Search Results (113)

Internal combustion engine vehicles damage the environment and public health by emitting toxic fumes, such as CO₂ or CO and other trace compounds. The use of electric cars helps to reduce the emission of pollutants into the environment due to the use of batteries with no direct and local emissions. However, accidents of battery electric vehicles pose new challenges, such as thermal runaway. Such accidents can be serious and, in some cases, may result in uncontrolled overheating that causes the battery pack to spontaneously ignite. In particular, the most dangerous vehicles are heavy goods vehicles (HGVs), as they release a large amount of energy that generate high temperatures, poor visibility, and respiratory damage. This study aims to determine the potential consequences of large BEV fires in road tunnels using computational fluid dynamics (CFD). Furthermore, a comparison between a BEV and an ICEV fire shows the differences related to the thermal and the toxic impact. Furthermore, the adoption of a longitudinal ventilation system in the tunnel helped to mitigate the BEV fire risk, keeping a safer environment for tunnel users and rescue services through adequate smoke control. Full article

Road traffic accidents remain a critical global issue with approximately 1.19 million fatalities each year, on which excessive and inappropriate speeds contribute significantly. Managing vehicle speeds is essential for improving road safety, yet predicting and understanding operating speeds remains a challenge. Among different road elements, tangents play a crucial role, as they serve as transition segments between curves and allow for free acceleration, making them particularly relevant for speed management and road design. This study investigates the operating speeds on both single- and dual-carriageway road tangents to identify the key influencing factors. Data were collected from 24 single-carriageway and 20 dual-carriageway road tangents in Croatia, comprising a total of 14,854 speed observations (filtered sample size). The analysis focuses on the impact of geometric, traffic, and roadside environment characteristics on operating vehicle speeds. The results reveal that for single-carriageway road tangents, the most influential factors were traffic volume and terrain type, while for dual-carriageway road tangents, the factors traffic flow density, average summer daily traffic, and heavy goods vehicle share. These findings provide essential insights for the future development of operating speed prediction models, enhancing road design guidelines, and improving speed management strategies. Full article

Road transport remains a dominant mode of transportation in Europe, yet it significantly contributes to fatalities and injuries, particularly in crashes involving heavy goods vehicles and trucks. Advanced Driver Assistance Systems (ADAS) are widely recognized as a promising solution for improving truck safety. However, given that the average age of the EU truck fleet is 12 years and ADAS technologies is mandatory for new vehicles from 2024, their full impact on crash reduction may take over a decade to materialize. To address this delay, retrofitting ADAS onto existing truck fleets presents a viable strategy for enhancing road safety more promptly. This study integrates a systematic literature review, cost–benefit analysis, and a pilot field test to assess the feasibility and effectiveness of retrofitting ADAS. The literature review categorizes ADAS technologies based on their crash prevention potential, cost-effectiveness, market availability, and overall efficacy. A cost–benefit analysis applied to the Italian context estimates that ADAS retrofitting could save over 250 lives annually and reduce societal costs by more than €350 million. Moreover, the economic analysis indicates that the installation cost of retrofitted ADAS is outweighed by the societal savings associated with prevented crashes. Finally, pilot field testing suggests high user acceptance, providing a foundation for further large-scale studies. In conclusion, retrofitting ADAS onto existing truck fleets represents an effective and immediate strategy for significantly reducing truck-related crashes in Europe, bridging the gap until newer, ADAS-equipped vehicles dominate the fleet. Full article

The hydrogen fuel quality is critical to the efficiency and longevity of fuel cell electric vehicles (FCEVs), with ISO 14687:2019 grade D establishing stringent impurity limits. This study compared two different sampling techniques for assessing the hydrogen fuel quality, focusing on the National Physical Laboratory hydrogen direct sampling apparatus (NPL DirSAM) from a 35 MPa heavy-duty (HD) dispenser and qualitizer sampling from a 70 MPa light-duty (LD) nozzle, both of which were deployed on the same day at a local hydrogen refuelling station (HRS). The collected samples were analysed as per the ISO 14687:2019 contaminants using the NPL H₂-quality laboratory. The NPL DirSAM was able to sample an HD HRS, demonstrating the ability to realise such sampling on an HD nozzle. The comparison of the LD (H₂ Qualitizer sampling) and HD (NPL DirSAM) devices showed good agreement but significant variation, especially for sulphur compounds, non-methane hydrocarbons and carbon dioxide. These variations may be related to the HRS difference between the LD and HD devices (e.g., flow path, refuelling conditions and precooling for light duty versus no precooling for heavy duty). Further study of HD and LD H₂ fuel at HRSs is needed for a better understanding. Full article

