Overtaking is a maneuver that consists of passing another vehicle traveling on the same trajectory, but at a slower speed. Overtaking is considered one of the most dangerous, delicate and complex maneuvers performed by a vehicle, as it requires a quick assessment of the distance and speed of the vehicle to be overtaken, and also the estimation of the available space for the maneuver. In particular, most drivers have difficulty overtaking a vehicle in the presence of vehicles coming on other trajectories. To solve these overtaking problems, this article proposes a method of performing safe, autonomous-vehicle maneuvers through the PreScan simulation program. In this environment, the overtaking-maneuver scenario (OMS) is composed of highway infrastructure, vehicles and sensors. The proposed OMS is based on the solution of minimizing the risks of collision in the presence of any oncoming vehicle during the overtaking maneuver. It is proven that the overtaking maneuver of an autonomous vehicle is safe to perform through the use of advanced driver-assistance systems (ADAS) such as adaptive cruise control (ACC) and technology-independent sensors (TIS) that detect the driving environment of the maneuver.

Vehicle congestion and the environmental problems associated with the increasing vehicle fleet have led stakeholders to create solutions to these problems. Park-and-Ride (P&R) facilities are provided as a solution for public transportation to avoid increasing vehicular flow and using private vehicles. However, the optimal location of these facilities is still a challenge to be considered. Therefore, this article aims to present a systematic review of the mathematical models applied for P&R localization, using the PRISMA protocol to ensure a comprehensive analysis. A total of 44 articles between 2002 and 2025 were identified into four categories: decision support models, econometric models, optimization models, and other models. The review also examines the term distribution of urban contexts where the mathematical models are applied, distinguishing between Global North versus Global South urban contexts. The results showed the efficiency of mathematical models within the decision support models category due to their integration with multiple criteria. The econometric models analyze factors influencing user behavior, while the optimization models improve and optimize the efficiency of transport networks despite facing computational challenges. Finally, other models, such as multilevel programming and fuzzy logic, offer adaptive solutions for highly variable urban environments. The primary contribution of this study is its comprehensive application of the mathematical models used for the location of P&R facilities. This offers a systematic approach for anticipating future urban situations, developing supporting policies, and analyzing their effects.

Connected Autonomous Vehicles (CAVs) are changing transportation networks and introducing new ways to strengthen and adapt to disruptive events. This systematic scoping review uses PRISMA-ScR to examine how CAVs affect transportation network resilience, focusing on four key attributes of resilience: Robustness, Redundancy, Resourcefulness, and Rapidity (“4Rs”). The review analyzes 106 peer-reviewed articles published between 2014 and 2025. Quantitative synthesis of the “4Rs” centered framework reveals a significant research imbalance: Robustness ( n = 106) and Resourcefulness ( n = 102) are the most extensively documented attributes, followed by Redundancy ( n = 83), while Rapidity ( n = 31) remains the least explored. The review identifies Traffic Congestion (34.91%), Cyber Disruptions (14.15%), and Communication Failures (12.26%) as the primary disruptive events quantified in the literature. The study found that CAVs significantly improve the Robustness and Redundancy by maintaining a constant flow of traffic and creating alternative routes during disruptions. Likewise, Resourcefulness is benefited due to the ability of CAVs to collect information in real time, allowing for effective resource allocation and decision-making during system recovery, while Rapidity remains the least explored attribute, particularly in the post-disruption phase. Furthermore, the empirical basis distribution is predominantly simulation/analytical studies (71 out of 106), while purely empirical studies remain limited (13/106), and pilot/field/FOT evidence is scarce (4/106). Overall, the study highlights the main factors to be taken into account in the implementation of CAVs.

Introduction: Linking projects have become integral to Higher Education in Ecuador, necessitating thorough planning, budgeting, continuous monitoring, and rigorous presentation of outcomes. This study evaluates the impact of such projects on student competency development and society, focusing on the Logistics and Transportation Engineering program at Universidad Técnica Particular de Loja (UTPL). A linking project conducted between October 2023 and February 2024 aimed to enhance public transportation in underserved areas of Loja. Methodology: Methodology involved ten seventh-semester students who conducted a detailed analysis of Loja's urban transportation system using advanced tools to assess demand, service schedules, and route availability. Subsequent stages included designing efficient transportation routes and presenting proposals to stakeholders. Results: Results indicate significant improvements in students' technical skills, project management, and communication and teamwork abilities, alongside heightened social responsibility and community engagement. Discussions: This research underscores the pivotal role of linking projects in fostering holistic student development and addressing societal needs through practical application. Conclusions: The study's findings highlight the transformative impact of linking projects on student learning outcomes and their ability to contribute meaningfully to societal challenges, particularly in the context of public transportation development.