Quantifying inter-agency coordination impacts on highway network recovery after disruptions
以东京为例，用代理模型模拟不同协调水平下高速公路网络震后恢复，发现更高协调能显著加速恢复并减少拥堵，路线策略在早期与高效恢复间取得良好平衡。
This study quantifies the impact of inter-agency coordination on the recovery of large-scale highway networks after earthquakes, using Tokyo as a case study. A semi-dynamic agent-based traffic model simulates recovery under varying levels of coordination between two operators. Recovery is evaluated using two complementary rapidity metrics (area under the curve and trajectory skewness) which capture both overall efficiency and the timing of functionality restoration. Coordination is modeled across four levels, from uncoordinated to full joint planning and resource pooling, and combined with three recovery strategies: volume-based, betweenness-based, and route-based. Results show that higher coordination substantially accelerates recovery and reduces congestion spillovers. Route-based strategies achieve strong trade-offs between early and efficient recovery, while volume-based strategies provide effective baselines when coordination is limited. The findings highlight the central role of institutional collaboration in post-disaster transportation resilience, addressing a gap in recovery models that typically assume centralized decision-making in complex, multi-operator highway systems.