Search Results (54)

Previous research showed that a tsunami similar to the 1755 event would inundate Caxias’ low-ground areas in Oeiras municipality, Portugal. However, the streets of downtown Caxias were not well reproduced, which is a limitation of the area’s mitigation strategies and evacuation plan. For these reasons, new Lidar data were used for the first time in Portugal. The new local topography data allowed the construction of a more accurate DEM, which was used in the tsunami numerical model to update and improve the inundation results. As a complement, a field survey was conducted in several locations to assess coastal features and protection. The numerical model results show that low-ground areas up to 6 m in height were inundated by the tsunami, including the residential area, the road, and the railway. To stop the tsunami waves from inundating these areas, it is proposed that the construction of more sea walls up to 7 m in height and a third bridge over the Barcarena Stream, only for pedestrians, ranging from 5 to 7 m in height, which will serve as a gate for the incoming tsunami waves. These coastal protections should be part of the strategy to mitigate coastal overtopping (winter storm surges and tsunamis) not only in Caxias but also in other coastal zones. Full article

The impact of environmental noise on the health and well-being of people living in cities is an issue that has been addressed in the scientific literature to try to develop effective environmental policies. In this context, road traffic is the main source of noise in urban environments, but it is not the only source of noise that pedestrians hear. This paper presents an experimental study using in situ surveys and acoustic measurements to analyse the capacity of acoustic variables related to sound energy to estimate the occurrence and importance of noise effects in urban environments. The results revealed that average sound energy indicators can be considered most significant in terms of the perception of the noise effects studied on pedestrians. When estimating noise effects from them, frequency weightings related to flat or nearly flat spectra (Z and C weightings) were found to provide better results than an A weighting; however, it was also concluded that if the average energy is considered, the use of a temporal I weighting did not lead to improvements. The perception of how noisy a street is, it is strongly associated with a low frequency, and annoyance was the effect that generally showed the strongest significant correlations with acoustic indicators. The indicators of minimum sound levels explained a larger proportion of the variability of noise effects than the indicators of maximum energy; they were even better in this regard than any of the average energy indicators in terms of explaining the variability of startle and annoyance in the ears, and they were found to be equivalent when interruption of a telephone conversation was assessed. Both acoustic variables associated with sound energy in different parts of the audible spectrum and Leq in each one-third octave band showed significant correlations with the effects of noise on pedestrians. Similarities in the structure of the spectra were found between some of these effects. Full article

This study explores the integration of Spiking Neural Networks (SNNs) with Dynamic Vision Sensors (DVSs) to enhance pedestrian street-crossing detection in adverse weather conditions—a critical challenge for autonomous vehicle systems. Utilizing the high temporal resolution and low latency of DVSs, which excel in dynamic, low-light, and high-contrast environments, this research evaluates the effectiveness of SNNs compared to traditional Convolutional Neural Networks (CNNs). The experimental setup involved a custom dataset from the CARLA simulator, designed to mimic real-world variability, including rain, fog, and varying lighting conditions. Additionally, the JAAD dataset was adopted to allow for evaluations using real-world data. The SNN models were optimized using Temporally Effective Batch Normalization (TEBN) and benchmarked against well-established deep learning models, concerning their accuracy, computational efficiency, and energy efficiency in complex weather conditions. This study also conducted a comprehensive analysis of energy consumption, highlighting the significant reduction in energy usage achieved by SNNs when processing DVS data. The results indicate that SNNs, when integrated with DVSs, not only reduce computational overhead but also dramatically lower energy consumption, making them a highly efficient choice for real-time applications in autonomous vehicles (AVs). Full article

This study analyses five months of continuous monitoring of different lighting warning systems at a pedestrian crosswalk through video surveillance cameras during nighttime. Three different light signalling systems were installed near a pedestrian crossing to improve the visibility and safety of vulnerable road users: in-curb LED strips, orange flashing beacons, and asymmetric enhanced LED lighting. Seven different lighting configurations of the three systems were studied and compared with standard street lighting. The speed of vehicles for each pedestrian–driver interaction was also evaluated. This was then compared to the speed that vehicles should maintain in order to stop in time and allow pedestrians to cross the road safely. In all of the conditions studied, speeds were lower than those maintained in the five-month presence of standard street lighting (42.96 km/h). The results show that in conditions with dedicated flashing LED lighting, in-curb LED strips, and orange flashing beacons, most drivers (72%) drove at a speed that allowed the vehicle to stop safely compared to standard street lighting (10%). In addition, with this lighting configuration, the majority of vehicles (85%) stopped at pedestrian crossings, while in standard street lighting conditions only 26% of the users stopped to give way to pedestrians. Full article

