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Real-Time Parking Information on Parking-Related Travel Cost

 

TRIP Internship Presentation 2014

 Kory HarbJuly 24, 2014

Advisor: Dr. Yafeng YinCoordinator: 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

Parking Reservation

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Approach

· Modeling the behavior of individual drivers in a complex system is difficult to do mathematically.

· To account for the complexity of the problem, agent-based 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.

D

Optimal Space

Space to be chosen if the parked cars remained parked.

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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 at any point in the searching process

If the optimal space becomes available, the driver will not turn around.

If the space becomes occupied while the vehicle is traveling, it will re-search for the best-available spot.

D

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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

indicates the space has been reserved.

D

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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.

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.

2.91% increase

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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.

Optimal Point

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.

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Results – Simple Case 2

· Average walking distances resulting from the different search strategies are displayed below.

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.

1.74% increase

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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–

• 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 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:

• 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

· This strategy was compared with both reservation-capable and informed driver types as in the simple cases.

Symbol Value

μ 20

Φ -2

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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.

The informed driver type produced the lowest travel cost, using the same advantage described previously.

26.66% increase

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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.