![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/4796826/Screenshot_from_2018-04-11_16-04-26.png)
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Use cases for multiagent simulation in passenger and freight transport
Sebastian Hörl
17 May 2024
CentraleSupélec
IRT SystemX
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
- 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://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/9068432/pasted-from-clipboard.png)
MATSim @ IRT SystemX
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
- On-demand mobility simulation
- First/last mile mobility simulation
Passenger transport
Freight transport
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/9068438/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8517156/pasted-from-clipboard.png)
- First/last mile delivery
- VRP Solvers
- Development of electric VRP for JSprit
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
http://www.loc.gov/pictures/item/2016800172/
The street in 1900
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
The street today
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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?
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
I. Transport simulation
Classic transport planning
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- Zones
- Flows
- Peak hours
- User groups
Aggregated
Agent-based models
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
MATSim
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900001/senozon.png)
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![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7912429/pasted-from-clipboard.png)
- 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, ...
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/4796826/Screenshot_from_2018-04-11_16-04-26.png)
matsim-org/matsim-libs
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7899952/GitHub-Mark-64px.png)
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MATSim
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900001/senozon.png)
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Synthetic demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
MATSim
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900001/senozon.png)
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Mobility simulation
Synthetic demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
MATSim
Decision-making
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900001/senozon.png)
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10:00 - 17:30
17:45 - 21:00
22:00 - 0:00
Mobility simulation
Synthetic demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
MATSim
Decision-making
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900001/senozon.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900007/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7912429/pasted-from-clipboard.png)
Mobility simulation
Synthetic demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
MATSim
Decision-making
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900001/senozon.png)
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Mobility simulation
Analysis
Synthetic demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7381314/pasted-from-clipboard.png)
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?
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
What do we know about automated taxis?
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5226050/Screenshot_from_2018-09-03_19-30-02.png)
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?
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5226055/survey_example_1.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5226056/survey_example_2.png)
Felix Becker, Institute for Transport Planning and Systems, ETH Zurich.
VTTS
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![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5837943/noun_Car_1784918.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5837943/noun_Car_1784918.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5837981/noun_Bus_1348506.png)
13 CHF/h
AMoD
Taxi
19 CHF/h
Conventional
Car
12 CHF/h
Public
Transport
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
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![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5837943/noun_Car_1784918.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5837943/noun_Car_1784918.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5837981/noun_Bus_1348506.png)
13 CHF/h
AMoD
Taxi
19 CHF/h
Conventional
Car
12 CHF/h
Public
Transport
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5838008/noun_Wait_2249597.png)
21 CHF/h
32 CHF/h
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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
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Fleet sizing with dynamic demand
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Fleet sizing with dynamic demand
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Visualisation
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Automated taxi
Pickup
Dropoff
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7980940/pasted-from-clipboard.png)
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
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![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7980968/pasted-from-clipboard.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8336669/pasted-from-clipboard.png)
Other aspects
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5724072/Screenshot_from_2019-01-29_12-05-30.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5724073/Screenshot_from_2019-01-29_12-05-42.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/4336423/4528290409_ac717b1447_b.jpg)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/4336396/2000px-Zeichen_365-65_-_Ladestation_f_r_Elektrofahrzeuge__StVO_2014.svg.png)
Operational constraints
Spatial constraints
Intermodality
Pooling
III. Demand data
https://pixabay.com/en/paris-eiffel-tower-night-city-view-3296269/
Synthetic travel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Population census (RP)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Population census (RP)
Income data (FiLoSoFi)
Synthetic travel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Population census (RP)
Income data (FiLoSoFi)
Commuting data (RP-MOB)
Synthetic travel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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 travel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Population census (RP)
Income data (FiLoSoFi)
Commuting data (RP-MOB)
Household travel survey (EDGT)
Enterprise census (SIRENE)
Address database (BD-TOPO)
Synthetic travel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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, ...)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8437454/population.png)
Synthetic travel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8437454/population.png)
Synthetic travel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8437451/video_126.png)
Synthetic travel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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)
Synthetic travel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Population census (RP)
Income data (FiLoSoFi)
Commuting data (RP-MOB)
National HTS (ENTD)
Enterprise census (SIRENE)
OpenStreetMap
GTFS (SYTRAL / SNCF)
Address database (BD-TOPO)
Synthetic travel demand
EDGT
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Population census (RP)
Income data (FiLoSoFi)
Commuting data (RP-MOB)
Enterprise census (SIRENE)
OpenStreetMap
GTFS (SYTRAL / SNCF)
Address database (BD-TOPO)
Synthetic travel demand
Open
Data
Open
Software
+
=
Reproducible research
Integrated testing
