Geosimulation for
Urban Parking Policy-Making
Itzhak Benenson
bennya@post.tau.ac.il
http://www.tau.ac.il/~bennya/
http://g...
Geosimulation
Spatially Explicit
Agent-Based
Dynamic
Modeling of
Geographic
Phenomena
High-resolution data +
Understanding...
Geosimulation and Spatial Analysis Lab

InDOG Olomouc 14-17 October 2013
Geosimulation of Parking

InDOG Olomouc 14-17 October 2013
What are the components of parking knowledge?

Parking
demand
and supply

Parking
management
and policy
assessment

Driver...
Parking
demand
and supply

InDOG Olomouc 14-17 October 2013

Parking
spatial
pattern
PARKING DEMAND AND SUPPLY

GIS + Aerial photos + Population Census
URBAN GIS and AERIAL PHOTOS

PARKING DEMAND BY TAZ

Est...
PARKING DEMAND AND SUPPLY

Parking turnover: Field surveys
For a certain day of the week and hour,
parameters of the parki...
PARKING PATTERNS

Destination-parking place distance: Field surveys
The distance between the parking place
and the destina...
GIS, Remote Sensing and census data,
together with the properly conducted surveys,
provide reliable estimates of character...
Drivers’
parking
behavior

InDOG Olomouc 14-17 October 2013
DRIVERS’ PREFERENCES DURING THE PARKING SEARCH:

GPS data logging, GIS analysis, interviews with drivers
Car speed during ...
DRIVERS’ BEHAVIOR ON THE WAY TO DESTINATION

GPS data logging, GIS analysis, interviews with drivers

Drivers do not take ...
DRIVER’S BEHAVIOR AFTER MISSING THE DESTINATION

GPS data logging, GIS analysis, interviews with drivers

Drivers’
parking...
Analysis of drivers’ parking trajectories
provides adequate heuristic algorithms
of drivers’ parking behavior

Drivers’
pa...
Parking
dynamics
in space
and time

InDOG Olomouc 14-17 October 2013
PARKING SPATIO-TEMPORAL PATTERNS

PARKAGENT: Agent-Based modeling of the parking search
Every parking
inspector is an agen...
PARKAGENT is a spatially explicit model

Parking
dynamics
in space
and time

InDOG Olomouc 14-17 October 2013
PARKAGENT is easily adjustable to a new city

Tel Aviv city street network

Simplified grid network

Parking
dynamics
in s...
PARKAGENT generates great variety of parking statistics

Occupancy rate per street segment/any area

Distance to destinati...
A closer look at the PARKAGENT

Parking
dynamics
in space
and time

InDOG Olomouc 14-17 October 2013
PARKAGENT validation - fits very well to the field data

Parking
dynamics
in space
and time

InDOG Olomouc 14-17 October 2...
PARKAGENT REVEALS UNIVERSAL DEPENDENCIES
Average cruising time, fraction of drivers failed to park, fraction of drivers pa...
PARKAGENT adequately describes
parking dynamics in space and in time and is
easily adjusted to a new city.
PARKAGENT revea...
PARKAGENT provides a straightforward imitation of the parking
phenomena, but has its own problems:
Theoretical:
We do not ...
Do we really need dynamics? PARKFIT algorithm

High-resolution GIS data on parking
demand and supply enable static estimat...
PARKFIT algorithm:
Randomly distribute destinations’ demand over the parking space around

3

7

Parking
dynamics
in space...
PARKFIT output: Average distance to destination and the number of cars in
a building that failed to find a parking place a...
PARKFIT output: Average distance to destination and the number of cars in
a building that failed to find a parking place a...
PARKFIT output: Average distance to destination and the number of cars in
a building that failed to find a parking place a...
PARKFIT output: Average distance to destination and the number of cars in
a building that failed to find a parking place a...
PARKFIT output: Average distance to destination and the number of cars in
a building that failed to find a parking place a...
PARKFIT output: Average distance to destination and the number of cars in
a building that failed to find a parking place a...
PARKFIT output: Average distance to destination and the number of cars in
a building that failed to find a parking place a...
PARKFIT output
Bat Yam, average distance to parking place

