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This research presents a dual-pronged bibliometric and systematic review of the integration of phase change materials (PCM) in asphalt pavements to counteract the urban heat island (UHI) effect. The bibliometric approach discerns the evolution of PCM-inclusion asphalt research, highlighting a marked rise in the number of publications between 2019 and 2022. Notably, Chang’an University in China has emerged as a leading contributor. The systematic review addresses key questions like optimal PCM types for UHI effect mitigation, strategies for PCM leakage prevention in asphalt, and effects on mechanical properties. The findings identify polyethylene glycols (PEGs), especially PEG2000 and PEG4000, as prevailing PCM due to their wide phase-change temperature range and significant enthalpy during phase transitions. While including PCM can modify asphalt’s mechanical attributes, such mixtures typically stay within performance norms. This review emphasises the potential of PCM in urban heat management and the need for further research to achieve optimal thermal and mechanical balance.

As autonomous vehicles (AVs) become part of urban environments, pedestrian safety and interactions with these vehicles are critical to creating sustainable, walkable cities. Intuitive pedestrian-vehicle communication is essential not only for reducing crash risk but also for supporting policies that promote active mobility and efficient traffic flow. This study investigates pedestrian crossing behavior in a fully immersive virtual reality environment, building on previous work by the authors conducted in a CAVE-type simulator. Participants crossed between a conventional vehicle and an AV when they perceived it was safe. The analysis examines how external human–machine interfaces (eHMIs) influence crossing decisions, collisions, safety margins, and crossing initiation time (CIT) across different vehicle speeds and traffic gaps. Three hypotheses were tested regarding the effects of eHMIs on CIT, risk-taking behavior, and perceived safety. Results show that eHMIs significantly affect pedestrian decisions: participants delayed crossings when the eHMI indicated non-yielding behavior and initiated crossings earlier when yielding was signaled. Risk-taking behavior increased at higher vehicle speeds and shorter time gaps. Although perceived safety did not increase, behavioral results indicate reliance on visual cues. These findings underscore the importance of standardizing eHMIs to support pedestrian safety and sustainable urban mobility.

This paper presents a method for position correction in collaborative robots, applied to a case study in an industrial environment. The case study is aligned with the GreenAuto project and aims to optimize industrial processes through the integration of various hardware elements. The case study focuses on tightening a specific number of nuts onto bolts located on a partition plate, referred to as “Cloison”, which is mounted on commercial vans produced by Stellantis, to secure the plate. The main challenge lies in deviations that may occur in the plate during its assembly process, leading to uncertainties in its fastening to the vehicles. To address this and optimize the process, a collaborative robot was integrated with a 3D vision system and a screwdriving system. By using the 3D vision system, it is possible to determine the bolts’ positions and adjust them within the robot’s frame of reference, enabling the screwdriving system to tighten the nuts accurately. Thus, the proposed method aims to integrate these different systems to tighten the nuts effectively, regardless of the deviations that may arise in the plate during assembly.

Virtual studies involving pedestrians have gained relevance due to the advantage of not exposing them to actual risk, and simulation setups have benefitted from rapid technical advancements, becoming increasingly complex and immersive. However, it remains unclear whether complex setups affecting participants’ freedom of movement impact their decision-making. This research evaluated the effects of a more realistic approach to studying pedestrian crossing behavior by comparing a perception-action task requiring participants to walk effectively along a semi-virtual crosswalk with a similar experiment using static crossing conditions. Using a CAVE system, two real-world streets were modeled in two different virtual scenarios, varying vehicle speed patterns and distance from the crosswalk. Visual stimuli were presented to two groups of 30 participants, with auditory stimuli adapted accordingly. The impact of various factors on participants’ crossing decisions was evaluated by examining the percentage of crossings, crossing start time, and time-to-passage. Overall, the experimental approach did not significantly affect participants’ crossing decisions.

