# A Systematic Review: To Increase Transportation Infrastructure Resilience to Flooding Events
## 3 Results

Table 5: _Cont._Inclusion of the interconnected infrastructure elements and display with spatial analysis was important for heightened accuracy in vulnerability analysis. [PERSON], et al. (2021) combined building information modeling, geographic information system, and domain-specific computational engines to investigate vulnerabilities of infrastructure, specifically a stormwater drainage-building-road transport combination during urban flooding from extreme rainfall. This allowed for the investigation of all the affected infrastructure systems to generate a reliable vulnerability study [91]. [PERSON] and [PERSON] (2005) investigated vulnerability of rural settlements in Gangetic West Bengal, India by observing presence of flood and proximity to elevated areas. This was conducted with remote imagery and displayed with spatial analysis [115]. Since accessibility and mobility were also major factors impacting levels of vulnerability, several studies within this category considered them. These studies are distinct from category C, as they investigated the road network's vulnerability based on the impacts and transportation network information. For example, [PERSON], et al. (2021) investigated the vulnerability of road networks based on modified accessibility measures which included populations affected by floods, opportunities, and shortest travel time in Bern, Switzerland [92]. [PERSON], et al. (2013) assessed road vulnerability by considering an accessibility index in the Golestan province, Iran. This was accomplished by using flood analysis with flood peak volume and flood frequency as well as traffic volume modeling which enabled them to estimate the traffic volume and travel time [109]. [PERSON] and [PERSON] (2020) assessed the vulnerability of road networks and zones that needed traffic analysis were ranked by change in accessibility in response to tidal flooding in Honolulu, Hawaii, USA. This study used spatial analysis, population, and trip information to show the exposure and disruptions [99]. [PERSON], et al. (2018) assessed the vulnerability of urban road networks in Bangalore, India with hydrodynamic modeling and spatial analysis with 10-year and 100-year flood return periods. They found a relationship between flood depth and vehicle speed reduction to quantify vulnerability [104]. Table 6 represents the 20 studies within Research category E, the response approaches or preparation methods towards involving transportation infrastructure with flood events [116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Study Title:** & **Year:** & **Authors:** & **Country of Study Area:** & **Journal:** \\ \hline \multirow{3}{*}{1} & A multi-step assessment framework for & & & \\  & optimization of flood mitigation strategies in & 2021 & [PERSON] and & & \\  & transportation networks [116] & 2020 & [PERSON] & & \\ \hline \multirow{3}{*}{2} & When floods hit the road: Resilience to & & & \\  & flood-related traffic disruption in the San Francisco & 2020 & [PERSON], & & \\  & Bay Area and beyond [117] & & & \\ \hline \multirow{3}{*}{3} & Highways protection from flood hazards, a case & & & \\  & study: New Tama road, KSA [118] & 2020 & [PERSON], et al. & Saudi Arabia & _Natural Hazards_ \\ \hline \hline \end{tabular}
\end{table} 
Table 6: 20 Studies of category E. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Study Title:** & **Year:** & **Authors:** & **Country of Study Area:** & **Journal:** \\ \hline \multirow{3}{*}{24} & Development an accessibility approach to & & & \\  & rank the transportation network & & & \\  & components during the occurrence of flood & & & \\  & crisis (Golestan province case study) [113] & & & \\ \hline \multirow{3}{*}{25} & Landslide and flood hazard index for & & & \\  & mountain roads an example from the Stura & 2000 & [PERSON] and & & \\  & di Demonte Valley, Italy [114] & & [PERSON] & & \\ \hline \hline \end{tabular}
\end{table}