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

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
Deployment scenarios for an Urban Consolidation Center

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

eqasim-org/eqasim-java

eqasim-org/ile-de-france

Methodology: Parcel demand

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

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

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