William H. Whyte took on the challenge of assessing the amount of public space in a city based on its carrying capacity, pointing out that popular public spaces offer more room for social activities. However, the absence of qualitative characteristics makes this assessment even more challenging to implement. This study aims to find a method to gauge the carrying capacity of urban public spaces by calculating the social space ratio for pedestrian-only streets in Karlstad, Sweden, and quantifying this relationship. The social space ratio represents the proportion of public spaces that foster social interaction throughout their entire area. The method began by selecting the most relevant conceptual framework for social public spaces and then sought theory-based characteristics to assign to seven social activities on Karlstad’s pedestrian-only streets. The authors performed a comprehensive search of the literature utilizing the PRISMA approach, gathering information from credible references, placemaking toolkits, transportation toolkits, and academic sources. This was performed to determine the weighting factors and effective social areas by evaluating these activities in terms of nine categories of the chosen framework: accessibility, traffic, social infrastructure, security, places to meet, senses and experience, architecture and aesthetics, development and maintenance, and control and programming. We devised a method to calculate the carrying capacity and social space ratio of Karlstad’s pedestrian-only streets, resulting in a ratio of 0.38. The research led to the development of eight quality-control tools to analyze the seven social activities in public places. This innovative approach helps researchers and municipal planners evaluate the benefits and drawbacks of these spaces, contributing significantly to Swedish urban planning and enabling future studies to create a social area factor. Full article

Connected cooperative and automated (CAM) vehicles and self-driving cars need to achieve robust and accurate environment understanding. With this aim, they are usually equipped with sensors and adopt multiple sensing strategies, also fused among them to exploit their complementary properties. In recent years, artificial intelligence such as machine learning- and deep learning-based approaches have been applied for object and pedestrian detection and prediction reliability quantification. This paper proposes a procedure based on the YOLOv8 (You Only Look Once) method to discover objects on the roads such as cars, traffic lights, pedestrians and street signs in foggy weather conditions. In particular, YOLOv8 is a recent release of YOLO, a popular neural network model used for object detection and image classification. The obtained model is applied to a dataset including about 4000 foggy road images and the object detection accuracy is improved by changing hyperparameters such as epochs, batch size and augmentation methods. To achieve good accuracy and few errors in detecting objects in the images, the hyperparameters are optimized by four different methods, and different metrics are considered, namely accuracy factor, precision, recall, precision–recall and loss. Full article

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

Environmental factors have been identified as having a direct relationship with physical activity (PA) and sedentary behavior. The main aim of this study was to investigate the relationship between microscale built-environment attributes and the levels of PA and sedentary behavior in young people. This study included 465 adolescents (55% girls) between 14 and 18 years from Valencia, Spain. Accelerometers and self-reported questionnaires were used to measure PA, including active commuting, and sedentary behavior, and the MAPS (Microscale Audit of Pedestrian Streetscapes)-Global tool was used for microscale variables. Mixed-effects regression models were used for data analysis. Higher levels of moderate-to-vigorous activity were identified when more positive elements were found in the street characteristics. Greater active commuting in the neighborhood had a positive relationship not only with more positive elements of land use and destinations but also with the overall score of the MAPS-Global tool. The sedentary levels were higher when higher levels of negative aesthetics and social characteristics were identified, and the participants were less sedentary when more bike facilities were observed. The main results of this study provide us with evidence of the relationship between the microscale variables of the built environment and both PA and sedentary behavior. Full article

