# Macro Perspective Research on Transportation Safety: An Empirical Analysis of Network Characteristics and Vulnerability

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data

#### 2.1.1. The Layout of the China Comprehensive Transportation Corridors

#### 2.1.2. The Layout of the China Comprehensive Transportation Hubs

#### 2.2. Network Model

#### 2.3. General Properties

#### 2.4. Correlation Analysis

#### 2.5. Vulnerability Assessment

#### 2.5.1. Main Measures of Network Performance

- 1.
- Efficiency E (k)

- 2.
- Origin–destination considered efficiency ODE(k)

#### 2.5.2. Node Failure Simulation

#### 2.5.3. Vulnerability Assessment Model

- 1.
- The influence of node disruption on network efficiency ${V}_{k/n}$

- 2.
- The influence of node disruption on origin–destination considered efficiency ${U}_{k/n}$

## 3. Results

#### 3.1. Topological Characteristics of the CCTCH

#### 3.2. Correlation Analysis of Different Factors and Type of Hub

#### 3.3. Vulnerability Assessment

#### 3.3.1. Vulnerability of the Whole Network

#### 3.3.2. Vulnerability of the Main Origin and Destination of International Freight Corridors

## 4. Discussion

#### 4.1. Vulnerability of the CCTCH

#### 4.2. Correlation Analysis between Node Centrality and Hub Type

#### 4.3. Economic and Demographic Factors, and Hub Type

#### 4.4. Vulnerability of International Freight Network

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 3.**(

**a**) Description of the degree of comprehensive transportation corridor plan nodes; (

**b**) the topology reconfiguration of the network after deleting the top ten nodes by degree; (

**c**) a description of the betweenness of comprehensive transportation corridor plan nodes; (

**d**) the topology reconfiguration of the network after deleting the top ten nodes by betweenness.

**Figure 4.**(

**a**) Description of node degree K and hub type; (

**b**) description of node betweenness B and hub type; (

**c**) description of node degree K and node betweenness B; (

**d**) description of GDP and hub type; (

**e**) description of population and hub type; (

**f**) description of GDP per capita and hub type.

**Figure 5.**Network efficiency variations in three scenarios: deleting the k

^{th}node in a descending sequence of degree (DD), betweenness (DB) and average GDP (DGDP).

**Figure 6.**Invulnerability of the network based on efficiency variations in three scenarios: deleting the k

^{th}node in a descending sequence of degree (DD), betweenness (DB) and average GDP (DGDP).

**Figure 7.**Origin–destination considered efficiency variations in three scenarios: deleting the k

^{th}node in a descending sequence of degree(DD), betweenness (DB) and average GDP (DGDP).

**Figure 8.**Invulnerability of the network based on origin–destination considered efficiency variations in three scenarios: deleting the k

^{th}node in a descending sequence of degree (DD), betweenness (DB) and average GDP (DGDP).

No. | Name | No. | Name |
---|---|---|---|

V1 | Coastal Corridor | H1 | Suifenhe–Manzhouli Corridor |

V2 | Beijing–Shanghai Corridor | H2 | Hunchun–Erlianhot Corridor |

V3 | Beijing–Hong Kong, Macao and Taiwan Corridor | H3 | Northwest Corridor |

V4 | Heihe–Hong Kong and Macao Corridor | H4 | Qingdao–Lhasa Corridor |

V5 | Erenhot–Zhanjiang Corridor | H5 | Land bridge Corridor |

V6 | Baotou–Fangchenggang Corridor | H6 | Riverside Corridor |

V7 | Linhe–Mohan Corridor | H7 | Shanghai–Ruili Corridor |

V8 | Beijing–Kunming Corridor | H8 | Shantou–Kunming Corridor |

V9 | Ejina–Guangzhou Corridor | H9 | Fuzhou–Yinchuan Corridor |

V10 | Yantai–Chongqing Corridor | H10 | Xiamen–Kashi Corridor |

Type | International Comprehensive Transportation Hub (ICTH) | National Comprehensive Transportation Hub (NCTH) | Regional Comprehensive Transportation Hub and Border Hub (RCT-BH) |
---|---|---|---|

