Search Results (7)

Controlling vehicle speed is crucial for reducing exhaust emissions and ensuring the sustainable development of road transportation. Currently, speed limits on expressways are primarily set from a safety perspective, with limited research addressing speed limits from an environmental protection standpoint. In this study, based on real-world vehicle experiments and a vehicle flow exhaust emission model, we investigated the exhaust emission characteristics of light passenger vehicles (categorized as M1) and freight vehicles (categorized as N, including N1-minivans, N2-light heavy-duty vehicles, N3-medium heavy-duty vehicles, and N4-large heavy-duty vehicles) both individually and in traffic flows at varying speeds. We take carbon monoxide (CO), nitrogen oxides (NOx), particular matter (PM), and hydrocarbons (HCs) as representative emission components. The emission rate ranking of typical exhaust factors differs between M1-light passenger vehicles and N-freight vehicles. For M1-light passenger vehicles, the order is CO > HC > NOx > PM2.5, while for N-freight vehicles, it is NOx > CO > PM2.5 > HC. Conversely, for freight vehicles, higher speeds correlate with increased exhaust emissions in general, although carbon emissions specifically decrease as the speed increases. The results indicate the following speed limits conducive to sustainable road transportation development and low exhaust and carbon emissions: 90–110 km/h for light passenger vehicles and 80–100 km/h for freight vehicles. Full article

In this study a novel microsimulation-based methodology for environmental assessment of urban systems is developed to address the performance of autonomous mass-mobility against conventional approaches. Traffic growth and microsimulation models, calibrated using real data, are utilised to assess four traffic management scenarios: business-as-usual; public bus transport case; public-bus rapid transit (BRT) case; and, a traffic-demand-responsive-autonomous-BRT case, focusing on fuel energy efficiency, headways, fleet control and platooning for lifecycle analysis (2015–2045) of a case study 3.5 km long 5-lane dual-carriageway section. Results showed that both energy consumption and exhaust emission rates depend upon traffic volume and flow rate factors of vehicle speed-time curves; acceleration-deceleration; and braking rate. The results measured over-reliance of private cars utilising fossil fuel that cause congestions and high environmental footprint on urban roads worsen causing excessive travel times. Public transport promotion was found to be an effective and easy-to-implement environmental burden reduction strategy. Results showed significant potential of autonomous mass-mobility systems to reduce environmental footprint of urban traffic, provided adequate mode-shift can be achieved. The study showed utility of microsimulations for energy and emissions assessment, it linked bus network performance assessment with environmental policies and provided empirical models for headway and service frequency comparisons at vehicle levels. The developed traffic fleet operation prediction methodology for long-term policy implications and tracking models for accurate yearly simulation of real-world vehicle operation profiles are applicable for other sustainability-oriented urban traffic management studies. Full article

The development of the COVID-19 pandemic and the related lockdown had a major impact on vehicle traffic in cities. Based on available data from the selected city of Rzeszow, Poland, it was decided to assess changes in vehicle traffic and the impact of these changes on roadside environmental pollution. As part of the research, data from the first half of 2020 regarding vehicle traffic on selected streets of the city and on the city’s inlet routes were analyzed. For the selected road sections, changes in hourly traffic volume in 2020, compared with 2019, were also determined. With data on traffic volume, an attempt was made to estimate the impact of changes in traffic volume on air pollution in the city. Research on air pollution from motor vehicles was focused on a selected section of a city road that was equipped with an automatic air pollution measurement station located very close to the road. Additionally, at the road intersection and in the vicinity of the measuring station, a sensor was installed in the roadway to count passing vehicles. A preliminary analysis of air pollution data revealed that factors such as wind speed and direction and outside temperature had a large impact on measurement results. To eliminate the influence of these factors and to obtain data concerning only contamination originating from motor vehicles traveling along the road, an appropriate mathematical model of the traffic flow–roadside environment system was built. This model was designed to determine the air pollution in the vicinity of the road generated by traffic flow. The constructed model was verified, and the conditions for its use were determined in order to study the impact of traffic and roadside environment on the level of air pollution from harmful exhaust substances. It was shown that at certain times of the day, especially at low temperatures, other sources of harmful emissions related to home heating play a major role in air pollution in the city. Full article

