Search Results (312)

Grading and providing early warning of traffic congestion density is crucial for the timely coordination and optimization of traffic management. However, current traffic density detection methods primarily rely on historical traffic flow data, resulting in ambiguous thresholds for congestion classification. To overcome these challenges, this paper proposes a traffic density grading algorithm for remote sensing images that integrates adaptive clustering and multi-scale fusion. A dynamic neighborhood radius adjustment mechanism guided by spatial distribution characteristics is introduced to ensure consistency between the density clustering parameter space and the decision domain for image cropping, thereby addressing the issues of large errors and low efficiency in existing cropping techniques. Furthermore, a hierarchical detection framework is developed by incorporating a dynamic background suppression strategy to fuse multi-scale spatiotemporal features, thereby enhancing the detection accuracy of small objects in remote sensing imagery. Additionally, we propose a novel method that combines density analysis with pixel-level gradient quantification to construct a traffic state evaluation model featuring a dual optimization strategy. This enables precise detection and grading of traffic congestion areas while maintaining low computational overhead. Experimental results demonstrate that the proposed approach achieves average precision (AP) scores of 32.6% on the VisDrone dataset and 16.2% on the UAVDT dataset. Full article

With the accelerated commercialization of autonomous vehicles, new accident types and complex risk factors have emerged beyond the scope of existing traffic safety management systems. This study aims to contribute to sustainable safety by establishing a quantitative basis for early recognition and response to high-risk situations in urban traffic environments where autonomous and conventional vehicles coexist. To this end, high-risk factors were identified through a combination of literature meta-analysis, accident history and image analysis, autonomous driving video review, and expert seminars. For analytical structuring, the six-layer scenario framework from the PEGASUS project was redefined. Using the analytic hierarchy process (AHP), 28 high-risk factors were identified. A risk prediction model framework was then developed, incorporating observational indicators derived from expert rankings. These indicators were structured as input variables for both road segments and autonomous vehicles, enabling spatial risk assessment through agent-based strategies. This space–object integration-based prediction model supports the early detection of high-risk situations, the designation of high-enforcement zones, and the development of preventive safety systems, infrastructure improvements, and policy measures. Ultimately, the findings offer a pathway toward achieving sustainable safety in mixed traffic environments during the initial deployment phase of autonomous vehicles. Full article

To study the generation and dissipation process of agglomerate fog in mountainous expressways and deeply understand the hazard mechanisms of agglomerate fog sections in mountainous expressways, based on the analysis of the geographical location characteristics of mountainous expressways and the spatial and temporal distribution characteristics of agglomerate fog, the airspace constitutive model of agglomerate fog in mountainous expressways was constructed based on Newton constitutive theory. Firstly, the properties of the Newtonian fluid and cluster fog were compared and analyzed, and the influence mechanism of environmental factors such as the altitude difference, topography, water system, valley effect, and vegetation on the generation and dissipation of agglomerate fog in mountainous expressways was analyzed. Based on Newton’s constitutive theory, the constitutive model of temperature, humidity, wind speed, and agglomerate fog points in the foggy airspace of the mountainous expressway was established. Then, the time and spatial distribution of fog in Chongqing and Guizhou from 2021 to 2023 were analyzed. Finally, the model was verified by using the meteorological data and fog warning data of Liupanshui City, Guizhou Province in 2023. The results show that the foggy airspace of mountainous expressways can be defined as “the space occupied by the agglomerate fog that occurs above the mountain expressway”; The temporal and spatial distribution of foggy airspace on expressways in mountainous areas is closely related to the topography, water system, vegetation distribution, and local microclimate formed by thermal radiation. The horizontal and vertical movements of the atmosphere have little influence on the foggy airspace on expressways in mountainous areas. The specific manifestation of time distribution is that the occurrence of agglomerate fog is concentrated from November to April of the following year, and the daily occurrence time is mainly concentrated between 4:00–8:00 and 18:00–22:00. The calculation results of the foggy airspace constitutive model of the expressway in the mountainous area show that when there is low surface radiation or no surface radiation, the fogging value range is [90, 100], and the fogging value range is [50, 70] when there is high surface radiation (>200), and there is generally no fog in other intervals. The research results can provide a theoretical basis for traffic safety management and control of mountainous expressway fog sections. Full article

