Effective road pavement management is vital for maintaining the functionality and safety of transportation infrastructure. This review examines the integration of Machine Learning (ML) into Pavement Management Systems (PMS), presenting an analysis of state-of-the-art ML techniques, algorithms, and challenges for application in the field. We discuss the limitations of conventional PMS and explore how Artificial Intelligence (AI) algorithms can overcome these shortcomings by improving the accuracy of pavement condition assessments, enhancing performance prediction, and optimizing maintenance and rehabilitation decisions. Our findings indicate that ML significantly advances PMS capabilities by refining data collection processes and improving decision-making, thereby addressing the intricacies of pavement deterioration. Additionally, we identify technical challenges such as ensuring data quality and enhancing model interpretability. This review also proposes directions for future research to overcome these hurdles and to help stakeholders develop more efficient and resilient road networks. The integration of ML not only promises substantial improvements in managing pavements but is also in line with the increasing demands for smarter infrastructure solutions.

A highway in poor conditions can raise transportation costs. Due to budgetary constraints, pavement maintenance programming is considered a difficult decision-making problem. In this article we propose a novel mathematical model and a different variant of the pavement maintenance management problem, solved with integer linear programming. The novelty of this approach is the use of the Pavement Surface Rating as the condition indicator, along with a proposed conversion strategy between most used performance indices. Additionally, we propose a simpler and broader deterioration model, when compared to existent ones, using a table system. This renders the model to be solved easily, allowing it to be implemented worldwide, given its generic characteristics. Many computational experiments were performed, both on artificial benchmark instances and on a real-world case study. The proposed model is shown to obtain optimal solutions in short computational times, and it is able to solve much larger instances than the ones found in the literature. Optimal solutions from benchmark instances, consisting of 5,000 segments and an analysis period of 30 years, were found in less than 45 minutes. Additionally, the optimal solutions have a difference of more than 20% in average, when compared to a greedy algorithm.

Micromobility responds to urban transport challenges by reducing emissions, mitigating traffic, and improving accessibility. Nevertheless, the safety of micromobility users, particularly cyclists, remains a concern in urban environments. This study aims to construct a safety map and a risk-averse routing system for micromobility users in diverse urban environments, as exemplified by a case study in Lisbon. A data-driven methodology uses object detection algorithms and image segmentation techniques to identify potential risk factors on cycling routes from Google Street View images. The ‘Bikeable’ Multilayer Perceptron neural network measures these risks, assigning safety scores to each image. The method analyzed 5321 points across 24 parishes in Lisbon, with an average safety score of 4.5, indicating a generally safe environment for cyclists. Carnide emerged as the safest area, while Alcântara exhibited a higher level of potential risks. Additionally, an equation is proposed to compute route efficiency, enabling comparisons between different routes for identical origin-destination pairs. Preliminary findings suggest that the presented routing solution exhibits higher efficiency than the commercial routing benchmark. Risk-averse routes did not result in a substantial rise in travel distance or time, with increments of 7% on average. The study also contributed to increasing the existing amount of cycle path data in Lisbon by 12%, correcting inaccuracies, and updating the network in OpenStreetMap, providing access to more precise information and, consequently, more routes. The key contributions of this study, such as the safety map and risk-averse router, underscore the potential of data-driven tools for boosting urban micromobility. The solutions proposed demonstrate modularity and adaptability, making them fit for a range of urban scenarios and highlighting their value for cities prioritizing safe, sustainable urban mobility.

Connected and automated vehicles (CAVs) have the potential to significantly impact transportation systems in terms of mobility, the environment, safety, and the economy. These vehicles rely on a range of sensors and cameras to detect road signs and lane markings, as well as to scan their surroundings, and they are connected to other vehicles and infrastructures. Previous research has highlighted the need for transport asset management processes to manage the intrinsic aspects of CAVs more effectively, with a view to improving performance, resource utilization, and budget planning. However, little literature was found with a deep discussion of how CAVs will impact asset management. In this article we offer an initial discussion on the impacts of CAVs on road asset management. To do so, we first provide a short introduction to CAVs, followed by an overview of road asset management. We then comprehensively discuss many asset management aspects that are affected by CAVs. Finally, future research opportunities, challenges, and important subjects are outlined.

As global infrastructure ages under fiscal constraints, traditional Pavement Management Systems often rely on sequential workflows that ignore the dynamic impact of interventions on future deterioration. This paper introduces a data-driven framework integrating a recursive multi-year forecasting engine with network-level Genetic Algorithm (GA) optimization. Unlike static models, this approach establishes a dynamic feedback loop where maintenance decisions at year t update the pavement state inputs for the prediction model at year $$t+1$$ . The framework couples a Light Gradient Boosting Machine (LightGBM), with hyperparameters tuned via Bayesian optimization (Optuna), trained on Long-Term Pavement Performance (LTPP) data ( $$R^2=0.88$$ , $$RMSE=0.19$$ ) with a GA optimization module for resource allocation. Using a section-based splitting strategy to prevent data leakage, results show this recursive integration outperforms reactive strategies, improving network quality by 3% while reducing maintenance costs by 22%. This framework demonstrates the potential of recursive data-driven models to optimize life-cycle costs and reliability in modern pavement management.