parking benenson olomouc

Geosimulation for Urban Parking Policy-Making

Itzhak Benenson

[email protected] http://www.tau.ac.il/~bennya/

http://geosimlab.tau.ac.il/

Geosimulation and Spatial Analysis Lab,

Department of Geography and Human Environment, Tel Aviv University, Israel

1InDOG Olomouc 14-17 October 2013

mailto:[email protected]

http://www.tau.ac.il/~bennya/

http://geosimlab.tau.ac.il/

InDOG Olomouc 14-17 October 2013 2

Spatially Explicit Agent-Based Dynamic Modeling of Geographic Phenomena

Geosimulation

High-resolution data + Understanding of Agents Behavior + Simulation + Complex System Theory

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Geosimulation and Spatial Analysis Lab

InDOG Olomouc 14-17 October 2013

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Geosimulation of Parking


Parking spatial pattern

Drivers’ parking

behavior

Parking demand

and supply

Parking dynamics in space and time

Parking m anagem ent

and policy assessm ent

Parking management

and policy assessment


WE STUDY THE CURRENT STATE WE AIM AT FORECASTING

What are the components of parking knowledge?


Parking demand

and supply


PARKING DEMAND BY TAZURBAN GIS and AERIAL PHOTOS

PARKING DEMAND AND SUPPLY

GIS + Aerial photos + Population Census

Estimation of demand: Night: Householders*car ownership rate Day: Office area/20 or proportional to Shops’ turnover

Estimation of supply: Curb: Length of streets /5 m – prohibited placesLots: Lots area /8 sq m * number of floors

7InDOG Olomouc 14-17 October 2013Parking

demand and supply



PARKING DEMAND AND SUPPLY

Parking turnover: Field surveys

For a certain day of the week and hour, parameters of the parking system are stable

Residents VisitorsAverage occupancy

(weekdays) STD Average occupancy (weekdays) STD

61.8% 0.94% 17.4% 1.77%

Parking demand

and supply


Parking demand

and supply 9InDOG Olomouc 14-17 October 2013

PARKING PATTERNS

Destination-parking place distance: Field surveys


The distance between the parking place and the destination

Estimated based on the data of owners’ addresses

10InDOG Olomouc 14-17 October 2013Parking

demand and supply


GIS, Remote Sensing and census data, together with the properly conducted surveys,

provide reliable estimates of characteristics of the parking demand, supply, and spatial patterns


Drivers’ parking

behavior


Drivers’ parking

behavior

DRIVERS’ PREFERENCES DURING THE PARKING SEARCH:

GPS data logging, GIS analysis, interviews with drivers

0 20 40 60 80 100 1200

5000

10000

15000

20000

25000

30000

Car speed versus distance to parking

Speed (km/h)

Dis

stan

ce fr

om h

ome

15:40:35 15:44:51 15:48:21 15:51:51 15:55:21 16:00:01 16:06:09 17:32:17 18:19:030

20

40

60

80

100

120

Car speed during the trip


Drivers’ parking

behavior

DRIVERS’ BEHAVIOR ON THE WAY TO DESTINATION


Drivers do not take the shortest path…


Drivers’ parking

behavior

DRIVER’S BEHAVIOR AFTER MISSING THE DESTINATION



Drivers’ parking

behavior

Analysis of drivers’ parking trajectoriesprovides adequate heuristic algorithms

of drivers’ parking behavior



PARKING SPATIO-TEMPORAL PATTERNS

PARKAGENT: Agent-Based modeling of the parking search

Residents

Commuters

Guests

Customers

Every parking inspector is an agent

Every car that searches for parking or parks is an agent



PARKAGENT is a spatially explicit model

Tel Aviv city street network

Simplified grid network

Antwerp city street network

Ramat Gan, diamond stock exchange



PARKAGENT is easily adjustable to a new city

Cruising time

No of issued tickets per route/area

Occupancy rate per street segment/any area

Distance to destination



PARKAGENT generates great variety of parking statistics



A closer look at the PARKAGENT



PARKAGENT validation - fits very well to the field data



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PARKAGENT REVEALS UNIVERSAL DEPENDENCIES Average cruising time, fraction of drivers failed to park, fraction of drivers parking

at a certain distance to destination as a function of occupancy rate



PARKAGENT adequately describesparking dynamics in space and in time and is

easily adjusted to a new city.

