Search Results (169)

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

The growth of urban and industrial areas is accompanied by an increase in vehicle traffic, resulting in rising concentrations of various air pollutants. This is a global issue that causes environmental damage and risks to human health. The dataset presented in this research contains records with measurements of the air pollutants ozone (O₃) and carbon monoxide (CO), as well as meteorological parameters such as temperature (T), relative humidity (RH), and barometric pressure (BP). This dataset was collected using a set of low-cost sensors over a four-month study period (March to June) in 2024. The monitoring of air pollutants and meteorological parameters was conducted in a city with high industrial activity, heavy traffic, and close proximity to a petrochemical refinery plant. The data were subjected to a series of statistical analyses for visualization using plots that allow for the identification of their behavior. Finally, the dataset can be utilized for air quality studies, public health research, and the development of prediction models based on mathematical approaches or artificial intelligence algorithms. Full article

In the era of “carbon dioxide peaking and carbon neutrality”, low-carbon development of road traffic and transportation has now become a rigid demand in China. Considering the fact that socioeconomic and demographic characteristics vary significantly across Chinese cities, proper city-level transportation development strategies should be established. Using detailed data from cities at prefecture level and above in China, this study investigates the spatially heterogeneous effects of various factors on road traffic CO₂ emissions. Another theoretical issue is concerned with the analytic method for zonal CO₂ emission modeling. We combine the concepts of geographically weighted regression (GWR) and machine learning for nonparametric regression, proposing a modified random forest (RF) algorithm, named “geographically weighted random forest” (GWRF). Our empirical analysis indicates that, when an appropriate weight parameter is applied, GWRF is able to achieve significantly superior performance compared to both the traditional RF and GWR methods. Moreover, the influences of various explanatory variables on CO₂ emissions differ across cities. These findings suggest that low-carbon transportation strategies should be customized to reflect regional heterogeneity, rather than relying on a unified national policy. Full article

This study investigates the heavy metal contamination in urban and peri-urban soils of Thessaloniki, Greece, over a two-year period (2023–2024). A total of 208 composite soil samples were systematically collected from 52 sites representing diverse land uses, including high-traffic roadsides, industrial zones, residential neighborhoods, parks, and mixed-use areas, with sampling conducted both after the wet (winter) and dry (summer) seasons. Soil physicochemical properties (pH, electrical conductivity, texture, organic matter, and calcium carbonate content) were analyzed alongside the concentrations of heavy metals such as Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn. A pollution assessment employed the Geoaccumulation Index (I_geo), Contamination Factor (Cf), Pollution Load Index (PLI), and Potential Ecological Risk Index (RI), revealing variable contamination levels across the city, with certain hotspots exhibiting a considerable to very high ecological risk. Multivariate statistical analyses (PCA and HCA) identified distinct anthropogenic and geogenic sources of heavy metals. Health risk assessments, based on USEPA models, evaluated non-carcinogenic and carcinogenic risks for both adults and children via ingestion and dermal contact pathways. The results indicate that while most sites present low to moderate health risks, specific locations, particularly near major transport and industrial areas, pose elevated risks, especially for children. The findings underscore the need for targeted monitoring and remediation strategies to mitigate the ecological and human health risks associated with urban soil pollution in Thessaloniki. Full article

