Representation of mobility demand in agent-based transport simulation
Sebastian Hörl
28 April 2024
ENPC
http://www.loc.gov/pictures/item/2016800172/
The street in 1900
The street today
https://commons.wikimedia.org/wiki/File:Atlanta_75.85.jpg
Julius Bär / Farner
The street of tomorrow?
Julius Bär / Farner
I. Transport simulation
Classic transport planning
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
Disaggregated
Icons on this and following slides: https://fontawesome.com
MATSim
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
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
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
13 CHF/h
AMoD
Taxi
19 CHF/h
Conventional
Car
12 CHF/h
Public
Transport
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
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/
Macroscopic four-step model
Generation
Distribution
Mode choice
Assignment
Macroscopic four-step model
Generation
Distribution
Mode choice
Assignment
Macroscopic four-step model
Generation
Distribution
Mode choice
Assignment
Travel demand
Travel supply
Macroscopic four-step model
Generation
Distribution
Mode choice
Assignment
Travel demand
Travel supply
Equilibrium
I) Trip Generation
Number of inabitants in Île-de-France
I) Trip Generation
Generated trips
in zone s in group g
Trip generation model
Model inputs
I) Trip Generation
Model parameters (linear factors)
Census data (inhabitants)
Reference data
II) Trip Distribution
Number of daily commutes arriving from 13th arrondissement
II) Trip Distribution
Flow between s and t
Model
O1 | ... | ... | On | |
---|---|---|---|---|
D1 | ||||
... | Fst | |||
... | ||||
Dn |
III) Mode choice
III) Mode choice
Connection A
Connection B
-0.6
-1.0
-0.6 * 20 - 1.0 * 1 = -13
-0.6 * 30 - 1.0 * 0 = -19
Connection A is better alternative.
III) Mode choice
III) Mode choice
( IV: Assignment )
( IV: Assignment )
S
E
Route A
Route B
How many people use each road? What are the travel times?
( IV: Assignment )
S
E
Route A
(*)
from (**)
(**)
Route B
( IV: Assignment )
S
E
Route A
(*)
from (**)
(**)
Route B
N = 1000
( IV: Assignment )
S
E
Route A
(*)
from (**)
(**)
Route B
N = 1000
( IV: Assignment )
How to disaggregate the demand?
Population census (RP)
Income data (FiLoSoFi)
Synthetic travel demand
Population census (RP)
Income data (FiLoSoFi)
Commuting data (RP-MOB)
Synthetic travel demand
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
Population census (RP)
Income data (FiLoSoFi)
Commuting data (RP-MOB)
Household travel survey (EDGT)
Enterprise census (SIRENE)
Address database (BD-TOPO)
Synthetic travel demand
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 travel demand
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 travel demand
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
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
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
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
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
Current use cases
Nantes
Current use cases
Lille
Current use cases
Toulouse
Current use cases
Rennes
Current use cases
Paris / Île-de-France
Current use cases
Paris / Île-de-France
Current use cases
Paris / Île-de-France
Current use cases
Lyon (IRT SystemX)
Current use cases
Current use cases
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, ...
Emissions in Paris
Grand Paris Express
Automated taxis in Paris
Automated taxis in Paris
IV. Logistics
LEAD Project
Low-emission Adaptive last-mile logistics supporting on-demand economy through Digital Twins
Lyon Living Lab
Modeling methodology
Agent-based simulation of Lyon
Demand in parcel deliveries
Distribution by operators
KPI Calculation
Methodology: Parcel demand
Synthetic population
+
Synthetic population
Out-of-home purchase survey
Achats découplés des ménages
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
Synthetic population
+
Synthetic population
Out-of-home purchase survey
Achats découplés des ménages
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
Synthetic population
+
Synthetic population
Out-of-home purchase survey
Achats découplés des ménages
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
Synthetic population
+
Synthetic population
Out-of-home purchase survey
Achats découplés des ménages
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
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
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
Iterative proportional fitting (IFP)
Methodology: 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
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
Maximum entropy approach
Methodology: 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
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
Methodology: 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
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
Methodology: 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
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
Methodology: 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
Based on sociodemographic attributes of the households, parcels are generated for the city on an average day.
Presence of household members
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 (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.
SIRENE: Delivery centers
Scaling up: City-wide baseline
Using shares of customer preference, parcels are assigned to operators and the closest distribution centers.
SIRENE: Delivery centers
Scaling up: City-wide baseline
Using shares of customer preference, parcels are assigned to operators and the closest distribution centers.
SIRENE: Delivery centers
Scaling up: City-wide baseline
Using shares of customer preference, parcels are assigned to operators and the closest distribution centers.
SIRENE: Delivery centers
Methodology: Supply side
Vehicle routing and fleet composition problem
Novel approximation method
Visualisation platform
UCC platform
Questions?