Search Results (4,269)

Traffic-induced vibrations from road and rail systems pose a significant threat to the structural integrity and operational safety of buildings, especially masonry structures located near planned infrastructure such as tunnels. This study investigates the dynamic impact of such vibrations on a representative early 20th-century masonry building situated within the influence zone of a design railway tunnel. A comprehensive analysis combining geological, structural, and vibration propagation data was conducted. A detailed 3D finite element model was developed in Diana FEA v10.7, incorporating building material properties, subsoil conditions, and anticipated train-induced excitations. Various vibration isolation strategies were evaluated, including the use of block supports and vibro-isolation mats. The model was calibrated using pre-construction measurements, and simulations were carried out in the linear-elastic range to prevent resident-related claims. Results showed that dynamic stresses in masonry walls and wooden floor beams remain well below critical thresholds, even in areas with stress concentration. Among the tested configurations, vibration mitigation systems significantly reduced the transmitted forces. This research highlights the effectiveness of integrated numerical modelling and vibration control solutions in protecting structures from traffic-induced vibrations and supports informed engineering decisions in tunnel design and urban development planning. Full article

This paper investigates the dispersion of atmospheric pollutants in urban environments using a fractional-order convection–diffusion-reaction model with dynamic line sources associated with vehicle traffic. The model includes Caputo fractional time derivatives and Riesz fractional space derivatives to account for memory effects and non-local transport phenomena characteristic of complex urban air flows. Vehicle trajectories are generated stochastically on the road network graph using Dijkstra’s algorithm, and each moving vehicle acts as a mobile line source of pollutant emissions. To reflect the daily variability of emissions, a time-dependent modulation function determined by unknown parameters is included in the source composition. These parameters are inferred by solving an inverse problem using synthetic concentration measurements from several fixed observation points throughout the area. The study presents two main contributions. Firstly, a detailed numerical analysis of how fractional derivatives affect pollutant dispersion under realistic time-varying mobile source conditions, and secondly, an evaluation of the performance of the proposed parameter estimation method for reconstructing time-varying emission rates. The results show that fractional-order models provide increased flexibility for representing anomalous transport and retention effects, and the proposed method allows for reliable recovery of emission dynamics from sparse measurements. Full article

Frequent transit services in urban areas have the potential to increase their accessibility to transit-dependent riders and reduce congestion by attracting new ridership through a modal shift. However, bus services operating in mixed traffic face operational challenges that reduce reliability and hinder their attractiveness. The sources of unreliability can range from local-level conditions, like the road infrastructure, to higher-level decisions, like the service plan. For the effective planning of improvement strategies, both scales of analysis must be considered. This paper uses a novel modeling framework to understand reliability by analyzing the route and segment factors separately. The Chicago Transit Authority (CTA) bus network is used as a case study for the analysis. The data reflect the operational, demand, and urban conditions of 50 high-frequency bus routes. At the route level, we use the coefficient of headway variation as the dependent variable and diverse route characteristics as explanatory variables. The results indicate that the most significant contributors to the variability of headways are variability in schedules and dispatching at terminals. It is also found that driver experience impacts reliability and that east–west routes are more unreliable than north–south routes. At the segment level, we use data from trips involved in bunching and gaps. As the dependent variable, a novel measure is formulated to capture how quickly bunching or gaps are formed. The bunching and gap events are treated as separate regression models. Findings suggest that link and dwell time variability are the most significant contributors to gap and bunching formation. In terms of infrastructure, bus lane segments reduce gap formations, and left turns increase bunching and gap formations. The insights presented can inform improvements in service and transit infrastructure planning to improve transit level of service (LOS) and support the future of sustainable, smart cities. Full article