Conducting research on the effectiveness of driving simulators is of great significance for enhancing the availability of driving simulator systems. However, in risky environments, traditional methods have the limitation of making it difficult to conduct real vehicle experiments. Therefore, this study proposes a method based on driver physiological indicators to evaluate the effectiveness of driving simulators in risky environments. On the one hand, the two-dimensional extended time to collision theoretical model (2D-TTC) was used to calculate the risk degree. Then, the similarity between the risk degree and the drivers’ electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) data sequences was calculated based on the dynamic time warping (DTW) model. On the other hand, we used the complexity and sample entropy of ECG and EMG as indicators to assess the drivers’ physiological load. This paper used intersections as risk scenarios to conduct driving simulation experiments to verify the feasibility of the above method. It was found that changes in drivers’ physiological indicators were consistent with changes in risk degree, with the DTW values of risk degree and drivers’ EDA tending to become smaller and the two sequence values closer to being similar. It was also found that the complexity and the sample entropy of the driver’s ECG and EMG showed higher values in the simulated poor sight intersection scenario compared to the intersection with good sight. In addition, in the simulated heavy traffic intersection scenario, physiological parameters such as EMG complexity and sample entropy, as well as ECG complexity, were higher than in the low traffic flow intersection. These findings are highly consistent with the characteristics of physiological responses in real driving environments, fully demonstrating the effectiveness of the test-driving simulation system in simulating risky traffic scenarios. The method proposed in this paper overcomes the limitations of traditional approaches and effectively validates the effectiveness of driving simulation systems in risky environments. The research results can drive further development and application of driving simulation technology. Full article

Asphalt pavements are subjected to both repeated vehicle loads and erosive deterioration from complicated environments in service. Salt erosion exerts a serious negative impact on the service performance of asphalt pavements in salt-rich areas such as seasonal frozen areas with snow melting and deicing, coastal areas, and saline soils areas. In recent years, the performance evolution of asphalt materials under salt erosion environments has been widely investigated. However, there is a lack of a systematic summary of salt erosion damage for asphalt materials from a multi-scale perspective. The objective in this paper is to review the performance evolution and the damage mechanism of asphalt mixtures and binders under salt erosion environments from a multi-scale perspective. The salt erosion damage and damage mechanism of asphalt mixtures is discussed. The influence of salt categories and erosion modes on the asphalt binder is classified. The salt erosion resistance of different asphalt binders is determined. In addition, the application of microscopic test methods to investigate the salt damage mechanism of asphalt binders is generalized. This review finds that the pavement performance of asphalt mixtures decreased significantly after salt erosion. A good explanation for the salt erosion mechanism of asphalt mixtures can be provided from the perspective of pores, interface adhesion, and asphalt mortar. Salt categories and erosion modes exerted great influences on the rheological performance of asphalt binders. The performance of different asphalt binders showed a remarkable diversity under salt erosion environments. In addition, the evolution of the chemical composition and microscopic morphology of asphalt binders under salt erosion environments can be well characterized by Fourier Infrared Spectroscopy (FTIR), Gel Permeation Chromatography (GPC), and microscopic tests. Finally, the major focus of future research and the challenges that may be encountered are discussed. From this literature review, pore expansion mechanisms differ fundamentally between conventional and salt storage asphalt mixtures. Sulfate ions exhibit stronger erosive effects than chlorides due to their chemical reactivity with asphalt components. Molecular-scale analyses confirm that salt solutions accelerate asphalt aging through light-component depletion and heavy-component accumulation. These collective findings from prior studies establish critical theoretical foundations for designing durable pavements in saline environments. Full article

In a scenario where fuel costs are notably high and the policies that we are currently witnessing tend to limit the fossil fuel resource that powers most heavy goods transport services, the optimization of space in vehicles transporting these goods, such as trucks and shipping containers, becomes an indisputable and urgent need. This urgency is manifested in the need to minimize the costs associated with transport, given its increasing growth. This experiment aims to study and implement an Internet of Things (IoT)-based solution to the problem previously presented. The developed system comprises a computer and a millimeter-wave (mmWave) sensor. The computer processes the data captured by the sensor through code in Python language and displays, through a web page allocated in a cloud/server, the volume occupied by the load, as well as the percentage of occupied and free space, considering the volume provided by the user. The validation tests consisted of checking the results in 2D and 3D, all carried out in a controlled environment focused on the detection of static objects. For the 3D analysis, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm was used to obtain the points for extracting the volume of the detected object. Several objects with different dimensions were used and the error ranged from 0.6% to 7.61%. These results denote the confirmation of the reliability and efficacy of the presented solution. With this, it was concluded that this new solution has significant potential to enter the market and compete with other existing technologies. Full article

