Search Results (240)

Village streets are indispensable spaces for people to perform outdoor activities, and they also directly affect the outdoor wind environment in villages. At present, people are paying more attention to the wind environment comfort of urban residential areas and urban commercial streets, but there is a lack of attention and research on the wind environment comfort of village and town streets. By summarizing the field research and meteorological data of Lefeng Village, we propose the outdoor wind environment evaluation requirements applicable to the Hanjiang River’s Chuan Dao area in the winter and summer seasons. We found that more than 80% of the outdoor wind environment in the summer is less than 1 m/s. Based on the numerical simulation method of computational fluid dynamics, and on the basis of the characteristics of the streets and lanes in the Hanjiang River’s Chuan Dao area, we found that the wind environment is poor in the winter and summer seasons; regarding streets and lanes, we propose three appropriate values, namely building density, building height, and street width. It is suggested that it is appropriate for the building density of the area to be less than 36%, the height of the building to be less than 15 m, and the width of the street to be 6–11 m when the street is open to traffic and 3–6 m when only pedestrians are passing through the area. Full article

With the advancement of child-friendly urban planning initiatives, the significance of Active School Travel Spaces (ASTSs) in shaping urban development and promoting the physical and mental well-being of children has become increasingly apparent. This research focuses on 151 public primary schools in the central urban area of Lanzhou City. Utilizing the Amap pedestrian route planning API, we establish a walking route network, evaluate the paths using spatial syntax and street view recognition methods, and analyze their influencing factors using a Geographic Detector model. The results show the following: ① The overall friendliness of ASTSs in Lanzhou City is moderate, with 44% of school districts exhibiting low friendliness. ② The distribution of child friendliness in ASTS exhibits a “core-periphery” pattern. Anning District demonstrates higher friendliness compared to Chengguan District and Qilihe District, while Xigu District exhibits the lowest level of friendliness. ③ Different levels of friendliness have different tendencies for access, safety, and comfort. A high degree of friendliness favors comfort. Low friendliness has the lowest requirements for safety and comfort. ④ Population density and transportation convenience exert a significant positive impact on friendliness, while the size of the school district and the centrality of schools have a negative impact. The synergistic effects among these influencing factors notably enhance the explanatory power of friendliness. Full article

In this paper, a quantitative ground risk assessment mechanism is proposed in which urban ground features are extracted based on high-resolution data in a satellite image when unmanned aerial vehicles (UAVs) operate in urban areas. Ground risk distributions are estimated and a risk map is constructed with a multi-layer method considering the comprehensive risk imposed by UAV operations. The urban ground feature extraction is first implemented by employing a K-Means clustering method to an actual satellite image. Five main categories of the ground features are classified, each of which is composed of several sub-categories. Three more layers are then obtained, which are a population density layer, a sheltering factor layer, and a ground obstacle layer. As a result, a three-dimensional (3D) risk map is formed with a high resolution of 1 m × 1 m × 5 m. For each unit in this risk map, three kinds of risk imposed by UAV operations are taken into account and calculated, which include the risk to pedestrians, risk to ground vehicles, and risk to ground properties. This paper also develops a method of the resolution conversion to accommodate different UAV operation requirements. Case study results indicate that the risk levels between the fifth and tenth layers of the generated 3D risk map are relatively low, making these altitudes quite suitable for UAV operations. Full article

In this paper, we propose a vehicle pedestrian detection and tracking method based on the improved YOLOv7 and DeepSort algorithms. We aim to improve the quality of vehicle pedestrian detection and tracking, addressing the challenges that current commercially available autonomous driving technologies face in complex and changing road traffic situations. First, the NMS (non-maximum suppression) algorithm in YOLOv7 is replaced with a modified Soft-NMS algorithm to ensure that targets can be accurately detected at high densities, and second, the CCBA (coordinate channel attention module) attention mechanism is incorporated to improve the feature extraction and perception capabilities of the network. Finally, a multi-scale feature network is introduced to extract features of small targets more accurately. Finally, the MobileNetV3 lightweight module is introduced into the feature extraction network of DeepSort, which not only reduces the number of model parameters and network complexity, but also improves the tracking performance of the target. The experimental results show that the improved YOLOv7 algorithm improves the average detection accuracy by 3.77% compared to that of the original algorithm; on the MOT20 dataset, the refined DeepSort model achieves a 1.6% increase in MOTA and a 1.9% improvement in MOTP; in addition, the model volume is one-eighth of the original algorithm. In summary, our model is able to achieve the desired real-time and accuracy, which is more suitable for autonomous driving. Full article

