# Vulnerability of Transport Networks to Multi-Scenario Flooding and Optimum Location of Emergency Management Centers

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## Abstract

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## 1. Introduction

## 2. Description of the GIS Approach to Evaluate Vulnerability and Obtain an Optimum Location of PEC

#### 2.1. Design Workflow

- Network: The road network consists of a system of interconnected carriageways which are designed to carry road vehicles.
- Service area: A network service area is a region that encompasses all accessible streets (that is, streets that are reached by an emergency center first).
- PEC: New (Proposed) Emergency Management Center (PEC).
- Cost Matrix: The OD cost matrix finds and measures the least-cost paths along with the network from multiple origins to multiple destinations.
- Flood scenarios: Each of the possible combinations without repetition of the flood zones.
- Network scenarios: Each one of the simulations made in the displacements from the emergency centers to each node of the network, considering the interruptions that may occur due to the flood scenarios.

#### 2.2. Block I: Obtain Current Centers

#### 2.2.1. (A) Data Gathering, Filtering and Topology Creation

- The first problem relates to the length of the vectors that usually configure the road layer. The lines corresponding to primary roads are usually too long, which diminishes the spatial resolution and impoverishes the cartography results. For this reason, the network must be segmented into stretches of 50 m.
- This type of network cartography usually lacks topological structure, while its generation usually involves adding spatial information to the road network, such as the relations existing between the different elements. To do this, the researchers used PgRouting as network manager. PgRouting is an extension for PostGIS which allows analyses based on topological networks using SQL. By means of the PgRouting function pgr_createTopology we attach topology to the network. With this process, a table of roads with the created topology is obtained and another of nodes (Network with topology in Figure 1), both used as entry parameters for the vulnerability network analysis.

#### 2.2.2. (B) Preprocessing of Information

#### 2.2.3. (C) Cost Matrices

#### 2.2.4. (D) O-D Matrices

#### 2.3. Block II: Optimal Location of New PEC

- Identification of candidate PEC. Ten possible nodes for locating the PEC are chosen for computational purposes. The points selected must satisfy the following criteria: the journey cost or response time from the existing emergency control centers to these new points in a normal situation (with no flooding) must not be more than 600 s , which is the minimum response time usually considered in the bibliography on optimal localizations [40]. This will ensure that the optimal location of PEC will not be excessively close to one of the three existing centers. It is, therefore, understood that the spatial distribution will affect (but will not determine) the new site.
- Calculation of ODM from the selected nodes to the rest of the nodes of the network for every flood scenario. As an example, in the study case, the result gives ten matrices with the dimensions (1 × 31; center/network scenarios) for each possible location of PEC. The matrices depict the costs (time from each node selected to destination nodes) for each network scenario. These ten matrices, one for each new PEC, are compared with the Min ODM for block I. In this way, the results for the new points will have been compared with the minima obtained in block I. If the new time taken to arrive at the same destination exceeds the previous minimum, the previous time remains, and if the response time from the new center is improved, the cost of the new one is added. This procedure is very similar to that depicted in Figure 2, which, on this occasion, is repeated ten times (once for each candidate).
- Once the previous calculations have been made, the mean access times are compared in order to identify the optimal location of the new PEC, selecting the candidate that minimizes this time. This process is based on a statistical hypothesis test (one-way ANOVA). To evaluate the statistical requirements of normality and homoscedasticity necessary in this type of parametric test, Kolmogorov–Smirnov’s test for more than 50 cases [41] and Levene’s test were used [42]. If these requirements are not met, a robust version of one-way ANOVA using trimmed means and Welch correction is used [43]. When the ANOVA F-test is significant, a post hoc analysis is carried out using linear constraints for multiple comparisons, based on T3 Dunnet’s method [43].
- Once the optimum point in terms of time has been identified statistically, the means of the minimum network scenarios for each center, including the new center (PEC), are calculated. By proceeding iteratively for each node selected, the vulnerability value of the network for the existing centers concerning the candidate center is obtained. To evaluate differences in access time in the new scenario, the current network scenario and the future scenario (based on a new PEC) are compared using a robust t-test for repeated measurement based on trimmed means [43].
- Finally, maps 3 (Future network vulnerability) and 4 (Future service area) are constructed (Figure 9a,b). Map 3 is made in a similar way to map 1, that is, with the results of the mean of the minima of the table Mean ODM New. Map 4 is made like map 2 that is, selecting center name instead of cost.

