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UC3 Urban Logistics in Madrid

Description

The Madrid use case (UC3) investigates and proposes last-mile delivery solutions based on the integration of urban distribution of goods with existing demand-responsive transport services, enabled by CCAM (referred to as the DRT-CCAM service). The proposed passenger and goods coordination strategies aim to reduce traffic related to last-mile parcel delivery, taking advantage of the synergies of both services.

The study area is a subregion of Madrid city centre, enclosed by the M-30 ring road. It includes the main streets of the city, where urban freight distribution is most critical and has a higher impact.

Scenarios and Testing

UC3 is being tested and validated within a simulation environment, using the Aimsun Next and Aimsun Ride transport simulation software. This setup enables the evaluation of multiple scenarios of increasing complexity under different demand levels for both services, and analysis of the impact of reducing delivery vehicles in the most affected areas.

  • Baseline scenario: DRT-CCAM and parcel delivery services operate independently.
  • Initial integration scenario: Routes based on DRT-CCAM demand; only parcels fitting those routes are integrated.
  • Optimised integration scenario: Routes based on combined service demand, with two sub-scenarios:
    • Without constraints on parcel delivery times
    • With parcel delivery time windows

Passenger transport schedules are prioritised in all scenarios to ensure no negative impact on users.

Validation Exercises

  1. Compare baseline vs. initial integration
  2. Compare initial integration vs. optimised (no time constraints)
  3. Compare initial integration vs. optimised (with time constraints)

Exercises #02 and #03 help assess trade-offs between routing flexibility, accuracy, and efficiency.

Key Features

📦

Last-mile delivery demand estimation

Understanding parcel needs for optimal distribution in dense urban areas.

🚕

DRT-CCAM demand estimation

Forecasting passenger transport needs via on-demand systems.

🚦

Traffic impact simulation

Evaluating how coordination affects congestion and flow.

🧭

Route optimisation

Planning efficient combined routes for passengers and parcels.

Key Performance Indicators (KPIs)

🚚

Vehicles Used

Number of vehicles used for goods delivery.

🕒

Idle Trips

Idle trips by DRT-CCAM service when not transporting passengers or goods.

⛽

Fuel Consumption

Total fuel consumed by delivery and passenger services.

🌿

Emissions

COâ‚‚ and other pollutants emitted by all vehicles involved.

Additional KPIs address social, economic, human performance, and liability risk aspects.

Expected Benefits

  • Reduced average travel time
  • Fewer vehicles used for delivery
  • Lower total distance travelled
  • Reduced transport emissions

Contribution to the CONDUCTOR Project

  • Optimised DRT-CCAM routing based on demand levels
  • Enhanced resilience for passenger and goods transport
  • Balanced mobility network load

Deployment Architecture

Figure 1 illustrates the process for generating demand estimates for DRT-CCAM and last-mile delivery services. Data from multiple sources is processed to feed the route optimisation algorithms for each scenario.


Figure 1. Process view of demand estimation for DRT-CCAM and last-mile delivery.

Figures 2 and 3 show the DRT-CCAM and parcel delivery integration process. Figure 2 addresses scenarios with parcel delivery time windows, while Figure 3 represents flexible integration using a FleetPy–Aimsun co-simulation environment.


Figure 2. Integration with delivery time constraints.



Figure 3. Flexible integration in co-simulation environment.