Search Results (32)

Understanding the carbon emissions of multimodal travel—comprising walking, metro, bus, cycling, and ride-hailing—is essential for promoting sustainable urban mobility. However, most existing studies focus on single-mode travel, while underlying spatiotemporal and behavioral determinants remain insufficiently explored due to the lack of fine-grained data and interpretable analytical frameworks. This study proposes a novel integration of high-frequency, real-world mobility trajectory data with interpretable machine learning to systematically identify the key drivers of carbon emissions at the individual trip level. Firstly, multimodal travel chains are reconstructed using continuous GPS trajectory data collected in Beijing. Secondly, a model based on Calculate Emissions from Road Transport (COPERT) is developed to quantify trip-level CO₂ emissions. Thirdly, four interpretable machine learning models based on gradient boosting—XGBoost, GBDT, LightGBM, and CatBoost—are trained using transportation and built environment features to model the relationship between CO₂ emissions and a set of explanatory variables; finally, Shapley Additive exPlanations (SHAP) and partial dependence plots (PDPs) are used to interpret the model outputs, revealing key determinants and their non-linear interaction effects. The results show that transportation-related features account for 75.1% of the explained variance in emissions, with bus usage being the most influential single factor (contributing 22.6%). Built environment features explain the remaining 24.9%. The PDP analysis reveals that substantial emission reductions occur only when the shares of bus, metro, and cycling surpass threshold levels of approximately 40%, 40%, and 30%, respectively. Additionally, travel carbon emissions are minimized when trip origins and destinations are located within a 10 to 11 km radius of the central business district (CBD). This study advances the field by establishing a scalable, interpretable, and behaviorally grounded framework to assess carbon emissions from multimodal travel, providing actionable insights for low-carbon transport planning and policy design. Full article

Ambient PM concentrations are influenced by various emission sources and weather conditions such as temperature, wind speed, and direction. Measurements using optical sensors cannot directly link pollution levels to specific sources. Data from roadside monitoring often show that a significant portion of PM originates from non-traffic sources. Therefore, vehicle-related PM emissions are typically estimated using simulation models based on average emission factors. This study uses the COPERT (Computer Programme to Calculate Emissions from Road Transport) model to estimate emissions from road vehicles under current conditions and future scenarios. These include the introduction of Euro 7 standards and a shift from internal combustion engine (ICE) vehicles to battery electric vehicles (BEVs). The analysis considers exhaust and non-exhaust emissions, as well as indirect emissions from electricity generation for BEV charging. The conducted study showed, among other findings, that replacing internal combustion engine vehicles with electric ones could reduce PM2.5 emissions by approximately 6% (2% when including indirect emissions from electricity generation) and PM10 emissions by about 10% (5% with indirect emissions), compared to the Euro 7 scenario. Full article

This article aimed to assess the accuracy of the COPERT model in predicting CO₂ emissions and energy consumption in real operating conditions, represented by the WLTP homologation tests. Experimental data obtained for a Euro 6 vehicle were compared with the values estimated by the COPERT model, assuming identical speed conditions. MLP and SANN artificial neural networks were also used to create a model describing the complex relationships between emissions, speed, and energy consumption. The results indicate an apparent overestimation of CO₂ and energy consumption values by the COPERT model, especially in the low-speed range typical of urban traffic. The minimum energy consumption values were observed at speeds of 50–70 km/h, indicating the existence of an optimal drive system operation zone. The neural models showed high efficiency in predicting the tested parameters—the best results were obtained for the MLP 6-10-1 architecture, whose correlation coefficient exceeded 0.98 in the validation set. The paper highlights the need to calibrate the COPERT model using local experimental data and integrate artificial intelligence methods in modern emission inventories. Full article

The transition to sustainable transport poses significant challenges for urban mobility, requiring shifts in fuel consumption, emissions reductions, and economic adjustments. This study conducts a scenario-based analysis of Budapest’s transport energy consumption, emissions, and monetary implications for 2020, 2030, and 2050 using the Budapest Transport Model (EFM), which integrates COPERT and HBEFA within PTV VISUM. This research examines the evolution of diesel, gasoline, and electric vehicle (EV) energy use alongside forecasted fuel prices, using the ARIMA model to assess the economic impact of transport decarbonisation. The findings reveal a 32.8% decline in diesel consumption and a 64.7% drop in gasoline usage by 2050, despite increasing vehicle kilometres travelled (VKT). Electricity consumption surged 97-fold, highlighting fleet electrification trends, while CO₂ emissions decreased by 48%, demonstrating the effectiveness of policies, improved vehicle efficiency, and alternative energy adoption. However, fuel price forecasts indicate significant cost escalations, with diesel and gasoline prices doubling and CO₂ pricing increasing sevenfold by 2050, presenting financial challenges in the transition. This study highlights the need for EV incentives, electricity price regulation, public transport investments, and carbon pricing adjustments. Future research should explore energy grid resilience, mobility trends, and alternative fuel adoption to support Budapest’s sustainable transport goals. Full article

