Simulation scenarios for automated mobility
Static vs. dynamic demand
Sebastian Hörl
IVT Brown Bag, ETH Zurich
18 Mar 2019
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?
- Autonomous Mobility
- Mobility as a Service
- Mobility on Demand
- Electrification
- Aerial Mobility
Julius Bär / Farner
I. MATSim
Mesoscopic Transport Simulation
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
Network simulation
Scoring of the plans
Selection and modification
Scenario
MATSim with Discrete Choice Model
Network simulation
Scoring
Replanning
Network simulation
Choice Model
Hörl, S., M. Balac and K.W. Axhausen (2019) Pairing discrete mode choice models and agent-based transport simulation with MATSim, presented at the Annual meeting of the Transport Research Board 2019, January 2019, Washington D.C.
https://pixabay.com/en/zurich-historic-center-churches-933732/
II. AMoD in Zurich
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
Fleet control
Feedforward Fluidic Optimal Rebalancing Policy
Linear program matching a priori known trip rates
Adaptive Uniform Rebalancing Policy
Linear program distributing vehicles equally
Car by Adrien Coquet from the Noun Project
Hail by Bradley Avison from the Noun Project
?
?
?
?
Redistribution
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.
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.
Fleet sizing with dynamic demand
Fleet sizing with dynamic demand
Fleet sizing with dynamic demand
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
Model integration
Model integration
MATSim
RScript
Results
Maximum
38k rides
Results
Maximum
38k rides
Results
III. Paris Scenario
https://pixabay.com/en/paris-eiffel-tower-night-city-view-3296269/
Sociodemographics
Activity Chains
Locations
MATSim
Census data, FILOSOFI
Enquête globale de transport (HTS)
- OD from census data
- Specifically designed sampling algorithm
MATSim Scenario for Île-de-France
- OpenStreetMap, GTFS
MATSim Scenario for Île-de-France
MATSim Scenario for Île-de-France
MATSim Scenario for Île-de-France
AMoD in Paris
Hörl, S. M. Balac, and K.W. Axhausen (2019) Dynamic demand estimation for an AMoD system in Paris, Under review.
AMoD in Paris
Travel Behaviour
Zurich model, calibrated for Paris population
AMoD Cost
Costs for Berlin
Fleet control
Heuristic algorithm
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, Under review.
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, Under review.
Work in progress ...
Spatial constraints
Operational constraints
Intermodality
https://commons.wikimedia.org/wiki/File:Zeichen_365-65_-_Ladestation_f%C3%BCr_Elektrofahrzeuge,_StVO_2014.svg
https://www.flickr.com/photos/viriyincy/4528290409
https://de.wikipedia.org/wiki/Datei:Zeichen_316_-_Parken_und_Reisen,_StVO_1992.svg
AMoD in Paris (or elsewhere)
Potential studies
- Financial aspects: Profits, subsidies
- Dispatching algorithms under customer behaviour
- Sensible scenario diameters
- Interaction with land use
- Emissions
Final remarks
Thanks!
Questions?
Contact: sebastian.hoerl@ivt.baug.ethz.ch
Simulation scenarios for automated mobility
By Sebastian Hörl
Simulation scenarios for automated mobility
Brown Bag, IVT, ETH Zurich, 18 March 2019
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