The growing density of space objects in low Earth orbit (LEO), driven by the deployment of large satellite constellations, has elevated the risk of orbital collisions and the need for high-precision conjunction analysis. Traditional methods based on Two-Line Element (TLE) data suffer from limited accuracy and insufficient uncertainty modeling. This study proposes a hybrid collision assessment framework that combines Monte Carlo simulation, spline-based refinement of the time of closest approach (TCA), and a multi-stage deterministic refinement process. The methodology begins with probabilistic sampling of TLE uncertainties, followed by a coarse search for TCA using the SGP4 propagator. A cubic spline interpolation then enhances temporal resolution, and a hierarchical multi-stage refinement computes the final TCA and minimum distance with sub-second and sub-kilometer accuracy. The framework was validated using real-world TLE data from over 2600 debris objects and active satellites. Results demonstrated a reduction in average TCA error to 0.081 s and distance estimation error to 0.688 km. The approach is computationally efficient, with average processing times below one minute per conjunction event using standard hardware. Its compatibility with operational space situational awareness (SSA) systems and scalability for high-volume screening make it suitable for integration into real-time space traffic management workflows. Full article

Aging railroad bridges present complex challenges due to advancing deterioration and outdated design assumptions. This study develops a comprehensive analytical approach for assessing an aging steel truss railroad bridge through finite element (FE) modeling, sensitivity analysis, and model updating, supported by field testing. An initial FE model of the bridge was created based on original drawings and field observations. Field testing using a laser Doppler vibrometer captured the bridge’s dynamic response (vibrations and deflections) under regular train traffic. Key structural parameters (material properties, section properties, support conditions) were identified and varied in a sensitivity analysis to determine their influence on model outputs. A hybrid sensitivity analysis combining log-normal sampling and a genetic algorithm (GA) was employed to explore the parameter space and calibrate the model. The GA optimization tuned the FE model parameters to minimize discrepancies between simulated results and field measurements, focusing on vertical deflections and natural frequencies. The updated FE model showed significantly improved agreement with observed behavior; for example, vertical deflections under a representative train were matched within a few percent, and natural frequencies were accurately reproduced. This validated model provides a more reliable tool for predicting structural performance and fatigue life under various loading scenarios. The results demonstrate that integrating field data, sensitivity analysis, and model updating can greatly enhance the accuracy of structural assessments for aging railroad bridges, supporting more informed maintenance and management decisions. Full article

This review examines the effect of geometric properties and the spacing of road humps on vehicle speed and noise, with a particular emphasis on South Asian contexts, especially Malaysia. Road humps are widely used traffic-calming devices designed to reduce vehicle speed and enhance road safety. The effectiveness of these measures is strongly influenced by parameters such as height, width, profile, and placement intervals. While the geometric optimization of humps generally improves speed-reduction outcomes, several studies indicate that braking and acceleration at humps can lead to increased traffic noise, particularly in residential and high-density areas. This review also explores design strategies and material choices (e.g., asphalt use, sinusoidal profiles) that may help mitigate noise impacts. Overall, a balance between speed control and noise management is necessary to ensure both safety and community acceptance. Full article

Real-time train rescheduling is a widely used strategy to minimize knock-on delays in railway networks. While recent research has introduced intelligent solutions to railway traffic management, the tight interdependence of train timetables and the intrinsic complexity of railway networks have hindered the scalability of these approaches to large-scale systems. This paper proposes a multi-agent system (MAS) that addresses these challenges by decomposing the network into single-junction levels, significantly reducing the search space for real-time rescheduling. The MAS employs a Condorcet voting-based collaborative approach to ensure global feasibility and prevent overly localized optimization by individual junction agents. This decentralized approach enhances both the quality and scalability of train rescheduling solutions. We tested the MAS on a railway network in the UK and compared its performance with the First-Come-First-Served (FCFS) and Timetable Order Enforced (TTOE) routing methods. The computational results show that the MAS significantly outperforms FCFS and TTOE in the tested scenarios, yielding up to a 34.11% increase in network capacity as measured by the defined objective function, thus improving network line capacity. Full article