After a fire breaks out, pedestrians simultaneously move towards the exit and quickly form a crowded area near the exit. With the intensification of pedestrians’ tendencies towards unfair competition, there is an increase in pushing and collisions within the crowd. The possibility of stampedes within the crowd also gradually increases. Analyzing the causes and psychological tendencies behind pedestrian pushing and collisions has a positive effect on reducing crowd instability and improving evacuation efficiency. This research proposes a modified social force model considering the unfair competition tendency of pedestrians. The model considers factors such as the gap between pedestrians’ actual and maximum achievable speed, effective radius, and their distance from the exit. In order to overcome the shortage of “deadlock” in the classical social force model in a high-density environment, this research introduces the feature of variable pedestrian effective radius. The effective radius of pedestrians dynamically changes according to the density of the surrounding crowd and queuing time. Through validation, the evacuation efficiency of this model aligns well with the actual situation and effectively reflects pedestrians’ pushing and squeezing behaviors in high-density environments. This research also analyzes how to strategically arrange obstacles to mitigate the exacerbating effect of unfair pedestrian competition on exit congestion. Five experiments were conducted to analyze how the relative position of obstacles and exits, the number of evacuation paths, and the size of the obstacle-free area before the exit affect evacuation efficiency in the presence of unfair pedestrian competition. The results show that evacuation efficiency can be improved when obstacles play a role in guiding or reducing the interaction of pedestrians in different queues. However, when obstacles hinder pedestrians, the evacuation efficiency is reduced to a certain extent. Full article

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions. Full article

Understanding the determinants of the availability and spatial fairness of street greenery is crucial for improving urban green spaces and addressing green justice concerns. While previous studies have mainly examined factors influencing street greenery from an aerial perspective, there has been limited investigation into determinants at eye level, which more closely aligns with people’s actual encounters with green spaces. To address this, the Green View Index (GVI) and Gini coefficient were used to assess the availability and spatial fairness of street greenery from a pedestrian’s perspective, using Baidu Street View (BSV) images across 49 subdistricts in Changchun City, China. A dataset of 33,786 BSV images from 1877 sites was compiled. Additionally, 21 explanatory factors were collected and divided into three groups: socioeconomic, biogeographic, and landscape patterns. The Boosted Regression Tree (BRT) method was employed to assess the relative influence and marginal effects of these factors on street greenery’s availability and spatial fairness. The results showed that street greenery’s availability and spatial fairness are predominantly influenced by landscape patterns. Specifically, the percentage of landscape and edge density emerged as the most significant factors, exhibiting a threshold effect on the availability and fairness of street greenery. Increasing the proportion and complexity of urban green spaces can efficiently enhance the availability and spatial fairness of street greenery. These findings lay a new foundation for urban green infrastructure management. Full article

With the increase in car domination, air pollution, traffic congestion, and urban sprawl, sustainable, livable, creative, and walkable cities are critical, now more than ever, for improving quality of life. The effect of neighborhood urban morphologies on walkability has received much attention in recent years. In this vein, the main research question is: how do different neighborhood urban morphologies impact the level of walkability in urban environments, and what are the essential elements impacting the walkability index? Thus, this research aims to determine the impact of urban morphology on walkability in the city of Alexandria, Egypt, as a case study by utilizing multi-spatial analysis. In particular, the study focused on assessing the walkability of four different study areas that vary according to their urban morphology: Kafr–Abdo, Smouha, Latin Quarter, and Roushdy areas. The analysis utilized GIS to calculate a number of indicators to reach the final walkability index for each study area. Results helped to identify the neighborhoods characterized by the lowest level of pedestrian walkability in relation to the area’s urban morphology in an attempt to help decision-makers suggest the appropriate interventions for those areas. The aggregated index results showed that the highest walkability index was that of the gridiron morphology, followed by the linear morphology, with the radial and organic morphologies coming in behind them, respectively. The composite walkability index values were 0.364, 0.247, 0.232 and 0.225, respectively. The reason for this is mainly the presence of the commercial density, intersection density, street density, services density, BCR, and residential density. Full article