Assignment | 3 | 2 | 1 |

Number of hub cities | 12 | 63 | 29 |

Symbol | Description |
---|---|

N | The number of nodes |

E | The number of links |

K_{i} | The degree of node i |

B_{i} | The betweenness of node i |

K | The average degree value |

B | The average betweenness value |

$\rho $ | Network density |

C_{K} | Degree centrality |

C_{B} | Betweenness centrality |

Network | N | E | $\overline{\mathbf{K}}$ | $\overline{\mathbf{B}}$ | $\mathit{\rho}$ | C_{K} | C_{B} |
---|---|---|---|---|---|---|---|

Value | 104 | 361 | 3.48 | 404.65 | 0.0337 | 5.46% | 24.32% |

Distance | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|

Frequency | 360 | 909 | 1430 | 1735 | 1791 | 1623 | 1272 | 816 | 371 | 120 | 20 | 6 |

Proportion | 0.034 | 0.087 | 0.137 | 0.166 | 0.171 | 0.155 | 0.122 | 0.078 | 0.035 | 0.011 | 0.002 | 0.001 |

Value of r | |r| < 0.3 | 0.3 ≤ |r| < 0.5 | 0.5 ≤ |r| < 0.8 | 0.8 < |r| ≤ 1 |
---|---|---|---|---|

Strength of relationship | Very weak | Weak | Moderate | Strong |

Experiment | Variable x | Variable y | Objective |
---|---|---|---|

1 | Hub type | Degree K | To investigate the relationship between the three types of hubs and the node degree, and whether the higher-level comprehensive hub is directly connected with more nodes. |

2 | Hub type | Betweenness B | To explore the relationship between the three types of hubs and the frequency of the shortest path passing through the nodes, and whether the higher-level comprehensive hub means higher frequency of passing by. |

3 | Degree K | Betweenness B | To examine the relationship between the important nodes that are identified by two measures, and whether the node connected directly to more cities is also passed by more frequently. |

4 | Hub type | GDP | To determine the relationship between the type of planned hub and the GDP of node cities, and whether a higher-level comprehensive hub indicates a more advanced economy. |

5 | Hub type | Population | To assess the relationship between the type of planned hub and the number of permanent residents in node cities, and whether a higher-level comprehensive hub owns more population. |

6 | Hub type | GDP per capita | To evaluate the relationship between the type of planned hub and the GDP per capita of node cities, and whether a higher level comprehensive hub shows a higher GDP per capita. |

No. | City under DGDP | City under DB | City under DD | No. | City under DGDP | City under DB | City under DD |
---|---|---|---|---|---|---|---|

1 | Shanghai | Beijing | Beijing | 19 | Fuzhou | Xiangyang | Xiangyang |

2 | Beijing | Chengdu | Chengdu | 20 | Changchun | Guangzhou | Changchun |

3 | Shenzhen | Xi’an | Xi’an | 21 | Shenyang | Urumchi | Yulin |

4 | Guangzhou | Chongqing | Taiyuan | 22 | Harbin | Zhanjiang | Yinchuan |

5 | Chongqing | Kunming | Chongqing | 23 | Shijiazhuang | Tongliao | Golmud |

6 | Tianjin | Wuhan | Changsha | 24 | Nanchang | Golmud | Guangzhou |

7 | Chengdu | Baotou | Hefei | 25 | Kunming | Korla | Urumchi |

8 | Wuhan | Shenyang | Wuhan | 26 | Xiamen | Huaihua | Tsitsihar |

9 | Hangzhou | Nanning | Tongliao | 27 | Nanning | Lanzhou | Jiuquan-Jiayuguan |

10 | Nanjing | Yinchuan | Nanchang | 28 | Guiyang | Baoji | Datong |

11 | Qingdao | Jiuquan-Jiayuguan | Guiyang | 29 | Taiyuan | Shangqiu | Shangrao |