The occurrence of haze pollution significantly deteriorates air quality and threatens human health, yet persistent knowledge gaps in real-time source apportionment of fine particulate matter (PM_2.5) hinder sustained improvements in atmospheric pollution conditions. Thus, this study employed single-particle aerosol mass spectrometry (SPAMS) to investigate PM_2.5 sources and dynamics during winter haze episodes in Yinchuan, Northwest China. Results showed that the average PM_2.5 concentration was 57 μg·m⁻³, peaking at 218 μg·m⁻³. PM_2.5 was dominated by organic carbon (OC, 17.3%), mixed carbonaceous particles (ECOC, 17.0%), and elemental carbon (EC, 14.3%). The primary sources were coal combustion (26.4%), fugitive dust (25.8%), and vehicle emissions (19.1%). Residential coal burning dominated coal emissions (80.9%), highlighting inefficient decentralized heating. Source contributions showed distinct diurnal patterns: coal combustion peaked nocturnally (29.3% at 09:00) due to heating and inversions, fugitive dust rose at night (28.6% at 19:00) from construction and low winds, and vehicle emissions aligned with traffic (17.5% at 07:00). Haze episodes were driven by synergistic increases in local coal (+4.0%), dust (+2.7%), and vehicle (+2.1%) emissions, compounded by regional transport (10.1–36.7%) of aged particles from northwestern zones. Fugitive dust correlated with sulfur dioxide (SO₂) and ozone (O₃) (p < 0.01), suggesting roles as carriers and reactive interfaces. Findings confirm local emission dominance with spatiotemporal heterogeneity and regional transport influence. SPAMS effectively resolved short-term pollution dynamics, providing critical insights for targeted air quality management in arid regions. Full article

The road construction sector urgently requires environmentally friendly, low-carbon, and high-performance base materials. Traditional materials exhibit issues of high energy consumption and carbon emissions, making it difficult for them to align with sustainable development requirements. While slag- and fly ash-based geopolymers demonstrate promising application potential in civil engineering, research on their application in road-stabilized soils remains insufficient. To address the high energy consumption and carbon emissions associated with conventional road base materials and to fill this research gap, this study investigated the utilization of industrial solid wastes through slag-based geopolymer and fly ash as stabilizers, systematically evaluating the pavement performance of two distinct soil types. Unconfined compressive strength tests and freeze–thaw cycling tests were conducted to elucidate the effects of stabilizer dosage, fly ash co-stabilization, and compaction degree on mechanical properties. The results demonstrated that the compressive strength of both stabilized soils increased significantly with higher slag-based geopolymer content, achieving peak values of 5.2 MPa (soil sample 1) and 4.5 MPa (soil sample 2), representing a 30% improvement over cement-stabilized soils with identical mix proportions. Fly ash co-stabilization exhibited more pronounced reinforcement effects on soil sample 2. At a 98% compaction degree, soil sample 1 maintained a stable 50% strength enhancement, whereas soil sample 2 displayed a dose-dependent exponential strength increase. Freeze–thaw resistance tests revealed the superior performance of soil sample 1, showing a loss of compressive strength (BDR) of 78% with 8% geopolymer stabilization alone, which improved to 90% after fly ash co-stabilization. For soil sample 2, the BDR increased from 64% to 80% through composite stabilization. This study confirms that slag/fly ash-based geopolymer-stabilized soils not only meet the strength requirements for heavy-traffic subbases and light-traffic base courses, but also demonstrates its great potential as a low-carbon and environmentally friendly material to replace traditional road base materials. Full article

Mitigating traffic-related carbon emissions is pivotal for achieving carbon peaking targets and advancing sustainable urban development. This study employs spatial autocorrelation and high-low clustering analyses to analyze the spatial correlation and clustering patterns of urban road traffic carbon emissions in Shaanxi Province. The spatiotemporal evolution and structural impacts of emissions are quantified through a systematic framework, while the GTWR (Geographically Weighted Temporal Regression) model uncovers the multidimensional and heterogeneous driving mechanisms underlying carbon emissions. Findings reveal that road traffic CO₂ emissions in Shaanxi exhibit an upward trajectory, with a temporal evolution marked by distinct phases: “stable growth—rapid increase—gradual decline”. Emission dynamics vary significantly across transport modes: private vehicles emerge as the primary emission source, taxi/motorcycle emissions remain relatively stable, and bus/electric vehicle emissions persist at low levels. Spatially, the province demonstrates a pronounced high-carbon spillover effect, with persistent high-value clusters concentrated in central Shaanxi and the northern region of Yan’an City, exhibiting spillover effects on adjacent urban areas. Notably, the spatial distribution of CO₂ emissions has evolved significantly: a relatively balanced pattern across cities in 2010 transitioned to a pronounced “M”-shaped gradient along the north–south axis by 2015, stabilizing by 2020. The central urban cluster (Yan’an, Tongchuan, Xianyang, Baoji) initially formed a secondary low-carbon core, which later integrated into the regional emission gradient. By focusing on the micro-level dynamics of urban road traffic and its internal structural complexities—while incorporating built environment factors such as network layout, travel behavior, and infrastructure endowments—this study contributes novel insights to the transportation carbon emission literature, offering a robust framework for regional emission mitigation strategies. Full article

