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Keywords = traffic measurements

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17 pages, 5314 KiB  
Article
The Settlement Ratio and Settled Area: Novel Indicators for Analyzing Land Use in Relation to Road Network Functions and Performance
by Giulia Del Serrone, Giuseppe Cantisani and Paolo Peluso
Eng 2025, 6(8), 188; https://doi.org/10.3390/eng6080188 - 5 Aug 2025
Abstract
Land use significantly influences mobility dynamics, affecting both travel behavior and mode choice. Traditional indicators such as the Floor Area Ratio, Land-Use Mix Index, and Built-up Area Ratio are widely used to describe settlement patterns; yet, they often fail to capture their functional [...] Read more.
Land use significantly influences mobility dynamics, affecting both travel behavior and mode choice. Traditional indicators such as the Floor Area Ratio, Land-Use Mix Index, and Built-up Area Ratio are widely used to describe settlement patterns; yet, they often fail to capture their functional impacts on road networks. This study introduces two complementary indicators—Settlement Ratio (SR) and Settled Area (SA)—developed through a spatial analysis framework integrating GIS data and MATLAB processing. SR offers a continuous typological profile of built-up functions along the road axis, while SA measures the percentage of anthropized land within fixed analysis windows. Applied to two Italian state roads, SS14 and SS309, in the Veneto Region, the dual-indicator approach reveals how the intensity (SR) and extent (SA) of settlement vary across different territorial contexts. In suburban segments, SR values exceeding 15–20, together with SA levels between 10% and 15%, highlight the significant spatial impact of isolated development clusters—often not evident from macro-scale observations. These findings demonstrate that the SR–SA framework provides a robust tool for analyzing land use in relation to road function. Although the study focuses on spatial structure and indicator design, future developments will explore correlations with traffic flow, speed, and crash data to support road safety analyses. Full article
(This article belongs to the Special Issue Interdisciplinary Insights in Engineering Research)
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27 pages, 2929 KiB  
Article
Comparative Performance Analysis of Gene Expression Programming and Linear Regression Models for IRI-Based Pavement Condition Index Prediction
by Mostafa M. Radwan, Majid Faissal Jassim, Samir A. B. Al-Jassim, Mahmoud M. Elnahla and Yasser A. S. Gamal
Eng 2025, 6(8), 183; https://doi.org/10.3390/eng6080183 - 3 Aug 2025
Viewed by 65
Abstract
Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values [...] Read more.
Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R2) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article
(This article belongs to the Section Chemical, Civil and Environmental Engineering)
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16 pages, 1176 KiB  
Article
Evaluating the Use of Rice Husk Ash for Soil Stabilisation to Enhance Sustainable Rural Transport Systems in Low-Income Countries
by Ada Farai Shaba, Esdras Ngezahayo, Goodson Masheka and Kajila Samuel Sakuhuka
Sustainability 2025, 17(15), 7022; https://doi.org/10.3390/su17157022 - 2 Aug 2025
Viewed by 214
Abstract
Rural roads are critical for connecting isolated communities to essential services such as education and health and administrative services, as well as production and market opportunities in low-income countries. More than 70% of movements of people and goods in Sub-Saharan Africa are heavily [...] Read more.
Rural roads are critical for connecting isolated communities to essential services such as education and health and administrative services, as well as production and market opportunities in low-income countries. More than 70% of movements of people and goods in Sub-Saharan Africa are heavily reliant on rural transport systems, using both motorised but mainly alternative means of transport. However, rural roads often suffer from poor construction due to the use of low-strength, in situ soils and limited financial resources, leading to premature failures and subsequent traffic disruptions with significant economic losses. This study investigates the use of rice husk ash (RHA), a waste byproduct from rice production, as a sustainable supplement to Ordinary Portland Cement (OPC) for soil stabilisation in order to increase durability and sustainability of rural roads, hence limit recurrent maintenance needs and associated transport costs and challenges. To conduct this study, soil samples collected from Mulungushi, Zambia, were treated with combinations of 6–10% OPC and 10–15% RHA by weight. Laboratory tests measured maximum dry density (MDD), optimum moisture content (OMC), and California Bearing Ratio (CBR) values; the main parameters assessed to ensure the quality of road construction soils. Results showed that while the MDD did not change significantly and varied between 1505 kg/m3 and 1519 kg/m3, the OMC increased hugely from 19.6% to as high as 26.2% after treatment with RHA. The CBR value improved significantly, with the 8% OPC + 10% RHA mixture achieving the highest resistance to deformation. These results suggest that RHA can enhance the durability and sustainability of rural roads and hence improve transport systems and subsequently improve socioeconomic factors in rural areas. Full article
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27 pages, 22029 KiB  
Article
Evaluating the Siphon Effect on Airport Cluster Resilience Using Accessibility and a Benchmark System for Sustainable Development
by Xinglong Wang, Weiqi Lin, Hao Yin and Fang Sun
Sustainability 2025, 17(15), 7013; https://doi.org/10.3390/su17157013 - 1 Aug 2025
Viewed by 133
Abstract
The siphon effect between airports has amplified the polarization in passenger throughput, undermining the balanced development and sustainability of airport clusters. The airport siphon effect occurs when one airport attracts a disproportionate share of passengers, concentrating traffic at the expense of others, which [...] Read more.
