Search Results (1,098)

The transportation sector is a major consumer of primary energy and is a significant contributor to greenhouse gas emissions. Sustainable transportation requires identifying and quantifying factors influencing transport-related CO₂ emissions. This research aims to establish an adaptable, precise, and transparent forecasting structure for transport CO₂ emissions of the United States. For this reason, we proposed a multi-stage method that incorporates explainable Machine Learning (ML) and Feature Selection (FS), guaranteeing interpretability in comparison to conventional black-box models. Due to high multicollinearity among 24 initial variables, hierarchical feature clustering and multi-step FS were applied, resulting in five key predictors: Total Primary Energy Imports (TPEI), Total Fossil Fuels Consumed (FFT), Annual Vehicle Miles Traveled (AVMT), Air Passengers-Domestic and International (APDI), and Unemployment Rate (UR). Four ML methods—Support Vector Regression, eXtreme Gradient Boosting, ElasticNet, and Multilayer Perceptron—were employed, with ElasticNet outperforming the others with RMSE = 45.53, MAE = 30.6, and MAPE = 0.016. SHAP analysis revealed AVMT, FFT, and APDI as the top contributors to CO₂ emissions. This framework aids policymakers in making informed decisions and setting precise investments. Full article

Accurate prediction of pedestrian movements is vital for autonomous driving, smart transportation, and human–computer interactions. To effectively anticipate pedestrian behavior, it is crucial to consider the potential spatio-temporal interactions among individuals. Traditional modeling approaches often depend on absolute position encoding to discern the positional relationships between pedestrians. Unfortunately, this method overlooks relative spatio-temporal relationships and fails to simulate ongoing interactions adequately. To overcome this challenge, we present a relative spatio-temporal encoding (RSTE) strategy that proficiently captures and analyzes this essential information. Furthermore, we design a multi-spatio-temporal graph (MSTG) modeling technique aimed at modeling and characterizing spatio-temporal interaction data across several individuals over time and space, with the goal of representing the movement patterns of pedestrians accurately. Additionally, an attention-based MSTT model has been developed, which utilizes an end-to-end approach for learning the structure of the MSTG. The findings indicate that an understanding of an individual’s preceding trajectory is crucial for forecasting the subsequent movements of other individuals. Evaluations using two challenging datasets reveal that the MSTT model markedly outperforms traditional trajectory-based modeling methods in predictive performance. Full article

In the summer of 2022, central and eastern China experienced prolonged extreme high temperatures and severe drought, leading to significant economic losses. To gain a more profound understanding of this drought event and furnish a reference for forecasting similar events in the future, this study examines the circulation anomalies associated with the drought. Employing a diagnostic method focused on temperature and moisture anomalies, this study introduces a novel approach to quantify and compare the relative significance of moisture transport and warm air dynamics in contributing to the drought. This study examines the atmospheric circulation anomalies linked to the drought event and compares the relative contributions of water vapor transport and warm air activity in causing the drought, using two parameters defined in the paper. The results show the following: (1) The West Pacific Subtropical High (WPSH) was more intense than usual and extended westward, consistently controlling the Yangtze River Basin. Simultaneously, the polar vortex area was smaller and weaker, the South Asian High area was larger and stronger, and it shifted eastward. These factors collectively led to weakened water vapor transport conditions and prevailing subsiding air motions in the Yangtze River Basin, causing frequent high temperatures. (2) By defining I_q and I_t to represent the contributions of moisture and temperature to precipitation, we found that the drought event in the Yangtze River Basin was driven by both reduced moisture supplies in the lower troposphere and higher-than-normal temperatures, with temperature playing a dominant role. Full article