12 | Changsha | Taiyuan | Shangqiu | 30 | Hohhot | Changchun | Zhanjiang |

13 | Ningbo | Changsha | Baotou | 31 | Urumchi | Yulin | Fuzhou |

14 | Zhengzhou | Hefei | Zhengzhou | 32 | Lanzhou | Tsitsihar | Yuzhou |

15 | Dalian | Nanchang | Shenyang | 33 | Yinchuan | Datong | Luzhou-Yibin |

16 | Xi’an | Guiyang | Kunming | 34 | Haikou | Shangrao | Harbin |

17 | Ji’nan | Zhengzhou | Nanning | 35 | Xi’ning | Fuzhou | Nanjing |

18 | Hefei | Ji’nan | Jinan | 36 | Lhasa | Yuzhou | Lhasa |

Correlation Coefficient r | Degree | Betweenness | Hub Type |
---|---|---|---|

Degree | 1 | 0.821 | 0.663 |

Betweenness | 0.821 | 1 | 0.508 |

Hub type | 0.663 | 0.508 | 1 |

Correlation Coefficient r | GDP | Population | GDP per capita |
---|---|---|---|

Hub type | 0.540022 | 0.408739 | 0.35175 |

k/n | 4.81% | 10.58% | 15.39% | 20.19% | 25.00% | 30.77% | |

k | 5 | 11 | 16 | 21 | 26 | 32 | |

${V}_{k/n}$ | DD | 0.685 | 0.360 | 0.213 | 0.135 | 0.095 | 0.077 |

DB | 0.631 | 0.395 | 0.228 | 0.166 | 0.111 | 0.054 | |

DGDP | 0.814 | 0.517 | 0.359 | 0.229 | 0.165 | 0.070 |

Origin | No. | Origin | No. | Destination | No. |
---|---|---|---|---|---|

Hongqilap | 93 | Yadong | 95 | Shanghai | 3 |

Jimunai | 88 | Ceke | 87 | Beijing | 1 |

Turgat | 92 | Mandula | 85 | Shenzhen | 5 |

Ruili | 96 | Erlianhot | 84 | Guangzhou | 4 |

Mohan | 97 | Manzhouli | 83 | Chongqing | 7 |

Hekou | 98 | Heihe | 82 | Tianjin | 2 |

Longbang | 99 | Tongjiang | 81 | Chengdu | 6 |

Pingxiang | 100 | Hunchun | 103 | Wuhan | 13 |

Dongxing | 102 | Dandong | 102 | Hangzhou | 23 |

Zhangmu | 94 | Nanjing | 21 |

k/n% | 4.81 | 9.62 | 15.39 | 20.19 | 25.00 | 30.77 | |

k | 5 | 10 | 16 | 21 | 26 | 32 | |

${U}_{k/n}$ | DD | 0.438 | 0.023 | 0.000 | 0.000 | 0.000 | 0.000 |

DB | 0.496 | 0.268 | 0.065 | 0.000 | 0.000 | 0.000 | |

DGDP | 0.462 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Liu, J.; Lu, H.; Chen, M.; Wang, J.; Zhang, Y.
Macro Perspective Research on Transportation Safety: An Empirical Analysis of Network Characteristics and Vulnerability. *Sustainability* **2020**, *12*, 6267.
https://doi.org/10.3390/su12156267

**AMA Style**

Liu J, Lu H, Chen M, Wang J, Zhang Y.
Macro Perspective Research on Transportation Safety: An Empirical Analysis of Network Characteristics and Vulnerability. *Sustainability*. 2020; 12(15):6267.
https://doi.org/10.3390/su12156267

**Chicago/Turabian Style**

Liu, Jing, Huapu Lu, Mingyu Chen, Jianyu Wang, and Ying Zhang.
2020. "Macro Perspective Research on Transportation Safety: An Empirical Analysis of Network Characteristics and Vulnerability" *Sustainability* 12, no. 15: 6267.
https://doi.org/10.3390/su12156267