The siphon effect between airports has amplified the polarization in passenger throughput, undermining the balanced development and sustainability of airport clusters. The airport siphon effect occurs when one airport attracts a disproportionate share of passengers, concentrating traffic at the expense of others, which affects the overall resilience of the entire airport cluster. To address this issue, this study proposes a siphon index, expands the range of ground transportation options for passengers, and establishes a zero-siphon model to assess the impact of siphoning on the resiliency of airport clusters. Using this framework, four major airport clusters in China were selected as research subjects, with regional aviation accessibility serving as a measure of resilience. The results showed that among the four airport clusters, the siphon effect is most pronounced in the Guangzhou region. To explore the implications of this effect further, three airport disruption scenarios were simulated to assess the resilience of the Pearl River Delta airport cluster. The results indicated that the intensity and timing of disruptive events significantly affect airport cluster resilience, with hub airports being particularly sensitive. This study analyzes the risks associated with excessive route concentration, providing policymakers with critical insights to enhance the sustainability, equity, and resilience of airport clusters. The proposed strategies facilitate coordinated infrastructure development, optimized air–ground intermodal connectivity, and risk mitigation. These measures contribute to building more sustainable and adaptive aviation networks in rapidly urbanizing regions. Full article
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11 pages, 3192 KiB  
Data Descriptor
Carbon Monoxide (CO) and Ozone (O3) Concentrations in an Industrial Area: A Dataset at the Neighborhood Level
by Jailene Marlen Jaramillo-Perez, Bárbara A. Macías-Hernández, Edgar Tello-Leal and René Ventura-Houle
Data 2025, 10(8), 125; https://doi.org/10.3390/data10080125 - 1 Aug 2025
Viewed by 151
Abstract
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 [...] Read more.
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
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30 pages, 4409 KiB  
Article
Accident Impact Prediction Based on a Deep Convolutional and Recurrent Neural Network Model
by Pouyan Sajadi, Mahya Qorbani, Sobhan Moosavi and Erfan Hassannayebi
Urban Sci. 2025, 9(8), 299; https://doi.org/10.3390/urbansci9080299 - 1 Aug 2025
Viewed by 260
Abstract
Traffic accidents pose a significant threat to public safety, resulting in numerous fatalities, injuries, and a substantial economic burden each year. The development of predictive models capable of the real-time forecasting of post-accident impact using readily available data can play a crucial role [...] Read more.
Traffic accidents pose a significant threat to public safety, resulting in numerous fatalities, injuries, and a substantial economic burden each year. The development of predictive models capable of the real-time forecasting of post-accident impact using readily available data can play a crucial role in preventing adverse outcomes and enhancing overall safety. However, existing accident predictive models encounter two main challenges: first, a reliance on either costly or non-real-time data, and second, the absence of a comprehensive metric to measure post-accident impact accurately. To address these limitations, this study proposes a deep neural network model known as the cascade model. It leverages readily available real-world data from Los Angeles County to predict post-accident impacts. The model consists of two components: Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN). The LSTM model captures temporal patterns, while the CNN extracts patterns from the sparse accident dataset. Furthermore, an external traffic congestion dataset is incorporated to derive a new feature called the “accident impact” factor, which quantifies the influence of an accident on surrounding traffic flow. Extensive experiments were conducted to demonstrate the effectiveness of the proposed hybrid machine learning method in predicting the post-accident impact compared to state-of-the-art baselines. The results reveal a higher precision in predicting minimal impacts (i.e., cases with no reported accidents) and a higher recall in predicting more significant impacts (i.e., cases with reported accidents). Full article
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13 pages, 2055 KiB  
Article
Design and Characterization of Ring-Curve Fractal-Maze Acoustic Metamaterials for Deep-Subwavelength Broadband Sound Insulation
by Jing Wang, Yumeng Sun, Yongfu Wang, Ying Li and Xiaojiao Gu
Materials 2025, 18(15), 3616; https://doi.org/10.3390/ma18153616 - 31 Jul 2025
Viewed by 197
Abstract
Addressing the challenges of bulky, low-efficiency sound-insulation materials at low frequencies, this work proposes an acoustic metamaterial based on curve fractal channels. Each unit cell comprises a concentric circular-ring channel recursively iterated: as the fractal order increases, the channel path length grows exponentially, [...] Read more.
