An Agent-Based Cellular Automaton Cruising-For-Parking Simulation

A. Horni, L. Montini, R. A. Waraich, K. W. Axhausen

IVTETH Zürich

July 2012

Thompson and Richardson (1998), A Parking Search Model

"Parking plays an important role in urban transport systems."

"Motorists have been observed spending a significant percentage of their total trip time searching for a car park ( [Huber, 1962] and [Axhausen and Polak, 1991]). […]”

Shiftan, Burd-Eden (2001), Modeling Response to Parking Policy

"Parking policy is one of the most powerful means urban planners and policy makers can use to manage travel demand and traffic in city centers.“

Arnott and Inci (2005), An Integrated Model of Downtown Parking and Traffic Congestion

“[…] In fact, traffic experts simply do not know what proportion of cars on downtown city streets are cruising for parking.”

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Scale of Parking Search

Munich and Regensburg

Montini et al. (2012), Searching for Parking in GPS Data (S11)

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Scale of Parking Search

RP and SP surveys (Axhausen and Polak, 1991, Weis et al., 2011)laboratory experiments (Bonsall et al., 1998)car-following (Wright and Orram, 1976)riding with a searcher (Laurier, 2005)GPS surveys (Montini et al. 2012)

simulations (PARKAGENT, PARKIT, ...)

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Parking Search Modeling

GPS Processing(Montini et al.)

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Destination Choice(Horni et al.)

ca-based cruising for parking simulation

MATSim Parking Choice and Search (Waraich et al.)

parameter extraction for

calibration

MATSim

interaction effectsapplication

Context

t0

t1 t0

t1

transition process

equilibrium (iterative) models

needs to be efficient butnot behaviorally sound

characteristics or uniqueness needs to be defined (not under-determined)

rule-based (sequential) models

needs to be behaviorally sound

needs to be clearily defined

does not matteras long as within boundary conditions

search process

q0

q1

t0 t1 t0

t1

simulated period

simulated period

s1

s0

Simulation Concept - A Rule-Based Model

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Overall goal:Implementation task

Goal here:Generate aggregate models for parking search key measures (here tsearch) …

similar to estimated functions such as …

«parking fundamental diagram»

… for hybrid application

Goal

Axhausen et al. (1994)PGI Frankfurt a. M.

Simulation Main Components: Framework - Implementation

SIMULATION

input Supply (network & parking infrastructure)Demand (population trips)

output «Parking Fundamental Diagram»

Output Generation

Analyzer

SpatialElementDrawer

ScenarioPlotter

Parking Decision Modeling

AcceptanceRadius/Linear/Quadtratic

ParkingDecision/Linear/Quadratic

RandomRouteChoice

WeightedRandomRouteChoice

Initi

aliz

ation InfrastructureCreator

XMLReader

PopulationCreator

Infrastructure

ParkingLotNNodeNLink

LCell

SpatialElement

contains

attached to

derived

creates

Drive simulation componets

CA

simulate() update() plot()end

SQueue

CAServer

queueHandling

supports

Popu

latio

n Agent

Route

contains

provide parking decisions

Glo

bal

Controller

setup()

simulate()

SConfig

update

update

populates

Simulation Main Components: Cellular Automaton

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• update process on randomly chosen links, nodes and parking lots as in famous Nagel and Schreckenberg (1992) CA

• future: parking search speeds

• CAServer class for update process:• not naively iterating over all agents and infrastructure elements (e.g.,

cells) but only over occupied ones -> queues of agents, links nodes and parking lots

• resolution• queue models – CA – car following models

• jam density used for cell size as in Wu and Brilon (1997)• future: maybe pool cells in free flow conditions

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Simulation Main Components: Cellular Automaton

• parking type choice• exogenously, derived from supply (for ZH scenario only)

• search tactic• search starting point (latent, GPS study …)• weighted random walk

• destination approaching efficiency• agent’s memory of parking lots with free spaces

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Simulation Main Components: Parking Search Modeling

• parking lot choice• Acceptance radius

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Simulation Main Components: Parking Search Modeling

• 3 small-scale scenarios for development and calibration

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• Zurich Inner-city scenario • derived from real-world data

(MATSim demand), navigation network

• ready, but not yet calibrated & speed issues!

Results and Scenarios

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• 100 agents• 2 origins, 1 destination• 30 min simulated

Results: Chessboard Scenario

Future Calibration: GPS Data

Montini et al. (2012), Searching for parking in GPS data (IATBR, S11)approx. 32’000 person days from Zurich and Geneva, raw data (x, y, z, timestamp)

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Future: Application in MATSim - Hybrid Approach

equilibrium model

parking simulation

mobility simulation

rule-based model

Future: Application in MATSim - Hybrid Approach

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tsearch = sample from aggregate functions

tsearch = simulate with CA

really necessary apart from parking studies?simulation costs?

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Future: Destination Choice Interaction Effects

+ e no agglomeration terms and e iid

Discussion

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• estimated aggregate functions reproduced• combination of SOA techniques• software structure very similar to MATSim -> easy migration• high simulation costs

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