Various studies have been recently conducted to predict pavement condition, but most of them were developed in a certain region where climate conditions were kept constant and/or the research focused on specific road distresses using single parameters. Thus, this research aimed at determining the influence of pavement structure, traffic demand, and climate factors on urban flexible pavement condition over time. To do this, the Structural Number was used as an indicator of the pavement capacity, various traffic and climate variables were defined, and the Pavement Condition Index was used as a surrogate measure of pavement condition. The analysis was focused on the calibration of regression models by using the K-Fold Cross Validation technique. As a result, for a given pavement age, pavement condition worsens as the Equivalent Single Axle Load and the Annual Average Height of Snow increased. Likewise, a cold Annual Average Temperature (5–15 °C) and a large Annual Average Range of Temperature (20–30 °C) encourage a more aggressive pavement deterioration process. By contrast, warm climates with low temperature variations, which are associated with low precipitation, lead to a longer pavement service life. Additionally, a new classification of climate zones was proposed on the basis of the weather influence on pavement deterioration.

Major cities in Europe have seen a significant increase in micromobility infrastructure, including cycling infrastructure, with 42 European Metropolitan cities implementing 1421.54 km of cycling infrastructure in a year. However, the design principles for bikeways primarily rely on conventional road design for bicycles and lack consistency in accommodating emerging powered micromobility devices like e-scooters. To address this research gap, this paper conducts a systematic review and scientometric analysis to explore safe bikeway infrastructure design. It identifies three overlooked topics (marking and signing, grading, and mode choice) and nine understudied areas (vibration, distress, skidding, alignment features, clearance, lateral control, connectivity, traffic composition, and intersection presence) that significantly impact micromobility safety. The study’s comprehensive understanding and use of scientometric tools reveal patterns and relationships within the literature. It also highlights criteria influencing micromobility safety and the need for research on pavement and user behavior. The findings contribute to evidence-based decision-making for practitioners and researchers, emphasizing the importance of tailored infrastructure design to enhance micromobility safety and achieve cost-effective improvements.

The number of road safety studies that are based on driving simulators is growing significantly. The Polytechnic University of Valencia, Spain, developed a low-cost driving simulator for the assessment, training, and rehabilitation of drivers (SE²RCO). The main objective of this research was the validation of the driving simulator so that studies about road safety and highway geometric design that considered human factors could be performed. The validation was based on continuous speed profiles collected from 28 volunteers on a 30-km-long, two-lane rural road section. The same volunteers drove through the same road section built in SE²RCO. Speed data of 79 curves and 52 tangents were selected for the analysis. Comparison of the real and simulated speeds ensured the simulator’s objective validity according to average and operating speeds. Two models were developed to predict field speeds from simulated speeds. Results showed that a simulated average speed lower than approximately 90 km/h was linked to a similar real average speed. For higher simulated speeds, the average speed in the real environment was lower than the simulated one. In addition, the actual operating speed was around 5 km/h lower than the operating speed in the driving simulator. Most volunteers assessed the quality and similarity of the virtual environment compared with the real world as medium or high and assessed the driving tasks similarly, thus achieving subjective validation of the simulator.

Pavement management systems can be used to efficiently allocate limited maintenance budgets to better align with pavement deterioration. However, pavement deterioration is subject to uncertain factors that complicate the prediction of future pavement conditions accurately, entailing differences in the optimum maintenance strategy. This paper addresses this challenge by introducing a method to aid local engineers in optimising the scheduling of maintenance activities under uncertain pavement deterioration conditions. Markov chains are used to simulate the variability of life-cycle performance. Moreover, a multi-objective optimisation of an urban network is carried out to find the maintenance programme that minimises the mean life-cycle cost, maximises the mean user benefit, and minimises the standard deviation of life-cycle cost. This third objective enables the optimisation routine to minimise the possibility of unintentionally increasing the life-cycle cost due to system variability. This approach results in a reduction of the life-cycle cost variability by up to 62%, provides pavement strategies that benefit road users as a result of better pavement conditions, and reduces the risk of resorting to costly future maintenance activities.