## 5 Conclusions and Recommendations  
### Conclusions  
This paper proposes a dynamic resilience assessment and optimization framework for sparse road networks in regions prone to geological disasters. The effectiveness of this framework was validated using road networks in Tibet. The study reveals that sparse road networks, characterized by long segments, few nodes, and a lack of alternative routes, are particularly susceptible to severe impacts from geological disasters. When critical segments are completely blocked, the network's traffic capacity significantly decreases or even fails entirely. Compared to dense networks, sparse networks are more vulnerable to frequent geological disasters, with greater recovery difficulty and longer recovery times. The simulation results also indicate that the damage to critical nodes, especially interchanges, tunnels, and bridges, significantly affects the performance of sparse road networks, potentially causing a sharp decline in network connectivity.  
The duration of the disaster, disaster type, and remaining traffic capacity significantly impact network resilience. In the case of partial blockage, sparse road networks exhibit higher evacuability and adaptability, achieving the highest overall resilience (0.7294). However, as disaster duration increases, network resilience notably decreases, with a nearly 10% drop in resilience when disaster duration extends to 69 h compared to 6 h. Additionally, sparse road networks show high resilience to collapse events but have slower recovery speeds. In contrast, they have low survivability but fast recovery speed and adaptability when facing debris flows. Subsidence disasters result in moderate survivability and evacuability, fast recovery speeds, and relatively balanced overall resilience. Water damage events lead to the lowest overall resilience.  
Finally, it is important to note that this study assumes a uniformly distributed failure probability for infrastructure components within the road network at the time of disaster occurrence. It does not account for the time-dependent risks associated with aging or accumulated load effects on critical nodes such as bridges and tunnels. In recent years, emerging technologies such as MTInSAR and UAV have been widely and successfully applied to long-term displacement monitoring and pre-disaster risk assessment of infrastructure. Therefore, future research could explore integrating early warning data obtained from MTInSAR and UAV monitoring technologies with dynamic resilience assessment models to establish a closed-loop framework encompassing pre-disaster risk prediction, real-time resilience quantification, and post-disaster recovery optimization. This integration would further enhance the overall disaster resistance of sparse mountainous road networks. Meanwhile, this study has not yet considered the impact of traffic congestion, information accessibility, and service availability on network performance. In future research, we plan to further integrate dynamic traffic flow models into the resilience framework, incorporating congestion effects, information dissemination, and essential service facilities (e.g., fuel stations, rest areas, etc.). This will enable us to assess how disruptions affect route choices, delays, and detours, ultimately developing a resilience assessment framework that accounts for traveler behavior.