Exploring the system-level impacts of urban parcel deliveries using synthetic data

Results from the project H2020 LEAD

Sebastian Hörl

28 March 2023

IRT SystemX

• 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/

Transport Modeling @ IRT SystemX

• First/last mile mobility simulation
• Grand Paris Express

Passenger transport

• On-demand mobility simulation
  
   

Passenger transport

Transport Modeling @ IRT SystemX

European projects

Social Innovation to foster inclusive cooperative connected and automated mobility

Low-Emission 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 in the transport domain

Safe, Efficient and Autonomous: Multimodal Library of European Shortsea and inland Solutions

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

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

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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

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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.

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.

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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.

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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.

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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 (2022) From synthetic population to parcel demand: Modeling pipeline and case study for last-mile deliveries in Lyon, TRA 2022, Lisbon.

First case study

Hörl, S. and J. Puchinger (2022) From synthetic population to parcel demand: Modeling pipeline and case study for last-mile deliveries in Lyon, TRA 2022, Lisbon.

JSprit

First case study

Hörl, S. and J. Puchinger (2022) From synthetic population to parcel demand: Modeling pipeline and case study for last-mile deliveries in Lyon, TRA 2022, Lisbon.

Solve VRP-TW based on generated parcels and household presence

JSprit

First case study

Solve VRP-TW based on generated parcels and household presence

JSprit

Hörl, S. and J. Puchinger (2022) From synthetic population to parcel demand: Modeling pipeline and case study for last-mile deliveries in Lyon, TRA 2022, Lisbon.

Methodology: Study area

How many parcels need to be delivered on one day?

Methodology: Study area

Perimeter

• City of Lyon
• Grand Lyon metropolitan region (dashed)
• Bordering municipalities including relevant logistics infrastructure

Demand

• 1.6M personas
• 790k households
• 16,252 parcels

How many parcels need to be delivered on one day?

Methodology: Distribution centers

From where do operators delivery the parcels?

Methodology: Distribution centers

Approach

• Facilities per operator extracted from SIRENE
• Geolocated using public BAN API
• La Poste: Facilities with 20+ employees

From where do operators delivery the parcels?

Methodology: Market shares

How many parcels are delivered by each operator?

Methodology: Market shares

How many parcels are delivered by each operator?

Approach

• For some operators, we know the annual national volumes from gray literature
   
• We know that La Poste (Colissimo + DPD + Chronopost) add up to about 65% of all parcels in France
   
• For the rest, we approximate their market share using their annual turnover values

Methodology: Market shares

How many parcels are delivered by each operator?

Methodology: Assignment

How many parcels are delivered by each distribution center?

Methodology: Assignment

How many parcels are delivered by each distribution center?

Approach

• For each parcel, sample an operator
   
• Find the operator's distribution center that is closest (shortest distance) to the parcel

Methodology: Assignment

Approach

• For each parcel, sample an operator
   
• Find the operator's distribution center that is closest (shortest distance) to the parcel

Outcome

• Nine centers with 300+ parcels
• Remaining centers with less than 300

How many parcels are delivered by each distribution center?

Methodology: Cost structures

What influences operators decisions?

Methodology: Cost structures

What influences operators decisions?

Salaries

Vehicles

Distance

Daily cost

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Methodology: Cost structures

What influences operators decisions?

Assumption (from grey literature)

• 1,300 EUR net per month
• 1,700 EUR gross per month
• 3,400 EUR staff cost per month
   
• 25 active days per month
• 136 EUR per day

Salaries

Vehicles

Distance

Daily cost

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Methodology: Cost structures

What influences operators decisions?

Salaries

Distance

Daily cost

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• We examined long-duration rental offers (LLD) of French vehicle manufacturers

Vehicles

Methodology: Cost structures

What influences operators decisions?

Daily cost

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• Insight: Rental costs depend linearly on the transport volume

Salaries

Distance

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Vehicles

Methodology: Cost structures

What influences operators decisions?

Daily cost

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• Seven prototypical vehicle types: 3 sizes thermal or electric plus cargo-bike

Salaries

Distance

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Vehicles

Methodology: Cost structures

What influences operators decisions?

Daily cost

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• Distance-costs depend on consumption of fuel and electricity
• Multiplied by fuel or electricity prices (example 1.45 EUR/L and 9ct/kWh)

Salaries

Distance

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Vehicles

Methodology: Cost structures

What influences operators decisions?

Daily cost

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• Additional information from our manufacturer analysis: Emissions
• Assuming 90 gCO2eq/kWh for electric vehicles

Salaries

Distance

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Vehicles

Methodology: Optimization

Minimize costs

Methodology: Optimization

Heterogeneous Vehicle Routing Problem

• Minimize cost per distribution center
• Operator can choose vehicles (7 types) and routes
   
• Operator must deliver all assigned parcels
• Maximum active time per day 10h
• Active time is travel time + 120s delivery + 60s pick-up per parcel
   
• Vehicles can not exceed capacity (we assume 10 parcels per m3)
• Multiple tours per vehicle are allowed
   
• Vehicles start and end the day at the distribution center

Minimize costs

Methodology: Optimization

Heterogeneous Vehicle Routing Problem

• Minimize cost per distribution center
• Operator can choose vehicles (7 types) and routes
   
• Operator must deliver all assigned parcels
• Maximum active time per day 10h
• Active time is travel time + 120s delivery + 60s pick-up per parcel
   
• Vehicles can not exceed capacity (we assume 10 parcels per m3)
• Multiple tours per vehicle are allowed
   
• Vehicles start and end the day at the distribution center

Minimize costs

Data

• OpenStreetMap network
• Extracted using osmnx
   
• Distance matrix between parcels and depot
• Travel time matrix using congestion factors
   

Solver

• Open-source
• VROOM

Visualisation platform

UCC platform

Discussion & Open questions

• Validation
  • What data can we use to validate the model?
  • Validation of individual operators?
     
• Coherence
  • Are our cost structures coherent? Did we miss some aspects?
     
• Replicability
  • Data available anywhere in France (but ADM survey only for Lyon)
  • Theoretically applicable anywhere in France

Next steps

• Scenarios
  • Define more interesting and detailed scenarios
  • Especially in terms of policies
     
• Integration of new components
  • Upstream reorganization
  • Pickup points
  • Parcel lockers
  • ZFE
     
• Approximation model
  • VRPs show specific structure
  • Possibility to approximate solutions
  • Allows for in-the-loop optimization (optimally placing UCCs, ...)

Questions?

https://slides.com/sebastianhorl/spl-28mar23