# Applied Study of the Fluidization Model of Logistics Transportation through the Prism of the Impact Generated on the Environment

^{1}

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## Abstract

**:**

## 1. Introduction

## 2. Discussion Regarding Urban Transport Characteristics and the Evolution of Urban Densities

#### 2.1. Presentation of Approaches Regarding Transport Policies and Imposed Limitations

#### 2.2. Studies and Analyzes Regarding the Restriction of Freight Transport and Methods

## 3. Presentation and Investigation of the Transport Structure Analysis Model

#### 3.1. Discussion Regarding the Route Optimization Model for Oversize Vehicles Taking into Account the Impact of Environmental Pollution

#### Analysis of the Costs Generated by Traffic Congestion, Referring also to the Cost of CO_{2} Emissions

## 4. Implementation of the Simulation Model and Analysis of the Urban Area

_{ij}represents the heuristic information, available a priori, later having the possibility to calculate the reciprocal between the distances of nodes i and j, defined by ${\eta}_{ij}^{\beta}$. We can say that the values of α and β are usually applied dependently, they define the importance of the pheromone and the heuristic values [35]. The existence of a distinct potential from the multitude of different nodes, the probability of transition also appears, however, Equation (5) presents the basis in terms of ACO algorithms and the most used by specialized literature. The updating of the pheromone trail depends directly on the alternative paths, but also on the specific modality through which the algorithm traces the respective routes, but mostly it has a general form. If we take into account the fact that, they evaporate:

#### 4.1. Simulative Implementation of the Case Study in Relation to the Analyzed Urban Area

^{2}, which includes approximately 8 main roads and approximately 30 secondary roads, without highways or high-speed road sections. The population of the city, the county seat, is approximately 90,000 inhabitants, and the density is 1771 inhabitants/km

^{2}. The areas were demarcated according to the utility of the land, being represented with different colors, keeping only the main traffic lines.

#### 4.2. The Implementation of the Simulation Model and the Presentation of the Architecture Created in order to Obtain a Logistic Flow for the Transport of Large Tonnage

_{2}emissions decreases considerably. The simulation was performed in tandem with the maximum degree of occupation of the habitable and passable area, and the degree of satisfaction and comfort of the population being affected. We can see that the total distance traveled is clearly superior to the other routes and that transport vehicles are moved away from high-congestion areas to other routes. These routes have been mapped distinctly from previous simulations, although the random movement of densities is in the same viable nodes that have been used to increase the performance of the transport network. We can say that the most important results pertain to the analysis of the longest lengths and in the case of peak times, the ACO alternatives seem much more feasible from all points of view, exceeding the expectations outlined in this regard.

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Representative diagram of a structure that optimizes the transport of large tonnage. The delimitation of the point of origin and the presentation of the destination point D, but also the division of the restricted areas into approximately 17 zones with 27 nodes capable of covering 5 km

^{2}.

**Figure 2.**Graphic representation of the urban area and the distinct presentation of the economically and technologically developed components of Suceava. Distribution by important areas from (A–H) and the number of entrances and exits with several traffic lanes from ① to ⑥.

**Figure 3.**Presentation of the distribution in urban areas of the population in relation to the peak hour average.

**Figure 5.**Simulative scenario in which the ACO model are presented in relation to traffic densities and transit capacity in relation to the capacity of areas and nodes through which transportation vehicles can pass.

**Figure 6.**Simultaneous scenario showing the instability of transit routes and the degree of congestion created, as well as CO

_{2}emissions.

**Figure 7.**Demonstration scenario showing the contribution brought by the ACO model to the transport network, the exposure of the solutions generated by the algorithm and the distinct highlighting of the new transit areas with the aim of streamlining traffic and reducing polluting emissions.

Speed Vehicles | Emissions | Heavy-Duty Gasoline Vehicles | Light-Duty Diesel Vehicles | Medium-Duty Diesel Trucks | Very Large-Duty Vehicles |
---|---|---|---|---|---|

5 | CO NOx | 68.12 2.56 | 27.44 25.51 | 29.65 27.33 | 32.53 32.79 |

10 | CO NOx | 58.22 2.66 | 21.38 24.78 | 22.45 24.72 | 28.44 30.70 |

15 | CO NOx | 44.51 2.70 | 17.31 21.44 | 20.28 22.15 | 26.51 28.83 |

20 | CO NOx | 40.33 2.79 | 20.87 15.21 | 23.67 18.12 | 25.79 24.65 |

25 | CO NOx | 39.74 2.83 | 19.54 16.65 | 21.81 16.21 | 22.74 21.07 |

30 | CO NOx | 38.33 2.91 | 12.17 10.22 | 20.78 14.41 | 20.49 18.91 |

35 | CO NOx | 37.01 3.07 | 11.77 13.52 | 12.83 11.49 | 19.01 17.38 |

40 | CO NOx | 40.14 3.28 | 8.91 12.49 | 11.14 10.51 | 18.83 18.49 |

45 | CO NOx | 41.66 3.45 | 7.26 11.84 | 10.84 9.91 | 16.02 19.28 |

50 | CO NOx | 43.59 3.73 | 6.29 10.93 | 9.46 9.22 | 10.45 17.73 |

Arrival Time (veh/h) | ① | ② | ③ | ④ | ⑤ | ⑥ |
---|---|---|---|---|---|---|

A | 30 | 28 | 20 | 17 | 10 | 9 |

B | 18 | 22 | 21 | 16 | 19 | 11 |

C | 22 | 33 | 11 | 19 | 24 | 13 |

D | 17 | 21 | 9 | 22 | 17 | 23 |

E | 14 | 9 | 11 | 16 | 9 | 17 |

F | 7 | 15 | 13 | 21 | 8 | 12 |

G | 12 | 19 | 8 | 4 | 5 | 4 |

H | 9 | 5 | 4 | 2 | 2 | 7 |

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**MDPI and ACS Style**

Zadobrischi, E.; Negru, M.
Applied Study of the Fluidization Model of Logistics Transportation through the Prism of the Impact Generated on the Environment. *Sensors* **2022**, *22*, 9255.
https://doi.org/10.3390/s22239255

**AMA Style**

Zadobrischi E, Negru M.
Applied Study of the Fluidization Model of Logistics Transportation through the Prism of the Impact Generated on the Environment. *Sensors*. 2022; 22(23):9255.
https://doi.org/10.3390/s22239255

**Chicago/Turabian Style**

Zadobrischi, Eduard, and Mihai Negru.
2022. "Applied Study of the Fluidization Model of Logistics Transportation through the Prism of the Impact Generated on the Environment" *Sensors* 22, no. 23: 9255.
https://doi.org/10.3390/s22239255