Throughout the lifetime, road markings (RMs) accumulate dirt, oils, and greases, which reduce visibility, shorten service life, and compromise road safety. If RMs could degrade these pollutants, their service life would increase. When exposed to UV light and humidity, semiconductors, such as titanium dioxide (TiO2), can interact with contaminants and promote their chemical degradation. Semiconductors are commonly used on different types of substrates to achieve self-cleaning ability. In this study, 0.25–3 wt% TiO2 was incorporated into a commercial RM paint for this purpose. After functionalization, the RM paint samples were contaminated with Methylene Blue and Rhodamine B. After pollution, the specimens were irradiated with a light source that simulates sunlight. To assess the self-cleaning capacity of the paints, visual analysis, color variation and discoloration by using CIELAB color coordinates, diffuse reflectance, and digital image processing techniques were applied. In both techniques, the samples with 2% and 3% of TiO2 showed a greater capacity to degrade pollutants. Further, the chemical and morphological characteristics of the reference paint and the samples that showed the best self-cleaning results were analyzed by using Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and X-ray Diffraction (XRD). They identified the polymer, filler, and pigment in the commercial paint and confirmed the TiO2 increase after functionalization. This study demonstrated the innovative potential of incorporating semiconductors to achieve a new capability (self-cleaning) for RM paints. This breakthrough not only has the potential to extend the RM service life, but also to improve road safety through greater visibility.

This paper presents the development of fleet management software for mobile robots, including AGV and AMR technologies, within the scope of a case study from the GreenAuto project. The system was designed to integrate position and status data from different robots, unifying this information into a single map. To achieve this, a web-based platform was developed to allow the simultaneous, real-time visualization of all robots in operation. However, the main challenge of this research lies in the heterogeneity of the fleet, which comprises robots of different makes and models from various manufacturers, each using distinct data formats. The proposed approach addresses this by facilitating fleet monitoring and management, ensuring a greater efficiency and coordination in the robot movement. The results demonstrate that the platform improves the traceability and operational supervision, promoting the optimized management of mobile robots. It is concluded that the proposed solution contributes to industrial automation by providing an intuitive and centralized interface, enabling future expansions for new functionalities and the integration with other emerging technologies. The proposed system demonstrated efficiency in updating and supervising operations, with an average latency of 120 ms for task status updates and an interface refresh rate of less than 1 s, enabling near real-time supervision and facilitating operational decision-making.

When crossing a road, pedestrians must detect traffic, combine data coming from different perceptual modalities, evaluate the time envelope for safely cross the street, and monitor the position of oncoming vehicles to perform corrective actions if needed. This study analyzed the influence of noise emitted by vehicles, or its absence, on pedestrians’ crossing decision-making. Experiments were performed in a virtual environment using two road scenarios. Participants were presented with stimuli of approaching vehicles that varied regarding speed, movement patterns, and auditory condition: one concerning the approaching of an electric vehicle, another regarding the approaching of a gasoline combustion vehicle, and, finally, a condition regarding the absence of auditory cues. Participants were tasked with indicating the moment when they decided to cross the street. The results show that, despite the noise variations caused by the type of vehicle and its speed pattern, the participants’ decision to cross was mostly based on vehicle distance. When a vehicle approaches the crosswalk from a short distance and with no occlusion to the pedestrian’s visibility, the sound does not seem to influence the pedestrians’ crossing decision-making.

Road markings (RMs) typically consist of a paint layer and a retroreflective layer. They play a crucial role in road safety by offering visibility and guidance to drivers. Over their lifetime, dirt particles, oils, and greases are adsorbed on the RM surface, reducing their visibility and service life. A self-cleaning ability has been widely studied in several substrates. However, for RMs, this represents a breakthrough and a sustainable advance, while having the potential to increase their service life and enhance road safety. In this context, nanotechnology can be a strong ally through the application of semiconductor materials, such as TiO2, to develop the self-cleaning ability. In addition to this novelty in RMs, quantifying this ability in terms of pollutant removal efficiency is also a challenge. In this sense, artificial intelligence (AI) and colorimetry can be combined to achieve improved results. The aims of the work herein reported were to assess the self-cleaning capability in an RM paint through the mass incorporation of semiconductors, evaluate their photocatalytic efficiency using traditional (spectrophotometric) and modern (AI-enhanced) colorimetry techniques, and compare the results obtained using both techniques. To this end, a water-based acrylic RM paint was modified through the mass incorporation of 0.5%, 1%, 2%, and 3% of nano-TiO2, and a pollutant model widely used, Rhodamine B, was applied onto their surface. The samples were irradiated with a light source that simulates sunlight for 0, 3, 6, 12, 24, and 48 h. Visual analysis and spectrophotometric and artificial intelligence-enhanced colorimetry techniques were used and compared to evaluate the pollutant removal. The results confirm that RM paints with 2% and 3% nano-TiO2 incorporated have a significantly higher pollutant removal ability and that both colorimetric techniques used are suitable for this assessment.