With the growth of the urban population and economic level, the issue of urban parking space shortages (UPSSs) has assumed growing prominence. This persistent issue not only exacerbates traffic congestion but also contributes to environmental pollution, highlighting the need for system-oriented mitigation strategies. First, an algorithm for mitigating UPSSs based on nudge theory was constructed, in order to determine how the nudge strategies work. Second, nudge tools, including gain disclosure, salience, and outcome notification, were integrated to construct a mitigation model for UPSSs, which synthesizes nudge theory, the model of self-regulatory processes involved in behavioral change, and system dynamics (NT-SPBC-SD theory). Finally, four scenarios of natural development, guide adjustment, balanced regulation, and enhanced change were simulated. The findings of this study are as follows: (1) The UPSS mitigation had multiple overlapping effects and critical point effects, and the nudge strategy gradually decayed or even rebounded over time. (2) Under the enhanced change scenario, the degree of UPSSs, the amount of illegal parking, and CO₂ emissions from civil vehicles decreased by 21.2%, 6.93%, and 14.54%, respectively. (3) After quantitative comparisons, the balanced regulation scenario with lower implementation costs instead demonstrated superior overall performance. The results support subsequent research and guide the enhancement of urban parking management policies to advance urban sustainability. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

Since 1957, the near-Earth population of trackable space objects has grown in number to over 36,000. Of these 36,000+ trackable objects now in low Earth orbit, just a few thousand are working spacecraft. The rest are Earth-orbiting objects which are no longer operational and are considered to be space junk. Because this junk can no longer receive maneuvering commands from its Earth-based owners, the survivability of other spacecraft traveling through or operating in Earth orbit can be jeopardized by the impacts of any number of pieces of this space junk, whose origins can usually be traced back to defunct satellites. As a result, a major design parameter for Earth-orbiting spacecraft is the possibility of such high-speed impacts and the damage they can cause. Furthermore, several private companies are now launching several thousand spacecraft into Earth orbit, many of which are satellites built for communication purposes. Other satellites have been launched to expand the reach of the World Wide Web and to provide better tools for disaster management. Two questions quickly become evident, namely, what is the beneficial purpose of these large satellite constellations, and what are some of the deleterious consequences of their proliferation? Numerous topics related to space debris will be discussed in this paper, including issues in space law that concern the growing problem of orbital debris. In the end, several areas of concern will be noted that are vital to the continuing presence of humans in near-Earth space and must be addressed as the near-Earth orbital environment becomes more congested and space traffic management becomes more difficult. Full article

Indoor environmental quality (IEQ) of higher education (HE) buildings significantly impacts the built environment sector. This research aimed to optimize learning environments and enhance student comfort, especially post-COVID-19. The study adopts the principles of Post-occupancy Evaluation (POE) to collect and analyze various quantitative and qualitative data through environmental data monitoring, a user perceptions survey, and semi-structured interviews with professionals. Although the environmental conditions generally met existing standards, the findings indicated opportunities for further improvements to better support university communities’ comfort and health. A significant challenge identified by this research is the inability of the facility management to physically manage and operate the vast and complex spaces within HE buildings with contemporary IEQ standards. In response to these findings, this research developed a BIM-based prototype for the real-time monitoring and automated control of IEQ. The prototype integrates a BIM model with Arduino-linked sensors, motors, and traffic lights, with the latter visually indicating IEQ status, while motors automatically adjust environmental conditions based on sensor inputs. The outcomes of this study not only contribute to the ongoing discourse on sustainable building management, especially post-pandemic, but also demonstrate an advancement in the application of BIM technologies to improve IEQ and by extension, occupant wellbeing in HE buildings. Full article

