Search Results (129)

This paper presents a new approach to optimizing route selection in urban road networks with sparsely placed traffic counters. By leveraging graph theory and Dijkstra’s algorithm, we propose a new method to determine the shortest path between origins and destinations in city traffic networks with sparsely placed counters. The method is based on the similarities between traffic flows recorded at the counter and the streets that generate traffic for a given counter. The advantage of this method is the use of a secondary counter function to obtain data that are built into the shortest path determination model and the free choice of the time of day for which the path is searched. The proposed method is implemented using the programming language AutoLISP 2022 and program AutoCAD 2022, providing a valuable tool for transportation engineers and urban planners. This paper presents a model of the shortest path that integrates one-way streets, the average speed of the car, as well as the delay time at traffic-lighted and non-traffic intersections. The model was applied to the traffic network of the city of Sarajevo (Bosnia and Herzegovina), but there are no restrictions for application to any network equipped with traffic counters. The obtained results show a high agreement with the Google Maps service as a reference system. Full article

This study introduces a new framework for combining calibrated mobile mapping system (MMS) data and low-cost unmanned aerial vehicle (UAV) images to generate seamless, high-fidelity 3D urban maps. This approach addresses the limitations of single-source mapping, such as occlusions in aerial top views and insufficient vertical detail in ground-level data, by utilizing the complementary strengths of the two technologies. The proposed approach combines cloth simulation filtering for ground point extraction from MMS data with deep-learning-based segmentation (U²-Net) for feature extraction from UAV images. Street-view MMS images are projected onto a top-down viewpoint using inverse perspective mapping to align diverse datasets, and precise cross-view alignment is achieved using the LightGlue technique. The spatial accuracy of the 3D model was improved by integrating the matched features as ground control points into a structure from the motion pipeline. Validation using data from the campus of Yonsei University and the nearby urban area of Yeonhui-dong yielded notable accuracy gains and a root mean square error of 0.131 m. Geospatial analysis, infrastructure monitoring, and urban planning can benefit from this flexible and scalable method, which enhances 3D urban mapping capabilities. Full article

The development of the fluorescent lamp and the air-conditioning system resulted in buildings being lit inexpensively without having to rely on daylighting to save energy, as was the case during the incandescent lamp era. Consequently, architects were able to design buildings with deep floor plates for maximum occupancy, placing workstations far away from windows since daylighting was no longer a necessity. Floor-to-ceiling heights became lower to minimize the inhabitable volumes that needed to be cooled or heated. With the rising costs of land in some major American cities such as New York City and Chicago at the beginning of the twentieth century, developers sought to optimize their investments by erecting tall structures, giving rise to densely inhabited city centers with massive street canyons that limit sunlight access in the streets. Today, there is growing awareness in terms of the impact of the built environment on people’s health especially in terms of the health benefits of natural light. The fact that buildings, through their shapes and envelope, filter a large amount of daylight, which may impact building occupants’ health and well-being, should cause architects and building developers to take this issue seriously. The amount and quality of light we receive daily impacts many of our bodily functions and consequently several aspects of our health and well-being. The human circadian rhythm is entrained by intrinsically photosensitive retinal ganglion cells (ipRGCs) in our eyes that are responsible for non-visual responses due to the presence of a short-wavelength sensitive pigment called melanopsin. The entrainment of the circadian rhythm depends on several factors such as the intensity, wavelength, timing, and duration of light exposure. Recently, this field of research has gained popularity, and several researchers have tried to create metrics to quantify photopic light, which is the standard way of measuring visual light, into a measure of circadian effective lighting. This paper discusses the relationship between different parameters of daylighting and their non-visual effects on the human body. It also summarizes the existing metrics of daylighting, especially those focusing on its effects on the human circadian rhythm and its shortcomings. Finally, it discusses areas of future research that can address these shortcomings and potentially pave the way for a universally acceptable standardized metric. Full article