National HTS (ENTD)
EDGT
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Population census (RP)
Income data (FiLoSoFi)
Commuting data (RP-MOB)
Enterprise census (SIRENE)
OpenStreetMap
GTFS (SYTRAL / SNCF)
Address database (BD-TOPO)
Synthetic travel demand
Open
Data
Open
Software
+
=
Reproducible research
Integrated testing
National HTS (ENTD)
EDGT
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8857690/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Current use cases
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937904/departements.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8329182/pasted-from-clipboard.png)
Nantes
- Noise modeling
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8654601/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Current use cases
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937904/departements.png)
Lille
- Park & ride applications
- Road pricing
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8329185/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8654605/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Current use cases
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937904/departements.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7912429/pasted-from-clipboard.png)
Toulouse
- Placement and use of shared offices
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Current use cases
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937904/departements.png)
Rennes
- Micromobility simulation
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8329201/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8654613/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Current use cases
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937904/departements.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/6322792/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8329182/pasted-from-clipboard.png)
Paris / Île-de-France
- Scenario development for sustainable urban transformation
- New mobility services
Mahdi Zargayouna (GRETTIA / Univ. Gustave Eiffel)
Nicolas Coulombel (LVMT / ENPC)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Current use cases
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937904/departements.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8654614/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8654616/pasted-from-clipboard.png)
Paris / Île-de-France
- Cycling simulation
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Current use cases
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937904/departements.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Paris / Île-de-France
- Simulation of dynamic mobility services
- Fleet control through reinforcement learning
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Current use cases
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937904/departements.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Lyon (IRT SystemX)
- Low-emission first/last mile logistics
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Current use cases
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7899952/GitHub-Mark-64px.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7899952/GitHub-Mark-64px.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937904/departements.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Current use cases
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7899952/GitHub-Mark-64px.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/1013243/images/8384763/WorldMap.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7899952/GitHub-Mark-64px.png)
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
Sao Paulo, San Francisco Bay area, Los Angeles five-county area, Switzerland, Montreal, Quebec City, Jakarta, Casablanca, ...
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Emissions in Paris
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900001/senozon.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900007/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7912429/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Grand Paris Express
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900001/senozon.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900007/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7912429/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8118304/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Automated taxis in Paris
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900001/senozon.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900007/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7912429/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8329161/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Automated taxis in Paris
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900001/senozon.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7900007/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7912429/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5724337/Screenshot_from_2019-01-29_13-02-30.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/5724339/Screenshot_from_2019-01-29_13-02-58.png)
IV. Logistics
LEAD Project
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Lyon Living Lab
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
- 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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/9068490/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Methodology: Overview
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10705538/pasted-from-clipboard.png)
Methodology: Parcel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Synthetic population
+
Synthetic population
Out-of-home purchase survey
Achats découplés des ménages
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434461/pasted-from-clipboard.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Synthetic population
+
Synthetic population
Out-of-home purchase survey
Achats découplés des ménages
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434461/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434468/pasted-from-clipboard.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Synthetic population
+
Synthetic population
Out-of-home purchase survey
Achats découplés des ménages
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434461/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434468/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434474/pasted-from-clipboard.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Synthetic population
+
Synthetic population
Out-of-home purchase survey
Achats découplés des ménages
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434461/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434468/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434474/pasted-from-clipboard.png)
Gardrat, M., 2019. Méthodologie d’enquête: le découplage de l’achat et de la récupération des marchandises par les ménages. LAET, Lyon, France.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434484/pasted-from-clipboard.png)
Methodology: Parcel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
Iterative proportional fitting (IPF)
- 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.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434532/pasted-from-clipboard.png)
Methodology: Parcel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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.
Methodology: Parcel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434861/region.png)
Methodology: Parcel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434863/lyon.png)
Methodology: Parcel demand
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/8434868/confluence.png)
Methodology: Study area
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
How many parcels need to be delivered on one day?