This study explores the impact of road transport on the environment, focusing on noise pollution. Using high-resolution, one-minute data from a low-cost environmental sensor, this research examines traffic flow dynamics, meteorological influences, and their relationship to noise along a major transport corridor. The methodology combines cluster analysis and descriptive statistics to evaluate the effects of deploying a Smart Motorway Variable Speed Limit (SMVSL) system over a six-month monitoring period. Results indicate that SMVSL systems not only smooth traffic flow but also significantly reduce noise variability, particularly during peak hours, thus mitigating noise peaks associated with adverse health outcomes. LAeq values were found to differ modestly between day and night, with clustering revealing a reduction in extreme noise events (LAmax > 70 dB(A)) in SMVSL scenarios dominated by heavy goods vehicles. This study further identifies associations between unmanaged speed regimes and elevated noise levels, enriching our understanding of the environmental impacts of unregulated traffic conditions. These findings inform sustainable planning and policy strategies aimed at improving urban environmental quality and enhancing public health outcomes. Full article

Noise coming from road traffic represents a major contributor to the high levels of noise to which people are continuously exposed—especially in urban areas—throughout all of Europe. Since it represents a very detrimental pollutant, the assessment of such noise is an important procedure. Noise levels can be measured or simulated, and, in this second case, for the building of a valid model, a proper collection of input data cannot be left out of consideration. In this paper, the authors present the development of a methodology for the collection of the main inputs for a road traffic noise model, i.e., vehicle number, category, and speed, from a video recording of traffic on an Italian highway. Starting from a counting and recognition tool already available in the literature, a self-written Python routine based on image inference has been developed for the instantaneous detection of the position and speed of vehicles, together with the categorization of vehicles (light or heavy). The obtained data are coupled with the CNOSSOS-EU model to estimate the noise power level of a single vehicle and, ultimately, the noise impact of traffic on the selected road. The results indicate good performance from the proposed model, with a mean error of −1.0 dBA and a mean absolute error (MAE) of 3.6 dBA. Full article

The transportation sector plays a vital role in enabling the movement of people, goods, and services, but it is also a major contributor to energy consumption and greenhouse gas emissions. Accurate modeling of fuel consumption and pollutant emissions is critical for effective transportation management and environmental sustainability. This study investigates the use of real-world driving data from gasoline and diesel vehicles to model fuel consumption and exhaust emissions (${CO}_2$ and ${NO}_x$). The models were developed using ensemble bagged and decision tree algorithms with inputs derived from both vehicle speed and GPS speed data. The results demonstrate high predictive accuracy, with the ensemble bagged model consistently outperforming the decision tree model across all datasets. Notably, GPS speed-based models showed comparable performance to vehicle speed-based models, indicating the feasibility of using GPS data for real-time predictions. Furthermore, the combined gasoline and diesel engine dataset improved the accuracy of ${CO}_2$ emission predictions, while the gasoline-only dataset yielded the highest accuracy for fuel consumption. These findings underscore the potential of integrating GPS-based machine learning models into Intelligent Transportation Systems (ITS) to enhance real-time monitoring and policymaking. Future research should explore the inclusion of heavy-duty vehicles, additional pollutants, and advanced modeling techniques to further improve predictive capabilities. Full article

Heavy goods transportation is responsible for around 27% of CO₂ emissions from road transport in the EU and for 5% of total CO₂ emissions in the EU. The decarbonization of long-distance transport in particular remains a major challenge. The combination of battery electric trucks (BETs) with on-route high-power charging (HPC) offers a promising solution. Planning and setting up the required infrastructure is a critical success factor here. We propose a methodology to evaluate the charging infrastructure needed to support the large-scale introduction of heavy-duty BETs in Germany, considering different levels of electrification, taking the European driving and rest time regulations into account. Our analysis employs MATSim, an activity-based multi-agent transport simulation, to assess potential bottlenecks in the charging infrastructure and to simulate the demand-based distribution of charging stations. The MATSim simulation is combined with an extensive pre-processing of transport-related data and a suitable post-processing. This approach allows for a detailed examination of the required charging infrastructure, considering the impacts of depot charging solutions and the dynamic nature of truck movements and charging needs. The results indicate a significant need to augment HPC with substantial low power overnight charging facilities and highlight the importance of strategic infrastructure development to accommodate the growing demand for chargers for BETs. By simulating various scenarios of electrification, we demonstrate the critical role of demand-oriented infrastructure planning in reducing emissions from the road freight sector until 2030. This study contributes to the ongoing discourse on sustainable transportation, offering insights into the infrastructure requirements and planning challenges associated with the transition to battery electric heavy-duty vehicles. Full article