Addressing the challenges of bulky, low-efficiency sound-insulation materials at low frequencies, this work proposes an acoustic metamaterial based on curve fractal channels. Each unit cell comprises a concentric circular-ring channel recursively iterated: as the fractal order increases, the channel path length grows exponentially, enabling outstanding sound-insulation performance within a deep-subwavelength thickness. Finite-element and transfer-matrix analyses show that increasing the fractal order from one to three raises the number of bandgaps from three to five and expands total stop-band coverage from 17% to over 40% within a deep-subwavelength thickness. Four-microphone impedance-tube measurements on the third-order sample validate a peak transmission loss of 75 dB at 495 Hz, in excellent agreement with simulations. Compared to conventional zigzag and Hilbert-maze designs, this curve fractal architecture delivers enhanced low-frequency broadband insulation, structural lightweighting, and ease of fabrication, making it a promising solution for noise control in machine rooms, ducting systems, and traffic environments. The method proposed in this paper can be applied to noise reduction of transmission parts for ceramic automation production. Full article
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29 pages, 3400 KiB  
Article
Synthetic Data Generation for Machine Learning-Based Hazard Prediction in Area-Based Speed Control Systems
by Mariusz Rychlicki and Zbigniew Kasprzyk
Appl. Sci. 2025, 15(15), 8531; https://doi.org/10.3390/app15158531 (registering DOI) - 31 Jul 2025
Viewed by 203
Abstract
This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a [...] Read more.
This work focuses on the possibilities of generating synthetic data for machine learning in hazard prediction in area-based speed monitoring systems. The purpose of the research conducted was to develop a methodology for generating realistic synthetic data to support the design of a continuous vehicle speed monitoring system to minimize the risk of traffic accidents caused by speeding. The SUMO traffic simulator was used to model driver behavior in the analyzed area and within a given road network. Data from OpenStreetMap and field measurements from over a dozen speed detectors were integrated. Preliminary tests were carried out to record vehicle speeds. Based on these data, several simulation scenarios were run and compared to real-world observations using average speed, the percentage of speed limit violations, root mean square error (RMSE), and percentage compliance. A new metric, the Combined Speed Accuracy Score (CSAS), has been introduced to assess the consistency of simulation results with real-world data. For this study, a basic hazard prediction model was developed using LoRaWAN sensor network data and environmental contextual variables, including time, weather, location, and accident history. The research results in a method for evaluating and selecting the simulation scenario that best represents reality and drivers’ propensities to exceed speed limits. The results and findings demonstrate that it is possible to produce synthetic data with a level of agreement exceeding 90% with real data. Thus, it was shown that it is possible to generate synthetic data for machine learning in hazard prediction for area-based speed control systems using traffic simulators. Full article
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20 pages, 10603 KiB  
Article
A Safety-Based Approach for the Design of an Innovative Microvehicle
by Michelangelo-Santo Gulino, Susanna Papini, Giovanni Zonfrillo, Thomas Unger, Peter Miklis and Dario Vangi
Designs 2025, 9(4), 90; https://doi.org/10.3390/designs9040090 (registering DOI) - 31 Jul 2025
Viewed by 141
Abstract
The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper [...] Read more.