The transportation sector, as a major source of carbon emissions, plays a crucial role in the realization of dual carbon goals worldwide. In this study, an improved least absolute shrinkage and selection operator (LASSO) is used to identify six key factors affecting transportation carbon emissions (TCEs) in China. Aiming at the spatio-temporal characteristics of transportation carbon emissions, a CNN-BiLSTM neural network model is constructed for the first time for prediction, and an improved whale optimization algorithm (EWOA) is introduced for hyperparameter optimization, finding that the prediction model combining spatio-temporal characteristics has a more significant prediction accuracy, and scenario forecasting was carried out using the prediction model. Research indicates that over the past three decades, TCEs have demonstrated a rapid growth trend. Under the baseline, green, low-carbon, and high-carbon scenarios, peak carbon emissions are expected in 2035, 2031, 2030, and 2040. The adoption of a low-carbon scenario represents the most advantageous pathway for the sustainable progression of China’s transportation sector. Consequently, it is imperative for China to accelerate the formulation and implementation of low-carbon policies, promote the application of clean energy and facilitate the green transformation of the transportation sector. These efforts will contribute to the early realization of dual-carbon goals with a positive impact on global sustainable development. Full article

Against the backdrop of multifaceted strategies to combat climate change, understanding soil erosion’s role in carbon cycling is critical due to terrestrial carbon pool vulnerability. This study integrates bibliometric methods with visualization tools (CiteSpace, VOSviewer) to analyze 3880 Web of Science core publications (1994–2024, inclusive), constructing knowledge graphs and forecasting trends. The results show exponential publication growth, shifting from slow development (1994–2011) to rapid expansion (2012–2024), aligning with international climate policy milestones. The Chinese Academy of Sciences led productivity (519 articles), while the US demonstrated major influence (H-index 117; 52,297 citations), creating a China–US bipolar research pattern. It was also found that Dutch journals dominate this research field. A keyword analysis revealed a shift from erosion-driven carbon transport to ecosystem service assessments. Emerging hotspots include microbial community regulation, climate–erosion feedback, and model–policy integration, though developing country collaboration remains limited. Future research should prioritize isotope tracing, multiscale modeling, and studies in ecologically vulnerable regions to enhance global soil carbon management. This study provides a novel analytical framework and forward-looking perspective for the soil erosion research on soil carbon cycling, serving as an extension of climate change mitigation strategies. Full article

This study addresses critical safety challenges in sustainable urban mobility by developing a probabilistic framework for lane-change risk prediction on congested expressways. Utilizing unmanned aerial vehicle (UAV)-captured trajectory data from 784 validated lane-change events, we construct a Bayesian network model integrated with an I-CH scoring-enhanced MMHC algorithm. This approach quantifies risk probabilities while accounting for driver decision dynamics and input data uncertainties—key gaps in conventional methods like time-to-collision metrics. Validation via the Asia network paradigm demonstrates 80.5% reliability in forecasting high-risk maneuvers. Crucially, we identify two sustainability-oriented operational thresholds: (1) optimal lane-change success occurs when trailing-vehicle speeds in target lanes are maintained at 1.0–3.0 m/s (following-gap < 4.0 m) or 3.0–6.0 m/s (gap ≥ 4.0 m), and (2) insertion-angle change rates exceeding 3.0°/unit-time significantly elevate transition probability. These evidence-based parameters enable traffic management systems to proactively mitigate collision risks by 13.26% while optimizing flow continuity. By converting behavioral insights into adaptive control strategies, this research advances resilient transportation infrastructure and low-carbon mobility through congestion reduction. Full article

Urban population growth is intensifying pressure on mobility systems, with road transportation contributing to environmental and sustainability challenges. Policymakers must navigate complex uncertainties in addressing rising mobility demand while pursuing sustainability goals. Advanced technologies offer promise, but their real-world effectiveness in urban contexts remains underexplored. This meta-review comprised three complementary studies: a broad analysis of sustainable mobility with Norwegian case studies, and systematic literature reviews on digital twins and Big Data/AI applications in urban mobility, covering the period of 2019–2024. Using structured criteria, we synthesised findings from 72 relevant articles to identify major trends, limitations, and opportunities. The findings show that mobility policies often prioritise technocentric solutions that unintentionally hinder sustainability goals. Digital twins show potential for traffic simulation, urban planning, and public engagement, while machine learning techniques support traffic forecasting and multimodal integration. However, persistent challenges include data interoperability, model validation, and insufficient stakeholder engagement. We identify a hierarchy of mobility modes where public transit and active mobility outperform private vehicles in sustainability and user satisfaction. Integrating electrification and automation and sharing models with data-informed governance can enhance urban liveability. We propose actionable pathways leveraging Big Data and AI, outlining the roles of various stakeholders in advancing sustainable urban mobility futures. Full article