Urban agglomerations close to road infrastructure are particularly exposed to harmful exhaust emissions from motor vehicles and this problem is exacerbated at road intersections. Roundabouts are one of the most popular intersection designs in recent years, making traffic flow smoother and safer, but especially at peak times they are subject to numerous stop-and-go operations by vehicles, which increase the dispersion of emissions with high particulate matter rates. The study focused on a specific area of the city of Rzeszow in Poland. This country is characterized by the current composition of vehicle fleets connected to combustion engine vehicles. The measurement of the concentration of particulate matter (PM2.5 and PM10) by means of a preliminary survey campaign in the vicinity of the intersection made it possible to assess the impact of vehicle traffic on the dispersion of pollutants in the air. The present report presents some strategies to be implemented in the examined area considering a comparison of current and project scenarios characterized both by a modification of the road geometry (through the introduction of a turbo roundabout) and the composition of the vehicular flow with the forthcoming diffusion of electric vehicles. The study presents an exemplified methodology for comparing scenarios aimed at optimizing strategic choices for the local administration and also shows the benefits of an increased electric fleet. By processing the data with specific tools and comparing the scenarios, it was found that a conversion of 25% of the motor vehicles to electric vehicles in the current fleet has reduced the concentration of PM10 by about 30% along the ring road, has led to a significant reduction in the length of particulate concentration of the motorway, and it has also led to a significant reduction in the length of the particulate concentration for the access roads to the intersection. Full article

The work proposes a methodological approach to studying and assessing the environmental sustainability of the transport system of the city. The authors have selected parameters for assessing the environmental sustainability of the transport system and identified significant factors affecting environmental sustainability. A coefficient of environmental sustainability of the urban transport system and a formula for its calculation are proposed. A simulation was used to assess the amount of emissions of harmful substances from the car exhaust gases if the demand structure changes with respect to the means of transport and transportation methods. The paper presents the results of changing the parameters of the traffic flow and demand by means of transport and transportation methods when introducing a parking fee in the central part of the city, changing the cost of parking and expanding the paid parking area. Full article

Botnets are some of the most recurrent cyber-threats, which take advantage of the wide heterogeneity of endpoint devices at the Edge of the emerging communication environments for enabling the malicious enforcement of fraud and other adversarial tactics, including malware, data leaks or denial of service. There have been significant research advances in the development of accurate botnet detection methods underpinned on supervised analysis but assessing the accuracy and performance of such detection methods requires a clear evaluation model in the pursuit of enforcing proper defensive strategies. In order to contribute to the mitigation of botnets, this paper introduces a novel evaluation scheme grounded on supervised machine learning algorithms that enable the detection and discrimination of different botnets families on real operational environments. The proposal relies on observing, understanding and inferring the behavior of each botnet family based on network indicators measured at flow-level. The assumed evaluation methodology contemplates six phases that allow building a detection model against botnet-related malware distributed through the network, for which five supervised classifiers were instantiated were instantiated for further comparisons—Decision Tree, Random Forest, Naive Bayes Gaussian, Support Vector Machine and K-Neighbors. The experimental validation was performed on two public datasets of real botnet traffic—CIC-AWS-2018 and ISOT HTTP Botnet. Bearing the heterogeneity of the datasets, optimizing the analysis with the Grid Search algorithm led to improve the classification results of the instantiated algorithms. An exhaustive evaluation was carried out demonstrating the adequateness of our proposal which prompted that Random Forest and Decision Tree models are the most suitable for detecting different botnet specimens among the chosen algorithms. They exhibited higher precision rates whilst analyzing a large number of samples with less processing time. The variety of testing scenarios were deeply assessed and reported to set baseline results for future benchmark analysis targeted on flow-based behavioral patterns. Full article

Research assessing on-road emission flow patterns from motor vehicles is essential in monitoring urban air quality, since it helps to mitigate atmospheric pollution levels. To reveal the influence of vehicle induced turbulence (VIT) caused by both front- and rear-vehicles on traffic exhaust and verify the applicability of the simplified line source emission model, a Computational Fluid Dynamics (CFD) numerical simulation was used to investigate the micro-scale vehicle pollutant flow patterns. The simulation results were examined through sensitivity analysis and compared with the field measured carbon monoxide (CO) concentration. Conclusions indicate that the vehicle induced turbulence caused by the airflow blocking effect of both front- and rear-vehicles impedes the diffusion of front-vehicle traffic exhaust, compared with that of the rear vehicle. The front-vehicle isosurface with the CO mass fraction of 0.0012 extended to 6.0 m behind the vehicle, while that of the rear-vehicle extends as far as 12.7 m. But for the entire motorcade, VIT is beneficial to the diffusion of pollutants in car-following situations. Meanwhile, within the range of 9 m behind the rear of the lagging vehicle lies a vehicle induced turbulence zone. Furthermore, the influence of vehicle induced turbulence on traffic exhaust flow pattern is obvious within a range of 1 m on both sides of the vehicle body, where the concentration gradient of on-road emission is larger and contains severe mechanical turbulence. As a result, in the large concentration gradient area of the pollutant flow field, which accounts for 99.85% of the total concentration gradient, using the line source models to represent the on-road emission might introduce considerable errors due to neglecting the influence of vehicle induced turbulence. Findings of this study may shed lights on predicting emission concentrations in multiple locations by selecting appropriate on-road emission source models. Full article