1 Real-Time Parking Information on Parking-Related Travel Cost TRIP Internship Presentation 2014 Kory Harb July 24, 2014 Advisor: Dr. Yafeng Yin Coordinator: Zhibin Chen

2 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 SpotHeroParkWhiz ParkNowParkingPanda ParkMeSFpark Best ParkingParker ParkmobileParkMate Parking FinderParkBud Spot AgentAA Parking Parking Reservation

3 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.

4 Software Introduction - NetLogo  Programmable Modeling Environment – Developed by Dr. Uri Wilensky at Northwestern University – Used by over 10,000 researchers, students, and teachers worldwide. – Agent-based modeling software Definition: “a class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole” 1computational modelssimulating – Comes with multiple built-in agents that developers can use together to create scenarios relevant in social media, biology, and transportation.

5 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

6 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.

7 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.

8 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

9 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

10 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).

11 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

12 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.

13 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

14 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

15 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.

16 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

17 Complex Case

18 New Search Strategy - Uninformed Utility SymbolValue μ20 Φ-2

19 Setup of complex case

20 Complex Case Visual

21 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. – When the driver reaches an intersection, the driver selects an intersection to travel towards randomly. The probability that a specific intersection is selected is dependent upon a utility function created that takes into account the driver’s previous 3 intersections visited, as well as the next intersection’s distance to the destination. Probability of Being Selected: α(M) + β (D) – Here M represents a memory value, which is at its maximum if the driver did not visit that intersection in his last 3 decision points. D represents the distance from the intersection in question to the destination. – α >0 represents the weight a driver gives to memory, while β < 0 and represents the weight given to distance from the destination – In this model, β had a diminishing value as time went on, under the assumption that drivers become more willing to park farther from the destination as their search time lengthens.  This strategy was compared with both reservation-capable and informed driver types as in the simple cases.

22 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 % increase

23 Informed Vehicle Domination Explanation  The better performance of the informed vehicle over the reservation capable is not intuitive.  Although the informed vehicle has the advantage of switching parking spaces at any time, in a real-world scenario, these spaces could become occupied while the vehicle is traveling towards it.  If this trend repeats, this could elevate the cruising time and travel cost of the informed vehicle as it could potentially travel “in circles” as it backtracks between the newly available and unavailable optimal spaces. – This phenomena was not observed in the simplistic grid models, as there was no competition as all vehicles started from the same starting point, and generated after 100 ticks. – 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. – Although a model of this nature was developed, the running time of the simulation did not allow for analyzable data to be produced in time for this presentation.

24 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.

25 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.