Urban networks are a key force in reshaping regional resilience patterns. However, existing research has not yet systematically elucidated, from a physical–virtual integration perspective, the underlying mechanisms through which composite urban networks shape multidimensional urban resilience in regions confronted with severe environmental and infrastructural challenges. Northwest China, characterized by its extreme arid climate, pronounced core–periphery structure, and heavy reliance on overland transportation, provides an important empirical context for examining the unique relationship between network centrality and the mechanisms of resilience formation. Based on the panel data of 33 prefecture-level cities in northwest China from 2011 to 2023, this article empirically examines the impact of the composite urban network constructed by traffic and information flows on urban resilience from the perspective of network node centrality using a two-way fixed-effects model. It is found that (1) the spatial evolution of urban resilience in northwest China is characterized by “core leadership—gradient agglomeration”: provincial capitals demonstrate significantly the highest resilience levels, while non-provincial cities are predominantly characterized by medium resilience and contiguous distribution, and the growth rate of low-resilience cities is faster, which pushes down the relative gap in the region, but the absolute gap persists; (2) the urban network in this region is characterized by a highly centralized topology, which improves the efficiency of resource allocation yet simultaneously introduces systemic vulnerability due to its over-reliance on a limited number of core hubs; (3) urban network centrality exerts a significant positive impact on resilience enhancement (β = 0.002, p < 0.01) and the core nodes of the city through the control of resources to strengthen the economic, ecological, social, and infrastructural resilience; (4) multi-dimensional factors synergistically drive the resilience, with the financial development level, economic density, and informationization level as a positive pillar. The population size and rough water utilization significantly inhibit the resilience of the region. Accordingly, the optimization path of “multi-center resilience network reconstruction, classified measures to break resource constraints, regional wisdom, and collaborative governance” is proposed to provide theoretical support and a practical paradigm for the construction of resilient cities in northwest China. Full article

Reducing emissions through the invisible hand of the market has become an important way to promote sustainable environmental development. The shadow price of carbon dioxide ($C{O}_2$) is the core element of the carbon market, and its accuracy depends on the micro level of the measurement data. In view of this, this paper innovatively uses enterprise level input-output data and combines the stochastic frontier method to obtain $C{O}_2$ shadow prices in China’s industrial sector. On this basis, the impacts of research and development (R&D) intensity, opening up level, traffic development level, population density, industrial structure, urbanization level, human resources level, degree of education, and environmental governance intensity on shadow price are discussed. In further analysis, this study introduces a Spatial Durbin Model (SDM) to evaluate the spatial spillover effects of $C{O}_2$ shadow price itself and its influencing factors. The research results indicate that market-oriented emission abatement measures across industries and regions can reduce total costs, and it is necessary to consider incorporating carbon tax into low-carbon policies to compensate for the shortcomings of the carbon Emission Trading Scheme (ETS). In addition, neighboring regions should coordinate emission abatement tasks in a unified manner to realize a sustainable reduction in $C{O}_2$ emissions. Full article

Small object detection in UAV-based remote sensing imagery is crucial for applications such as traffic monitoring, emergency response, and urban management. However, aerial images often suffer from low object resolution, complex backgrounds, and varying lighting conditions, leading to missed or false detections. To address these challenges, we propose DCS-YOLOv8, an enhanced object detection framework tailored for small target detection in UAV scenarios. The proposed model integrates a Dynamic Convolution Attention Mixture (DCAM) module to improve global feature representation and combines it with the C2f module to form the C2f-DCAM block. The C2f-DCAM block, together with a lightweight SCDown module for efficient downsampling, constitutes the backbone DCS-Net. In addition, a dedicated P2 detection layer is introduced to better capture high-resolution spatial features of small objects. To further enhance detection accuracy and robustness, we replace the conventional CIoU loss with a novel Scale-based Dynamic Balanced IoU (SDBIoU) loss, which dynamically adjusts loss weights based on object scale. Extensive experiments on the VisDrone2019 dataset demonstrate that the proposed DCS-YOLOv8 significantly improves small object detection performance while maintaining efficiency. Compared to the baseline YOLOv8s, our model increases precision from 51.8% to 54.2%, recall from 39.4% to 42.1%, $mA{P}_{0.5}$ from 40.6% to 44.5%, and $mA{P}_{0.5:0.95}$ from 24.3% to 26.9%, while reducing parameters from 11.1 M to 9.9 M. Moreover, real-time inference on RK3588 embedded hardware validates the model’s suitability for onboard UAV deployment in remote sensing applications. Full article

