TRIP Internship Presentation 2014
Real-Time Parking Information
on Parking-Related Travel Cost
Kory Harb
July 24, 2014
Advisor: Dr. Yafeng Yin
Coordinator: Zhibin Chen
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Introduction
 In recent years, real-time parking information has
become more and more available to drivers.
 Often this information is accessed through the use
of smartphone applications.
Parking App
SpotHero
ParkWhiz
ParkNow
ParkingPanda
ParkMe
SFpark
Best Parking
Parker
Parkmobile
ParkMate
Parking Finder
ParkBud
Spot Agent
AA Parking
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Parking
Reservation
Approach
 Modeling the behavior of individual drivers in a complex
system is difficult to do mathematically.
 To account for the complexity of the problem, agentbased simulation models were created using the software
NETLOGO to study various dynamic parking scenarios
and the information’s effect.
 3 Cases were studied:
– Simple Case: One lane, one way street with curbside parking.
– Simple Case 2: Two lane, two way street with curbside parking
– Complex Case: Block grid network composed of two-way streets
with parking garages.
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Simple Case
 Characteristics:
– One-way street
– One lane
– Curbside parking
 Driver Types to Compare:
– Uninformed
– Informed without reservation capabilities
– Informed with reservation capabilities
 Objective: Minimize walking distance
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Model Parameters
 Arrival and Departure Rate: The arrival and
departure rates were modeled to be exponentially
distributed.
– Average time between arrivals: 100 units of time
– Average duration of parking: 900 units of time
 The street was initially empty, and the length of the
street was 30 parking spaces wide.
 Speed: A vehicle moves one length of parking space
for every passing unit of time.
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Search Strategy- Uninformed
 Driver selects a certain number of spots away from
the destination to begin searching for parking
 Once the driver is searching for parking, the first
available spot will be selected for parking.
Optimal
Space
Space to be
chosen if
the parked
cars
remained
parked.
D
7
Search Strategy- Informed without
Reservations Ability
 At every passing unit of time, the driver observes
which space minimizes walking distance, and
travels towards that spot.
 The driver does not have the ability to turn around
If the space
at any point in the searching process
If the optimal
space becomes
available, the
driver will not
turn around.
D
8
becomes
occupied
while the
vehicle is
traveling, it
will re-search
for the bestavailable spot.
Search Strategy - Informed with
Reservation Capability
 Once the vehicle is generated, it searches for the
“best available” parking space and reserves it.
 Once the space is reserved, no other vehicle may
park there, and the vehicle travels to that reserved
space despite what better spaces may become
available during travel.
Magenta color
D
9
indicates the
space has been
reserved.
Optimal Search Point- Simple Case

To compare the uninformed search strategy with the real-time
information parking, the best uninformed search starting point had
to be determined to get a “best case” uninformed search.
 Using the parameters discussed, the simple model was run with
varying search start points, and the results are summarized below.
Distance of 5 is the
optimal starting point,
as it minimizes
walking distance (avg
of 3.24 units).
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Optimal Point Explanation

To determine why the optimal search starting point is 5, the parking
parameters must be studied.
– In the model, a vehicle, on average, arrived every 100 units of
time (ticks).
– On average, a vehicle parked for 900 ticks.
– As a result, after 900 ticks, on average, one car would arrive and
park as another was leaving their parking space. This makes for
an equilibrium number of occupied spaces to be about 9.
9 Space Occupancy
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Verification of Explanation
 Observe the optimal point when the parameters are
changed from a 900 tick average parking duration to a
1400 tick parking duration.
– Per the explanation, this would result in a 14 space average
occupancy zone, making 7 the start point that centers that zone
around the destination.
As displayed by the
optimal point analysis, the
optimal starting point is 7
as it minimizes the
average walking distance.
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Results – Simple Case
 As displayed below, the parking type with the lowest
average walking distance is the informed without
reservation capabilities type.
2.91%
increase
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This can be
attributed to the
driver’s ability
to change
destinations if a
better spot
becomes
available, and
the lack of
competition in
the scenario.
Simple Case 2

Characteristics:
– Two-way street
– Two lanes
– Curbside parking for both lanes
 Driver Types to Compare:
– Uninformed
– Informed without reservation capabilities
– Informed with reservation capabilities
 Assumptions
– Demand and driver behavior is symmetric in both directions
 Objective: Minimize walking distance
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Optimal Search Point - Simple Case 2
 Keeping model parameters constant for the second
case, the optimal starting point for the uninformed
search must be determined.
Note the left side of
the curve is much
less steep than that
of the simple case,
which can be
attributed to the
symmetric nature of
the drivers from
opposing directions.
Optimal Point
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Results – Simple Case 2
 Average walking distances resulting from the
different search strategies are displayed below.
1.74%
increase
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As explained
previously, the
informed vehicle’s
advantage of being
able to change its
destination parking
space most likely
accounts for its lower
average walking time.
Complex Case

Characteristics:
– Network Scenario – Uniform grid of streets with parking garages
placed randomly in the grid.
– 2-way streets only
 Objective: Minimize travel cost
– 𝒄𝒐𝒔𝒕 = 𝜶𝒙 + 𝜷𝒚
• Here x represents the walking distance while y represents the cruise
distance
– It is assumed that α > β as people are more willing to drive a
distance than walk it.
 Assumptions:
– α =4 β=1
– Arrival every 100 ticks
– Departure every 9000 ticks
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New Search Strategy - Uninformed
Utility

Characteristics:
– Driver will approach the destination until a specified distance away from
Symbol
the destination is reached.
– Upon reaching this distance, the driver will search for parking much like
the search was conducted in the simple cases.
– At an intersection, a direction is chosen based on a utility function u that
is dependent on memory, and distance for every intersection i:
Value
– 𝑼𝒊 = 𝝁𝑴 + 𝝋𝑫
μ
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Φ
-2
• M: Memory factor. M = 4 if the intersection was not one of the last 3 visited
• D: Distance. This is the distance from the future intersection to the destination.
– Using the logic model, the probability of choosing a specific intersection
to travel towards was calculated as 𝑷𝒊 =

𝒆𝑼𝒊
𝑼𝒊
𝒊𝒆
This strategy was compared with both reservation-capable and
informed driver types as in the simple cases.
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Complex Case Visual
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Complex Case: Garages Near
Destination
 Grid scenario with a garage in the same block as
the destination, with vehicles only competing with
drivers of the same type.
26.66%
increase
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The informed
driver type
produced the
lowest travel
cost, using the
same advantage
described
previously.
Conclusions
 Uninformed Search Strategies:
– Optimal Point for search strategies is dependent
on the arrival and departure rates for the
parking area.
 Search Strategy Selection
– The informed driver without reservation
abilities recorded the lowest travel costs in all
situations.
– This result can be attributed to the assumptions
and parameters that influence driver behavior
and vehicular competition.
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Future Opportunities
 Competition-driven Models
– A more complex model involving competition among
driver types and random starting positions would most
likely result in the appearance of a reservation-capable
vehicle advantage.
– Vehicle decision-making based on the probability of finding
a parking space in a garage with a high occupancy %
 Traffic without Parking Intention
 Pedestrian traffic of recently parked vehicles
 The implications of such projects can play a large role in the
routing of vehicles in GPS applications to determine the
optimal path with respect to not only time, and distance, but
also parking and parking cost.
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