The growing popularity of Personal Light Electric Vehicles (PLEVs), such as e-scooters, has revolutionized urban mobility by offering compact, cost-effective, and environmentally friendly transportation solutions. However, safety concerns, including inadequate infrastructure, poor protective measures, and high accident rates, remain critical challenges. This paper presents the design and development of an innovative self-balancing microvehicle under the H2020 LEONARDO project, which aims to address these challenges through advanced engineering and user-centric design. The vehicle combines features of monowheels and e-scooters, integrating cutting-edge technologies to enhance safety, stability, and usability. The design adheres to European regulations, including Germany’s eKFV standards, and incorporates user preferences identified through representative online surveys of 1500 PLEV users. These preferences include improved handling on uneven surfaces, enhanced signaling capabilities, and reduced instability during maneuvers. The prototype features a lightweight composite structure reinforced with carbon fibers, a high-torque motorized front wheel, and multiple speed modes tailored to different conditions, such as travel in pedestrian areas, use by novice riders, and advanced users. Braking tests demonstrate deceleration values of up to 3.5 m/s2, comparable to PLEV market standards and exceeding regulatory minimums, while smooth acceleration ramps ensure rider stability and safety. Additional features, such as identification plates and weight-dependent motor control, enhance compliance with local traffic rules and prevent misuse. The vehicle’s design also addresses common safety concerns, such as curb navigation and signaling, by incorporating large-diameter wheels, increased ground clearance, and electrically operated direction indicators. Future upgrades include the addition of a second rear wheel for enhanced stability, skateboard-like rear axle modifications for improved maneuverability, and hybrid supercapacitors to minimize fire risks and extend battery life. With its focus on safety, regulatory compliance, and rider-friendly innovations, this microvehicle represents a significant advancement in promoting safe and sustainable urban mobility. Full article
(This article belongs to the Section Vehicle Engineering Design)
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22 pages, 580 KiB  
Article
The Choice of Training Data and the Generalizability of Machine Learning Models for Network Intrusion Detection Systems
by Marcin Iwanowski, Dominik Olszewski, Waldemar Graniszewski, Jacek Krupski and Franciszek Pelc
Appl. Sci. 2025, 15(15), 8466; https://doi.org/10.3390/app15158466 - 30 Jul 2025
Viewed by 270
Abstract
Network Intrusion Detection Systems (NIDS) driven by Machine Learning (ML) algorithms are usually trained using publicly available datasets consisting of labeled traffic samples, where labels refer to traffic classes, usually one benign and multiple harmful. This paper studies the generalizability of models trained [...] Read more.
Network Intrusion Detection Systems (NIDS) driven by Machine Learning (ML) algorithms are usually trained using publicly available datasets consisting of labeled traffic samples, where labels refer to traffic classes, usually one benign and multiple harmful. This paper studies the generalizability of models trained on such datasets. This issue is crucial given the application of such a model to actual internet traffic because high-performance measures obtained on datasets do not necessarily imply similar efficiency on the real traffic. We propose a procedure consisting of cross-validation using various sets sharing some standard traffic classes combined with the t-SNE visualization. We apply it to investigate four well-known and widely used datasets: UNSW-NB15, CIC-CSE-IDS2018, BoT-IoT, and ToN-IoT. Our investigation reveals that the high accuracy of a model obtained on one set used for training is reproducible on others only to a limited extent. Moreover, benign traffic classes’ generalizability differs from harmful traffic. Given its application in the actual network environment, it implies that one needs to select the data used to train the ML model carefully to determine to what extent the classes present in the dataset used for training are similar to those in the real target traffic environment. On the other hand, merging datasets may result in more exhaustive data collection, consisting of a more diverse spectrum of training samples. Full article
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12 pages, 1078 KiB  
Article
Aerostability of Sin Nombre Virus Aerosol Related to Near-Field Transmission
by Elizabeth A. Klug, Danielle N. Rivera, Vicki L. Herrera, Ashley R. Ravnholdt, Daniel N. Ackerman, Yangsheng Yu, Chunyan Ye, Steven B. Bradfute, St. Patrick Reid and Joshua L. Santarpia
Pathogens 2025, 14(8), 750; https://doi.org/10.3390/pathogens14080750 (registering DOI) - 30 Jul 2025
Viewed by 242
Abstract
Sin Nombre virus (SNV) is the main causative agent of hantavirus cardiopulmonary syndrome (HCPS) in North America. SNV is transmitted via environmental biological aerosols (bioaerosols) produced by infected deer mice (Peromyscus maniculatus). It is similar to other viruses that have environmental [...] Read more.
