AV Dispatching in MATSim
Tools and recent results
Sebastian Hörl
Bauhaus Luftfahrt
20 January 2020
Fleet control
- Assigning vehicles to requests
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Assignment / Dispatching
Car by Adrien Coquet from the Noun Project
Hail by Bradley Avison from the Noun Project
Fleet control
Car by Adrien Coquet from the Noun Project
Hail by Bradley Avison from the Noun Project
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Rebalancing
- Movement of empty vehicles
- Anticipating demand patterns
Fleet control
Routing
Car by Adrien Coquet from the Noun Project
Hail by Bradley Avison from the Noun Project
- Avoid congestion
- Mitigate congestion
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Fleet control
Parking
Car by Adrien Coquet from the Noun Project
Hail by Bradley Avison from the Noun Project
- Avoid empty distance
- Restricted by infrastructure
Complementary Problems
Infrastructure placement
Infrastructure sizing
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Example algorithms
Load-balancing heuristic
Undersupply
- When request pops up
find closest vehicle
Oversupply
- When vehicle gets available
find closest request
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
- Find matching of vehicles and requests that minimizes total Euclidean (or network-based) empty distance
- Nice benchmark because sole objective is to minimize empty distance
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
Feed-forward fluidic rebalancing strategy
Travel time
Rebalancing flows
Arrival rate
Transition probability
- Informed algorithm from historical data
- Minimization of rebalancing time
- Minimization of waiting time?
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
- Minimization of rebalancing time
- Working on current information
- Uniform distribution of vehicles
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.
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.
Case study: Fleet control
Case study: Routing
Lu, Chengqi (2019) Congestion-Aware Operation of Coordinated Autonomous Mobility-on-Demand Systems, Master thesis, Institute for Dynamic Systems and Control (IDSC), ETH Zurich, Zürich, Switzerland.
Case study: Parking
Ruch, C., S. Hörl, R. Ehrler, M. Balac, E. Frazzoli (2020) How Many Parking Spaces Does a Mobility-on-Demand System Require?, Under review.
Case study: Parking
Ruch, C., S. Hörl, R. Ehrler, M. Balac, E. Frazzoli (2020) How Many Parking Spaces Does a Mobility-on-Demand System Require?, Under review.
Case study: Parking
Case study: Parking
Case study: Serving taxi demand
San Francisco
Chicago
Zurich
Further topics
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Dynamic demand
How do agents with choice-making behaviour react to strategies?
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Pick-up and drop-off dynamics
How does limited pickup/dropoff space limit performance and network efficiency?
Thanks!
Questions?
Contact: sebastian.hoerl@ivt.baug.ethz.ch
Dispatching in MATSim
By Sebastian Hörl