In our interconnected society, prioritizing the resilience and sustainability of road infrastructure has never been more critical, especially in light of growing environmental and climatic challenges. By harnessing data from various sources, we can proactively enhance our ability to detect road damage. This approach will enable us to make well-informed decisions for timely maintenance and implement effective mitigation strategies, ultimately leading to safer and more durable road systems. This paper presents a new method for detecting road potholes during low-light conditions, particularly at night when influenced by street and traffic lighting. We examined and assessed various advanced machine learning and computer vision models, placing a strong emphasis on deep learning algorithms such as YOLO, as well as the combination of Grad-CAM++ with feature pyramid networks for feature extraction. Our approach utilized innovative data augmentation techniques, which enhanced the diversity and robustness of the training dataset, ultimately leading to significant improvements in model performance. The study results reveal that the proposed YOLOv11+FPN+Grad-CAM model achieved a mean average precision (mAP) score of 0.72 for the 50–95 IoU thresholds, outperforming other tested models, including YOLOv8 Medium with a score of 0.611. The proposed model also demonstrated notable improvements in key metrics, with mAP50 and mAP75 values of 0.88 and 0.791, reflecting enhancements of 1.5% and 5.7%, respectively, compared to YOLOv11. These results highlight the model’s superior performance in detecting potholes under low-light conditions. By leveraging a specialized dataset for nighttime scenarios, the approach offers significant advancements in hazard detection, paving the way for more effective and timely driver alerts and ultimately contributing to improved road safety. This paper makes several key contributions, including implementing advanced data augmentation methods and a thorough comparative analysis of various YOLO-based models. Future plans involve developing a real-time driver warning application, introducing enhanced evaluation metrics, and demonstrating the model’s adaptability in diverse environmental conditions, such as snow and rain. The contributions significantly advance the field of road maintenance and safety by offering a robust and scalable solution for pothole detection, particularly in developing countries. 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

With the help of traffic lights and street cameras, optical camera communication (OCC) can be adopted in Internet of Vehicles (IoV) applications to realize communication between vehicles and roadside units. However, the encoded light emitted by these OCC transmitters (LED infrastructures on the roadside and/or LED-based headlamps embedded in cars) will generate stripe patterns in image frames captured by existing license-plate recognition systems, which seriously degrades the accuracy of the recognition. To this end, we propose and experimentally demonstrate a method that can reduce the interference of OCC stripes in the image frames captured by the license-plate recognition system. We introduce an innovative pipeline with an end-to-end image reconstruction module. This module learns the distribution of images without OCC stripes and provides high-quality license-plate images for recognition in OCC conditions. In order to solve the problem of insufficient data, we model the OCC strips as multiplicative noise and propose a method to synthesize a pairwise dataset under OCC using the existing license-plate dataset. Moreover, we also build a prototype to simulate real scenes of the OCC-based vehicle networks and collect data in such scenes. Overall, the proposed method can achieve a recognition performance of $81.58$% and $79.35$% on the synthesized dataset and that captured from real scenes, respectively, which is improved by about $31.18$% and $24.26$%, respectively, compared with the conventional method. 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

The study of light pollution is a relatively new and specific field of measurement. The current literature is dominated by articles that describe the use of ground and satellite data as a source of information on light pollution. However, there is a need to study the phenomenon on a microscale, i.e., locally within small locations such as housing estates, parks, buildings, or even inside buildings. Therefore, there is an important need to measure light pollution at a lower level, at the low level of the skyline. In this paper, the authors present a new drone design for light pollution measurement. A completely new original design for an unmanned platform for light pollution measurement is presented, which is adapted to mount custom sensors (not originally designed to be mounted on a unmanned aerial vehicles) allowing registration in the nadir and zenith directions. The application and use of traditional photometric sensors in the new configuration, such as the spectrometer and the sky quality meter (SQM), is presented. A multispectral camera for nighttime measurements, a calibrated visible-light camera, is used. The results of the unmanned aerial vehicle (UAV) are generated products that allow the visualisation of multimodal photometric data together with the presence of a geographic coordinate system. This paper also presents the results from field experiments during which the light spectrum is measured with the installed sensors. As the results show, measurements at night, especially with multispectral cameras, allow the assessment of the spectrum emitted by street lamps, while the measurement of the sky quality depends on the flight height only up to a 10 m above ground level. Full article