Methodology: Study area
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265600/pasted-from-clipboard.png)
Methodology: Distribution centers
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
From where do operators delivery the parcels?
Methodology: Distribution centers
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265608/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265675/pasted-from-clipboard.png)
Methodology: Market shares
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
How many parcels are delivered by each operator?
Methodology: Market shares
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265675/pasted-from-clipboard.png)
Methodology: Market shares
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
How many parcels are delivered by each operator?
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265623/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265626/pasted-from-clipboard.png)
Methodology: Assignment
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
How many parcels are delivered by each distribution center?
Methodology: Assignment
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/9031681/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/9031682/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265669/pasted-from-clipboard.png)
Methodology: Assignment
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265596/pasted-from-clipboard.png)
How many parcels are delivered by each distribution center?
Methodology: Cost structures
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
What influences operators decisions?
Methodology: Cost structures
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
What influences operators decisions?
Salaries
Vehicles
Distance
Daily cost
+
+
=
Methodology: Cost structures
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
What influences operators decisions?
Salaries
Vehicles
Distance
Daily cost
+
+
=
Assumption (from grey literature)
- 1,300 EUR net per month
- 1,700 EUR gross per month
- 2,550 EUR staff cost per month
- 25 active days per month
- 102 EUR per day
Methodology: Cost structures
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
What influences operators decisions?
Salaries
Distance
Daily cost
+
+
=
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265662/pasted-from-clipboard.png)
- We examined long-duration rental offers (LLD) of French vehicle manufacturers
Vehicles
Methodology: Cost structures
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
What influences operators decisions?
Daily cost
=
- Insight: Rental costs depend linearly on the transport volume
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/9375365/pasted-from-clipboard.png)
Salaries
Distance
+
+
Vehicles
Methodology: Cost structures
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
What influences operators decisions?
Daily cost
=
- Seven prototypical vehicle types: 3 sizes thermal or electric plus cargo-bike
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265689/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265689/pasted-from-clipboard.png)
Salaries
Distance
+
+
Vehicles
Methodology: Cost structures
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265689/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265689/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265689/pasted-from-clipboard.png)
Salaries
Distance
+
+
Vehicles
Methodology: Cost structures
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
What influences operators decisions?
Daily cost
=
- Additional information from our manufacturer analysis: Emissions
- Assuming 90 gCO2eq/kWh for electric vehicles
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265689/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10265689/pasted-from-clipboard.png)
Salaries
Distance
+
+
Vehicles
Methodology: Optimization
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Minimize costs
Methodology: Optimization
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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
Scenarios and Results
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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Baseline 2022: Estimate today's situation with today's price structure and demand
- Baseline 2030: Parcel delivery demand increased 100%
Scenarios and Results
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10705668/pasted-from-clipboard.png)
Today vs. Future
Scenarios and Results
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10705668/pasted-from-clipboard.png)
Today vs. Future
Scenarios and Results
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10705668/pasted-from-clipboard.png)
Today vs. Future
Scenarios and Results
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10705668/pasted-from-clipboard.png)
Today vs. Future
Scenarios and Results
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10705668/pasted-from-clipboard.png)
Today vs. Future
Scenarios and Results
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10705668/pasted-from-clipboard.png)
Today vs. Future
Scenarios and Results
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
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Baseline 2022: Estimate today's situation with today's price structure and demand
-
Baseline 2030: Parcel delivery demand increased 100%
-
Quantitative mitigation scenarios
- Increasing the price of thermic vehicles
- Implementing a substantial carbon tax
-
Qualitative mitigation scenarios
- Forbidding thermic vehicles in Lyon's LEZ
- Forbidding thermic vehicles in the whole region
Scenarios and Results
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7937920/pasted-from-clipboard.png)
Mitigation
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/10705696/pasted-from-clipboard.png)
Questions?
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/4796826/Screenshot_from_2018-04-11_16-04-26.png)
![](https://s3.amazonaws.com/media-p.slid.es/uploads/66822/images/7596875/systemx_transparent.png)
Use cases for multi-agent simulation in passenger and freight transport
By Sebastian Hörl
Use cases for multi-agent simulation in passenger and freight transport
CentraleSupelec, 17 May 2024
- 169