The Horizon LEAD project

Preliminary results and tools

Sebastian Hörl

9 September 2022

ATEC

LEAD Project

Low-emission Adaptive last-mile logistics supporting on-demand economy through Digital Twins

  • H2020 Project from 2020 to 2023
     
  • Six living labs with different innovative logistics concepts
    • Lyon, The Hague, Madrid, Budapest, Porto, Oslo
    • One partner for implementation and one for research each
       
  • Development of a generic modeling library for last-mile logistics scenario simulation and analysis

Lyon Living Lab

  • Peninsula Confluence between Saône and Rhône
     
  • Interesting use case as there are limited access points
     
  • Implementation of an urban consolidation center (UCC) to collect the flow of goods and organize last-mile distribution
    • using cargo-bikes
    • using electric robots
    • and others
       
  • Analysis and modeling through
    • Flow estimation through cameras
    • Simulation of future scenarios
       
  • Focus on parcel deliveries due to data availability

Approach

  1. Estimating the impact of parcel deliveries in a city

    - City model, to prepare data processing for local analyses
    - Mitigating the lack of demand and supply information

     
  2. Deployment scenarios for an Urban Consolidation Center

    - Local analysis for Lyon Confluence
    - Tool to integrate and assess real-world data

     
  3. Outlook: Open modeling of regional goods flows

Modeling methodology

  • Main question: What is the impact on traffic and population of implementing an Urban Consolidation Center in Confluence?
     
  • Focus on B2C parcel deliveries due to data availability

Modeling methodology

Agent-based simulation of Lyon

  • Main question: What is the impact on traffic and population of implementing an Urban Consolidation Center in Confluence?
     
  • Focus on B2C parcel deliveries due to data availability

Modeling methodology

Agent-based simulation of Lyon

Demand in parcel deliveries

  • Main question: What is the impact on traffic and population of implementing an Urban Consolidation Center in Confluence?
     
  • Focus on B2C parcel deliveries due to data availability

Modeling methodology

Agent-based simulation of Lyon

Demand in parcel deliveries

Distribution by operators

  • Main question: What is the impact on traffic and population of implementing an Urban Consolidation Center in Confluence?
     
  • Focus on B2C parcel deliveries due to data availability

Modeling methodology

Agent-based simulation of Lyon

Demand in parcel deliveries

Distribution by operators

KPI Calculation

  • Main question: What is the impact on traffic and population of implementing an Urban Consolidation Center in Confluence?
     
  • Focus on B2C parcel deliveries due to data availability

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

National HTS (ENTD)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Methodology: Synthetic travel demand

EDGT

Synthetic travel demand has been generated for Lyon in order to perform agent-based mobility simulation of all residents' movements.

0:00 - 8:00

08:30 - 17:00

17:30 - 0:00

0:00 - 9:00

10:00 - 17:30

17:45 - 21:00

22:00 - 0:00

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

National HTS (ENTD)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Methodology: Synthetic travel demand

EDGT

Synthetic travel demand has been generated for Lyon in order to perform agent-based mobility simulation of all residents' movements.

Methodology: Synthetic travel demand

Open
Data

Open
Software

+

=

Reproducible research

Integrated testing

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

National HTS (ENTD)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

EDGT

Synthetic travel demand has been generated for Lyon in order to perform agent-based mobility simulation of all residents' movements.

Methodology: Synthetic travel demand

Open
Software

=

Reproducible research

Integrated testing

Open
Data

Open
Software

+

=

Reproducible research

Integrated testing

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

National HTS (ENTD)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

EDGT

Synthetic travel demand has been generated for Lyon in order to perform agent-based mobility simulation of all residents' movements.

Methodology: Synthetic travel demand

Methodology: Parcel demand

Synthetic population

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

Methodology: Parcel demand

Synthetic population

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

Methodology: Parcel demand

Synthetic population

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

Methodology: Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

Methodology: Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Iterative proportional fitting (IFP)

  • Based on synthetic population, find average number of purchases delivered to a household defined by socioprofesional class, age of the reference person and household size per day.
\mu_{a,h,s} = d_{s} \cdot \frac{w_{a,h,s}}{365}

Methodology: Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Maximum entropy approach

  • We now the average number of parcels, but we do not now the distribution of the number of parcels for a household on an average day.
     
  • We know it must be non-negative, and we know the mean.
     
  • Without additional data, we assume maximum entropy distribution, which is Exponential in this case.
F(N \leq n) = \text{Pois}(\mu)

Methodology: Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Methodology: Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Methodology: Parcel demand

Synthetic population

Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.

+

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.

Methodology: Supply side

Using shares of customer preference, parcels are assigned to operators and the closest distribution centers.

SIRENE: Delivery centers

Methodology: Supply side

Using shares of customer preference, parcels are assigned to operators and the closest distribution centers.

SIRENE: Delivery centers

Methodology: Supply side

Using shares of customer preference, parcels are assigned to operators and the closest distribution centers.

SIRENE: Delivery centers

Methodology: Supply side

Vehicle routing and fleet composition problem

  • Minimize total cost of delivering all parcels for each center
    • Distance cost influenced by fuel / electricity cost
    • Fixed cost (per day) influenced by salaries and vehicle cost
       
  • Derive
    • Distances driven and related emissions
    • Fleet composition for each center

Novel approximation method

Visualisation platform

UCC platform

Outlook: Open goods transport model

  • Model the flows between companies on a territory
     
  • Based on FRETURB methodology, but using open data
    • SIRENE (company locations, sizes, classification)
    • Logistics surveys (Île-de-France, Bordeaux, Dijon)
    • High-level statistics

 

Generate demand

Generate movements

Generate routes

Next steps

  • Research publications
     
  • Making results accessible
     
  • Packaging up
     
  • Integration with MATSim + Impact assessment

Questions?

Preliminary results and scenarios

Grand Lyon 2022 (Baseline)

 

Preliminary results and scenarios

Grand Lyon 2030

 

Preliminary results and scenarios

Zoom: Confluence 2022

Preliminary results and scenarios

Zoom: Confluence 2030

+ 17,000 habitants

Preliminary results and scenarios

Scenario: Urban Consolidation Center in Confluence 2030

  • UCC in Confluence, last-mile delivery through cargo bikes