Background: Bicycles constitute a primary means of transportation, particularly within the scope of urban micromobility. However, the use of this mode of transport is associated with the risk of traffic accidents and subsequent maxillofacial trauma. Cyclists are classified as vulnerable road users, among whom the assessment of injury patterns is a significant issue. This study aimed to identify the most common maxillofacial fractures resulting from bicycle-related traffic accidents. Methods: A retrospective analysis was conducted on the medical records of patients treated at the Clinic of Maxillofacial Surgery at the University Clinical Hospital in Poznan, who sustained maxillofacial injuries as a result of bicycle-related accidents between 2019 and 2023. Results: A total of 99 patients met the inclusion criteria. Most of the study population was males (70.7%), with a median age of 38. Accidents most frequently occurred during the summer months and on Fridays and weekends. The most common fracture site was the mandible (40.4%), with double fractures being the predominant type. Additionally, zygomatic-orbital fractures were frequently observed (30.3%). In terms of treatment, surgical intervention was predominant, and the mean duration of hospitalisation was 6 days. Only 5.1% of patients were under the influence of alcohol at the time of the incident. Furthermore, it was found that isolated mandibular fractures occurred more frequently in younger patients, whereas midface fractures of the Le Fort II and III types were more commonly observed in individuals under the influence of alcohol at the time of the event. Moreover, accidents involving alcohol consumption were associated with a higher incidence of concomitant cranio-cerebral injuries. Conclusions: Defining the profile of maxillofacial fractures resulting from bicycle accidents constitutes a clinically relevant issue. Additionally, identifying the main risk factors and developing preventive measures is of critical importance. Full article

Exploring the pollution characteristics and ecological risks of urbanization on lakes in urban fringe areas has guiding significance for the control and scientific management of heavy metal pollution in lakes in urban fringe areas. Taking the West Lake in Kaifeng city as an example, the samples of the sediments and surface water of the lake were collected, and the contents of heavy metals (As, Cd, Cr, Cu, Ni, Pb, and Zn) were measured, assessing the degree and ecological risk of heavy metal pollution using the Geo-Accumulation Index (I_geo) and Potential Ecological Risk Index methods (RI); and the sources of pollution were identified. The results show that the heavy metal concentrations in the surface water of the West Lake in Kaifeng city are generally low; average concentrations of Cd, Cu, Zn, Cr, Ni, Pb, and As in sediments are 3.120, 1.810, 1.700, 1.540, 1.000, 0.990, and 0.430 times higher than the background value of fluvo-aquic soil, respectively. The sequence of the average I_geo from high to low is Cd (1.020) > Cu (0.220) > Zn (0.160) > Cr (0.000) > Pb (−0.610) > Ni (−0.640) > As (−1.850). Among them, contaminations with Pb are classed as moderately polluted; As pollution is relatively light, while other heavy metals are unpolluted. The average Potential Ecological Risk Coefficient (E) values for seven heavy metals are Cd (93.500) > Cu (9.040) > Ni (4.990) > Pb (4.950) > As (4.290) > Cr (3.080) > Zn (1.700). Cd is at a considerable potential ecological risk, while other heavy metals are at low ecological risks. Heavy metal pollution in sediment of West Lake in Kaifeng mainly comes from traffic activities such as yacht machinery wear and gasoline burning. The research findings provide a scientific foundation for developing effective mitigation strategies against heavy metal contamination in peri-urban lacustrine ecosystems. Full article

