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A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

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Page 1: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.
Page 2: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

A. Horni and K.W. Axhausen IVT, ETH Zürich

GRIDLOCK MODELING WITH MATSIM

Page 3: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

MOTIVATION

• gridlocks only roughly covered in current version of microsimulationse.g., MATSim models average working day traffic

+

• relevance/risk of gridlocks (?)

Page 4: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

• context: joint seed project VPFW ETH

• analyze “gridlock” events (Zürich 2013, Aargau 2014)

• MATSim simulation experiments- network level- impedance components (e.g., look at intersections)

• extend solid base of gridlock modeling for aggregate transport modelinge.g., gridlocks and network-level MFDs (Geroliminis,

Daganzo, …)- microsimulation modeling- gridlock characteristics

PROCEEDING

Page 5: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

METHOD• MATSim simulation experiments

outputoutput

executionexecution

replanningreplanning

scoringscoring

inputinput

planplanplanplan

planplan

planplan

planplan utils

Page 6: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

METHOD• Zürich scenario

- full population- navigation net- car mode simulated

• simulation configuration- relaxed state normal conditions- tunnels blockage due to truck accident 17.04.2013

15:30- within-day rerouting with increasing replanning range

• analysis based on city count data to begin with- 10. / 16. / 17. / 18. / 25. of April 2013

Page 7: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

RESULTS• link storage capacity, a.k.a.

jam densitynormal state

blocked state

Page 8: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

RESULTS• link storage capacity, a.k.a. jam density

Page 9: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

RESULTS

Page 10: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

RESULTS• link storage capacity, a.k.a. jam density

Page 11: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

RESULTS

Page 12: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

RESULTS

blocked state

simcounts

Page 13: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

RESULTS

Bucheggplatz morning rush hour

Page 14: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

RESULTS• impedance modeling: missing components

- intersection dynamics: signals, priority rules, …- mode interactions: crosswalks, lane merging, …- driver behavior: slow drivers, driveaway delays, parking searchers, …- road geometry

• network modeling & its influence on the simulation dynamics

Page 15: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

RESULTSissues with link storage capacity, … jam density

Page 16: A. Horni and K.W. Axhausen IVT, ETH Zürich GRIDLOCK MODELING WITH MATSIM.

CONCLUSIONS & FUTURE WORK• inspection

- microsimulation (MATSim) capability to simulate gridlocks

- relevance/risk of gridlocks and concise definition- gridlock characteristics

• more and more suitable data- police department ZH, …

• other events- Aargau 2014- Zug 2011- Chur 2014

• impedance modeling issues- missing components, intersections- storage capacity


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