Post on 19-Aug-2018
Parking Policy-Making Academic View of Practical Needs
Itzhak Benenson1 bennya@post.tau.ac.il
Nadav Levy1 and Karel Martens2
1 Department of Geography and Human Environment, Tel Aviv University, Israel 2 Institute for Management Research, Radboud University Nijmegen, the Netherlands
http://geosimlab.tau.ac.il/
1 POLIS, HELSINKI, SEPTEMBER 20, 2012
Establishing parking policy…
Parking spatial pattern
Drivers’ parking
behavior
Parking demand
and supply
Parking dynamics in space and time
Parking management
and policy assessment
3 POLIS, HELSINKI, SEPTEMBER 20, 2012
WE STUDY THE CURRENT STATE WE AIM AT FORECASTING
4 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking demand
and supply
Parking spatial pattern
Parking demand
and supply
Parking spatial pattern
PARKING DEMAND BY TAZ URBAN GIS and AERIAL PHOTOS
BASIC ESTIMATES OF 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 places Lots: Lots area /8 sq m * number of floors
5 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking demand
and supply
Parking spatial pattern
6 POLIS, HELSINKI, SEPTEMBER 20, 2012
TURNOVER and DRIVERS’ TYPES: Field surveys
For a certain day of the week and hour, parameters of the parking system are stable
Parking demand
and supply
Parking spatial pattern
Residents Visitors
Average occupancy
(weekdays) STD
Average occupancy
(weekdays) STD
61.8% 0.94% 17.4% 1.77%
Parking demand
and supply 7 POLIS, HELSINKI, SEPTEMBER 20, 2012
DESTINATION-PARKING PLACE DISTANCE: Field surveys + Population Census
Given the parking fees, driver’s satisfaction is defined by • Duration of the parking search • Distance between the parking place and destination
Parking spatial pattern
The distance between the parking place and the destination
Estimated based on the data of owners’ addresses
Obtained with the PARKAGENT model
8 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking demand
and supply
Parking spatial pattern
Parking demand
and supply
Parking spatial pattern
Standard GIS and census data together with the proper survey methodologies guarantee reliable estimates of parking demand, supply, and spatial patterns
For typical Western city,
field surveys demand 20-40 person-weeks
and are performed in 2-4 weeks
10 POLIS, HELSINKI, SEPTEMBER 20, 2012
Drivers’ parking
behavior
DRIVERS’ PREFERENCES DURING PARKING SEARCH: GPS data loggers and interviews with drivers
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30000
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stan
ce f
rom
ho
me
Speed (km/h)
Car speed versus distance to parking
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Car speed during the trip
11 POLIS, HELSINKI, SEPTEMBER 20, 2012
Drivers’ parking
behavior
DRIVERS’ BEHAVIOR ON THE WAY TO DESTINATION: GPS data logging + GIS + Heuristics
Drivers do not take the shortest path…
12 POLIS, HELSINKI, SEPTEMBER 20, 2012
Drivers’ parking
behavior
DRIVER’S BEHAVIOR AFTER MISSING THE DESTINATION: GPS data logging + GIS + Heuristics
13 POLIS, HELSINKI, SEPTEMBER 20, 2012
Drivers’ parking
behavior
Drivers’ parking
behavior
Analysis of drivers’ parking trajectories, as registered by the GPS,
provides adequate heuristic algorithms of drivers’ parking behavior
Parking behavior does not depend on gender and age. The only meaningful feature is driver’s experience of
parking in the area, and its effects are currently studied
by Geert Tasseron (Nijmegen)
Parking dynamics in space and time
14 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
Workers
demand
Resident
s demand
Underground
Parking
ID
5 20 10 34
Restrictions Curb
Parking
Road ID ID
Residents only 10 20394 4809
PARKING SPATIAL PATTERNS: PARKFIT algorithm
15 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
High-resolution data on parking demand and supply enable static estimate of the parking pattern for constant (usually maximal) demand
16 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
PARKING SPATIAL PATTERNS: PARKFIT algorithm
PARKFIT: fixed distance to destination
17 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
PARKFIT: varying distance to destination
18 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
Distance to destination
Bat Yam, distance to parking place , 2012
19 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
PARKFIT outputs
Distance to parking, by buildings
20 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
PARKING SPATIO-TEMPORAL PATTERNS Multi-Agent Simulation Model
PARKAGENT
Every parking car is an agent Every parking inspector is an agent
Residents
Commuters
Guests
Customers
21 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
PARKAGENT is spatially explicit
Tel Aviv city street network
Simplified grid network
Antwerp city street network
Ramat Gan, diamond stock exchange
22 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
PARKAGENT is easily adjustable to a new city
Cruising time
No of issued tickets
Occupancy rate per street
Distance to Destination
23 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
PARKAGENT generates great variety of parking statistics
24 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
A closer look at the PARKAGENT
25 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
PARKAGENT REVEALS UNIVERSAL DEPENDENCIES Driver’s cruising time as a function of occupation rate
POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
26
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
27 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking dynamics in space and time
Simulation models adequately describe parking dynamics in space and in time.
The models exploit data on parking demand, supply, turnover and on the
drivers’ parking behavior, and are easily adjusted to a new city
The models reveal several universal characteristics of the urban parking patterns. Model results fit very well to the survey data
Parking management
and policy assessment
28 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking management
and policy assessment
Different development plans were interpreted as the models scenarios:
1 – 10: Planned office buildings
Search time distance
Parked in Bialik
Garage
Average parking search
Average dist to the
office
Scenario,
certainty
348 8.8 min 150 A, low
514 6.6 min 243 B, low
600 5.5 min 214 C, high
Example of the model outputs
For each scenario, estimates of occupancy rate, distance to destination, search time
29 POLIS, HELSINKI, SEPTEMBER 20, 2012
PARKAGENT: Cost-benefit analysis of new parking facility
Parking management
and policy assessment
Multi-level garage
under the main road
The signpost system that directs drivers to the lots that have vacant places decreases search time by 30%
30 POLIS, HELSINKI, SEPTEMBER 20, 2012
PARKAGENT: New garage will not justify itself unless signpost system will be introduced
Parking management
and policy assessment
Base Route TYPE 1263 1268 Parking events
190 204 Parking violations
0 278 Number of empty parking's checked
0 2373 Number of valid driver's checked
0 31 Number of drivers that have a ticket
180 313 Number of tickets issued
180 5281 Total number of parking checked
1 0.046 Ticket / checks ratio
Route 1
Route 2
Base 2
Base 1
Base Route TYPE 1222 1282 Parking events
362 365 Parking violations
1 274 Number of empty parking's checked
0 1971 Number of valid driver's checked
0 35 Number of drivers that have a ticket
337 232 Number of tickets issued
338 2512 Total number of parking checked
0.99 0.09 Ticket / checks ratio
15% violations 30% violations
31 POLIS, HELSINKI, SEPTEMBER 20, 2012
PARKAGENT: Estimating the necessary level of inspection for a new censor technology
Parking management
and policy assessment
32 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking management
and policy assessment
At a level of a city, total demand is the same as a supply:
PARKFIT: Planning parking in Bat-Yam (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~
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
Antwerp application (Geert Tasseron, Nijmegen)
33 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking management
and policy assessment
Parking management
and policy assessment
34 POLIS, HELSINKI, SEPTEMBER 20, 2012
Parking management
and policy assessment
Our data and models are sufficient for the knowledge-based parking policy making at all levels,
from assessment of local parking solutions to establishment parking policy for the
neighborhood, region, or entire city
THANK YOU!