#### 2.4. Block III: Expert Evaluation

## 3. Study Area

## 4. Results

#### 4.1. Present Road Network Vulnerability Map

#### 4.2. Vulnerability of Future Network Following the Optimal Siting of a New Command Center

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

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**Figure 1.**Conceptual scheme of the work carried out. Flow line connectors show the direction that the process flows; Rectangles show spatial instructions or actions; Ellipses show partial results, and are the input information of the next process; as a result, octagons show final maps.

**Figure 2.**Process for constructing the table of means of the minimum times. Example for the case study (n = 31 scenarios and m = 3 emergency centers).

**Figure 6.**Mean access time (arithmetic and trimmed means) for each new PEC and scenario. Main effects, selected nodes factor (mean and 95% confidence interval). Means with different letters (rectangles) are significantly different (lincon multiple comparisons, $p<0.05$).

**Figure 7.**Main effects, network scenario factor (mean and 95 % confidence interval). Effects in terms of diminution in mean time (arithmetic and trimmed means) of present network vulnerability and future network vulnerability.

**Figure 9.**(

**a**) Map of the service areas in the current vulnerability network and (

**b**) with the new center.

Road Type | Length (km) |
---|---|

Primary road | 345.3 |

Secondary road | 114.2 |

Tertiary road | 147.2 |

Street | 1097.4 |

TOTAL | 1704.1 |

Road Type | Estimated Speed |
---|---|

Primary road | 140 |

Secondary road | 100 |

Tertiary road | 80 |

Street | 60 |

—Create a column |

Municipality | Area (km${}^{2}$) | % | Population | % | Length (km${}^{2}$) | % | Density (km/km${}^{2}$) | Road (Net./Pop.) |
---|---|---|---|---|---|---|---|---|

Alcantarilla | 16.241 | 3.25 | 41,406 | 8.39 | 117.919 | 6.92 | 7.26 | 2.85 |

Beniel | 10.092 | 2.02 | 11,057 | 2.24 | 27.041 | 1.59 | 2.68 | 2.45 |

Murcia | 429.804 | 85.91 | 425,465 | 86.22 | 1469.677 | 86.23 | 3.42 | 3.45 |

Santomera | 44.174 | 8.83 | 15,547 | 3.15 | 89.768 | 5.27 | 2.03 | 5.77 |

Total | 500.311 | 100 | 493,475 | 100 | 1704.405 | 100 |

**Table 5.**Results obtained with the lengths of the sections with the new center included, and difference concerning the present situation.

Center (Node) | Present Network Length Emergency Service Area (km) | % (100 = Total Network) | Length Emergency Service Area with New Center (km) | % (100 = Total Network) | % of Variation (100 = Present Emergency Service Area) | Difference (km) (Present-New Center) |
---|---|---|---|---|---|---|

Espinardo (1006) | 591.2 | 34.6 | 320.1 | 18.8 | −45.8 | 271.1 |

Alcantarilla (1304) | 324.8 | 19.0 | 324.8 | 19.0 | 0 | 0 |

Infante (6050) | 788.1 | 46.2 | 778.6 | 45.7 | −1.2 | 9.5 |

New (9700) | —- | —- | 280.6 | 16.4 | —- | —- |

Total | 1704.1 | 100 | 1704.1 | 100 | −16.4 | 280.6 |

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## Share and Cite

**MDPI and ACS Style**

Pérez-Morales, A.; Gomariz-Castillo, F.; Pardo-Zaragoza, P. Vulnerability of Transport Networks to Multi-Scenario Flooding and Optimum Location of Emergency Management Centers. *Water* **2019**, *11*, 1197.
https://doi.org/10.3390/w11061197

**AMA Style**

Pérez-Morales A, Gomariz-Castillo F, Pardo-Zaragoza P. Vulnerability of Transport Networks to Multi-Scenario Flooding and Optimum Location of Emergency Management Centers. *Water*. 2019; 11(6):1197.
https://doi.org/10.3390/w11061197

**Chicago/Turabian Style**

Pérez-Morales, Alfredo, Francisco Gomariz-Castillo, and Pablo Pardo-Zaragoza. 2019. "Vulnerability of Transport Networks to Multi-Scenario Flooding and Optimum Location of Emergency Management Centers" *Water* 11, no. 6: 1197.
https://doi.org/10.3390/w11061197