This study investigates the effects of key atmospheric features on mineral dust emissions and transport in the Sahara–Sahel region, focusing on the Bodélé Depression, during June 2006 and 2011. We use a combination of high-resolution atmospheric simulations (AROME model), satellite observations (MODIS), and reanalysis data (ERA5, ECMWF) to examine the roles of the low-level jet (LLJ), Saharan heat low (SHL), Intertropical Discontinuity (ITD), and African Easterly Jet (AEJ) in modulating dust activity. Our results reveal significant interannual variability in aerosol optical depth (AOD) between the two periods, with a marked decrease in June 2011 compared to June 2006. The LLJ emerges as a dominant factor in dust uplift over Bodélé, with its intensity strongly influenced by local topography, particularly the Tibesti Massif. The position and intensity of the SHL also play crucial roles, affecting the configuration of monsoon flow and Harmattan winds. Analysis of wind patterns shows a strong negative correlation between AOD and meridional wind in the Bodélé region, while zonal wind analysis emphasizes the importance of the AEJ and Tropical Easterly Jet (TEJ) in dust transport. Surprisingly, we observe no significant correlation between ITD position and AOD measurements, highlighting the complexity of dust emission processes. This study is the first to combine climatological context and case studies to demonstrate the effects of African monsoon variability on dust uplift at intra-seasonal timescales, associated with the modulation of ITD latitude position, SHL, LLJ, and AEJ. Our findings contribute to understanding the complex relationships between large-scale atmospheric features and dust dynamics in this key source region, with implications for improving dust forecasting and climate modeling efforts. Full article

This study proposes an intelligent patrol planning framework that leverages reinforcement learning, spatiotemporal crime forecasting, and simulated sensor telemetry to optimize autonomous vehicle (AV) navigation in urban environments. Crime incidents from Washington DC (2024–2025) and Seattle (2008–2024) are modeled as a dynamic spatiotemporal graph, capturing the evolving intensity and distribution of criminal activity across neighborhoods and time windows. The agent’s state space incorporates synthetic AV sensor inputs—including fuel level, visual anomaly detection, and threat signals—to reflect real-world operational constraints. We evaluate and compare three learning strategies: Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Proximal Policy Optimization (PPO). Experimental results show that DDQN outperforms DQN in convergence speed and reward accumulation, while PPO demonstrates greater adaptability in sensor-rich, high-noise conditions. Real-map simulations and hourly risk heatmaps validate the effectiveness of our approach, highlighting its potential to inform scalable, data-driven patrol strategies in next-generation smart cities. Full article

This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle acquisition, operation, charging, maintenance, and environmental impact costs during the lifecycle of the buses. The results show that, despite the higher initial investment in electric buses, these vehicles offer savings, especially when coupled with significantly reduced emissions of pollutants, which decreases indirect costs. However, local contexts differ, leading to a need to revise whether or not a municipality can finance the procurement and operations of such a fleet. The paper utilizes a robust methodological framework, integrating a proposal based on real-world data and demand and combining it with predictive analytics to forecast long-term benefits. The findings of the paper support the introduction of buses as a sustainable solution for Jastrebarsko, which provides insights for public transport planners, urban planners, and policymakers, with a discussion about the specific issues regarding the introduction, procurement, and operations of buses of different propulsion in a low-density area. Full article