The Internet of Things refers to a network of interconnected devices, objects, and systems, that can interact with one another without human intervention. The adoption of IoT technology has expanded rapidly, significantly impacting various fields, including smart healthcare, intelligent transportation, agriculture, and smart homes. This paper focuses on smart street lighting, which represents the core piece of the smart city and the key public service for citizens’ safety. Nevertheless, it poses substantial challenges related to energy consumption, especially during energy crises. This work aims to provide an advanced solution that enables intelligent control of street lighting, enhances human safety, reduces CO₂ emissions and light pollution, and optimizes energy consumption, as well as facilitates maintenance of the lighting network. The solution is twofold: First, it introduces IoT-based smart street lighting referential models; second, it presents a framework for controlling smart street lighting based on the referential models. The proposal uses an IoT-based fuzzy multi-agent systems approach to address the challenges of smart street lighting. The approach leverages the strengths and properties of fuzzy logic and multi-agent systems to address the system requirements. This is illustrated through a testbed case study conducted on a concrete IoT prototype. Full article

This paper addresses the growing importance of efficient street lighting management, driven by rising electricity costs and the need for municipalities to implement cost-effective solutions. Central to this study is the UNI 11248 Italian regulation, which extends the European EN 13201-1 standard introduced in 2016. These standards provide guidelines for designing, installing, operating, and maintaining lighting systems in pedestrian and vehicular traffic areas. Specifically, the UNI 11248 standard introduces the possibility to dynamically adjust light intensity through two alternative operating modes: (a) Traffic Adaptive Installation (TAI), which dims the light based solely on real-time traffic flow measurements; and (b) Full Adaptive Installation (FAI), which, in addition to traffic measurements, also requires evaluating road surface luminance and meteorological conditions. In this paper, we first present the general architecture and operation of an FAI-enabled lighting infrastructure, which relies on environmental sensors and a heterogeneous wireless communication network to connect intelligent, remotely controlled streetlights. Subsequently, we examine large-scale, in-field FAI infrastructures deployed in Vietnam and Italy as case studies, providing substantial measurement data. The paper offers insights into the measured energy consumption of these infrastructures, comparing them to that of conventional light-control strategies used in traditional installations. The measurements demonstrate the superiority of FAI as the most efficient solution. Full article

Urban Vitality (UV) is a critical indicator for measuring sustainable urban development and quality. It reflects the dynamic interactions and supply–demand coordination within urban systems, especially concerning the human–land relationship. This study aims to quantify the UV of Luohe City, China, for the year 2023, analyze its spatial characteristics, and investigate the driving patterns of socio-ecological land functions on UV intensity and heterogeneity under different scenarios. Utilizing multi-source data, including human mobility data from Baidu Location-Based Services (LBSs), Landsat-9, MODIS, and diverse geo-information datasets, we conducted factor screening and comprehensive assessments. Firstly, Self-Organizing Maps (SOMs) were employed to identify typical activity patterns, and the Urban Vitality Index (UVI) was calculated based on Human Mobility Intensity (HMI) data. Subsequently, a framework for quantity–quality–structure assessments weighted and aggregated sub-indicators to evaluate the Land Social Function (LSF) and Land Ecological Function (LEF). Following the screening process, a Multi-scale Geographically Weighted Regression (MGWR) was applied to analyze the scale and driving relationships between UVI and the land assessment sub-indicators. The results were as follows: (1) The UV distribution in Luohe City was highly uneven, with high vitality areas concentrated within the built-up regions. (2) UV showed significant correlations with both LSF and LEF. The influence of LSF on UV was stronger than that of LEF, with the effectiveness of LEF relying on the well-established provisioning of LSF. (3) Artificial Surface Ratio (ASR) and Corrected Night Lights (LERNCI) were identified as key drivers of UV across multiple scenarios. Under the weekend scenario, the Green Space Ratio (GSR) and the Vegetation Quality (VQ) notably enhanced the attractiveness of human activities. (4) The impacts of drivers varied at the urban, township, and street scales. The analysis focuses on factors with significant bandwidth changes across multiple scenarios: VQ, Remote-Sensing-based Ecological Index (RSEI), GSR, ASR, and ALSI. This study underscores the importance of socio-ecological land functions in enhancing urban vitality, offering valuable insights and data support for urban planning. Full article