D/S = 0.75

Distance to destination, 2012
Survey data
PARKFIT

...
Parking
management
and policy
assessment

Parking
management
and policy
assessment

InDOG Olomouc 14-17 October 2013
PARKAGENT: Cost-benefit analysis of new parking facility
Different development plans were
interpreted as the models scenar...
PARKAGENT: New garage will not justify itself
unless signpost system will be introduced

The signpost system that directs ...
PARKFIT and PARKIDW: Planning parking in Bat-Yam city (200,000) in 2030

At a level of a city, total demand is the same as...
PARKAGENT: Antwerp application (Geert Tasseron, Nijmegen)



Closure of huge free parking
lot of 3000 places along the
qu...
The series of models are sufficient for the
knowledge-based parking policy-making at
all levels, from local parking soluti...
Publications:
I. Benenson, K. Martens and Birfir, S. 2008,
"PARKAGENT: an agent-based model for parking in the city", Comp...
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Parking benenson olomouc

  1. 1. Geosimulation for Urban Parking Policy-Making Itzhak Benenson bennya@post.tau.ac.il 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 InDOG Olomouc 14-17 October 2013
  2. 2. Geosimulation Spatially Explicit Agent-Based Dynamic Modeling of Geographic Phenomena High-resolution data + Understanding of Agents Behavior + Simulation + Complex System Theory InDOG Olomouc 14-17 October 2013
  3. 3. Geosimulation and Spatial Analysis Lab InDOG Olomouc 14-17 October 2013
  4. 4. Geosimulation of Parking InDOG Olomouc 14-17 October 2013
  5. 5. What are the components of parking knowledge? Parking demand and supply Parking management and policy assessment Drivers’ parking behavior WE STUDY THE CURRENT STATE WE AIM AT FORECASTING Parking dynamics in space and time Parking spatial pattern InDOG Olomouc 14-17 October 2013
  6. 6. Parking demand and supply InDOG Olomouc 14-17 October 2013 Parking spatial pattern
  7. 7. PARKING DEMAND AND SUPPLY GIS + Aerial photos + Population Census URBAN GIS and AERIAL PHOTOS PARKING DEMAND BY TAZ 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 places Lots: Lots area /8 sq m * number of floors Parking demand and supply Parking spatial pattern InDOG Olomouc 14-17 October 2013
  8. 8. 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 Average occupancy (weekdays) 61.8% Parking demand and supply Parking spatial pattern STD 0.94% InDOG Olomouc 14-17 October 2013 Visitors Average occupancy (weekdays) 17.4% STD 1.77%
  9. 9. 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 Parking demand and supply Parking spatial pattern InDOG Olomouc 14-17 October 2013
  10. 10. 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 Parking demand and supply Parking spatial pattern InDOG Olomouc 14-17 October 2013
  11. 11. Drivers’ parking behavior InDOG Olomouc 14-17 October 2013
  12. 12. DRIVERS’ PREFERENCES DURING THE PARKING SEARCH: GPS data logging, GIS analysis, interviews with drivers Car speed during the trip Disstance from home Car speed versus distance to parking Drivers’ parking behavior Speed (km/h) InDOG Olomouc 14-17 October 2013
  13. 13. DRIVERS’ BEHAVIOR ON THE WAY TO DESTINATION GPS data logging, GIS analysis, interviews with drivers Drivers do not take the shortest path… Drivers’ parking behavior InDOG Olomouc 14-17 October 2013
  14. 14. DRIVER’S BEHAVIOR AFTER MISSING THE DESTINATION GPS data logging, GIS analysis, interviews with drivers Drivers’ parking behavior InDOG Olomouc 14-17 October 2013
  15. 15. Analysis of drivers’ parking trajectories provides adequate heuristic algorithms of drivers’ parking behavior Drivers’ parking behavior InDOG Olomouc 14-17 October 2013
  16. 16. Parking dynamics in space and time InDOG Olomouc 14-17 October 2013
  17. 17. PARKING SPATIO-TEMPORAL PATTERNS PARKAGENT: Agent-Based modeling of the parking search Every parking inspector is an agent Residents Commuters Guests Customers Parking dynamics in space and time Every car that searches for parking or parks is an agent InDOG Olomouc 14-17 October 2013
  18. 18. PARKAGENT is a spatially explicit model Parking dynamics in space and time InDOG Olomouc 14-17 October 2013
