Resilience assessment and enhancement of urban road networks subject to traffic accidents: a network-scale optimization strategy

The Effectiveness of Improvement Measures in Road Transport Network Resilience: A Systematic Review and Meta-Analysis

“…Providing clearer, more timely information to passengers and management can help improve the resilience of RTNs during disasters and the speed of recovery afterward. Yap et al [55] and Wang et al [57] also agreed with this view. The resilience of RTNs can also be improved by enhancing communication between operators and users [71].…”

“…Using the random effects model and combining the four studies, the total effect size was 0.04, i.e., 95% CI (0.01-0.06) (Figure 6). This study, after performing sensitivity analysis, found that the study by Tao et al [57] is an anomaly whose presence may bias the effect size and heterogeneity. When this study TSM: Four studies reported TSM measures that could be used for integrated analysis [39,40,50,57].…”

“…As a result, additional analysis was conducted after excluding this outlier, and the results showed minimal heterogeneity (effect size: 0.06, i.e., 95% CI (0.03-0.08), I 2 = 11.6%, and p = 0.323). TSM: Four studies reported TSM measures that could be used for integrated analysis [39,40,50,57]. Shang et al [40] used the relative area index to represent resilience in their paper, and they applied a method based on adaptive signal control with depth reinforcement to improve the resilience of RTNs, and this can increase the relative area index from 0.25 to 4.65 at a 75% capacity reduction in the affected section.…”

“…Dana Abudayyeh et al [50] defined travel time as resilience in their study, and they adopted a bilevel optimization framework using the CE algorithm to draw the conclusion that applying signal optimization can reduce travel time by almost 6%. To solve the suggested network-wide traffic signal optimization model, Tao et al [57] devised a two-level method based on the greedy approach and gradient descent, and the resilience of RTNs was characterized as resilience loss (RL). The findings showed that the suggested model might increase system resilience under various scenarios.…”

“…All the included studies were independent, with considerable inter-study heterogeneity. Some of the studies (e.g., [52,53,57]) showed an unclear-to-high risk of bias. Considering the broad range of improvement measures that have been demonstrated to be related to the resilience of RTNs, it is worth noting that these measures all improved the resilience of RTNs in all the studies.…”