Solid-state lighting technology, such as LED devices, is critical to improving energy efficiency in street lighting systems. In Ecuador, government policies have established the obligation to exclusively use LED systems starting in 2023, except in special projects. Ecuador, known for its vast biodiversity, protects its national parks, which are rich in flora, fauna and natural resources, through international institutions and agreements such as UNESCO, CBD and CITES. Although reducing electrical consumption usually measures energy efficiency, this article goes further. It considers aspects such as the correlated color temperature in the lighting design of protected areas, light pollution and the decrease in energy quality due to harmonic distortion. Measurements of the electromagnetic spectrum of the light sources were made in an area in the Galápagos National Park of Ecuador, revealing highly correlated color temperatures that can affect ecosystem cycles. In addition, the investigation detected levels of light pollution increasing the night sky brightness and a notable presence of harmonic distortion in the electrical grid. Using simulations to predict the behavior of these variables offers an efficient option to help preserve protected environments and the quality of energy supply while promoting energy savings. Full article

Over the coming years, the advancement of driverless transport systems for people and goods that are designed to be used on fixed routes will revolutionize the transportation system. Therefore, for a safe transportation system, detecting and recognizing traffic signals based on computer vision has become increasingly important. Deep learning approaches, particularly convolutional neural networks, have shown exceptional performance in various computer vision applications. The goal of this research is to precisely detect and recognize traffic signs that are present on the streets using computer vision and deep learning techniques. Previous work has focused on symbol-based traffic signals, where popular single-task learning models have been trained and tested. Therefore, several comparisons have been conducted to select accurate single-task learning models. For further improvement, these models are employed in a multi-task learning approach. Indeed, multi-task learning algorithms are built by sharing the convolutional layer parameters between the different tasks. Hence, for the multi-task learning approach, different experiments have been carried out using pre-trained architectures like, for instance, InceptionResNetV2 and DenseNet201. A range of traffic signs and traffic lights are employed to validate the designed model. An accuracy of 99.07% is achieved when the entire network has been trained. To further enhance the accuracy of the model for traffic signs obtained from the street, a region of interest module is added to the multi-task learning module to accurately extract the traffic signs available in the image. To check the effectiveness of the adopted methodology, the designed model has been successfully tested in real-time on a few Riyadh highways. Full article

The built environment’s impact on human activities has been a hot issue in urban research. Compared to motorized spaces, the built environment of pedestrian and cycling street spaces dramatically influences people’s travel experience and travel mode choice. The streets’ built environment data play a vital role in urban design and management. However, the multi-source, heterogeneous, and massive data acquisition methods and tools for the built environment have become obstacles for urban design and management. To better realize the data acquisition and for deeper understanding of the urban built environment, this study develops a new portable, low-cost Arduino-based multi-sensor array integrated into a single portable unit for built environment measurements of street cycling spaces. The system consists of five sensors and an Arduino Mega board, aimed at measuring the characteristics of the street cycling space. It takes air quality, human sensation, road quality, and greenery as the detection objects. An integrated particulate matter laser sensor, a light intensity sensor, a temperature and humidity sensor, noise sensors, and an 8K panoramic camera are used for multi-source data acquisition in the street. The device has a mobile power supply display and a secure digital card to improve its portability. The study took Beijing as a sample case. A total of 127.97 G of video data and 4794 Kb of txt records were acquired in 36 working hours using the street built environment data acquisition device. The efficiency rose to 8474.21% compared to last year. As an alternative to conventional hardware used for this similar purpose, the device avoids the need to carry multiple types and models of sensing devices, making it possible to target multi-sensor data-based street built environment research. Second, the device’s power and storage capabilities make it portable, independent, and scalable, accelerating self-motivated development. Third, it dramatically reduces the cost. The device provides a methodological and technological basis for conceptualizing new research scenarios and potential applications. Full article

Ubiquitous street lighting is essential for urban areas. While nowadays, LED-based “smart lamps” are commercially available, municipalities can only switch to them in the long run due to financial constraints. Especially, older types of lamps require frequent bulb replacements to maintain the lighting infrastructure’s function. To speed up the detection of defects and enable better planning, a non-invasively retrofittable IoT sensor solution is proposed that monitors lamps for defects via visible light sensors, communicates measurement data wirelessly to a central location via LoRaWAN, and processes and visualizes the resulting information centrally. The sensor nodes are capable of automatically adjusting to shifting day- and nighttimes thanks to a second sensor monitoring ambient light. The work specifically addresses aspects of energy efficiency essential to the battery-powered operation of the sensor nodes. Besides design considerations and implementation details, the paper also summarizes the experimental validation of the system by way of an extensive field trial and expounds upon further experiences from it. Full article