Large-scale simulations for automated mobility
Results for Zurich and Paris
Sebastian Hörl
at MINES ParisTech
on 26 February 2020
The street in 1900
http://www.loc.gov/pictures/item/2016800172/
The street today
https://commons.wikimedia.org/wiki/File:Atlanta_75.85.jpg
The street of tomorrow?
Julius Bär / Farner
I. MATSim
Agent-based models
Senozon VIA
https://pixabay.com/en/traffic-jam-stop-and-go-rush-hour-143391/
MATSim
Home
Work
Shop
Home
until 8am
9am to 6pm
6:15m to 6:30pm
from 6:45pm
walk
public
transport
walk
MATSim
Mobility simulation
Decision-making
Analysis
Scenario
https://pixabay.com/en/zurich-historic-center-churches-933732/
II. AMoD in Zurich
What do we know about automated vehicles?
Cost structures?
User preferences?
System impact?
Cost Calculator for automated mobility
Stated preference survey
MATSim simulation
1
2
3
AMoD Cost Calculator
Bösch, P.M., F. Becker, H. Becker and K.W. Axhausen (2018) Cost-based analysis of autonomous mobility services, Transport Policy, 64, 76-91
AMoD Survey
Felix Becker, Institute for Transport Planning and Systems, ETH Zurich.
AMoD Survey
13 CHF/h
AMoD
Taxi
19 CHF/h
Conventional
Car
12 CHF/h
Public
Transport
VTTS
Car by Adrien Coquet from the Noun Project
Bus by Simon Farkas from the Noun Project
Wait by ibrandify from the Noun Project
AMoD
AMoD Survey
Car by Adrien Coquet from the Noun Project
Bus by Simon Farkas from the Noun Project
Wait by ibrandify from the Noun Project
13 CHF/h
AMoD
Taxi
19 CHF/h
Conventional
Car
12 CHF/h
Public
Transport
VTTS
21 CHF/h
32 CHF/h
AMoD
Fleet sizing with dynamic demand
Fleet sizing with dynamic demand
Fleet sizing with dynamic demand
Model structure
Cost calculator
Plan modification
Discrete Mode Choice Extension
Mobility simulation
Prediction
Price
Trips
- Utilization
- Empty distance, ...
- Travel times
- Wait times, ...
Visualisation
Automated taxi
Pickup
Dropoff
Results
Results
Results
30% reduction in vehicles
but
50% increase in VKT
Results
Increase of vehicles on the road
Results
Increase in accessibility
Fleet control
Load-balancing heuristic
Simple heuristic, fast runtime
Gloal Euclidean Bipartite Matching
Standard algorithm in Operations Research
?
?
Assignment
Car by Adrien Coquet from the Noun Project
Hail by Bradley Avison from the Noun Project
Example algorithms
Load-balancing heuristic
Undersupply
Oversupply
able to serve remote demand
able to serve remote demand
able to serve peek demand
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.
Example algorithms
Global Bipartite Matching
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
Feedforward Fluidic Optimal Rebalancing Policy
Linear program matching a priori known trip rates
Adaptive Uniform Rebalancing Policy
Linear program distributing vehicles uniformly
Car by Adrien Coquet from the Noun Project
Hail by Bradley Avison from the Noun Project
?
?
?
?
Redistribution
Example algorithms
Feed-forward fluidic rebalancing strategy
Travel time
Rebalancing flows
Arrival rate
Transition probability
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.
Example algorithms
Feed-forward fluidic rebalancing strategy
Travel time
Rebalancing flows
Arrival rate
Transition probability
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.
Example algorithms
Adaptive Uniform Rebalancing Policy
Vehicles per zone
Current requests
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
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.
III. Paris Scenario
https://pixabay.com/en/paris-eiffel-tower-night-city-view-3296269/
The case of Île-de-France
Census data
Récensement de la population
RP
Icons on this and following slides: https://fontawesome.com // Background: Simunto VIA
The case of Île-de-France
Dispositif sur les revenus localisés sociaux et fiscaux
Income tax data
RP
FiLo
SoFi
The case of Île-de-France
RP: Flux de mobilité
Commuting data
RP
RP
Mob
FiLoSoFi
The case of Île-de-France
Enquête globale de transport
Household Travel Survey
RP
EGT
FiLoSoFi
RP Mob
Enquête national transports et deplacements
Household Travel Survey
ENTD
The case of Île-de-France
Enquête globale de transport
Household Travel Survey
RP
EGT
FiLoSoFi
RP Mob
Enquête national transports et deplacements
Household Travel Survey
ENTD
The case of Île-de-France
Base permanente des équipements
Enterprise census
RP
BPE
FiLoSoFi
RP Mob
EGT
ENTD
The case of Île-de-France
OpenStreetMap
Road network
RP
OSM
FiLoSoFi
RP Mob
EGT
ENTD
BPE
IDFm GTFS
Public transport schedule
GTFS
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
Discrete choice model
Agent behaviour
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
DCM
The case of Île-de-France
Open
Data
RP
FiLoSoFi
RP Mob
EGT
ENTD
BPE
OSM
GTFS
Open
Software
+
=
Reproducible research
Verifiable results
Integrated testing
DCM
The case of Île-de-France
CO2 Emmissions
AMoD in Paris
Hörl, S., M. Balac and K.W. Axhausen (2019) Dynamic demand estimation for an AMoD system in Paris, paper presented at the 30th IEEE Intelligent Vehicles Symposium, June 2019, Paris, France.
Travel Behaviour
Zurich model, calibrated
for Paris population
Cost sturcture
Adapted from Berlin
AMoD Simulation
Load-balancing heuristic
AMoD in Paris
Maximum static demand: 2.3M trips
Hörl, S., M. Balac and K.W. Axhausen (2019) Dynamic demand estimation for an AMoD system in Paris, paper presented at the 30th IEEE Intelligent Vehicles Symposium, June 2019, Paris, France.
AMoD in Paris
Maximum static demand: 2.3M trips
Hörl, S., M. Balac and K.W. Axhausen (2019) Dynamic demand estimation for an AMoD system in Paris, paper presented at the 30th IEEE Intelligent Vehicles Symposium, June 2019, Paris, France.
AMoD in ... ?
AMoD in ... ?
AMoD in ... ?
AMoD in ... ?
Further work
Spatial constraints
Operational constraints
Intermodality
Delivery
Routing
Freight
Further work
Serving real-world taxi demand
San Francisco
Chicago
Zurich
Ruch, C., S. Hörl, J. Hakenberg, E. Frazzoli (2020) The Impact of Fleet Coordination on Taxi Operations, Under review.
Thanks!
Questions so far?
Contact: sebastian.hoerl@ivt.baug.ethz.ch