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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
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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
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Geosimulation of Parking
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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
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WE STUDY THE CURRENT STATE WE AIM AT FORECASTING
What are the components of parking knowledge?
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Parking demand
and supply
Parking spatial pattern
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
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demand and supply
Parking spatial pattern
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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 spatial pattern
Parking demand
and supply 9InDOG Olomouc 14-17 October 2013
PARKING PATTERNS
Destination-parking place distance: Field surveys
Parking spatial pattern
The distance between the parking place and the destination
Estimated based on the data of owners’ addresses
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demand and supply
Parking spatial pattern
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
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Drivers’ parking
behavior
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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
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Drivers’ parking
behavior
DRIVERS’ BEHAVIOR ON THE WAY TO DESTINATION
GPS data logging, GIS analysis, interviews with drivers
Drivers do not take the shortest path…
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Drivers’ parking
behavior
DRIVER’S BEHAVIOR AFTER MISSING THE DESTINATION
GPS data logging, GIS analysis, interviews with drivers
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Drivers’ parking
behavior
Analysis of drivers’ parking trajectoriesprovides adequate heuristic algorithms
of drivers’ parking behavior
Parking dynamics in space and time
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Parking dynamics in space and time
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
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Parking dynamics in space and time
PARKAGENT is a spatially explicit model
Tel Aviv city street network
Simplified grid network
Antwerp city street network
Ramat Gan, diamond stock exchange
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Parking dynamics in space and time
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
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Parking dynamics in space and time
PARKAGENT generates great variety of parking statistics
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Parking dynamics in space and time
A closer look at the PARKAGENT
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Parking dynamics in space and time
PARKAGENT validation - fits very well to the field data
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Parking dynamics in space and time
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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
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Parking dynamics in space and time
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?
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Parking dynamics in space and time
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
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Parking dynamics in space and time
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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Parking dynamics in space and time
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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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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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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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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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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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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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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
D/S = 0.75
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PARKFIT output
Parking dynamics in space and time
Bat Yam, average distance to parking place
Distance to destination, 2012
D/S 0.75
Survey data
PARKFIT
Parking management
and policy assessment
Parking management
and policy assessment
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Parking management and policy assessment
Parking management
and policy assessment
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
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PARKAGENT: Cost-benefit analysis of new parking facility
Multi-level garage under the main road
Parking management and policy assessment
Parking management
and policy assessment
The signpost system that directs drivers to the lots that have vacant places decreases search time by 30%
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PARKAGENT: New garage will not justify itself unless signpost system will be introduced
Parking management and policy assessment
Parking management
and policy assessment
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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
and policy assessment
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)
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Parking management and policy assessment
Parking management
and policy assessment
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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 and policy assessment
Parking management
and policy assessment
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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
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