The transport sector is responsible for nearly a quarter of global CO₂ emissions annually, underscoring the urgent need for cleaner, more sustainable alternatives such as electric vehicles (EVs). However, the electrification of heavy goods vehicles (HGVs) has been slow due to the substantial power and battery capacity required to match the large payloads and extended operational ranges. This study addresses the research gap in battery pack design for commercial HGVs by investigating the electrical and thermal behaviour of a novel battery pack configuration using an electro-thermal model based on the equivalent circuit model (ECM). Through computationally efficient 1D modelling, this study evaluates critical factors such as cycle ageing, state of charge (SoC), and their impact on the battery’s range, initially estimated at 285 km. The findings of this study suggest that optimal cooling system parameters, including a flow rate of 18 LPM (litres per minute) and actively controlling the inlet temperature within ±7.8 °C, significantly enhance thermal performance and stability. This comprehensive electro-thermal assessment and the advanced cooling strategy set this work apart from previous studies centred on smaller EV applications. The findings provide a foundation for future research into battery thermal management system (BTMS) design and optimised charging strategies, both of which are essential for accelerating the industrial deployment of electrified HGVs. Full article

Accidents involving cyclists and trucks are among the most severe road accidents. In 2021, 199 cyclists were killed in accidents involving a truck in the EU. The main accident situation is a truck turning right and a cyclist going straight ahead. A large proportion of these accidents are caused by the inadequate visibility in an HGV (Heavy Goods Vehicle). The blind spot, in particular, is a significant contributor to these accidents. A BSD (Blind Spot Detection) system is expected to significantly reduce these accidents. There are only a few studies that estimate the potential of assistance systems, and these studies include a combined assessment of cyclists and pedestrians. In the present study, accident simulations are used to assess a warning and an autonomously intervening assistance system that could prevent truck to cyclist accidents. The main challenges are local sight obstructions such as fences, hedges, etc., rule violations by cyclists, and the complexity of correctly predicting the cyclist’s intentions, i.e., detecting the trajectory. Taking these accident circumstances into consideration, a BSD system could prevent between 26.3% and 65.8% of accidents involving HGVs and cyclists. Full article

Emissions standards describe the fuels, the procedures, and, among others, the analyzers to be used for the measurement of the different compounds during the type-approval of heavy-duty engines and vehicles. Traditionally, NOx, CO, hydrocarbons, and CO₂ were the gaseous compounds measured within the Euro standard, with the later addition of CH₄ and NH₃. Euro 7, introduced in early 2024, expanded those compounds, requiring the measurement of N₂O and HCHO. With an increasing number of molecules that need to be measured and introducing carbonless fuels, such as hydrogen, that present different requirements compared to carbon-based fuels, the test procedure needs to be updated. The performances of three laboratory-grade instruments and three portable emissions measurement systems based on Fourier-transformed infrared (FTIR) or quantum cascade laser infrared (QCL-IR) technologies were investigated while measuring from the tailpipe of a Diesel engine and a compressed natural gas (CNG) vehicle. All instruments presented good agreement when emissions of NOx, CO, CH₄, NH₃, N₂O, HCHO, and CO₂ were compared using: Z-score, F-test and two tail t-test of student. Water concentration measured by the four FTIRs was also in good agreement. Moreover, the dry emissions of CO₂ and CO measured by the laboratory non-dispersive infrared (NDIR) and corrected using water were a few percentages different from those obtained using the regulated carbon-based approach. The results indicate that all the investigated systems are suitable for the measurement of the investigated gaseous compounds, including CO₂ and H₂O. Full article

The efficiency of road freight transport determines—to a significant degree—the total environmental footprint and the amount of greenhouse gases and other pollutants released into the atmosphere by inland transport. The rate of empty or partially empty vehicles is one of the key metrics for improvement of the environmental performance of freight road transportation. This paper presents the characteristics of road freight transport in Poland on the basis of data collected by weigh-in-motion stations. Data aggregation for environmental analysis represents a novel aspect of the work. Indicators describing the degree of loading, the share of empty vehicles in traffic, and the share of vehicles of maximum permissible total weight in traffic were determined for a representative group of heavy goods vehicles. Based on the representative load factor value (LFA), a classification of the road section into four groups was proposed. The results obtained show a clear differentiation of the values of the indicators analyzed for individual groups and their variability in the three-year period covered by the analysis. The aggregation method presented can be used to identify the nature of the distribution of the weight categories of the heavy goods vehicles and provide input information for targeted analyses relating to sustainable road transport management and environmental protection. Finally, the grouped LFA values were linked with indicators of energy consumption (ECI) and on-road emissions (EI). Full article