PARKAGENT reveals universal characteristics of the urban parking patterns and fits very

well to the data.

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PARKAGENT provides a straightforward imitation of the parking phenomena, but has its own problems:

Theoretical: We do not fully understand driver’s parking behavior, especially driver’s reaction to the parking prices. The latter varies greatly depending on driver’s parameters.

Practical: Practitioners demand “fast and frugal” answers. PARKAGENT is too complicated for practitioners and has too complicated output.

How can we simplify the answer?



Workers demand

Residents demand

Underground Parking

ID

5 20 10 34

Restrictions Curb Parking Road ID ID

Residents only 10 20394 4809

Do we really need dynamics? PARKFIT algorithm



High-resolution GIS data on parking demand and supply enable static estimate of the parking pattern for given demand

PARKFIT algorithm: Randomly distribute destinations’ demand over the parking space around



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7

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PARKFIT output: Average distance to destination and the number of cars in a building that failed to find a parking place at 400 m or closer

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31



32



33



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D/S = 0.75


PARKFIT output


Bat Yam, average distance to parking place

Distance to destination, 2012

D/S 0.75

Survey data

PARKFIT

Parking management


Parking management



Parking management and policy assessment

Parking management


Different development plans were interpreted as the models scenarios:

1 – 10: Planned office buildings

Search time Distance to destination

Parked in the Bialik

Garage

Average parking search

Average distance to the office

Scenario, certainty

348 8.8 min 150 A, low514 6.6 min 243 B, low600 5.5 min 214 C, high

Example of the model outputs

For each scenario, estimates of occupancy rate, distance to destination, search time


PARKAGENT: Cost-benefit analysis of new parking facility

Multi-level garage under the main road


Parking management


The signpost system that directs drivers to the lots that have vacant places decreases search time by 30%


PARKAGENT: New garage will not justify itself unless signpost system will be introduced


Parking management



At a level of a city, total demand is the same as a supply:

PARKFIT and PARKIDW: Planning parking in Bat-Yam city (200,000) in 2030

67,000 Total parking places in 2030

65,000 Total cars in 2030

There are no parking places within a 400 m distance for 5000 cars!

TAZ Growth of car ownership

Growth of parking supply

Parking deficit

3601 560~ 100~ 460~

3602 400~ 0 400~

3603 650~ 50 600~

3605 950~ 0 950~

3609 330~ 0 330~

3701 380~ 0 380~

3703 720~ 360 360~

3704 360~ 0 360~

3705 325~ 0 325~Parking management and policy assessment

Parking management


Closure of huge free parking lot of 3000 places along the quay

Use PARKAGENT to study the effects of new underground paid parking lot of 1000 pp

Study on effects of closure of free parking lot Gedempte Zuiderdok

PARKAGENT: Antwerp application (Geert Tasseron, Nijmegen)



Parking management



The series of models are sufficient for the knowledge-based parking policy-making at

all levels, from local parking solutions to the neighborhood, city area, or entire city.

Different phenomena demand different models


Parking management


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Publications:

I. Benenson, K. Martens and Birfir, S. 2008, "PARKAGENT: an agent-based model for parking in the city", Computers, Environment and Urban Systems, 32, 431–439

N. Levy, K. Martens, I. Benenson (corresponding author), 2012, Exploring Cruising for On-Street Parking Using Agent-Based and Analytical Models, Transportmetrica, DOI:10.1080/18128602.2012.664575

Děkuji תודה

parking benenson olomouc

Education

destinationgps

field surveys

parking dynamics

parking demand

parking place

drivers behavior

parking search

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