The Horizon LEAD project

Preliminary results and tools

Sebastian Hörl

16 December 2022

IRT SystemX

• Public/private research institute situated in Paris
   
• Focus on fostering digital transition in a range of fields from transport, cybersecurity to circular economy
   
• Transferring research results and tools into active application by development and provision of industry platforms
   
• Various collaborative projects with multiple French companies (Renault, SNCF, ...) and academic partners (Université Paris Saclay, CentraleSupélec, Université Gustave Eiffel)
   
• Participation in European projects

https://www.irt-systemx.fr/

MATSim @ IRT SystemX

• First/last mile mobility simulation
• Grand Paris Express

Passenger transport

MATSim @ IRT SystemX

• On-demand mobility simulation
  
   

Passenger transport

MATSim @ IRT SystemX

• Peer-to-peer car-sharing
   

Passenger transport

European projects

Social Innovation to foster inclusive cooperative connected and automated mobility

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

Data-driven, Integrated, Synchromodal, Collaborative and Optimized urban freight meta-system for new generation of urban logistics and planning

• ERTICO Member
• Various European projects on transport
• Most EU projects of French institutions

LEAD Project

• 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

https://www.leadproject.eu/

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

Methodology: Synthetic travel demand

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

Methodology: Synthetic travel demand

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

Methodology: Synthetic travel demand

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

Methodology: Synthetic travel demand

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

Methodology: Synthetic travel demand

eqasim-org/eqasim-java

eqasim-org/ile-de-france

Methodology: Parcel demand

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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

+

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

+

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

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

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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

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

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

• 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.

Methodology: Parcel demand

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

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

• 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.

Methodology: Parcel demand

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

+

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

Methodology: Parcel demand

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

+

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

Methodology: Parcel demand

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

+

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

First case study

Hörl, S. and J. Puchinger (2021) From synthetic population to parcel demand: Modeling pipeline and case study for last-mile deliveries in Lyon, Working paper.

Solve VRP-TW based on generated parcels and household presence

JSprit

First case study

Hörl, S. and J. Puchinger (2021) From synthetic population to parcel demand: Modeling pipeline and case study for last-mile deliveries in Lyon, Working paper.

Solve VRP-TW based on generated parcels and household presence

JSprit

Methodology: Supply side

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

Methodology: Supply side

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

Methodology: Supply side

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