Use cases for multiagent simulation in passenger and freight transport

Sebastian Hörl

10 May 2023

CentraleSupélec

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

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

http://www.loc.gov/pictures/item/2016800172/

The street in 1900

The street today

https://commons.wikimedia.org/wiki/File:Atlanta_75.85.jpg

• Autonomous Mobility
  
  
• Mobility as a Service
  
  
• Mobility on Demand
  
  
• Electrification
  
  
• Aerial Mobility

Julius Bär / Farner

The street of tomorrow?

I. Transport simulation

Classic transport planning

• Zones
• Flows
• Peak hours
• User groups

Aggregated

Agent-based models

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

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

Disaggregated

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

MATSim

• 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-org/matsim-libs

MATSim

Synthetic demand

MATSim

Mobility simulation

Synthetic demand

MATSim

Decision-making

10:00 - 17:30

17:45 - 21:00

22:00 - 0:00

Mobility simulation

Synthetic demand

MATSim

Decision-making

Mobility simulation

Synthetic demand

MATSim

Decision-making

Mobility simulation

Analysis

Synthetic demand

Scenarios

https://pixabay.com/en/zurich-historic-center-churches-933732/

II. AMoD in Zurich

Cost structures?

User preferences?

System impact?

Cost Calculator for automated mobility

Stated preference survey

MATSim simulation

1

2

3

What do we know about automated taxis?

What do we know about automated taxis?

Bösch, P.M., F. Becker, H. Becker and K.W. Axhausen (2018) Cost-based analysis of autonomous mobility services, Transport Policy, 64, 76-91

What do we know about automated taxis?

Felix Becker, Institute for Transport Planning and Systems, ETH Zurich.

VTTS

AMoD

Car by Adrien Coquet from the Noun Project

Bus by Simon Farkas from the Noun Project

Wait by ibrandify from the Noun Project

VTTS

AMoD

Car by Adrien Coquet from the Noun Project

Bus by Simon Farkas from the Noun Project

Wait by ibrandify from the Noun Project

Model structure

Cost calculator

Plan modification

Discrete Mode Choice Extension

Mobility simulation

Prediction

Price

Trips

- Utilization

- Empty distance, ...

- Travel times

- Wait times, ...

Fleet sizing with dynamic demand

Fleet sizing with dynamic demand

Fleet sizing with dynamic demand

Visualisation

Automated taxi

Pickup

Dropoff

Hörl, S., F. Becker and K.W. Axhausen (2020) Automated Mobility on Demand: A comprehensive simulation study of cost, behaviour and system impact for Zurich

Hörl, S., F. Becker and K.W. Axhausen (2020) Automated Mobility on Demand: A comprehensive simulation study of cost, behaviour and system impact for Zurich

13% reduction in vehicles

100% increase in VKT

Hörl, S., F. Becker and K.W. Axhausen (2020) Automated Mobility on Demand: A comprehensive simulation study of cost, behaviour and system impact for Zurich

100% increase in VKT

Hörl, S., F. Becker and K.W. Axhausen (2020) Automated Mobility on Demand: A comprehensive simulation study of cost, behaviour and system impact for Zurich

Other aspects

Hörl, S., C. Ruch, F. Becker, E. Frazzoli and K.W. Axhausen (2019) Fleet operational policies for automated mobility: a simulation assessment for Zurich, Transportation Research: Part C, 102, 20-32.

Fleet control

Operational constraints

Spatial constraints

Intermodality

Pooling

III. Demand data

https://pixabay.com/en/paris-eiffel-tower-night-city-view-3296269/

Synthetic travel demand

Synthetic travel demand

Synthetic travel demand

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 travel demand

Synthetic travel demand

Person ID    

Age

Gender

Home (X,Y)

1

43

male

(65345, ...)

2

24

female

(65345, ...)

3

9

female

(65345, ...)

Synthetic travel demand

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 travel demand

Synthetic travel demand

Synthetic travel demand

Synthetic travel demand

Synthetic travel demand

+

=

Synthetic travel demand

+

=

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

Current use cases

eqasim-org/eqasim-java

eqasim-org/ile-de-france

Current use cases

eqasim-org/sao_paulo

eqasim-org/california

Balac, M., Hörl, S. (2021) Synthetic population for the state of California based on open-data: examples of San Francisco Bay area and San Diego County, presented at 100th Annual Meeting of the Transportation Research Board, Washington, D.C.

Sallard, A., Balac, M., Hörl, S. (2021) Synthetic travel demand for the Greater São Paulo Metropolitan Region, based on open data, Under Review

Emissions in Paris

Grand Paris Express

Automated taxis in Paris

Automated taxis in Paris

IV. Logistics

Photo by Tom Fisk from Pexels

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

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: 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

+

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.

+

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.

+

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.

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: Route optimization

Using a heursitic routing solver, an optimal distribution scheme per distribution center is obtained to arrive at a lower bound estimate for the distance covered for the deliveries.

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

How many parcels need to be delivered on one day?

Perimeter

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

Demand

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

Methodology: Distribution centers

From where do operators delivery the parcels?

Methodology: Distribution centers

From where do operators delivery the parcels?

Approach

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

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

+

+

=

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

+

+

=

Methodology: Cost structures

What influences operators decisions?

Salaries

Distance

Daily cost

+

+

=

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

Vehicles

Methodology: Cost structures

What influences operators decisions?

Daily cost

=

• Insight: Rental costs depend linearly on the transport volume

Salaries

Distance

+

+

Vehicles

Methodology: Cost structures

What influences operators decisions?

Daily cost

=

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

Salaries

Distance

+

+

Vehicles

Methodology: Cost structures

What influences operators decisions?

Daily cost

=

• 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

+

+

Vehicles

Methodology: Cost structures

What influences operators decisions?

Daily cost

=

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

Salaries

Distance

+

+

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

Questions?

https://slides.com/sebastianhorl/cs-may23