Transparent binder is used to substitute conventional black asphalt binder and to provide light-colored pavements, whereas nano-TiO2 has the potential to promote photocatalytic and self-cleaning properties. Together, these materials provide multifunction effects and benefits when the pavement is submitted to high solar irradiation. This paper analyzes the physicochemical and rheological properties of a transparent binder modified with 0.5%, 3.0%, 6.0%, and 10.0% nano-TiO2 and compares it to the transparent base binder and conventional and polymer modified binders (PMB) without nano-TiO2. Their penetration, softening point, dynamic viscosity, master curve, black diagram, Linear Amplitude Sweep (LAS), Multiple Stress Creep Recovery (MSCR), and Fourier Transform Infrared Spectroscopy (FTIR) were obtained. The transparent binders (base and modified) seem to be workable considering their viscosity, and exhibited values between the conventional binder and PMB with respect to rutting resistance, penetration, and softening point. They showed similar behavior to the PMB, demonstrating signs of polymer modification. The addition of TiO2 seemed to reduce fatigue life, except for the 0.5% content. Nevertheless, its addition in high contents increased the rutting resistance. The TiO2 modification seems to have little effect on the chemical functional indices. The best percentage of TiO2 was 0.5%, with respect to fatigue, and 10.0% with respect to permanent deformation.Transparent binder is used to substitute conventional black asphalt binder and to provide light-colored pavements, whereas nano-TiO2 has the potential to promote photocatalytic and self-cleaning properties. Together, these materials provide multifunction effects and benefits when the pavement is submitted to high solar irradiation. This paper analyzes the physicochemical and rheological properties of a transparent binder modified with 0.5%, 3.0%, 6.0%, and 10.0% nano-TiO2 and compares it to the transparent base binder and conventional and polymer modified binders (PMB) without nano-TiO2. Their penetration, softening point, dynamic viscosity, master curve, black diagram, Linear Amplitude Sweep (LAS), Multiple Stress Creep Recovery (MSCR), and Fourier Transform Infrared Spectroscopy (FTIR) were obtained. The transparent binders (base and modified) seem to be workable considering their viscosity, and exhibited values between the conventional binder and PMB with respect to rutting resistance, penetration, and softening point. They showed similar behavior to the PMB, demonstrating signs of polymer modification. The addition of TiO2 seemed to reduce fatigue life, except for the 0.5% content. Nevertheless, its addition in high contents increased the rutting resistance. The TiO2 modification seems to have little effect on the chemical functional indices. The best percentage of TiO2 was 0.5%, with respect to fatigue, and 10.0% with respect to permanent deformation.

This study investigates the effect of iron-modified nano-TiO2, using the co-precipitation method with different concentrations of FeCl3 (0.1, 1, and 10%), to improve its photocatalytic properties for outdoor applications. To this end, modified and unmodified nano-TiO2 were characterized using different techniques. The optical properties were characterized by diffuse reflectance spectroscopy (DRS) followed by band gap calculation. X-ray diffraction (XRD) was used to analyze the crystalline structure. Chemical and morphological characterization were carried out using energy-dispersive X-ray spectroscopy (EDS) and scanning electron microscopy (SEM). The photocatalytic activity was investigated by decolorizing Rhodamine B aqueous solutions under similar sunlight irradiation. The results indicate that the modification improved light absorption in the UV range for all iron concentrations; however, only the concentration of TiO2: FeCl3 (10%) shifted the absorption to the visible region. Also, including Fe3⁺ in TiO2 decreased the band gap energy from 3.14 to up to 2.80 eV. There were variations in crystallite size from 21.13 to up to 40.07 nm. The nano-TiO2 morphology analysis showed that it did not change after iron modification. EDS showed an FeCl3 peak only at higher concentrations (10%). In addition, the 0.1% Fe-modified TiO2 exhibited the highest activity in the photocatalytic process, with an efficiency of 95.23% after 3 h of irradiation.