Creating healthy street environments to encourage physical activity is an effective strategy against non-communicable diseases exacerbated by rapid urbanization globally. Developing countries face more significant health challenges than developed ones. However, existing research predominantly focuses on the perspective of developed countries. To address the health challenges in developing nations, studies should not only draw on the findings from developed countries but also clearly define unique research processes and pathways. Consequently, this study conducts a comparative analysis between China, representing developing countries, and developed nations, using databases like China National Knowledge Infrastructure (CNKI) and Web of Science (WOS) and tools such as Citespace, Bicomb, and Statistical Package for the Social Sciences (SPSS) to explore research hotspots, developmental trajectories, thematic categories, and trends. The findings reveal a shift in developed countries from macro-material to micro-environmental elements under multidisciplinary scrutiny, while future topics may include street space evaluations and psychological healing. In China, research has been dominated by different disciplines at various stages, starting with medical attention to chronic disease prevention, which then shifted to traffic engineering’s focus on constructing green travel environments, and finally expanded to disciplines like landscape architecture examining the impact of street environment elements on pedestrian behavioural perceptions. Future themes will focus on promoting elderly health and urban health transport systems. Generally, research in developed countries exhibits a “bottom-up” approach, with practical issues at a “post-evaluation” stage, primarily based on the “socio-ecological model” and emphasizing multidisciplinary collaboration. Chinese research shows a “top-down” characteristic, driven by national policies and at a “pre-planning” stage, integrating theories such as Maslow’s hierarchy of needs and attention restoration theory, with relatively loose disciplinary cooperation. Overall, research is shifting from macro to human-centric scales and is progressively utilizing multi-source and multi-scale big data analysis methods. Based on this, future research and development recommendations are proposed for developing countries, with China as a representative example. Full article

In Yaoundé, Cameroon, where walking dominates transport modes, pedestrian safety remains an issue as pedestrians account for a fair share of road traffic casualties, partly due to the lack of walking policies and pedestrian facilities safety data, hindering targeted intervention. This study used a pedestrian safety index (PSI) and the Global Walkability Index (GWI) to investigate 12 road segments frequented by diverse pedestrian groups. Indexes were graded from E—lowest to A—highest and analyzed using description and rank correlation. Main safety issues included lack of adequate and accessible sidewalks, bollards, pedestrian crossings, signage, shade, and street lighting. Only one segment (R7) achieved grade C, while the remainder scored D or E, indicating poor pedestrian safety conditions and an unpleasant walking experience. The correlation coefficient (0.69) between the PSI and GWI at a 99% significance level validated the safety assessment, providing confidence in the results. A seven-year (2024–2030) safety strategy is proposed to improve all roads to grade B. This strategy contains several interventions, including engineering improvement, which have been proven effective. This study offers evidence for city officials to improve pedestrian safety and informs walking policies and the implementation of upcoming projects. Future research should quantify the recommendations’ benefits and validate indexes with crash or conflict data. Full article

When it is realised meaningfully, barrier-free access enables pedestrians with disabilities to use streets without being impeded by non-existent or poorly maintained sidewalks, inaccessible overpasses or underpasses, crowded sidewalks, lack of traffic controls, lack of aids at street crossings, unsafe motorist behaviour, and poor signage and lighting. While Kenya has laws in place that are intended to facilitate barrier-free access, in reality, these laws are not implemented, resulting in the violations of rights of pedestrians in general, and pedestrians with disabilities in particular. Using the lived experiences of pedestrians with disabilities, this article reflects on the policy, legislative, and practical contexts which undermine access. It shows that despite the range of policy and legal instruments which Kenya has adopted or enacted to ensure the public in general can access streets, pedestrians with disabilities enjoy arising benefits only marginally. The article’s thesis is that continental policy and normative instruments and institutions may impel Kenya towards ensuring that pedestrians with disabilities have meaningful barrier-free access. Full article

This research aims to optimize the use of trees to enhance microclimate conditions, which has become necessary because of climate change and its impacts, especially for cities suffering from extreme heat stress, such as Cairo. It considers elements of urban morphology, such as the aspect ratio and orientation of canyons, which play an important role in changing microclimate conditions. It also considers both sides of each canyon because the urban shading is based on the orientation and the aspect ratio, which can provide good shade on one side of the canyon but leave the other side exposed to direct and indirect radiation, to ensure a complete assessment of how the use of trees can be optimized. As Cairo city is very large and has a variety of urban morphologies, a total of 144 theoretical cases have been tested for Cairo city using ENVI-met to cover the majority of the urban cases within the city (Stage 1). Then, the same tree scenarios used in the theoretical study are applied to an existing urban area in downtown Cairo with many urban morphology varieties to validate the results of the theoretical study (Stage 2). After testing all cases in both stages, it became very clear that the addition of trees cannot be the same for the different aspect ratios, orientations, and sides of the different canyons. For example, eastern roads should have more trees than other orientations for all aspect ratios, but the required number of trees is greater for the northern side than the southern side, as the southern side is partially shaded for a few hours of the day by buildings in moderate and deep canyons. Northern streets require a very limited number of trees, even in shallow canyons, on both sides. The correlation between the number of trees on each side for the different orientations and aspect ratios shows a strong negative relationship, but the correlation values change between the different sides and orientations. The results of applying trees to an existing urban area show almost the same results as the theoretical study’s results, with very slight differences occurring because of the irregularity of the existing study area. This proves that when adding trees, not only the aspect ratio and orientation but also the side of each canyon should be considered to ensure that pedestrians, in all cases, have better microclimate conditions and that the use of trees is optimized. Full article