Sin Nombre virus (SNV) is the main causative agent of hantavirus cardiopulmonary syndrome (HCPS) in North America. SNV is transmitted via environmental biological aerosols (bioaerosols) produced by infected deer mice (Peromyscus maniculatus). It is similar to other viruses that have environmental transmission routes rather than a person-to-person transmission route, such as avian influenza (e.g., H5N1) and Lassa fever. Despite the lack of person-to-person transmission, these viruses cause a significant public health and economic burden. However, due to the lack of targeted pharmaceutical preventatives and therapeutics, the recommended approach to prevent SNV infections is to avoid locations that have a combination of low foot traffic, receive minimal natural sunlight, and where P. maniculatus may be found nesting. Consequently, gaining insight into the SNV bioaerosol decay profile is fundamental to the prevention of SNV infections. The Biological Aerosol Reaction Chamber (Bio-ARC) is a flow-through system designed to rapidly expose bioaerosols to environmental conditions (ozone, simulated solar radiation (SSR), humidity, and other gas phase species at stable temperatures) and determine the sensitivity of those particles to simulated ambient conditions. Using this system, we examined the bioaerosol stability of SNV. The virus was found to be susceptible to both simulated solar radiation and ozone under the tested conditions. Comparisons of decay between the virus aerosolized in residual media and in a mouse bedding matrix showed similar results. This study indicates that SNV aerosol particles are susceptible to inactivation by solar radiation and ozone, both of which could be implemented as effective control measures to prevent disease in locations where SNV is endemic. Full article
(This article belongs to the Special Issue Airborne Transmission of Pathogens)
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32 pages, 3694 KiB  
Article
Decoding Urban Traffic Pollution: Insights on Trends, Patterns, and Meteorological Influences for Policy Action in Bucharest, Romania
by Cristiana Tudor, Alexandra Horobet, Robert Sova, Lucian Belascu and Alma Pentescu
Atmosphere 2025, 16(8), 916; https://doi.org/10.3390/atmos16080916 - 29 Jul 2025
Viewed by 360
Abstract
Traffic-related pollutants remain a challenging global issue, with significant policy implications. Within the European Union, Romania has the highest yearly societal cost per capita due to air pollution, which kills 29,000 Romanians every year, whereas the health and economic costs are also significant. [...] Read more.
Traffic-related pollutants remain a challenging global issue, with significant policy implications. Within the European Union, Romania has the highest yearly societal cost per capita due to air pollution, which kills 29,000 Romanians every year, whereas the health and economic costs are also significant. In this context, municipal authorities in the country, particularly in high-density areas, should place a strong focus on mitigating air pollution. In particular, the capital city, Bucharest, ranks among the most congested cities in the world while registering the highest pollution index in Romania, with traffic pollution responsible for two-thirds of its air pollution. Consequently, studies that assess and model pollution trends are paramount to inform local policy-making processes and assist pollution-mitigation efforts. In this paper, a generalized additive modeling (GAM) framework is employed to model hourly concentrations of nitrogen dioxide (NO2), i.e., a relevant traffic-pollution proxy, at a busy urban traffic location in central Bucharest, Romania. All models are developed on a wide, fine-granularity dataset spanning January 2017–December 2022 and include extensive meteorological covariates. Model robustness is assured by switching between the generalized additive model (GAM) framework and the generalized additive mixed model (GAMM) framework when the residual autoregressive process needs to be specifically acknowledged. Results indicate that trend GAMs explain a large amount of the hourly variation in traffic pollution. Furthermore, meteorological factors contribute to increasing the models’ explanation power, with wind direction, relative humidity, and the interaction between wind speed and the atmospheric pressure emerging as important mitigators for NO2 concentrations in Bucharest. The results of this study can be valuable in assisting local authorities to take proactive measures for traffic pollution control in the capital city of Romania. Full article
(This article belongs to the Special Issue Sources Influencing Air Pollution and Their Control)
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33 pages, 16026 KiB  
Article
Spatiotemporal Analysis of BTEX and PM Using Me-DOAS and GIS in Busan’s Industrial Complexes
by Min-Kyeong Kim, Jaeseok Heo, Joonsig Jung, Dong Keun Lee, Jonghee Jang and Duckshin Park
Toxics 2025, 13(8), 638; https://doi.org/10.3390/toxics13080638 - 29 Jul 2025
Viewed by 232
Abstract
Rapid industrialization and urbanization have progressed in Korea, yet public attention to hazardous pollutants emitted from industrial complexes remains limited. With the increasing coexistence of industrial and residential areas, there is a growing need for real-time monitoring and management plans that account for [...] Read more.