To support carbon peaking and neutrality goals in the city transportation sector, this paper accounts for CO₂ emissions from the transport sector in Xuzhou City, North Jiangsu Province, from 1995 to 2023. This study explores the relationship between transport-related carbon emissions and economic growth, using the TAPIO decoupling index. Meanwhile, a carbon emission prediction model based on the STIRPAT framework is constructed, with scenario analysis applied to forecast future emissions. Results show three decoupling stages: the first, dominated by weak and expansive negative decoupling, reflects extensive economic growth; the second features weak decoupling with expansive coupling, indicating a more environmentally coordinated phase; the third transitions from expansive negative decoupling and weak decoupling to strong decoupling and expansive coupling, suggesting a shift in development patterns. Under the baseline, low-carbon, and enhanced low-carbon scenarios, by 2030, the CO₂ emissions of the transportation industry in Xuzhou will be 10,154,700 tons, 9,072,500 tons, and 8,835,000 tons, respectively, and the CO₂ emissions under the low-carbon scenario and the enhanced low-carbon scenario will be reduced by 10.66% and 13.00%, respectively. Based on these findings, the study proposes carbon reduction strategies for Xuzhou’s transport sector, focusing on policy regulation, energy use, and structural adjustments. Full article

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article

Simulations are conducted using a microscale model framework to quantify differences in projected Annual Energy Production (AEP), Capacity Factor (CF) and wake losses for large offshore wind farms that arise due to different input datasets, installed capacity density (ICD) and/or wake parameterizations. Differences in CF (and AEP) and wake losses that arise due to the selection of the wake parameterization have the same magnitude as varying the ICD within the likely range of 2–9 MW km⁻². CF simulated with most wake parameterizations have a near-linear relationship with ICD in this range, and the slope of the dependency on ICD is similar to that in mesoscale simulations with the Weather Research and Forecasting (WRF) model. Microscale simulations show that remotely generated wakes can double AEP losses in individual lease areas (LA) within a large LA cluster. Finally, simulations with the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) model are shown to differ in terms of wake-induced AEP reduction from those with the WRF model by up to 5%, but this difference is smaller than differences in CF caused by the wind farm parameterization used in the mesoscale modeling. Enhanced evaluation of mesoscale and microscale wake parameterizations against observations of climatological representative AEP and time-varying power production from wind farm Supervisory Control and Data Acquisition (SCADA) data remains critical to improving the accuracy of predictive AEP modeling for large offshore wind farms. Full article

Highway accessibility plays a vital role in supporting local economic development, particularly in regions lacking access to sea or river ports. Recognizing the functional transformation of road infrastructure, the Chinese government has made substantial investments in its expansion. Nevertheless, a theoretical gap remains in justifying whether such investments yield significant economic returns. Drawing on the theory of economic externalities, this study investigates the causal relationship between highway development and regional economic growth, and assesses whether highway construction leads to an acceleration in growth rates. Utilizing panel data from 14 Chinese cities spanning 2000 to 2014, the synthetic control method (SCM) is employed to evaluate the economic externalities of highway investment. The results indicate a positive impact on surrounding industries. Furthermore, a growth rate forecasting analysis based on Back-Propagation Neural Networks (BPNNs) is conducted using industrial enterprise data from 2005 to 2014. The growth rate in the treated city is 1.144%, which is close to the real number 1.117%, higher than the number for the weighted control group, which is 1.000%. The findings suggest that the growth rate of total industrial output improved significantly, confirming the existence of positive spillover effects. This not only enriches the empirical literature on transport infrastructure but also provides targeted enlightenment for the sustainable development of urban economy in terms of policy guidance. Full article

Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational data, how energy consumption and charging behavior affect battery aging and how operational strategies can be optimized to extend battery life under realistic conditions. This article presents an energy consumption analysis with respect to ambient temperatures and average vehicle speed based exclusively on real-world data of an urban bus fleet, providing a data foundation for range forecasting and infrastructure planning optimized for public transport needs. Additionally, the State of Charge (SOC) window during operation and vehicle idle time as well as the charging power were analyzed in this case study to formulate recommendations towards a more battery-friendly treatment. The central research question is whether battery-friendly operational strategies—such as reduced charging power and lower SOC windows—can realistically be implemented in daily public transport operations. The impact of the recommendations on battery lifetime is estimated using a battery aging model on drive cycles. Finally, the reduction in CO₂ emissions compared to diesel buses is estimated. Full article