Railway track performance and durability face growing challenges from higher speeds, heavier axle loads, and changing environmental conditions. Crumb rubber-modified asphalt (CRMA) offers a sustainable solution by repurposing waste tires into a durable material for railway trackbeds, improving both performance and environmental impact. Following PRISMA-ScR guidelines, this scoping review synthesizes an extensive body of global research on the structural, mechanical, and environmental benefits of CRMA in railway trackbeds. A systematic literature search was conducted across major academic databases, covering studies published over several decades. Selection criteria focused on CRMA applications in railway trackbeds, using keywords such as “crumb rubber-modified asphalt”, “railway track vibration”, and “sustainable railway materials.” After rigorous screening and eligibility assessment, the most relevant peer-reviewed studies were included, emphasizing mechanical performance, durability, and environmental impact. Key findings indicate that CRMA effectively reduces ground vibrations, enhances load distribution, and lowers long-term maintenance costs while promoting sustainable waste management through tire recycling. However, challenges such as optimal mix design, potential emissions, and long-term bonding stability require further investigation. Additionally, the review was limited to English-language studies, potentially omitting relevant non-English research, and some reports were inaccessible during retrieval. This review maps critical research gaps, identifies key areas for future optimization, and highlights CRMA’s potential to advance resilient and eco-friendly railway infrastructure.

This study presents a comprehensive framework for assessing and prioritizing risks associated with railway tunnels, focusing on the Tehran-North railway in Iran. By integrating fuzzy Failure Modes and Effects Analysis with fuzzy Measurement of Alternatives and Ranking according to the Compromise Solution and fuzzy Criteria Importance Through Intercriteria Correlation methodologies, the research provides a robust multi-criteria approach to evaluate key risk factors such as seismic hazards, water ingress, and structural deformations. The analysis emphasized proactive mitigation strategies like seismic retrofitting and advanced monitoring systems, identifying tunnels at 243, 236, and 260 km as high priorities due to significant accident records. Additionally, tunnels at lower elevations were particularly vulnerable to flooding, landslides, and seismic hazards. This research enhances infrastructure risk management by offering actionable insights for resource allocation and retrofitting strategies while setting a foundation for real-time monitoring and artificial intelligence-driven models.

Recently, with an increasing number of people traveling by car, there has been a growing demand for effective traffic management, reduced travel times, and improved road and street maintenance plans. Here, it is evident that drivers make a well-informed decision on which route to take by utilizing smartphone routing, traffic announcements, and advancements in navigation technology. In the present study, the authors aim to develop a road maintenance plan that incorporates a bi-level optimization and simulation framework. They focus on the upper level by optimizing the road maintenance plan; at a lower level, intelligent agents acting as savvy passengers seek to minimize driving time and wait times in traffic. To evaluate the intelligent behavior of agents in reducing travel time on blocked routes (due to road repairs) under various scenarios, the authors first calculate the agents’ behavior in finding the optimal travel demand route and then integrate the optimization of the road maintenance plan. The results of this study demonstrated the effectiveness of informing passenger agents and their intelligence in correcting routes and reducing travel time.