Improving urban mobility, especially pedestrian mobility, is a current challenge in virtually every city worldwide. To calculate the least-cost paths and safer, more efficient routes, it is necessary to understand the geometry of streets and their various elements accurately. In this study, we propose a semi-automatic methodology to assess the capacity of urban spaces to enable adequate pedestrian mobility. We employ various data sources, but primarily point clouds obtained through a mobile laser scanner (MLS), which provide a wealth of highly detailed information about the geometry of street elements. Our method allows us to characterize preferred pedestrian-traffic zones by segmenting crosswalks, delineating sidewalks, and identifying obstacles and impediments to walking in urban routes. Subsequently, we generate different displacement cost surfaces and identify the least-cost origin–destination paths. All these factors enable a detailed pedestrian mobility analysis, yielding results on a raster with a ground sampling distance (GSD) of 10 cm/pix. The method is validated through its application in a case study analyzing pedestrian mobility around an educational center in a purely urban area of A Coruña (Galicia, Spain). The segmentation model successfully identified all pedestrian crossings in the study area without false positives. Additionally, obstacle segmentation effectively identified urban elements and parked vehicles, providing crucial information to generate precise friction surfaces reflecting real environmental conditions. Furthermore, the generation of cumulative displacement cost surfaces allowed for identifying optimal routes for pedestrian movement, considering the presence of obstacles and the availability of traversable spaces. These surfaces provided a detailed representation of pedestrian mobility, highlighting significant variations in travel times, especially in areas with high obstacle density, where differences of up to 15% were observed. These results underscore the importance of considering obstacles’ existence and location when planning pedestrian routes, which can significantly influence travel times and route selection. We consider the capability to generate accurate cumulative cost surfaces to be a significant advantage, as it enables urban planners and local authorities to make informed decisions regarding the improvement of pedestrian infrastructure. Full article

Due to their sparse distribution and placement in open areas, fixed air-quality-monitoring stations fail to characterize the effect of contextual factors such as buildings on the dispersion of PM2.5. This study evaluated the effects of building morphology on PM2.5 dispersion in a pedestrian-friendly area on the University of Texas at Dallas campus, spanning approximately 0.5 km². The study collected PM2.5 data along five distinct paths exhibiting varying building morphological characteristics in terms of size, height, density, and spacing at a high spatial resolution. The interquartile range of PM2.5 levels across nine data-collection runs varied from 0.3 µg/m³ to 1.7 µg/m³, indicating relatively uniform PM2.5 levels within the study area. Furthermore, weather-related variables played a dominant role in PM2.5 distribution as temporal variation over-powered spatial variation in the PM2.5 data. The study employed a fixed-effects model to assess the effect of time-invariant morphological characteristics of buildings on PM2.5 and found that the buildings’ morphological characteristics explained 33.22% variation in the fixed effects in the model. Furthermore, openness in the direction of wind elevated the PM2.5 concentration. Full article

Permeable-pavement design methodologies can improve the hydrologic and therefore the environmental benefits of rural and urban roadway systems. By contrast, conventional impervious pavements perturb the hydrologic cycle, altering the relationship between the rainfall loading and runoff response. Impervious pavements create a hydraulically conductive interface for the transport of traffic-generated chemicals and particulate matter (PM), deleteriously impacting their proximate environments. Permeable-pavement systems are countermeasures to mitigate hydrologic, chemical, and PM impacts. However, permeable pavements are not always equally implementable due to costs, PM loadings, and design constraints. A potential solution to facilitate environmental benefits while meeting the traffic load capacity is the combination of two filtration systems placed at the pavement shoulders and/or pedestrian sidewalks: a bituminous-pavement open-graded friction course (BPFC) and an aggregate-filled infiltration trench. This solution is presented in this manuscript together with the methodological framework and the first results of the investigations into designing and validating such a combined system. The research was conducted at the laboratories of the Polytechnic University of Bari and the University of Florida, while an operational and full-scale physical model was constructed in Bari, Italy. The first results presented characterize the PM deposition on public roads based on granulometry (particle size distributions (PSDs) and particle number densities (PNDs)). Samples (n = 16) were collected and analyzed at eight different sites with different land uses, traffic, and pavements from different cities (Bari and Taranto, Italy). The PM analysis showed similar distributions (PSDs and PNDs), except for two samples. The gravimetric-based PSDs of the PM had granulometric distributions in the sand-size range. In contrast, the PNDs, modeled by a Power Law Model (PLM) (R² ≥ 0.92), illustrated an exponentially increasing number of particles in the fine silt and clay-size range, representing less than 10% of the PSD mass. Moreover, the results indicate that PM sourced from permeable-pavement systems has differing impacts on the pavement service life. Full article