Black carbon (BC) from incomplete combustion sources including traffic emissions affects human health due to its physical characteristics and ubiquity in urban environments. We examined the effects of BC on microbial growth in the presence of particulate matter (PM), using Aquadag as a surrogate for BC. Brunauer–Emmett–Teller analysis showed BC had a specific surface area of 123.2 m² g⁻¹, with over 90% of particles smaller than 100 nm, indicating strong surface interaction potential. Pseudomonas aeruginosa PA14 was cultured for 7 days with various BC concentrations and fixed PM. Increasing BC (0–100 ng mL⁻¹) significantly inhibited growth, evidenced by a decline in cellular adenosine triphosphate (cATP) with a slope of −1.296 ± 0.258 cATP ng mL⁻¹/BC ng mL⁻¹. The seven-day mean cATP slope ranged from 77 to 131, with control at 161. The biomass stress index (BSI) increased by 56%, rising from 28.6 ± 8.8% (control) to 44.6 ± 16.1% under high BC. The BSI change was minimal on day 1 (<+0.1% per BC ng mL⁻¹) but greater on days 5 (+0.125 ± 0.052%) and 7 (+0.130 ± 0.075%). BC does not cause immediate microbial death, but prolonged exposure induces cumulative stress, damages synthetic enzymes, inhibits growth, and may lead to cell death, with potential public health implications. Full article

The metropolitan agglomeration in and around Bucharest, Romania’s capital and largest city, has experienced significant growth in recent decades, both economically and demographically. With over two million residents in its metropolitan area, Bucharest faces urban mobility challenges characterized by congested roads, overcrowded public transport routes, limited parking, and air pollution. This study evaluates the potential of AI-driven adaptive traffic signal control to address these challenges using an agent-based simulation approach. The authors focus on Bucharest’s north-western part, a critical congestion area. A detailed road network was derived from OpenStreetMap and calibrated with empirical traffic data from TomTom Junction Analytics and Route Monitoring (corridor-level speeds and junction-level turn ratios). Using the MATSim framework, the authors implemented and compared fixed-time and adaptive signal control scenarios. The adaptive approach uses a decentralized, demand-responsive algorithm to minimize delays and queue spillback in real time. Simulation results indicate that adaptive signal control significantly improves network-wide average speeds, reduces congestion peaks, and flattens the number of en-route agents throughout the day, compared to fixed-time plans. While simplifications remain in the model, such as generalized signal timings and the exclusion of pedestrian movements, these findings suggest that deploying adaptive traffic management systems could deliver substantial operational benefits in Bucharest’s urban context. This work demonstrates a scalable methodology combining open geospatial data, commercial traffic analytics, and agent-based simulation to rigorously evaluate AI-based traffic management strategies, offering evidence-based guidance for urban mobility planning and policy decisions. Full article

Urban traffic congestion remains a major contributor to vehicle emissions and travel inefficiency, prompting the need for adaptive and intelligent traffic management systems. In response, we introduce DeepSIGNAL-ITS (Deep Learning Signal Intelligence for Adaptive Lights in Intelligent Transportation Systems), a unified framework that leverages real-time traffic perception and learning-based control to optimize signal timing and reduce congestion. The system integrates vehicle detection via the YOLOv8 architecture at roadside units (RSUs) and manages signal control using Proximal Policy Optimization (PPO), guided by global traffic indicators such as accumulated vehicle waiting time. Secure communication between RSUs and cloud infrastructure is ensured through Transport Layer Security (TLS)-encrypted data exchange. We validate the framework through extensive simulations in SUMO across diverse urban settings. Simulation results show an average 30.20% reduction in vehicle waiting time at signalized intersections compared to baseline fixed-time configurations derived from OpenStreetMap (OSM). Furthermore, emissions assessed via the HBEFA-based model in SUMO reveal measurable reductions across pollutant categories, underscoring the framework’s dual potential to improve both traffic efficiency and environmental sustainability in simulated urban environments. Full article

This paper analyzes models of goods delivery to city centers, with a specific focus on the city of Rijeka. Urban areas are increasingly facing problems such as traffic congestion, lack of delivery space, and negative environmental impacts. The aim of the research is to examine existing delivery models and propose sustainable solutions that include consolidation centers, alternative fuel vehicles, and smart technologies. The paper presents three main delivery models: using consolidation centers, environmentally friendly vehicles, and modular BentoBox systems. Based on traffic data analysis and surveys with carriers and business entities, it was found that most deliveries are carried out by large diesel vehicles, which often face difficulties due to the lack of designated unloading zones. Building on these findings, several improvement scenarios were developed, including the introduction of one or two consolidation centers and the use of eco-friendly vehicles. The results indicate that the proposed models have the potential to reduce the number of large freight vehicles in the city center, ease traffic congestion, and lower emissions. However, quantitative confirmation of these effects will require the development and application of simulation models. This study therefore serves as a foundation for such future research. Full article

