HYBRID ECOSYSTEM RAIL ROAD & TRUCK TRANSPORT: APPLIED MODELLING

Transport Optimization Model – EU27 (Lagrange and Gauss Principles)

1. General Approach

This model aims to minimize the total transportation cost between multiple demand sectors and transport providers (road and rail), at the EU27 level, applying Lagrange optimization principles and Gauss' theorem for flow distribution.

2. Mathematical Model

Variables:

• D_i: demand from sector i
• S_j: supply from provider j
• x_ij: units transported from provider j to sector i
• c_ij: unit cost of transport

Objective Function:

min Σ Σ c_ij * x_ij

Constraints:

1. Demand satisfaction: Σ x_ij = D_i for all i
2. Supply limit: Σ x_ij ≤ S_j for all j
3. Non-negativity: x_ij ≥ 0

Lagrange Multipliers:

L = Σ Σ c_ij * x_ij + Σ λ_i(D_i - Σ x_ij) + Σ μ_j(Σ x_ij - S_j)

    

3. Python Simulation

We use scipy.optimize.linprog for linear programming:

import numpy as np

from scipy.optimize import linprog

demand = np.array([100, 150, 80])

supply = np.array([200, 180])

costs = np.array([[4, 6], [5, 3], [9, 2]])

c = costs.flatten()

A_eq = np.zeros((len(demand), costs.size))

for i in range(len(demand)):

    A_eq[i, i*len(supply):(i+1)*len(supply)] = 1

b_eq = demand

A_ub = np.zeros((len(supply), costs.size))

for j in range(len(supply)):

    A_ub[j, j::len(supply)] = 1

b_ub = supply

bounds = [(0, None)] * costs.size

res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq=b_eq, bounds=bounds, method='highs')

x_opt = res.x.reshape(costs.shape)

total_cost = res.fun

print("Optimal transport matrix (x_ij):", x_opt)

print("Total cost:", total_cost)

    

4. Visualized Transport Flow

5. Network Diagram (Basic Flow View)

6. Simulated on Python – Advanced Scenario

We extend the basic linear optimization model to include carbon emission penalties and delivery time constraints. This provides a more realistic transport planning scenario, aligned with EU Green Deal policies.

Extended Variables:

• e_ij: CO₂ emissions per unit transported from j to i (in kg)
• t_ij: delivery time from j to i (in hours)
• α: cost multiplier for emissions
• β: cost multiplier for delivery time

New Objective Function (combined cost):

    min Σ Σ (c_ij + α·e_ij + β·t_ij) * x_ij

Python Script:

import numpy as np

from scipy.optimize import linprog

demand = np.array([100, 150, 80])

supply = np.array([200, 180])

base_cost = np.array([[4, 6], [5, 3], [9, 2]])

emissions = np.array([[2, 4], [3, 2], [5, 1]])  # kg/unit

times = np.array([[8, 6], [7, 5], [12, 3]])     # hours

α = 0.5  # € per kg CO₂

β = 0.2  # € per hour delivery

total_cost = base_cost + α * emissions + β * times

c = total_cost.flatten()

A_eq = np.zeros((len(demand), total_cost.size))

for i in range(len(demand)):

    A_eq[i, i * len(supply):(i + 1) * len(supply)] = 1

b_eq = demand

A_ub = np.zeros((len(supply), total_cost.size))

for j in range(len(supply)):

    A_ub[j, j::len(supply)] = 1

b_ub = supply

bounds = [(0, None)] * total_cost.size

res = linprog(c, A_ub=A_ub, b_ub=b_ub, A_eq=A_eq, b_eq=b_eq, bounds=bounds, method='highs')

x_opt = res.x.reshape(base_cost.shape)

total_cost_value = res.fun

print("Optimal transport matrix:", x_opt)

print("Total weighted cost (with emissions/time):", total_cost_value)

    

This Python script demonstrates how to solve a transport optimization problem using scipy.optimize.linprog. It calculates the most cost-efficient distribution of goods between suppliers and demand sectors, while optionally incorporating environmental (CO₂ emissions) and logistical (delivery time) constraints. The model uses linear programming techniques to minimize the total cost under real-world constraints.

Future Enhancements:

• Use pandas and matplotlib for analysis and visualization.
• Feed real-time data from Eurostat or Open Data Portals.
• Integrate with GTFS feeds to simulate urban and regional public transport logistics.

Intelligent Hybrid Rail-Road-Truck Logistics Platform

Integrating AI, IIoT, IoT & Open Data

1. Vision & Impact

This platform modernizes the multimodal transport network by leveraging:

• AI, Machine Learning (ML), Deep Learning (DL) for real-time decision-making and optimization.
• IIoT & IoT devices for live data collection.
• Open data sources to enable forecasting and adaptive logistics.
• Digital twins and simulations for proactive maintenance and planning.

2. Smart Infrastructure Architecture

A. Industrial IoT (IIoT) Nodes

• Sensors on trains, wagons, terminals (temperature, vibration, GPS, load)
• PLCs in rail-to-truck hubs, communicating via MQTT/OPC-UA

B. IoT Nodes for Road Transport

• Fleet tracking: GPS, fuel consumption, accelerometers
• Environmental sensors on highways and crossings
• Mobile apps for driver feedback and incident reporting

C. Data & AI Platform

• Backend: Odoo + TimescaleDB + Apache Kafka
• AI Models: Scikit-learn, TensorFlow, PyTorch
• Dashboards: Node-RED, Grafana

3. Use Cases Powered by AI/ML/DL

• Multimodal Routing: DRL for container-level mode assignment, real-time re-routing.
• Fuel Forecasting: Predict consumption based on terrain, load, and driver behavior.
• Load Matching: Minimize empty miles using AI-driven load assignment (Uber Freight logic).
• Predictive Maintenance: Detect anomalies early via IIoT time-series data.
• Traffic & Weather Forecast: LSTM+CNN models using open meteorological datasets.
• Load Simulation: Monte Carlo models for cargo transfer optimization.
• Digital Twins: Virtual modeling of logistics nodes for planning and emergency simulations.