Air quality is becoming an important asset of modern society. Europe is adopting regulations that will enable better air quality for residents and encourage detailed study of emissions sources. Transport is recognized as a flourishing sector with the yearly growth of vehicle numbers. Even if the transport emissions trend slightly decreases, there is a concern that the increase in vehicle numbers on the road will slow down the process. Data from the bottom-up approach, estimating emissions from transit vehicles and tourism activities, was identified as a critical knowledge gap. Our study identifies and evaluates the issue of vehicle congestion on the roads during the summer, primarily driven by transit demands and tourism activities. The methodology to capture an understanding of traffic-related emissions from the summer vehicle peak was developed. Summer traffic peak was estimated by comparing the summer vehicle numbers with those of other parts of the year. Vehicle numbers were recognized by vehicle counters located on a Slovenian highway junction in the year 2021. Moreover, the study also revealed the emissions from the summer traffic peak, calculated by the COPERT emission model. We observed that, on an average summer day, there are up to 11,520 additional vehicles on Slovenian roads. It was estimated that the peak in summer passenger cars contributes up to 41,875 kg, 9542 kg, and 3057 kg of NO_x, NMVOCs, and PM_2.5 emissions. The maximum emissions of NO_x and PM_2.5 from light duty vehicles are 17,108 kg and 867 kg. There are non-negligible emissions of NMVOCs from motorcycles and these represent up to 3042 kg. Full article

The increasing number of heavy-duty vehicles (HDVs) on roads has become a major contributor to harmful emissions, posing critical environmental challenges and exacerbating global warming. This study aims to establish correlations between road types and the emissions they generate, offering actionable insights for logistics planning and strategies to mitigate diesel vehicle emissions. The analysis is based on input data from a selected transport company, covering parameters such as vehicle type, average mileage, speed, and driving style, as well as environmental conditions like ambient temperature and humidity. Emissions and energy consumption levels are estimated using the COPERT model. A key research challenge involves accurately predicting and managing air pollution caused by HDVs under varying vehicular, technological, and fuel conditions, as well as fluctuating atmospheric and operational factors. The findings indicate that highway driving produces the highest emissions of pollutants such as Se and Zn, while urban peak hours record the highest levels of NO_x, NO, and NO₂. These results emphasise the critical role of strategic route selection in reducing total emissions and managing levels of individual harmful substances. This research highlights the importance of integrating sustainable practices into transport planning to reduce environmental impacts, align with global climate objectives, and advance sustainable development in the transport sector. Full article

Expressway systems play a vital role in facilitating intercity travels for both passengers and freights, which are also a significant source of vehicle emissions within the transportation sector. This study investigates vehicle emissions from expressway systems using the COPERT model to develop multi-year emission inventories for different pollutants, covering the past and future trends from 2005 to 2030. Thereinto, an integrated SARIMA-SVR method is designed to portray the temporal variation of vehicle population, and the possible future trends of expressway vehicle emissions are predicted through policy scenario analysis. The Jiang–Zhe–Hu Region of China is taken as the case study to analyze emission control in expressway systems. The results indicate that (1) carbon monoxide (CO) and volatile organic compounds (VOCs) present a general upward trend primarily originating from passenger vehicles, while nitrogen oxides (NOx) and inhalable particles (PM) display a slowing upward trend with fluctuations mainly sourcing from freight vehicles; (2) vehicle population constraint is an effective emission control policy, but upgrading the medium- and long-haul transportation structure is necessary to meet the continuous growth of intercity trips. Expressway vehicle emission reduction effectiveness can be further enhanced by curtailing the update frequency of emission standards, along with the scrapping of high-emission vehicles. Full article

The rise of ride-hailing services presents innovative solutions for curbing urban carbon emissions, yet poses challenges such as fostering fair competition and integrating with public transit. Analyzing the factors influencing ride-hailing emissions is crucial for understanding their relationship with other travel modes and devising policies aimed at steering individuals towards more environmentally sustainable travel options. Therefore, this study delves into factors impacting ride-hailing emissions, including travel demand, land use, demographics, and transportation networks. It highlights the interplay among urban structure, multi-modal travel, and emissions, focusing on network features such as betweenness centrality and accessibility. Employing the COPERT (Computer Programme to Calculate Emissions from Road Transport) model, ride-hailing emissions are calculated from vehicle trajectory data. To mitigate statistical errors from multicollinearity, variable selection involves tests and correlation analysis. Geographically and temporally weighted regression (GTWR) with an adaptive kernel function is designed to understand key influencing mechanisms, overcoming traditional GTWR limitations. It can dynamically adjust bandwidth based on the spatio-temporal distribution of data points. Experiments in Shenzhen validate this approach, showing a 9.8% and 10.8% increase in explanatory power for weekday and weekend emissions, respectively, compared to conventional GTWR. The discussion of findings provides insights for urban planning and low-carbon transport strategies. Full article