Due to global warming, the impact of heat waves on the sentimental health of urban residents has significantly intensified. However, the associative mechanism between diverse urban functional layouts and residents’ emotions at the street scale remains underexplored. Taking the five core urban areas of Chengdu as an example, this study used natural language processing technology to quantify the sentiments in social media texts and combined traditional geographical information for spatial analysis and correlation analysis, to explore the spatial distribution pattern of sentiments during heat waves (SDHW), as well as the correlation between SDHW and the functional categories of streets (FCS). The findings are as follows: (1) There are significant differences in the spatial distribution pattern of residents’ sentiments in the five core urban areas, and positive emotions within the Second Ring Road exhibit a higher proportion than those of peripheral areas, while negative sentiments are more gathered in the eastern area. (2) The street categories of green space, park, and public show a significant promoting role on residents’ positive sentiments. (3) There is an association between the industrial and commercial categories and negative sentiments, and the impact of the traffic category on residents’ sentiments shows spatial differences. (4) The combination of the residential category and other functional categories has a strong correlation with sentiments, indicating that a reasonable functional combination within residential areas plays a crucial role in promoting residents’ positive sentiments. The current study revealed the influence mechanism of the functional categories of streets on residents’ sentiments during heat waves, providing a scientific basis from the sentimental dimension for the optimization of street functional categories, heat wave emergency management, and the construction of resilient cities. Full article

Urban mobility structure detection and functional distribution learning are significant for urban planning and management. However, existing methods have limitations in handling complex urban data and capturing global spatial structure features. To deal with these challenges, we proposed a multi-scale feature-aware urban mobility structure embedding method based on contrastive learning. First, we designed a multi-scale contrastive learning strategy to effectively learn local human activity features and global spatial structure features, determine the community affiliation of regions, and generate regional embedding vectors. Next, we introduced a correlation matrix to encode the functional synergy and competition of Point of Interests (POIs) and construct the complex correlation between urban mobility structure and urban functional distribution to evaluate the quality of regional embedding vectors. Experiments in Haikou City show that the proposed method can accurately detect the urban mobility structure and functional distribution. The analysis reveals that the central urban area of Haikou exhibits concentrated functions and significant traffic tidal effects, while the suburban areas have relatively weaker functions, with residents displaying a high level of dependence on the central area. Therefore, urban planning needs to optimize the functional layout, improve the functions of the suburbs, and promote the balance of urban space. Full article

Children’s outdoor physical activity (PA) serves as a crucial mechanism for health development, but its safety is affected by urban space design and management. However, most existing studies focus on isolated risk factors or singular spatial typologies, which lack a comprehensive safety assessment framework. This study aims to construct a safety assessment system for children’s outdoor public activity spaces and explore safety optimization strategies. This study employs a mixed methods approach to systematically analyze 13 outdoor public activity spaces across Shanghai, utilizing NVivo 12 Plus for qualitative coding of the data. Based on the coding results, a questionnaire survey targeting parents of children under 12 years old (with a balanced gender ratio) was designed and administered, yielding 509 valid responses. A 32-indicator assessment system was finally constructed via principal component analysis (PCA). The assessment system encompasses seven dimensions: site facilities (24.0%), spatial conditions (16.1%), site management (13.5%), material conditions (13.0%), service facilities (12.8%), traffic and landscape (10.3%), and ground conditions (10.3%). This study provides a quantitative safety assessment instrument for designing child-friendly urban public activity spaces, which has important implications for improving the public health service system and promoting the construction of healthy cities in the Sustainable Development Goals. Full article

In recent years, congestion on port motorways has become increasingly frequent, significantly constraining transportation efficiency and contributing to higher pollution emissions. This paper proposes a novel max-pressure-driven integrated control (IFC-MP) for port motorways, inspired by the max pressure (MP) concept, which continuously adjusts the weights of ramp metering (RM) and the variable speed limit (VSL) based on pressure feedback from the on-ramp and upstream, assigning greater control weight to the side with higher pressure. A queue management mechanism is incorporated to prevent on-ramp overflow. The effectiveness of IFC-MP is verified in SUMO, filling the gap where the previous integrated control methods for port motorways lacked micro-simulation validation. The results show that IFC-MP enhances bottleneck throughput by approximately 7% compared to the no-control case, optimizes the total time spent (TTS) by 26–27%, and improves total pollutant emissions (TPEs) by about 11%. Compared to strategies that use only RM and VSL control, or activate VSL control only after RM reaches its lower bound, the time–space distribution of speed under IFC-MP is more uniform, with smaller fluctuations in bottleneck occupancy. Additionally, IFC-MP maintains relatively stable performance under varying compliance levels. Overall, the IFC-MP is an effective method for alleviating congestion on port motorways, excelling in optimizing both traffic efficiency and pollutant emissions. Full article