With the increasing acceptance of green, low-carbon, and sustainable development principles, the rising number of new energy vehicles (NEVs) has raised public concern over traffic safety risks associated with these vehicles. To assist traffic management authorities in efficiently allocating rescue resources, this paper proposes a severity prediction method for the new energy vehicle accidents based on Cost-sensitive Fuzzy XGBoost (CFXGBoost). First, chi-square filtering and wrapper methods are used to extract 20 key features strongly cor-related with accident severity. Then, A fuzzy neural network is employed to combine fuzzy inference results with original features, forming an extended feature set. Moreover, These features are used as inputs to the XGBoost model for severity prediction of the new energy vehicle traffic accidents. Finally, the proposed approach is validated using traffic accident datasets from multiple provinces and cities. Results show that the FXGBoost model achieves a prediction accuracy of 0.92 and outperforms other models in terms of precision, recall, and F1 score, demonstrating its effectiveness in accurately predicting the severity of NEV-related traffic accidents. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper presents the first real-time quantum-enhanced electricity pricing framework for large-scale EV charging networks, marking a significant departure from existing approaches based on mixed-integer programming (MILP) and deep reinforcement learning (DRL). The proposed framework incorporates renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The objective is to dynamically determine spatiotemporal electricity prices that reduce system peak load, improve renewable utilization, and minimize user charging costs. A rigorous mathematical formulation is developed, integrating over 40 system-level constraints, including power balance, transmission limits, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber-resilience. Real-time electricity prices are treated as dynamic decision variables influenced by station utilization, elasticity response curves, and the marginal cost of renewable and grid electricity. The model is solved across 96 time intervals using a quantum-classical hybrid method, with benchmark comparisons against MILP and DRL baselines. A comprehensive case study is conducted on a 500-station EV network serving 10,000 vehicles, coupled with a modified IEEE 118-bus grid and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar/wind profiles are used to simulate realistic conditions. Results show that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% gain in renewable utilization, and up to 30% user cost savings compared to flat-rate pricing. Network congestion is mitigated at over 90% of high-traffic stations. Pricing trajectories align low-price windows with high-renewable periods and off-peak hours, enabling synchronized load shifting and enhanced flexibility. Visual analytics using 3D surface plots and disaggregated bar charts confirm structured demand-price interactions and smooth, stable price evolution. These findings validate the potential of quantum-enhanced optimization for scalable, clean, and adaptive EV charging coordination in renewable-rich grid environments. Full article

In this research, we present the development and validation of a compact, resource-efficient (low-cost, low-energy), distributed, real-time traffic and air quality monitoring system. Deployed since November 2023 in a small town that relies on burning various fuels and waste for winter heating, the system comprises three IoT units that integrate image processing and environmental sensing for sustainable urban and environmental management. Each unit uses an embedded camera and sensors to process live data locally, which are then transmitted to a central database. The image processing algorithm counts vehicles by type with over 95% daylight accuracy, while air quality sensors measure pollutants including particulate matter (PM), equivalent carbon dioxide (eCO₂), and total volatile organic compounds (TVOCs). Data analysis revealed fluctuations in pollutant concentrations across monitored areas, correlating with traffic variations and enabling the identification of pollution sources and their relative impacts. Recorded PM10 daily average levels even reached eight times above the safe 24 h limits in winter, when traffic values were low, indicating a strong link to household heating. This work provides a scalable, cost-effective approach to traffic and air quality monitoring, offering actionable insights for urban planning and sustainable development. Full article