Health factors concerning the well-being of the urban population urge us to better comprehend the impact of emissions in urban environments on the micro-scale. There is great necessity to depict and monitor pollutant concentrations with high precision in cities, by constructing an accurate and validated digital air quality network. This work concerns the development and application of a CFD model for the dispersion of particulate matter, CO, and NO_x from traffic activity in a highly busy area of the city of Augsburg, Germany. Emissions were calculated based on traffic activity during September of 2018 with COPERT Street software version 2.4. The needed meteorological data for the simulations were taken from a sensor’s network and the resulting concentrations were compared and validated with high-precision air quality station indications. The model’s solver used the steady-state RANS approach to resolve the velocity field and the convection–diffusion equation to simulate the pollutant’s dispersion, each one modelled with different molecular diffusion coefficients. A sensitivity analysis was performed to decide the most efficient computational mesh to be used in the modelling. A velocity profile for the atmospheric boundary layer (ABL) was implemented into the inlet boundary of each simulation. The cases concerned applications on the street level in steady-state conditions for one hour. The results were evaluated based on CFD validation metrics for urban applications. This approach provides a comprehensive state-of-the-art 3D digital pollution network for the area, capable of assessing contamination levels at the street scale, providing information for pollution reduction techniques in urban areas, and combining with existing sensor networks for a more thorough portrait of air quality. Full article

Emission factors serve as a valuable tool for quantifying the release of pollutants from road vehicles and predicting emissions within a specific time or area. In order to overcome the limitation of the computer program to calculate emissions from the road transport (COPERT) model in directly obtaining precise emission factors from on-board diagnostic (OBD) data, we propose a novel two-stream network that combines time-series features and time-frequency features to enhance the accuracy of the COPERT model. Firstly, for the instantaneous emission factors of NOx from multiple driving segments provided by heavy-duty diesel vehicles in actual driving, we select the monitored attributes with a high correlation to the emission factor of NOx considering the data scale and employing Spearman rank correlation analysis to obtain the final dataset composed of them and emission factors. Subsequently, we construct an information matrix to capture the impact of past data on emission factors. Each attribute of the time series is then converted into a time-frequency matrix using the continuous wavelet transform. These individual time-frequency matrices are combined to create a multi-channel time-frequency matrix, which represents the historical information. Finally, the historical information matrix and the time-frequency matrix are inputted into a two-stream parallel model that consists of ResNet50 and a convolutional block attention module. This model effectively integrates time-series features and time-frequency features, thereby enhancing the representation of emission characteristics. The reliability and accuracy of the proposed method were validated through a comparative analysis with existing mainstream models. Full article

Transformation of European transport belongs among the key challenges to achieve a reduction of 55% by 2030 and climate neutrality by 2050. This study focuses on GHG emissions in road transport in Slovakia, as it currently accounts for 19% of total GHG emissions (road transport emissions account for 99% of transport emissions). The main driver for this study was the preparation of Slovakia’s Climate Act and investigation of where are the limits of greenhouse gas emission reduction by 2050. With the aim of achieving maximum reduction in emissions by 2050 compared to 2005 levels, various scenarios were developed using the COPERT model to explore emission reduction strategies. The scenarios considered different subsectors of road transport, including passenger cars, light-commercial vehicles, heavy-duty vehicles (buses and trucks), and L-category vehicles and examined encompassed reduction of transport demand, improving energy efficiency, and utilizing advanced technologies with alternative fuels (hybrids, PHEV, CNG, LNG or LPG). However, the economic aspects of specific mitigation options were not considered in this analysis. The results show that there is a possibility of 77% GHG emission reduction by 2050 in comparison with the 2005 level. This reduction is accompanied by a shift in vehicle technologies to alternative fuels like electricity, hydrogen, and to a smaller extent biofuels and biomethane. This study shows that it will be possible to achieve 86.7% zero-emission cars and an additional 12.9% low emission and alternative fueled cars by 2050. By identifying and assessing these scenarios, policymakers and stakeholders can gain insights into the possibilities, challenges, and potential solutions for meeting the climate targets set by the European Union’s Fit for 55 climate package. Full article

