# An Optimization Route Selection Method of Urban Oversize Cargo Transportation

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review

## 3. Materials and Methods

#### 3.1. Influence Factor Analysis

#### 3.1.1. Total Mileage

#### 3.1.2. Horizontal Curves

#### 3.1.3. Longitudinal Gradient

#### 3.1.4. Road Width

#### 3.1.5. Bridges

- In all Tables of this paper:
- FC is the number of horizontal curves with a radius less than 20 m.
- TC is the number of switch-back curves.
- TM is the total mileage.
- LG is the road mileage with a longitudinal gradient greater than 5%.
- RW is the road mileage with a width greater than 25 m.
- RN is the road mileage with a width less than 15 m.
- BN is the number of bridges with headroom less than 5 m.

#### 3.2. Weight Calculation

#### 3.2.1. Standardize

#### 3.2.2. Determine the Weight of Influencing Factor

#### 3.3. Cloud Model Optimization

#### 3.3.1. Definition of Cloud Model

#### 3.3.2. Descriptive Statistics of Cloud Model

#### 3.3.3. Backward Cloud Generator

#### 3.4. TOPSIS Application

#### 3.4.1. Step 1

#### 3.4.2. Step 2

#### 3.4.3. Step 3

#### 3.4.4. Step 4

#### 3.4.5. Step 5

## 4. Discussion

## 5. Conclusions

- It summarizes the factors that need to be generally considered in urban oversize cargo transportation.
- Based on the characteristics of the cloud model that can reasonably handle subjective evaluation, it can be used to optimize the weight; the objective distribution of the weight is ensured and the subjective opinions of the practitioners are fully considered.
- The weight optimization method combined with TOPSIS is introduced into the field of oversize cargo transportation passing through a city for the first time, providing a new route selection solution. Its Pearson correlation coefficient for actual transportation is 0.018 higher than the entropy weight–TOPSIS method and 0.057 higher than the TOPSIS method.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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Route Section | FC (Quantity) | TC (Quantity) | TM (Kilometer) | LG (Kilometer) | RW (Kilometer) | RN (Kilometer) | BN (Quantity) |
---|---|---|---|---|---|---|---|

R1 | 4 | 0 | 23.13 | 2.45 | 7.01 | 4.3 | 5 |

R2 | 8 | 2 | 25.3 | 5.12 | 10.1 | 4.71 | 7 |

R3 | 3 | 1 | 23.77 | 3.34 | 5.77 | 1.22 | 10 |

R4 | 6 | 1 | 22.58 | 2.8 | 7.48 | 2.15 | 5 |

Factor | Minimum | Maximum | Mean | Standard Deviation |
---|---|---|---|---|

FC | 3 | 8 | 5.25 | 2.21736 |

TC | 0 | 2 | 1 | .8165 |

TM | 22.58 | 25.3 | 23.695 | 1.17532 |

LG | 2.45 | 5.12 | 3.4275 | 1.18624 |

RW | 5.77 | 10.1 | 7.59 | 1.82218 |

RN | 1.22 | 4.71 | 3.095 | 1.68017 |

BN | 5 | 10 | 6.75 | 2.36291 |

Factor | Information Entropy | Weight |
---|---|---|

FC | 0.676 | 0.129 |

TC | 0.750 | 0.100 |

TM | 0.658 | 0.137 |

LG | 0.587 | 0.165 |

RW | 0.686 | 0.125 |

RN | 0.707 | 0.117 |

BN | 0.432 | 0.227 |

Practitioners Number | FC | TC | TM | LG | RW | RN | BN |
---|---|---|---|---|---|---|---|

1 | 80 | 85 | 60 | 75 | 85 | 50 | 90 |

2 | 90 | 85 | 70 | 80 | 90 | 60 | 85 |

3 | 80 | 80 | 65 | 75 | 95 | 65 | 85 |

4 | 75 | 70 | 50 | 70 | 85 | 50 | 85 |

5 | 85 | 90 | 60 | 70 | 95 | 70 | 95 |

6 | 70 | 75 | 50 | 65 | 90 | 65 | 100 |

Factor | $\mathit{E}\mathit{x}$ | $\mathit{E}\mathit{n}$ | $\mathit{H}\mathit{e}$ |
---|---|---|---|

FC | 80 | 6.27 | 3.28 |

TC | 80.83 | 7.31 | 0.85 |

TM | 59.17 | 7.66 | 2.35 |

LG | 72.5 | 5.22 | 0.48 |

RW | 90 | 4.18 | 1.59 |

RN | 60 | 8.36 | 0.43 |

BN | 90 | 6.27 | 0.85 |

Factor | Weight |
---|---|

FC | 0.139 |

TC | 0.122 |

TM | 0.121 |

LG | 0.154 |

RW | 0.157 |

RN | 0.110 |

BN | 0.197 |

Route Section | ${\mathit{S}}_{\mathit{i}}^{+}$ | ${\mathit{S}}_{\mathit{i}}^{-}$ |
---|---|---|

R1 | 0.24 | 0.09 |

R2 | 0.08 | 0.26 |

R3 | 0.22 | 0.15 |

R4 | 0.22 | 0.1 |

Route Section | Relative Proximity | TOPSIS | Entropy Weight–TOPSIS | Optimization Weight–TOPSIS |
---|---|---|---|---|

R1 | ${C}_{1}$ | 0.27 | 0.27 | 0.28 |

R2 | ${C}_{2}$ | 0.69 | 0.73 | 0.77 |

R3 | ${C}_{3}$ | 0.5 | 0.46 | 0.4 |

R4 | ${C}_{4}$ | 0.25 | 0.27 | 0.32 |

Year | R1 | R2 | R3 | R4 |
---|---|---|---|---|

2016 | 0 | 2 | 1 | 2 |

2017 | 0 | 1 | 0 | 0 |

2018 | 1 | 1 | 2 | 1 |

2019 | 2 | 2 | 1 | 1 |

2020 | 0 | 3 | 1 | 1 |

Method | Pearson Correlation Coefficient |
---|---|

TOPSIS | 0.938 |

Entropy weight–TOPSIS | 0.977 |

Optimization weight–TOPSIS | 0.995 |

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**MDPI and ACS Style**

Huang, D.; Han, M.
An Optimization Route Selection Method of Urban Oversize Cargo Transportation. *Appl. Sci.* **2021**, *11*, 2213.
https://doi.org/10.3390/app11052213

**AMA Style**

Huang D, Han M.
An Optimization Route Selection Method of Urban Oversize Cargo Transportation. *Applied Sciences*. 2021; 11(5):2213.
https://doi.org/10.3390/app11052213

**Chicago/Turabian Style**

Huang, Da, and Mei Han.
2021. "An Optimization Route Selection Method of Urban Oversize Cargo Transportation" *Applied Sciences* 11, no. 5: 2213.
https://doi.org/10.3390/app11052213