This study examines the seasonality of Black Carbon (BC) and Brown Carbon (BrC) spectral absorption characteristics at a continental background site (Kozani) in southern Balkans (NW Greece). It aims to assess the seasonality and impact of different sources on light absorption properties, BC concentrations, and the fraction of BrC absorption. Moderate-to-low BC concentrations were observed, ranging from 0.05 µg m⁻³ to 2.44 µg m⁻³ on an hourly basis (annual mean: 0.44 ± 0.27 µg m⁻³; median: 0.39 µg m⁻³) with higher levels during winter (0.53 ± 0.33), reflecting enhanced emissions from residential wood burning (RWB) for heating purposes. Atmospheric conditions are mostly clean during spring (MAM) (BC: 0.34 µg m⁻³), associated with increased rainfall. BC components associated with fossil fuel combustion (BC_ff) and biomass burning (BC_bb), maximize in summer (0.36 µg m⁻³) and winter (0.28 µg m⁻³), respectively, while the absorption Ångstrôm exponent (AAE_370–880) values ranged from 1.09 to 1.93 on daily basis. The annual mean total absorption coefficient (b_abs,520) inferred by aethalometer (AE33) was 4.09 ± 2.65 Mm⁻¹ (median: 3.51 Mm⁻¹), peaking in winter (5.30 ± 3.35 Mm⁻¹). Furthermore, the contribution of BrC absorption at 370 nm, was also high in winter (36.7%), and lower during the rest of the year (17.3–29.8%). The measuring station is located at a rural background site 4 km outside Kozani City and is not directly affected by traffic and urban heating emissions. Therefore, the regional background atmosphere is composed of a significant fraction of carbonaceous aerosols from RWB in nearby villages, a characteristic feature of the Balkan’s rural environment. Emissions from the lignin-fired power plants, still operating in the region, have decreased during the last years and moderately affect the atmospheric conditions. Full article

To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI requires first determining the sub-indices for several pollutants, including respirable suspended particulates (PM₁₀), fine suspended particulates (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), and carbon monoxide (CO). Accurate prediction of AQI is crucial in providing early warnings to the public before pollution episodes occur. To improve AQI prediction accuracy, deep learning methods such as artificial neural networks (ANNs) and long short-term memory (LSTM) models were applied to forecast the six pollutants commonly found in the AQI. The data for this study was accessed from the Macau High-Density Residential Air Quality Monitoring Station (AQMS), which is located in an area with high traffic and high population density near a 24 h land border-crossing facility connecting Zhuhai and Macau. The novelty of this work lies in its potential to enhance operational AQI forecasting for Macau. The ANN and LSTM models were run five times, with average pollutant forecasts obtained for each model. Results demonstrated that both models accurately predicted pollutant concentrations of the upcoming 24 h, with PM₁₀ and CO showing the highest predictive accuracy, reflected in high Pearson Correlation Coefficient (PCC) between 0.84 and 0.87 and Kendall’s Tau Coefficient (KTC) between 0.66 and 0.70 values and low Mean Bias (MB) between 0.06 and 0.10, Mean Fractional Bias (MFB) between 0.09 and 0.11, Root Mean Square Error (RMSE) between 0.14 and 0.21, and Mean Absolute Error (MAE) between 0.11 and 0.17. Overall, the LSTM model consistently delivered the highest PCC (0.87) and KTC (0.70) values and the lowest MB (0.06), MFB (0.09), RMSE (0.14), and MAE (0.11) across all six pollutants, with the lowest SD (0.01), indicating greater precision and reliability. As a result, the study concludes that the LSTM model outperforms the ANN model in forecasting air pollutants in Macau, offering a more accurate and consistent prediction tool for local air quality management. Full article