As the transition towards connected and autonomous vehicles gradually happens, different phases with CAVs and human-driven vehicles sharing the same network will occur. This paper’s purpose is to increase the knowledge of these mixed situations, studying the impacts of an increasing number of CAVs within the vehicle fleet on road capacity, travel time savings and energy consumption, providing new insights into the debate that is still open. The methodology focused on a microsimulation-based approach on an urban motorway in the city of Rome. Some of the outcomes from simulations, run with the software PTV Vissim^TM 21, were used to analyse variations in general performances of the transportation system, whereas the remaining results were fed into the emission model COPERT for assessing the impacts of CAV penetration on the energy consumption of the fleet. Results show how, in congested cases, appreciable improvements can be recorded in terms of road capacity, mean speeds, and environmental impacts, while in lower-congested situations, any enhancement in traffic fluidification counteracts the environmental performances of the whole system. Full article

Current LCA-based environmental impact assessments rarely consider the environmental impact of traffic network deterioration due to temporary road closures during road infrastructure construction processes. This study proposes a quantification method to evaluate the environmental impact of traffic diversions during the road infrastructure construction process. The environmental impact assessment method ReCiPe 2016 was selected to evaluate the environmental impact of pollutant emissions from deteriorated traffic conditions. Ten types of traffic emissions were estimated by emission factors and traffic conditions. A case study quantified the potential environmental impact of traffic emissions resulting from four diversion plans based on an actual bridge-construction case study in Chongqing city of China. Results revealed that different diversion plans could lead to different final environmental impacts. “Global warming” dominated both “Human health” and “Ecosystems” impacts. In the “Human health” category, more than 95% of the environmental impact was contributed by global warming. Similarly, the impact of “Global warming” was higher than 75% in the “Ecosystems” category. CO₂ emissions were the main contributor to the overall “Global warming” impact in all four diversion plans. The traffic speed under traffic diversions before and during road infrastructure construction processes is the major factor influencing the overall environmental impact (endpoint). Full article

Urban air pollution has aroused growing attention due to its associated adverse health effects. A model which could promptly predict urban air quality with considerable accuracy is, therefore, important and will benefit the development of smart cities. However, only a computational fluid dynamics (CFD) model could better resolve the dispersion behavior within an urban canyon layer. A machine learning (ML) model using the Artificial Neural Network (ANN) approach was formulated in the current study to investigate vehicle-derived airborne particulate (PM₁₀) dispersion within a compact high-rise-built environment. Various measured meteorological parameters and PM₁₀ concentrations were adopted as the model inputs to train the ANN model. A building-resolved CFD model under the same environmental settings was also set up to compare its model performance with the ANN model. Our results showed that the ANN model exhibited promising performance (r = 0.82, fractional bias = 0.002) when comparing the > 1000 h PM₁₀ measurements. When comparing the diurnal hourly measured PM₁₀ variations in a clear-sky day, both the ANN and CFD models performed well (r > 0.8). The good performance of the CFD model relied on the knowledge of the in situ diurnal traffic profile, the adoption of suitable mobile source emission factor(s) (e.g., from MOBILE 6 and COPERT4), and the use of urban thermal and dynamical variables to capture PM₁₀ variations in both neutral and unstable atmospheric conditions. These requirements/constraints make it impractical for daily operation. On the contrary, the ML (ANN) model adopted here is free from these constraints and is fast (less than 0.1% computational time relative to the CFD model). These results demonstrate that the ANN model is a superior option for a smart city application. Full article

Community shuttle services have the potential to alleviate traffic congestion and reduce traffic pollution caused by massive short-distance taxi-hailing trips. However, few studies have evaluated and quantified the impact of community shuttle services on urban traffic and traffic-related air pollution. In this paper, we propose a complete framework to quantitatively assess the positive impacts of community shuttle services, including route design, traffic congestion alleviation, and air pollution reduction. During the design of community shuttle services, we developed a novel method to adaptively generate shuttle stops with maximum service capacity based on residents’ origin–destination (OD) data, and designed shuttle routes with minimum mileage by genetic algorithm. For traffic congestion alleviation, we identified trips that can be shifted to shuttle services and their potential changes in traffic flow. The decrease in traffic flow can alleviate traffic congestion and indirectly reduce unnecessary pollutant emissions. In terms of environmental protection, we utilized the COPERT III model and the spatial kernel density estimation method to finely analyze the reduction in traffic emissions by eco-friendly transportation modes to support detailed policymaking regarding transportation environmental issues. Taking Chengdu, China as the study area, the results indicate that: (1) the adaptively generated shuttle stops are more responsive to the travel demands of crowds compared with the existing bus stops; (2) shuttle services can replace 30.36% of private trips and provide convenience for 50.2% of commuters; (3) such eco-friendly transportation can reduce traffic emissions by 28.01% overall, and approximately 42% within residential areas. Full article