  19. 19. PARKAGENT is easily adjustable to a new city Tel Aviv city street network Simplified grid network Parking dynamics in space and time Antwerp city street network Ramat Gan, diamond stock exchange InDOG Olomouc 14-17 October 2013
  20. 20. PARKAGENT generates great variety of parking statistics Occupancy rate per street segment/any area Distance to destination No of issued tickets per route/area Cruising time Parking dynamics in space and time InDOG Olomouc 14-17 October 2013
  21. 21. A closer look at the PARKAGENT Parking dynamics in space and time InDOG Olomouc 14-17 October 2013
  22. 22. PARKAGENT validation - fits very well to the field data Parking dynamics in space and time InDOG Olomouc 14-17 October 2013
  23. 23. 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 Parking dynamics in space and time InDOG Olomouc 14-17 October 2013
  24. 24. PARKAGENT adequately describes parking 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. Parking dynamics in space and time InDOG Olomouc 14-17 October 2013
  25. 25. 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? Parking dynamics in space and time InDOG Olomouc 14-17 October 2013
  26. 26. 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 ID Underground Parking Residents demand Workers demand 34 10 20 5 ID Curb Parking Restrictions 4809 Parking dynamics in space and time Road ID 20394 10 Residents only InDOG Olomouc 14-17 October 2013
  27. 27. PARKFIT algorithm: Randomly distribute destinations’ demand over the parking space around 3 7 Parking dynamics in space and time InDOG Olomouc 14-17 October 2013
  28. 28. 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 InDOG Olomouc 14-17 October 2013
  29. 29. 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 InDOG Olomouc 14-17 October 2013
  30. 30. 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 InDOG Olomouc 14-17 October 2013
  31. 31. 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 InDOG Olomouc 14-17 October 2013
  32. 32. 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 InDOG Olomouc 14-17 October 2013
  33. 33. 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 InDOG Olomouc 14-17 October 2013
  34. 34. 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 InDOG Olomouc 14-17 October 2013
  35. 35. PARKFIT output Bat Yam, average distance to parking place D/S = 0.75 Distance to destination, 2012 Survey data PARKFIT D/S  0.75 Parking dynamics in space and time InDOG Olomouc 14-17 October 2013
  36. 36. Parking management and policy assessment Parking management and policy assessment InDOG Olomouc 14-17 October 2013
  37. 37. PARKAGENT: Cost-benefit analysis of new parking facility Different development plans were interpreted as the models scenarios: For each scenario, estimates of occupancy rate, distance to destination, search time Multi-level garage under the main road Search time Scenario, certainty A, low B, low C, high 1 – 10: Planned office buildings Parking management and policy assessment Distance to destination Average distance to the office 150 243 214 Average parking search min min min Parked in the Bialik Garage Example of the model outputs InDOG Olomouc 14-17 October 2013
  38. 38. PARKAGENT: New garage will not justify itself unless signpost system will be introduced The signpost system that directs drivers to the lots that have vacant places decreases search time by 30% Parking management and policy assessment InDOG Olomouc 14-17 October 2013
  39. 39. PARKFIT and PARKIDW: Planning parking in Bat-Yam city (200,000) in 2030 At a level of a city, total demand is the same as a supply: Total parking places in 2030 67,000 Total cars in 2030 65,000 Growth of car Growth of Parking There are no parking places ownership parking supply deficit within a 400 m distance for 5000 cars! TAZ Parking management and policy assessment InDOG Olomouc 14-17 October 2013
  40. 40. PARKAGENT: Antwerp application (Geert Tasseron, Nijmegen)  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 Parking management and policy assessment InDOG Olomouc 14-17 October 2013
  41. 41. 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 InDOG Olomouc 14-17 October 2013
  42. 42. 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 InDOG Olomouc 14-17 October 2013

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