With the proliferation of electric bicycle battery swapping models, spatial supply demand imbalances of battery resources across swapping stations have become increasingly prominent. Existing studies predominantly focus on location optimization but struggle to address dynamic operational challenges in battery allocation efficiency. This paper proposes an integer programming (IP)-based dynamic scheduling optimization method for shared batteries, aiming to minimize transportation costs and balance battery distribution under multi-constraint conditions. A resource allocation model is constructed and solved via an interior-point method (IPM) combined with a branch-and-bound (B&B) strategy, optimizing the dispatch paths and quantities of fully charged batteries among stations. This study contributes to urban sustainability by enhancing resource utilization efficiency, reducing redundant production, and supporting low-carbon mobility infrastructure. Using the operational data from 729 battery swapping stations in Shanghai, the spatiotemporal heterogeneity of rider demand is analyzed to validate the model’s effectiveness. Results reveal that daily swapping demand in core commercial areas is 3–10 times higher than in peripheral regions. The optimal scheduling network exhibits a ‘centralized radial’ structure, with nearly 50% of batteries dispatched from low-demand peripheral stations to high-demand central zones, significantly reducing transportation costs and resource redundancy. This study shows that the proposed model effectively mitigates battery supply demand mismatches and enhances scheduling efficiency. Future research may incorporate real-time traffic data to refine cost functions and introduce temporal factors to improve model adaptability. Full article

The construction of urban rail transit plays a crucial role in improving traffic conditions in large cities, promoting green urban development, and reducing carbon dioxide emissions. Based on Chinese urban data, this paper employs a time-varying difference-in-difference model combined with the Heckman two-step method to control the sample selection problem. The objective of this methodology is to ascertain whether urban rail transit exerts a traffic creation effect or a traffic substitution effect. The following results were found: (1) Urban rail transit notably reduces the bus ridership per capita and the carbon dioxide emissions per capita in cities, a finding which passes a series of robustness tests, and the traffic substitution effect increases as the number of urban rail transit lines increases. (2) Heterogeneity analysis reveals that the traffic substitution effect in terms of carbon reduction in urban rail transit is greater in non-resource-based cities, cities with large carbon emissions, and cities with low fiscal pressure. (3) Urban rail transit reduces the carbon dioxide emissions per capita by improving the allocation efficiency of factor resources and further generating technological innovation and structural upgrading effects. (4) Spatial econometric analysis shows that urban rail transit has a significant spatial spillover effect on the reduction in carbon dioxide emissions per capita in neighboring cities. In short, urban rail transit can reduce the carbon dioxide emissions per capita by improving resource allocation and support the attainment of carbon peak and carbon neutrality goals. This effect is greater in large cities where urban rail transit networks have been established. Therefore, cities should actively promote the construction of metro and other rail transit within the scope of urban financial resources and make full use of the carbon reduction and efficiency enhancement functions of urban rail transit. In this way, urban rail transit can become an effective tool for the realization of sustainable development. Full article

To address the needs of enhancing adaptive control and reducing emissions at intersections within intelligent traffic signal systems, this study innovatively proposes a deep reinforcement learning signal control model tailored for mixed traffic flows. Addressing shortcomings in existing models that overlook mixed traffic scenarios, neglect optimization of CO₂ emissions, and overly rely on high-performance algorithms, our model utilizes vehicle queue length, average speed, numbers of gasoline and electric vehicles, and signal phases as state information. It employs a fixed-phase strategy to decide between maintaining or switching signal states and incorporates a reward function that balances vehicle CO₂ emissions and waiting times, significantly lowering intersection carbon emissions. Following training with reinforcement learning algorithms, the model consistently demonstrates effective control outcomes. Simulation results using the SUMO platform reveal that our designed reward mechanism facilitates the rapid and stable convergence of intelligent agents. Compared with Fixed Time Control (FTC), Actuated Traffic Signal Control (ATSC), and Fuel-ECO TSC (FECO-TSC) methods, our model achieves superior performance in average waiting times and CO₂ emissions. Even across scenarios with gasoline–electric vehicle ratios of 25–75%, 50–50%, and 75–25%, the model exhibits significant advantages. These simulations validate the model’s rationality and effectiveness in promoting low-carbon travel and efficient signal control. Full article