Current Chinese campus planning and design have neglected to promote walking activity (WA). Lacking WA and developing sedentary and physical inactivity habits can lead to obesity, diabetes, and other noncommunicable diseases. Academia has confirmed that WA can be facilitated by planning and designing built environment (BE) interventions. Accordingly, this study aims to explore the effect of campus BE features on walking in different regions’ campuses and present nuanced campus planning and design strategies. We selected the objectively measured BE features of destination accessibility, land use, street connectivity, and spatial configuration. Environmental design qualities and pedestrian facilities were chosen as the micro-level BE features. We applied GIS 10.1 and sDNA to calculate gross BE features and field audit tools to measure street environmental features and pedestrian volume (PV). We built negative binomial regression models and eliminated spatial autocorrelation to investigate and compare the BE correlates of walking in urban and suburban campuses. Similarities and differences were found among the outcomes derived from the two regions. We found that campus Walk Score, land use attributes of facility density and park land ratio, complexity, and other features closely correlate with PV in the two types of campuses. Comparatively, closeness, transparency, and complexity only influence urban campuses’ PV, while block length, entropy, facility land ratio, and sidewalk quality only correlate with PV on suburban campuses. According to these findings, we proposed different and targeted campus renewal and planning strategies for WA and walkability promotion. Full article

Driving a tram in city traffic is a challenging task. It is especially difficult to drive smoothly (without unnecessary jerks) when the route runs through streets with many other vehicles, pedestrians, and traffic lights. A smooth driving style of the tram driver not only has a significant impact on the comfort of passengers being transported, but also affects the energy consumption of the tram. The paper focuses on the analysis of the tram driver’s way of driving and the resulting energy savings. The energy consumption of the tram was measured depending on the driver’s driving technique. For the analysis of the driving technique, an innovative electronic device was proposed to be installed on the tram. It detects jerks in the lateral and longitudinal directions. Based on vibration analysis, it evaluates the driver’s driving technique on an ongoing basis and displays the result of this assessment. The device is cheap and uses a popular minicomputer, a GPS system receiver, an IMU accelerometer, and a screen. It is independent of the electronic systems of the tram. Due to this, it is possible to increase passenger comfort and reduce electricity consumption. It can be useful when learning to drive a tram. Preliminary tests of this device were carried out on a real tram during rides with passengers in city traffic. Tests have confirmed its effectiveness. Full article

Cycling is a sustainable mode of transportation with significant benefits for society. The number of cyclists on the streets depends heavily on their perception of safety, which makes it essential to establish a common metric for determining and comparing risk factors related to road safety. This research addresses the identification of cyclists’ risk factors using deep learning techniques applied to a Google Street View (GSV) imagery dataset. The research utilizes a case study approach, focusing on London, and applies object detection and image segmentation models to extract cyclists’ risk factors from GSV images. Two state-of-the-art tools, You Only Look Once version 5 (YOLOv5) and the pyramid scene parsing network (PSPNet101), were used for object detection and image segmentation. This study analyzes the results and discusses the technology’s limitations and potential for improvements in assessing cyclist safety. Approximately 2 million objects were identified, and 250 billion pixels were labeled in the 500,000 images available in the dataset. On average, 108 images were analyzed per Lower Layer Super Output Area (LSOA) in London. The distribution of risk factors, including high vehicle speed, tram/train rails, truck circulation, parked cars and the presence of pedestrians, was identified at the LSOA level using YOLOv5. Statistically significant negative correlations were found between cars and buses, cars and cyclists, and cars and people. In contrast, positive correlations were observed between people and buses and between people and bicycles. Using PSPNet101, building (19%), sky (15%) and road (15%) pixels were the most common. The findings of this research have the potential to contribute to a better understanding of risk factors for cyclists in urban environments and provide insights for creating safer cities for cyclists by applying deep learning techniques. Full article