The impacts of flash flooding on road transportation, such as travel time delays, are crucial concerns by different stakeholders including neighborhoods and governments at all levels, which calls for a multi-scale flooding impact assessment. Although existing methods have such a capacity, they are not effectively validated, limiting our understanding of the impacts for successful flooding management. This paper develops a multi-scale flooding impact assessment framework and a validation method applied to road transportation in Wuhan, China. Using real-world traffic data in a historical flooding event, the validation results show the capability of the framework in predicting the post-flooding travel time of trips across different scales. The framework is further used to predict travel time delays in flooding at neighborhood, district, and city scales during peak-traffic periods. Results show that while flooding primarily impacts commuting times in central city areas, the integration of traffic accidents in flooding expands the impact to districts and communities farther away from the city center. At the city scale, our analysis indicates that the travel time to workplaces is mostly increased, compared to the increase in travel time to critical facilities. The proposed framework is also adopted to prioritize the treatment of flood-prone sites. These results show the potential of the framework to help flooding response and mitigation.

Road networks are typically connected at the national scale, thus investigating a city's road network vulnerability requires defining an urban boundary. However, the choice of boundary varies widely in research, resulting in divergent and at times contradictory findings. Moreover, analyzing larger cities' road networks with expanding boundaries helps to predict road network vulnerability patterns in smaller, similarly structured cities. Guided by these two objectives, this study investigates the gaps in road network vulnerabilities by comparing 1452 road networks from 363 cities in Mainland China across four distinct boundary types. The findings indicate a significant change in road network vulnerability across different boundaries, with the largest gap ranging between 0.5449 and 0.8743. The study further finds these vulnerability gaps correlate with the difference in the structural characteristics of those urban road networks and the urban population covered by different boundaries, with correlation coefficients ranging from 0.362 to 0.755. Notably, the cities' vulnerability rankings show relatively slight variations when adopting different boundaries, with Spearman's correlation coefficients between 0.442 and 0.879. This research offers valuable insights into the influence of urban expansion on road network vulnerability, particularly relevant in the context of rapid urbanization and the expansion of road networks.

Urban road networks (URNs), as simplified views and important components of cities, have different structures, resulting in varying levels of transport efficiency, accessibility, resilience, and many socio-economic indicators. Thus, topological characteristics of URNs have received great attention in the literature, while existing studies have used various boundaries to extract URNs for analysis. This naturally leads to the question of whether topological patterns concluded using small-size boundaries keep consistent with those uncovered using commonly adopted administrative boundaries or daily travel range-based boundaries. This paper conducts a large-scale empirical analysis to reveal the boundary effects on 22 topological metrics of URNs across 363 cities in mainland China. Statistical results show that boundaries have negligible effects on the average node degree, edge density, orientation entropy of road segments, and the eccentricity for the shortest or fastest routes, while other metrics including the clustering coefficient, proportion of high-level road segments, and average edge length together with route-related metrics such as average angular deviation show significant differences between road networks extracted using different boundaries. In addition, the high-centrality components identified using varied boundaries show significant differences in terms of their locations, with only 21%-28% of high-centrality nodes overlapping between the road networks extracted using administrative and daily travel range-based boundaries. These findings provide useful insights to assist urban planning and better predict the influence of a road network structure on the movement of people and the flow of socio-economic activities, particularly in the context of rapid urbanization and the ever-increasing sprawl of road networks.

Recent events, including COVID-19, extreme floods, and explosion accidents, commonly induced localized closures and disruptions of urban road networks (URNs), resulting in significant impacts on human mobility and socio-economic activities. Existing studies on URN resilience to those events mainly took few cases for empirical studies, limiting our understanding on the URN resilience patterns across different cities. By conducting a large-scale nationwide resilience analysis of URNs in 363 cities in mainland China, this study attempts to uncover the resilience patterns of URNs against the worst-case single (SLDs) and multiple localized disruptions (MLDs). Results show that the distance from the worst-case SLD to the city center would be less than 5 km in 62.3% cities, as opposed to more than 15 km in 14.3% cities. Moreover, the average road network resilience of cities in western China could be 7% and 13% smaller than that of the eastern cities under the worst-case SLDs and MLDs, respectively. This inequality in the worst-case resilience is partly attributable to variations in urban socio-economic, infrastructure-related, and topographic factors. These findings could inspire nationwide pre-disaster mitigation strategies to cope with localized disruptions and help transfer insights for mitigation strategies against disruptive events across cities.