Individuals are routinely exposed to traffic-related air pollution on their commutes, which has significant health impacts. Mitigating exposure to traffic-related pollution is a key urban sustainability concern. In Denver, Colorado, low-income Americans are more likely to rely on buses and spend time waiting at bus stops. Evaluating the contribution of traffic emissions at bus stops can provide important information on risks experienced by these populations. We measured PM_2.5 constituents at eight bus stops and one background reference site in Denver, in the summer of 2023. Source profiles, including gasoline emissions from traffic, were estimated using Positive Matrix Factorization (PMF) analysis of PM_2.5 constituents collected at a Chemical Speciation Network site in our study region. The contributions of the different sources at each bus stop were estimated by regressing the vector of species concentrations at each site (dependent variable) on the source-profile matrix from the PMF analysis (independent variables). Traffic-related emissions (~2.5–6.6 μg/m³) and secondary organics (~3–5 μg/m³) contributed to PM_2.5 at the bus stops in our dataset. The highest traffic-related emissions-derived PM_2.5 concentrations were observed at bus stops near local sources: a gas station and a car wash. The contribution of traffic-related emissions was lower at the background site (~1 μg/m³). Full article

Noise barriers are commonly used to reduce the adverse effects of traffic noise in both urban and suburban settings. While conventional systems constructed from concrete and steel provide reliable acoustic and structural performance, they raise sustainability concerns due to high embodied energy and carbon emissions. Glued-laminated (glulam) timber has emerged as a sustainable alternative, offering a reduced carbon footprint, aesthetic appeal, and effective acoustic performance. However, the crashworthiness of timber-based noise wall systems remains under investigated, particularly with respect to the safety criteria established in the 2016 edition of the American Association of State Highway and Transportation Officials (AASHTO) Manual for Assessing Safety Hardware (MASH). This study presents the full-scale crash testing and evaluation of glulam rubrail and noise wall systems under MASH Test Level 3 (TL-3) impact conditions. Building on a previously tested system compliant with National Cooperative Highway Research Program (NCHRP) Report 350, modifications were made to increase rubrail dimensions to meet higher lateral design loads. Three full-scale vehicle crash tests were conducted using 1100C and 2270P vehicles at 100 km/h and 25 degrees, covering both front- and back-mounted wall configurations. All tested systems demonstrated acceptable structural performance, effective vehicle redirection, and compliance with MASH 2016 occupant risk criteria. There was no penetration or potential for debris intrusion into the occupant compartment, and all measured occupant risk values remained well below allowable thresholds. Minimal damage to structural components was observed. The results confirm that the modified glulam noise wall system meets current impact safety standards and is suitable for use along high-speed roadways. This work supports the integration of sustainable materials into roadside safety infrastructure without compromising crash performance. Full article

Transportation safety studies identify and analyze different contributing factors affecting the safety of road users using virtual reality (VR) traffic simulations in game engines (e.g., Unity). They often either use simplified VR traffic simulation or develop a more advanced simulation requiring substantial technical expertise and resources. The Simulation of Urban Mobility (SUMO) software is widely employed in the field, offering extensive traffic simulation rules such as car-following models, lane changing models, and right-of-way rules. In this study, we develop an open-source virtual reality traffic co-simulation by integrating two different simulation software, SUMO and Unity, and developing a virtual reality traffic simulation where a VR user in Unity interacts with traffic generated by SUMO. In our methodology, we first explain the process of creating road networks. Next, we programmatically integrate SUMO and Unity. Finally, we measure how well this system works using two indicators: the real-time factor (RTF) and frames per second (FPS). RTF compares SUMO’s simulation time to Unity’s simulation time each second, while FPS counts how many images Unity draws each second. Our results showed that our proposed VR traffic simulation can create a realistic traffic environment generated by SUMO under various traffic densities. This work offers a new platform for driver-behavior research and digital-twin applications. Full article