Estimating the impact of parcel deliveries using multiagent simulation

Sebastian Hörl

1 November 2021

Brown Bag IVT

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

MATSim @ IRT SystemX

• On-demand mobility simulation
  
   

• First/last mile mobility simulation

Passenger transport

Freight transport

• First/last mile delivery
  
  
  
  
  
  
  
  
  
   
• VRP Solvers
  • Development of electric VRP for JSprit

MEMOLOG

• Research initiative with the goal to foster practical use of multiagent transport simulation for
  • Passenger mobility
  • Logistics
  • Territorial development
     
• In close collaboration with industrial partners and specific real-world use cases
   
• Focus on standardization of formats and methods, multiscale simulation, verification, and real-time capabilities
   
• Start expected beginning of 2021 over a duration of 4 years with 3-4 PhD students jointly supervised by IRT SystemX and Université Gustave Eiffel

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

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: Agent-based models

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• Discrete locations
• Individual travelers
• Individual behaviour
• Whole day analysis

Disaggregated

Icons on this and following slides: https://fontawesome.com

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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Paper published July 2021

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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.

+

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.

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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 (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

Scaling up: City-wide baseline

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

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

Scaling up: City-wide baseline

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

Scaling up: City-wide baseline

Methodology: Next steps

• Route and fleet optimization using JSprit
  • Currently, also eVRP extension under development
     
• Integration into MATSim baseline simulation of Lyon
   
• KPI Calculation

Methodology: KPI Calculation

GHG/PM Emissions: Simulation based on Hbefa database with detailed driving conditions (cold start, current speed, ...)

(Alternative in LEAD: COPERT)

Methodology: KPI Calculation

GHG/PM Emissions: Simulation based on Hbefa database with detailed driving conditions (cold start, current speed, ...)

(Alternative in LEAD: COPERT)

Noise Emissions: Simulation simulates noise emissions and emissions to arrive at exposure values

Methodology: KPI Calculation

GHG/PM Emissions: Simulation based on Hbefa database with detailed driving conditions (cold start, current speed, ...)

(Alternative in LEAD: COPERT)

Noise Emissions: Simulation simulates noise emissions and emissions to arrive at exposure values

Aggregation

• Global emissions are summed up over the territory (CO2)
• Local emissions need to be looked at in terms of exposure (PM)

Methodology: KPI Calculation

GHG/PM Emissions: Simulation based on Hbefa database with detailed driving conditions (cold start, current speed, ...)

(Alternative in LEAD: COPERT)

Noise Emissions: Simulation simulates noise emissions and emissions to arrive at exposure values

Aggregation

• Global emissions are summed up over the territory (CO2)
• Local emissions need to be looked at in terms of exposure (PM)

• Noise is local by definition, impact is calculated in terms of exposure (of the population) and expected social cost (in EUR)

Methodology: KPI Calculation

Energy: Based on vehicle consumption as a factor on the distance driven)

• Sum over all vehicles and distance

Congestion: Comparison of travel time of the population with and without delivery traffic added

Optional to be developed: Explicit simulation of lane-blocking behaviour due to deliveries

• Sum over all delays for the population

Example analysis

Caution: Current test results to exemplify the methodology. Model needs to be improved in multiple stages (see slide after).

Caution: Current test results to exemplify the methodology. Model needs to be improved in multiple stages (see slide after).

First main result: Estimate of the total distance driven for parcel deliveries in Lyon (by the most frequent operators).

Example analysis

Scenarios

Policy

• Closing whole confluence for parcel delivery
• Closing Confluence except boulevard

Consolidation centers

• One vs. multiple centers
• Different locations of consolidation centers

Consolidation fleet

• Electric vans
• Cargo-bikes
• Delivery robots
• Mix

Demand

• 10% / 20% / 100% of parcels

Validation

Traffic

• Road counts (open data for Lyon)

Air quality

• Observatoire Air Rhone-Alpes

Deliveries

• Data will be provided by La Poste

Gaps

Synthetic travel demand

Parcel demand

Parcel distribution

Route optimization

KPI calculation

Validation

Sendcloud survey is only a proxy. Ideally: Obtain data on use of parcel delivery operators through survey.

Gaps

Synthetic travel demand

Parcel demand

Parcel distribution

Route optimization

KPI calculation

Validation

Delivery center and fleet specifications are based on best guess information. Do we have more detailed data? (In progress for La Poste)

Gaps

Synthetic travel demand

Parcel demand

Parcel distribution

Route optimization

KPI calculation

Validation

Use heuristic optimization algorithm from JSprit instead of best-response assignment (currently running optimization)

Gaps

Synthetic travel demand

Parcel demand

Parcel distribution

Route optimization

KPI calculation

Validation

Emissions not detailed for the moment
Detailed Hbefa data set has been ordered

Gaps

Synthetic travel demand

Parcel demand

Parcel distribution

Route optimization

KPI calculation

Validation

Some KPIs need extensions for the simulation (lane-blocking through deliveries)

Gaps

Synthetic travel demand

Parcel demand

Parcel distribution

Route optimization

KPI calculation

Validation

Validation data needs to be assessed and integrated

Gaps

Synthetic travel demand

Parcel demand

Parcel distribution

Route optimization

KPI calculation

Validation

Experimental integration of B2B

• Internship on integration of FRETURB with our methodology for static background traffic (since November 2021)

• Academic exchange with TU Delft for the integration of our methodology and MASS-GT for dynamic B2B operations (End of 2021)

MATSim + MASS-GT

• Simulation of Lyon (and Rhônes-Alpes) with influence of traffic, including freight traffic

• Simulation of shipper behaviour (B2C and B2B)

• For instance: What will be the effect of a zero-emission zone in the city on traffic?

Questions?

https://slides.com/sebastianhorl/ivt-1nov21