# Analysing the Hidden Relationship between Long-Distance Transport and Information and Communication Technology Use through a Fuzzy Clustering Eco-Extended Apostle Model

^{1}

^{2}

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## Abstract

**:**

## 1. Introduction

## 2. Theoretical Framework and Literature Review

#### 2.1. Long- and Medium-Distance Trips

#### 2.2. ICT Use

## 3. Research and Analysis Methodology

#### 3.1. Data Collection

#### 3.2. Interurban Trips and ICT Use Latent Variables

#### 3.3. TOPSIS and Fuzzy Hybrid Analysis

#### 3.4. The Fuzzy Clustering Method

_{1}= w

_{2}= 0.5. The Lagrangian minimization problem is solved to obtain the membership function for each respondent given by the solution u

_{ic}. The membership function vector synthesizes the resemblance degree of each respondent with respect to the selected representative for each cluster (Membership Vector in Figure 1). The discussion of cluster validation and cluster profiles is omitted, and interested readers are referred again to [34,35,36].

#### 3.5. The Fuzzy Clustering Eco-Extended Apostle Model

#### 3.6. Relative Conditional Probability Ratios

## 4. Results

#### 4.1. Interurban Transport Trips and ICT Use

#### 4.2. The Classical vs. the Fuzzy Clustering Eco-Extended Apostle Model

#### 4.3. The Analysis of Gender, Age, Education, and Employment Status on the Relationship between Interurban Transport LDT and ICT Use

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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Variable | n | % * | Variable | n | % * |
---|---|---|---|---|---|

Gender | Education | ||||

Male | 12,986 | 49.00 | Primary | 738 | 2.78 |

Female | 13,514 | 51.00 | Secondary | 3167 | 11.95 |

Age | High School | 11,365 | 42.89 | ||

26–35 | 6449 | 24.34 | University | 11,230 | 42.38 |

36–45 | 6453 | 24.35 | Employment Status | ||

46–55 | 5467 | 20.63 | Full-time employed | 15,954 | 60.20 |

56–65 | 3330 | 12.57 | Part-time employed | 2845 | 10.74 |

66–75 | 1087 | 4.10 | Unemployed | 1696 | 6.40 |

75 years or older | 123 | 0.46 | Studying | 1933 | 7.29 |

Retired | 2490 | 9.40 | |||

Other | 1319 | 4.98 |

Interurban Transport Trips | High | Low | Intermediate | ICT Use | High | Low | Intermediate |
---|---|---|---|---|---|---|---|

LDT1 | 40 | 0 | 0 | ICT1 | 4 | 1 | 1 |

LDT2 | 45 | 0 | 0 | ICT2 | 4 | 1 | 4 |

LDT3 | 46 | 0 | 0 | ICT3 | 4 | 1 | 2 |

LDT4 | 47 | 0 | 2 | ICT4 | 4 | 1 | 2 |

ICT5 | 4 | 1 | 4 | ||||

ICT6 | 4 | 1 | 4 | ||||

ICT7 | 4 | 1 | 1 | ||||

ICT8 | 4 | 1 | 1 | ||||

ICT9 | 4 | 1 | 1 | ||||

ICT10 | 4 | 1 | 4 |

Name | Q1 (2.5) | Q1 (97.5) | Q2 (2.5) | Q2 (97.5) | Q3 (2.5) | Q3 (97.5) | Q4 (2.5) | Q4 (97.5) |
---|---|---|---|---|---|---|---|---|

Male | 0.976 | 0.977 | 1.038 | 1.039 | 0.970 | 0.972 | 1.042 | 1.044 |

Female | 1.022 | 1.023 | 0.962 | 0.964 | 1.027 | 1.028 | 0.958 | 0.960 |

Age. ≤25 | 0.927 | 0.930 | 0.997 | 1.001 | 1.051 | 1.055 | 1.123 | 1.129 |

Age. 26–35 | 0.956 | 0.958 | 1.019 | 1.022 | 0.964 | 0.967 | 1.136 | 1.140 |

Age. 36–45 | 1.004 | 1.006 | 0.992 | 0.995 | 1.014 | 1.017 | 0.970 | 0.974 |

Age. 46–55 | 1.021 | 1.023 | 1.026 | 1.029 | 0.992 | 0.995 | 0.896 | 0.900 |

Age. 56–65 | 1.068 | 1.071 | 0.944 | 0.948 | 1.002 | 1.007 | 0.886 | 0.890 |

Age. 66–75 | 1.098 | 1.103 | 0.995 | 1.001 | 0.924 | 0.931 | 0.816 | 0.824 |

Age. >75 | 1.304 | 1.325 | 0.525 | 0.529 | 1.171 | 1.210 | 0.638 | 0.645 |

Primary | 1.065 | 1.071 | 0.766 | 0.773 | 1.310 | 1.319 | 0.727 | 0.739 |

Secondary | 1.144 | 1.147 | 0.802 | 0.805 | 1.164 | 1.168 | 0.676 | 0.681 |

High School | 1.046 | 1.048 | 0.950 | 0.952 | 1.030 | 1.031 | 0.904 | 0.907 |

University | 0.906 | 0.908 | 1.119 | 1.120 | 0.900 | 0.902 | 1.202 | 1.205 |

Full-time employed | 0.944 | 0.944 | 1.066 | 1.067 | 0.944 | 0.946 | 1.125 | 1.127 |

Part-time employed | 1.101 | 1.104 | 0.931 | 0.935 | 0.986 | 0.991 | 0.838 | 0.843 |

Unemployed | 1.166 | 1.170 | 0.749 | 0.754 | 1.214 | 1.221 | 0.624 | 0.630 |

Studying | 0.958 | 0.961 | 1.005 | 1.010 | 1.053 | 1.059 | 1.012 | 1.019 |

Retired | 1.103 | 1.106 | 0.960 | 0.964 | 1.001 | 1.005 | 0.757 | 0.762 |

Other | 1.093 | 1.097 | 0.740 | 0.746 | 1.262 | 1.270 | 0.775 | 0.783 |

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**MDPI and ACS Style**

Christidis, P.; Martín, J.C.; Román, C.
Analysing the Hidden Relationship between Long-Distance Transport and Information and Communication Technology Use through a Fuzzy Clustering Eco-Extended Apostle Model. *Mathematics* **2024**, *12*, 791.
https://doi.org/10.3390/math12060791

**AMA Style**

Christidis P, Martín JC, Román C.
Analysing the Hidden Relationship between Long-Distance Transport and Information and Communication Technology Use through a Fuzzy Clustering Eco-Extended Apostle Model. *Mathematics*. 2024; 12(6):791.
https://doi.org/10.3390/math12060791

**Chicago/Turabian Style**

Christidis, Panayotis, Juan Carlos Martín, and Concepción Román.
2024. "Analysing the Hidden Relationship between Long-Distance Transport and Information and Communication Technology Use through a Fuzzy Clustering Eco-Extended Apostle Model" *Mathematics* 12, no. 6: 791.
https://doi.org/10.3390/math12060791