To solve this optimization problem using Gurobi, we first need to define the variables, the objective function, and the constraints as per the given problem description.

### Problem Description and Formulation

- **Variables**: 
  - $x_0$: Number of logistics companies
  - $x_1$: Number of signal platoons
  - $x_2$: Number of transportation companies

- **Objective Function**: Minimize $1x_0 + 9x_1 + 6x_2$

- **Constraints**:
  - $4.57x_0 = 4.57$ (This seems to be an equality constraint but given $x_0$ must be an integer and the nature of the problem, it might be better represented as a bound on $x_0$)
  - $1.6x_1$ contributes to mobility
  - $3.13x_2$ contributes to mobility
  - $4.57x_0 + 1.6x_1 \geq 31$
  - $4.57x_0 + 3.13x_2 \geq 23$
  - $4.57x_0 + 1.6x_1 + 3.13x_2 \geq 55$
  - $5x_0 - 2x_1 \geq 0$
  - $x_0, x_1, x_2$ are integers

### Gurobi Code

```python
import gurobipy as gp

# Create a new model
m = gp.Model("optimization_problem")

# Define variables
logistics_companies = m.addVar(name="logistics_companies", integer=True)
signal_platoons = m.addVar(name="signal_platoons", integer=True)
transportation_companies = m.addVar(name="transportation_companies", integer=True)

# Objective function: Minimize 1 * logistics_companies + 9 * signal_platoons + 6 * transportation_companies
m.setObjective(logistics_companies + 9 * signal_platoons + 6 * transportation_companies, gp.GRB.MINIMIZE)

# Constraints
m.addConstr(4.57 * logistics_companies <= 4.57, "mobility_rating_logistics")
m.addConstr(4.57 * logistics_companies >= 4.57, "mobility_rating_logistics_eq")
m.addConstr(4.57 * logistics_companies + 1.6 * signal_platoons >= 31, "mobility_rating_logistics_signal")
m.addConstr(4.57 * logistics_companies + 3.13 * transportation_companies >= 23, "mobility_rating_logistics_transportation")
m.addConstr(4.57 * logistics_companies + 1.6 * signal_platoons + 3.13 * transportation_companies >= 55, "mobility_rating_total")
m.addConstr(5 * logistics_companies - 2 * signal_platoons >= 0, "relationship_logistics_signal")

# Solve the model
m.optimize()

# Print the results
if m.status == gp.GRB.OPTIMAL:
    print("Optimal Solution:")
    print(f"Logistics Companies: {logistics_companies.varValue}")
    print(f"Signal Platoons: {signal_platoons.varValue}")
    print(f"Transportation Companies: {transportation_companies.varValue}")
    print(f"Objective: {m.objVal}")
else:
    print("No optimal solution found")
```