Parcel delivery simulations for Lyon

Sebastian Hörl

11 May 2021

H2020 Project LEAD

  • Sustainable first-/last-mile delivery services in the Confluence quarter of Lyon
     
  • Focus on household parcel delivery
     
  • Need for demand estimation
     
  • Detailed understanding of operational constraints for a delivery service

Study idea

Synthetic population

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

Parcel demand

Synthetic population

Out-of-home purchase survey
Achats découplés des ménages

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

+

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

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

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

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

First case study

  • Synthetic population parcel
    demand (101 parcels)
     
  • Parcel size: 1
     
  • Available vehicles: 10
  • Vehicle capacity: 5
  • Vehicle speed: 10 km/h
     
  • Pickup time (at depot):
    60s
     
  • Delivery time (at customer):
    5min
     
  • Minimize driven distance
  • Three vehicles used
  • Total distance 41.35 km

Next steps

Analysis

  • Define baseline case
  • Define more realistic cases (capacities, parcel sizes, ...)
  • Run series of scenarios (estimating hub size, ...)
  • Refinement of scenario with LL partners

 

Technical

  • More refined solution algorithms (charging stations, ...)

 

Future scenarios

  • General population growth up to 2050
  • Specific development of Confluence area

Scaling up to the digital twin

Synthetic popualtion

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

 

MATSim

MASS-GT

  • Simulation of shipper behaviour (B2C and B2B)

 

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

 

Scaling up to the digital twin

  • 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?

Baseline

Study idea

  • Synthetic population
     
  • Generated deliveries
     
  • Integration of background freight traffic

Baseline

Study idea

Hub analysis

Case scenarios

  • Delivery problem simulation
     
  • Sizing of distribution center
     
  • Sizing of distribution fleet
  • Size of distribution vehicles
     
  • Shared of deliveries
    (Strict / soft zero-emission zone)
     
  • Which configurations are feasible and effective?
  • Synthetic population
     
  • Generated deliveries
     
  • Integration of background freight traffic

Baseline

  • Synthetic population
     
  • Generated deliveries
     
  • Integration of background freight traffic

Study idea

Hub analysis

Case scenarios

  • Delivery problem simulation
     
  • Sizing of distribution center
     
  • Sizing of distribution fleet
  • Size of distribution vehicles
     
  • Shared of deliveries
    (Strict / soft zero-emission zone)
     
  • Which configurations are feasible and effective?

Hub analysis

Future scenarios

  • Increase of population
  • Change of demographics
  • ... and generated deliveries
     
  • Static increase of background freight (Interface Transport)

+

Baseline

  • Synthetic population
     
  • Generated deliveries

Study idea

Hub analysis

Case scenarios

Hub analysis

Future scenarios

  • Increase of population
  • Change of demographics
  • ... and generated deliveries
     
  • Static increase of background freight (Interface Transport)

+

 


 

  • Integration of background freight traffic

 

  • Sizing of distribution center
     
  • Sizing of distribution fleet
  • Size of distribution vehicles
     
  • Shared of deliveries
    (Strict / soft zero-emission zone)
     
  • Which configurations are feasible and effective?

 

  • Delivery problem simulation

Synthetic population

Population census (RP)

Synthetic population

Population census (RP)

Income data (FiLoSoFi)

Synthetic population

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Synthetic population

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

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

Synthetic population

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

Address database (BD-TOPO)

Synthetic population

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

Address database (BD-TOPO)

Person ID    

Age

Gender

Home (X,Y)

1

43

male

(65345, ...)

2

24

female

(65345, ...)

3

9

female

(65345, ...)

Synthetic population

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

Address database (BD-TOPO)

Person ID    

Activity

Start

End

Loc.

523

home

08:00

(x,y)

523

work

08:55

18:12

(x,y)

523

shop

19:10

19:25

(x,y)

523

home

19:40

(x,y)

Synthetic population

Population census (RP)

Income data (FiLoSoFi)

Commuting data (RP-MOB)

Household travel survey (EDGT)

Enterprise census (SIRENE)

OpenStreetMap

GTFS (SYTRAL / SNCF)

Address database (BD-TOPO)

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

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}

Out-of-home purchase survey
Achats découplés des ménages

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

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) = 1 - \exp(-\mu \cdot n)

Out-of-home purchase survey
Achats découplés des ménages

Routing problem

Vehicle routing problem with time windows (VRPTW)

  • We have a fleet of N vehicles (capacity, speed).
     
  • They need to delivery the parcels when somebody is home (i.e. they need to respect time windows).
     
  • Problem: Find optimal route that minimizes the distance covered while fulfilling the criteria.

Routing problem

Vehicle routing problem with time windows (VRPTW)

  • We have a fleet of N vehicles (capacity, speed).
     
  • They need to delivery the parcels when somebody is home (i.e. they need to respect time windows).
     
  • Problem: Find optimal route that minimizes the distance covered while fulfilling the criteria.

Routing problem

Vehicle routing problem with time windows (VRPTW)

  • We have a fleet of N vehicles (capacity, speed).
     
  • They need to delivery the parcels when somebody is home (i.e. they need to respect time windows).
     
  • Problem: Find optimal route that minimizes the distance covered while fulfilling the criteria.

First case study

  • Synthetic population parcel
    demand (101 parcels)
     
  • Parcel size: 1
     
  • Available vehicles: 10
  • Vehicle capacity: 5
  • Vehicle speed: 10 km/h
     
  • Pickup time (at depot):
    60s
     
  • Delivery time (at customer):
    5min
     
  • Minimize driven distance

Agent-based models

Senozon VIA

https://pixabay.com/en/traffic-jam-stop-and-go-rush-hour-143391/

MATSim

Senozon VIA

  • Flexible, extensible and well-tested open-source transport simulation framework
     
  • Used by many research groups and companies all over the world
     
  • Extensions for parking behaviour, signal control, location choice, freight, ...

http://www.matsim.org

matsim-org/matsim-libs

MATSim

Mobility simulation

Decision-making

Analysis

Scenario

  • Flexible, extensible and well-tested open-source transport simulation framework
     
  • Used by many research groups and companies all over the world
     
  • Extensions for parking behaviour, signal control, location choice, freight, ...

MATSim

Senozon VIA

MATSim

Home

Work

Shop

Home

until 8am

9am to 6pm

6:15m to 6:30pm

from 6:45pm

walk

public

transport

walk

Calibration

LEAD: Lyon use case

By Sebastian Hörl

LEAD: Lyon use case

11 May 2021

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