Rapid industrialization and urbanization have progressed in Korea, yet public attention to hazardous pollutants emitted from industrial complexes remains limited. With the increasing coexistence of industrial and residential areas, there is a growing need for real-time monitoring and management plans that account for the rapid dispersion of hazardous air pollutants (HAPs). In this study, we conducted spatiotemporal data collection and analysis for the first time in Korea using real-time measurements obtained through mobile extractive differential optical absorption spectroscopy (Me-DOAS) mounted on a solar occultation flux (SOF) vehicle. The measurements were conducted in the Saha Sinpyeong–Janglim Industrial Complex in Busan, which comprises the Sasang Industrial Complex and the Sinpyeong–Janglim Industrial Complex. BTEX compounds were selected as target volatile organic compounds (VOCs), and real-time measurements of both BTEX and fine particulate matter (PM) were conducted simultaneously. Correlation analysis revealed a strong relationship between PM10 and PM2.5 (r = 0.848–0.894), indicating shared sources. In Sasang, BTEX levels were associated with traffic and localized facilities, while in Saha Sinpyeong–Janglim, the concentrations were more influenced by industrial zoning and wind patterns. Notably, inter-compound correlations such as benzene–m-xylene and p-xylene–toluene suggested possible co-emission sources. This study proposes a GIS-based, three-dimensional air quality management approach that integrates variables such as traffic volume, wind direction, and speed through real-time measurements. The findings are expected to inform effective pollution control strategies and future environmental management plans for industrial complexes. Full article
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4 pages, 976 KiB  
Proceeding Paper
Developing a Risk Recognition System Based on a Large Language Model for Autonomous Driving
by Donggyu Min and Dong-Kyu Kim
Eng. Proc. 2025, 102(1), 7; https://doi.org/10.3390/engproc2025102007 - 29 Jul 2025
Viewed by 138
Abstract
Autonomous driving systems have the potential to reduce traffic accidents dramatically; however, conventional modules often struggle to accurately detect risks in complex environments. This study presents a novel risk recognition system that integrates the reasoning capabilities of a large language model (LLM), specifically [...] Read more.
Autonomous driving systems have the potential to reduce traffic accidents dramatically; however, conventional modules often struggle to accurately detect risks in complex environments. This study presents a novel risk recognition system that integrates the reasoning capabilities of a large language model (LLM), specifically GPT-4, with traffic engineering domain knowledge. By incorporating surrogate safety measures such as time-to-collision (TTC) alongside traditional sensor and image data, our approach enhances the vehicle’s ability to interpret and react to potentially dangerous situations. Utilizing the realistic 3D simulation environment of CARLA, the proposed framework extracts comprehensive data—including object identification, distance, TTC, and vehicle dynamics—and reformulates this information into natural language inputs for GPT-4. The LLM then provides risk assessments with detailed justifications, guiding the autonomous vehicle to execute appropriate control commands. The experimental results demonstrate that the LLM-based module outperforms conventional systems by maintaining safer distances, achieving more stable TTC values, and delivering smoother acceleration control during dangerous scenarios. This fusion of LLM reasoning with traffic engineering principles not only improves the reliability of risk recognition but also lays a robust foundation for future real-time applications and dataset development in autonomous driving safety. Full article
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19 pages, 6870 KiB  
Article
Impact of Urban Elevated Complex Roads on Acoustic Environment Quality in Adjacent Areas: A Field Measurement Study
by Guangrui Yang, Lingshan He, Yimin Wang and Qilin Liu
Buildings 2025, 15(15), 2662; https://doi.org/10.3390/buildings15152662 - 28 Jul 2025
Viewed by 157
Abstract
The current focus of urban environmental governance is on the traffic noise pollution caused by road transportation. Elevated complex roads, defined as transportation systems comprising elevated roads and underlying ground-level roads, exhibit unique traffic noise distribution characteristics due to the presence of double-decked [...] Read more.
The current focus of urban environmental governance is on the traffic noise pollution caused by road transportation. Elevated complex roads, defined as transportation systems comprising elevated roads and underlying ground-level roads, exhibit unique traffic noise distribution characteristics due to the presence of double-decked roads and viaducts. This study conducted noise measurements at two sections of elevated complex roads in Guangzhou, including assessing noise levels at the road boundaries and examining noise distribution at different distances from roads and building heights. The results show that the horizontal distance attenuation of noise in adjacent areas exhibits no significant difference from that of ground-level roads, but substantial discrepancies exist in vertical height distribution. The under-viaduct space experiences more severe noise pollution than areas above the viaduct height, and the installation of sound barriers alters the spatial distribution trend of traffic noise. Given that installing sound barriers solely on elevated roads is insufficient to improve the acoustic environment, systematic noise mitigation strategies should be developed for elevated composite road systems. Additionally, the study reveals that nighttime noise fluctuations are significantly greater than those during the day, further exacerbating residents’ noise annoyance. Full article
(This article belongs to the Special Issue Vibration Prediction and Noise Assessment of Building Structures)
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