In current transportation hubs, passengers travelling with wheeled luggage or suitcases is a common phenomenon. Due to the fact that most luggage occupies a certain space in dense passenger crowds with high mass inertia, its abnormal motion, such as drifting, can frequently trigger unavoidable local disturbances and turbulence in the surrounding pedestrian flows, further increasing congestion risk. Meanwhile, there still is a lack of quantitative disturbance propagation analysis, since most state-of-the-art achievements rely on either scenario-based experiments or the spatial characteristics of crowd distribution assessed qualitatively. Therefore, this study considers the luggage-laden passenger as a deformable particle. The resulting disturbance on surrounding non-luggage-carrying passengers is analyzed and quantified into a nonlinear pressure term. Subsequently, the disturbance propagation model of passenger-owned luggage is developed by adapting the classical Aw–Rascle traffic flow model with a pressure term. Simulation experiments of disturbances caused by luggage drifting and retrograding were conducted in Pathfinder 2022 Software. Experimental results showed that the disturbing force of a left-sided crowd can reach a peak of 238 N with a passenger density of 3.0 $\mathrm{p}/{\mathrm{m}}^2$, and the maximum force difference between the left- and right-sided disturbing force can reach 153 N, as confirmed by a case study in an L-shaped corridor of a transportation hub. Furthermore, it is recommended that the proposed model can be applied in crowd flow analysis and intelligent decision-making for passenger management in transportation hubs. Full article

(1) Background: Almost all trips include a walking leg. Pedestrian flow dynamics are an essential input to infrastructure design as well as efficient and safe operations. Pedestrian walking speed is the most influential traffic flow variable. This study examines the factors influencing pedestrian walking speed, categorizing them into pedestrian flow characteristics, pedestrian attributes, layout configuration, ambient conditions, and pedestrian behavioral patterns. (2) Methods: A comprehensive literature review was conducted, aggregating studies that investigate pedestrian walking speed across various environments and conditions. The identified factors were systematically categorized, and a meta-analysis was employed to synthesize the results. (3) Results: Speed measurements seem to be dependent on the method and technique employed, with experiments systematically overestimating speed and video recordings systematically underestimating it. Pedestrian density strongly influences speed as in motorized traffic. Being female, being of older age, walking in a group, engaging in social interactions or phone-related tasks, and moving under noise conditions are reported to have a negative impact on walking speed. Carrying baggage and moving under adverse weather conditions are also reported to have a statistically significant impact, but the direction of the impact is not always the same and seems to be very context dependent. (4) Conclusions: The findings highlight the significance of physiological, psychological, and environmental elements in shaping pedestrian behavior and thus speed. Valuable insights from this review can assist researchers, designers, and operators in providing safer, more inclusive, and reliable infrastructures for pedestrians. Future investigations should broaden the scope of data collection methods, particularly indoors. Full article

Ensuring the spatial equity of health service facilities (HSFs) is crucial for the well-being of residents. However, previous research has predominantly focused on the accessibility and equity of single-type facilities, neglecting the residents’ demand for diversified types of health services. This study proposes a multi-type, Gaussian-based, two-step floating catchment area method (MT-G2SFCA) to assess the comprehensive accessibility and equity of multi-type HSFs in different age groups in the Hedong District of Tianjin, with the Gini coefficient and the bivariate local Moran’s I. Furthermore, the key factors affecting the accessibility were explored through a geo-detector. The results indicate the following: (1) Neglecting the health benefits of facility type diversity can result in an underestimation of the accessibility and equity; (2) neglecting the differences in walking ability of the elderly can result in an overestimation of the accessibility and equity; and (3) the Pedestrian Route Directness is the key factor affecting the accessibility and equity in high-density urban areas, and especially that the facility density is the key factor for the elderly. This research emphasizes the impact of facility type diversity on the accessibility and equity of HSFs, which can offer more precise and holistic technical assistance and policy recommendations for optimizing the allocation of HSFs. Full article

Pedestrian monitoring in crowded areas like train stations has an important impact in the overall operation and management of those public spaces. An organized distribution of the different elements located inside a station will contribute not only to the safety of all passengers but will also allow for a more efficient process of the regular activities including entering/leaving the station, boarding/alighting from trains, and waiting. This improved distribution only comes by obtaining sufficiently accurate information on passengers’ positions, and their derivatives like speeds, densities, traffic flow. The work described here addresses this need by using an artificial intelligence approach based on computational vision and convolutional neural networks. From the available videos taken regularly at subways stations, two methods are tested. One is based on tracking each person’s bounding box from which filtered 3D kinematics are derived, including position, velocity and density. Another infers the pose and activity that a person has by analyzing its main body key points. Measurements of these quantities would enable a sensible and efficient design of inner spaces in places like railway and subway stations. Full article