4. Reliable Open Data Sources

Source	Use Case
OpenStreetMap + GTFS	Routing, Infrastructure
Copernicus, ERA5, AEMET	Weather forecasting
Eurostat, INE, World Bank	Logistics trends, Economic indicators
OpenData BCN, FIWARE	Smart city integration

5. KPI Forecasts

• –20% Empty mileage
• +30% Delivery reliability
• –12% Cost per ton/km
• –18% Carbon footprint
• +45% Predictive fault detection

6. Estimated Infrastructure & Cost

Component	Tool/Provider	Estimated Annual Cost
Server + DB	Odoo + TimescaleDB (Hetzner/OVH)	€1,200
IIoT Hardware	Advantech / Waveshare	€6,000 (100 nodes)
Fleet IoT Devices	Teltonika / Arduino MKR	€4,000
AI Frameworks	TensorFlow, PyTorch (Open Source)	€0
Dashboards	Node-RED / Grafana	€0

7. Implementation Roadmap

1. Pilot Zone: Identify key logistics corridor
2. Data Layer: Real-time ingestion + historical analysis
3. AI/ML Modeling: Train, simulate, validate
4. Deploy Digital Twin: Monitor, optimize, forecast
5. Stakeholder Engagement: Align industry & public actors
6. Scaling: Replicate across national/continental corridors

AI & ML-Driven Hybrid Ecosystem for Rail-Road-Truck Integration

This post proposes a technical framework using Python to implement Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Automated Learning (AL), and deployment strategies in a hybrid rail-road-truck transportation ecosystem. This vision aligns with the EU Horizon Europe goals for sustainable, smart, and inclusive mobility systems.

1. Data Collection and Preprocessing

import pandas as pd

import geopandas as gpd

# Load datasets

rail_data = pd.read_csv('rail_data.csv')

road_data = pd.read_csv('road_data.csv')

truck_data = pd.read_csv('truck_data.csv')

# Merge and create GeoDataFrame

combined = pd.concat([rail_data, road_data, truck_data])

gdf = gpd.GeoDataFrame(combined, geometry=gpd.points_from_xy(combined.longitude, combined.latitude))

2. Machine Learning for Demand Forecasting

from sklearn.ensemble import RandomForestRegressor

from sklearn.model_selection import train_test_split

X = combined[['feature1', 'feature2']]

y = combined['demand']

X_train, X_test, y_train, y_test = train_test_split(X, y)

model = RandomForestRegressor()

model.fit(X_train, y_train)

3. Deep Learning for Route Optimization

import torch

import torch.nn as nn

class RouteNet(nn.Module):

    def __init__(self):

        super().__init__()

        self.fc = nn.Sequential(nn.Linear(10, 32), nn.ReLU(), nn.Linear(32, 1))

    def forward(self, x):

        return self.fc(x)

4. Reinforcement Learning for Dynamic Scheduling

from stable_baselines3 import PPO

import gym

class TransportEnv(gym.Env):

    def __init__(self): pass

    def step(self, action): pass

    def reset(self): pass

env = TransportEnv()

model = PPO('MlpPolicy', env, verbose=1)

model.learn(total_timesteps=10000)

5. AutoML and Model Deployment

from fastapi import FastAPI

import joblib

app = FastAPI()

model = joblib.load('model.pkl')

@app.post("/predict")

def predict(data: dict):

    return {"prediction": model.predict([list(data.values())]).tolist()}

EU Horizon Europe Alignment

This approach directly supports the EU Horizon Europe objectives, particularly in the "Climate, Energy and Mobility" cluster:

• Smart Mobility: Integrating AI to enable intelligent routing, congestion prediction, and energy-efficient transportation.
• Intermodal Efficiency: Seamless coordination between rail, road, and truck systems to reduce emissions and enhance logistics efficiency.
• Digital Twins & Simulations: Using ML/RL models to simulate and optimize transport infrastructure planning.

Support for EU Defense Capabilities

This hybrid logistics AI ecosystem could also play a crucial role in strengthening European defense infrastructure and strategic autonomy. Here’s how it aligns with EU defense goals:

• Military Mobility: AI-optimized multimodal logistics can support rapid deployment of troops and equipment across the EU, as promoted by PESCO and the EU Military Mobility project.
• Resilient Dual-Use Infrastructure: The system enhances civil-military synergies by enabling shared, adaptive, and intelligent transport networks under both peace and crisis conditions.
• Situational Awareness: Real-time prediction, demand mapping, and routing optimization contribute to strategic logistics planning in NATO and EU operations.
• EDF Integration: This initiative may receive co-funding or integration within the European Defence Fund, under capabilities for logistics, surveillance, or cross-border defense readiness.

Conclusion

This technical framework is not just a solution—it’s a catalyst for a smarter, greener, and more resilient transportation ecosystem. The implementation of ML, DL, RL, and AL will enhance forecasting, routing, automation, and real-time decision-making, supporting both EU Horizon goals and EU strategic defense capabilities.

Would you like this system demoed or packaged for EU Horizon, CEF Transport, or private investment proposals? Contact me via the sidebar or comments!