the development of user-oriented planning tools, attractiveness indexes and strategies for cycling and micromobility networks. Moreover, the review contributes to an ongoing national research initiative and lays the groundwork for developing decision-making tools, attractiveness indexes and route recommendation systems. Full article

The transition to environmentally friendly mobility inevitably requires users to use sustainable modes of transport. Rapid urbanization, along with the growing demand for efficient, inclusive, and ecological transport systems, has highlighted the urgent need for research and analysis into the acceptability and experiences of transitioning to sustainable modes of transport. This article proposes a six-step procedure to support the selection of vehicles for car-sharing fleets in cities. The analysis utilizes the Analytic Hierarchy Process method, which allows for the comparison and evaluation of five vehicle variants with different powertrains, taking into account various evaluation criteria: ecological and economic. To refine the research, criterion weights were determined based on original surveys among six car-sharing operators and eighty-seven experts in the field of decarbonization of urban transport. The results indicated that plug-in hybrid vehicles are the most advantageous option for car-sharing fleets, providing a balance between emissions, cost-effectiveness and operational flexibility. The solution obtained is in line with expectations, confirming that the proposed analytical approach is a reliable decision support tool that reduces the risk of making the wrong decision regarding the choice of powertrains. Full article

The willingness to adopt Autonomous Vehicles (AVs) represents a crucial advancement from the sustainable mobility perspective. This is progressively continuing in the developed countries. A comparable shift is expected in developing nations; however, empirical studies remain limited, especially in areas where AVs have not yet been deployed. This study investigates the willingness to adopt AVs in a developing city where AVs have not been deployed yet. A comprehensive travel behavior questionnaire was conducted among local commuters in Alexandria, Egypt, to identify the influential variables affecting AV choice. The well-known machine learning classifier, Extreme Gradient Boosting (XGB), was employed to develop a forecasting model, which indicated a notable accuracy. The results indicated that trip cost was the most influential feature. On the other hand, there is a considerable level of mode captivity, since most travelers prefer to remain with their current mode, regardless of the effects of other variables. A significant share of travelers expressed concerns about shifting to AVs due to safety worries associated with the travel behavior of other transportation modes’ commuters. The analysis provides nuanced perspectives on the variables promoting modal shift toward the AVs, supporting future policies for smart urban mobility. Full article

In the digital era, rapid urban growth and the demand for sustainable mobility are placing increasing pressure on transport systems, where congestion, energy consumption, and schedule variability complicate intermodal journey planning. This work proposes an AI-enhanced transfer-graph framework that models each transport mode as an independent subnetwork connected through explicit transfer arcs. This modular structure captures modal interactions while reducing graph complexity, enabling algorithms to operate more efficiently in time-dependent contexts. A Deep Q-Network (DQN) agent is further introduced as an exploratory alternative to exact and meta-heuristic methods for learning adaptive routing strategies. Exact (Dijkstra) and meta-heuristic (ACO, DFS, GA) algorithms were evaluated on synthetic networks reflecting Casablanca’s intermodal structure, achieving coherent routing with favorable computation and memory performance. The results demonstrate the potential of combining transfer-graph decomposition with learning-based components to support scalable intermodal routing. Full article

The integration of electric buses into urban transportation networks is a priority for policymakers aiming to promote sustainable public mobility. Among available technologies, electric Bus Rapid Transit (eBRT) systems offer an environmentally friendly and operationally effective alternative to conventional modes. This study introduces a Machine Learning Decision Support Framework designed to assess the feasibility of deploying eBRT systems in urban environments. Using a dataset of 28 routes in the Athens Metropolitan Area, the framework integrates diverse variables such as land use, population coverage, proximity to public transport, points of interest, road characteristics, and safety indicators. The XGBoost model demonstrated strong predictive performance, outperforming traditional approaches and highlighting the significance of points of interest, land use diversity, green spaces, and roadway infrastructure in forecasting travel times. Overall, the proposed framework provides urban planners and policymakers with a robust, data-driven tool for evaluating the practical and environmental viability of eBRT systems. Full article

Public transit demand forecasting is a foundational component of sustainable urban mobility, enabling efficient operation, equitable service provision, and planning of public transit systems. Urban imagery, such as aerial images, contains rich information about urban sociodemographic characteristics and the built environment, offering particular value for data-scarce regions where conventional datasets are limited or outdated. However, there is limited research on using these images for public transit demand forecasting. This study introduces a deep learning approach for predicting transit ridership using aerial images. The method employs an encoder–decoder architecture to functionally separate image-derived latent representations into sociodemographic and physical environment vectors, which are subsequently used as inputs to a neural network for ridership prediction. Using data from Seoul, South Korea, the effectiveness of the proposed method is evaluated against three baseline configurations. The results show that the sociodemographic latent vector captures spatially organized residential characteristics, while the physical environment vector encodes distinct urban landscape patterns such as dense housing, traditional street grids, open spaces, and natural environments. The proposed model, which uses only imagery-derived latent features, substantially outperforms the pure image baseline and narrows the performance gap with census-informed models, reducing sMAPE by 25–60% depending on the mode. Combining imagery with census variables yields the highest accuracy, confirming their complementary nature. These findings highlight the potential of imagery-based approaches as a scalable, cost-efficient, and sustainable tool for data-driven transit planning. Full article

This paper presents a methodological framework for inferring energy-related operating states of plug-in hybrid electric vehicles (PHEVs) under conditions of limited and incomplete on-board diagnostic (OBD) data. The proposed approach is illustrated using a single short real-world urban trip recorded for one PHEV operating in electric mode. Unsupervised clustering based on k-means is applied in progressively expanded state spaces (3D–5D) to decompose the driving process into physically interpretable operating states, despite the absence of direct measurements of key variables such as regenerative braking power. Cluster validity indices, per-cluster silhouette values, temporal segmentation, and robustness checks are employed to support the interpretability and internal consistency of the results. The study demonstrates that even a single, non-representative OBD time series contains sufficient internal structure to recover meaningful energy-related information when appropriate state-space decomposition is applied. While no statistical generalization is intended, the results highlight the potential of the proposed framework for analyzing real-world vehicle operation under constrained data availability. Full article

Delays caused by motor vehicle traffic, accidents, and environmental pollution present considerable challenges to sustainable urban mobility. To address these issues, transportation system users are encouraged to adopt active transportation methods, micromobility options, and public transit. Electric scooters have become a notably popular micromobility choice, especially following the emergence of vehicle-sharing companies in 2018, a trend that gained further momentum during the COVID-19 pandemic. This study explored the demographic characteristics, attitudes, and behaviors of e-scooter users in Istanbul through an online survey conducted from 1 September 2023 to 1 May 2024. A total of 462 e-scooter users participated, providing valuable insights into their preferred modes of transportation across 24 different scenarios specifically designed for this research. The responses were analyzed using various machine learning techniques, including Artificial Neural Networks, Decision Trees, Random Forest, and Gradient Boosting methods. Among the models developed, the Decision Tree model exhibited the highest overall performance, demonstrating strong accuracy and predictive capabilities across all classifications. Notably, all models significantly surpassed the accuracy of discrete choice models reported in existing literature, underscoring the effectiveness of machine learning approaches in modeling transportation mode choices. The models created in this study can serve various purposes for researchers, central and local authorities, as well as e-scooter service providers, supporting their strategic and operational decision-making processes. Future research could explore different machine learning methodologies to create a model that more accurately reflects individual preferences across diverse urban environments. These models can assist in developing sustainable mobility policies and reducing the environmental footprint of urban transportation systems. Full article