To start, we formed a dedicated research team comprised of high school students interested in data science, urban planning, and community service. They decided to focus their efforts on the areas immediately surrounding UT Austin, specifically targeting the peak hours of 8 AM to 6 PM on weekdays.

We began by designing a survey to gather qualitative data on parking experiences from UT students, faculty, and staff. This survey included questions about the frequency of parking issues, specific problem areas, times when parking was most challenging, and any suggestions for improvement.

We distributed this survey through university mailing lists, social media, and by partnering with campus organizations.

At the same time, we conducted a series of parking lot observations. Over the course of three weeks, teams of students monitored various parking lots and streets around the campus, recording data on the number of available spots, duration of parking sessions, and peak usage times. We used smartphones with GPS capabilities to geotag specific parking locations.

For a more technological approach, we collaborated with the computer science department at our respective high schools to set up an automated data collection system using Raspberry Pi devices equipped with cameras and sensors. These devices were placed strategically around the busiest parking areas to continuously monitor parking space occupancy. The cameras captured real-time images, which were then processed using computer vision algorithms to detect empty and occupied parking spots.

After collecting the data, the we used Python libraries such as Pandas and Scikit-Learn to clean and preprocess the data, identifying patterns and anomalies. We applied clustering algorithms to group similar parking usage patterns and regression models to predict future parking demand based on historical data.

Next, we created simulations and models with MATLAB. These simulations visualized different parking scenarios, such as increased demand during university events or the impact of new parking regulations, which helped us understand how features such as weather or time of day, influenced parking availability and demand.

Throughout this process, we identified key problem areas where parking pressures were most acute and suggested preliminary solutions based on our data analysis. We found that:

2. The highest demand for parking occurred between 9 AM and 3 PM on weekdays, especially in the San Jacinto Garage and the Speedway Garage — both near major lecture halls and libraries.
   

3. The streets surrounding the Engineering and Natural Sciences buildings, including 24th Street and Dean Keeton Street, were identified as areas with severe parking shortages and high turnover rates.
   

4. The East Campus Garage and certain peripheral lots showed lower use rates during regular weekdays, despite having ample capacity, highlighting a mismatch between parking availability and user preferences.
   

5. Over 60% of parking spots were occupied for durations exceeding four hours, reducing turnover and limiting availability for short-term parkers.
   

6. University events, especially football games and large conferences, significantly increased parking demand, leading to a spillover effect into nearby residential areas, such as the neighborhoods north of Dean Keeton Street.
   

We held several brainstorming sessions to divergently ideate solutions, during which we used a decision matrix to prioritize solutions. We ranked each idea based on feasibility, viability, desirability, and potential impact. We eventually reached consensus on a 3-point plan:

2. Optimizing existing parking lots: Redistributing parking permits to balance use between high-demand and underused areas. We developed a detailed plan to encourage use of peripheral parking lots like the East Campus Garage by offering discounted rates and shuttle services to central campus locations. We also suggested implementing a reservation system for popular lots during peak hours.
   

3. Promoting alternative transport: To reduce reliance on cars, we recommended enhancing bike-sharing programs and publicizing existing transit options. We drafted a campaign to increase awareness of the benefits of biking and public transit, including environmental and financial savings.
   

4. Improving wayfinding: We proposed developing an app that provides real-time information on parking availability, including available spaces in each lot, predicted peak times, and suggested alternative lots.
   

We proposed a comprehensive financial plan aimed at encouraging the use of peripheral parking lots. This plan included discounted parking rates for students and faculty who opt to park in designated peripheral lots rather than in central, high-demand areas affected by the Orange Line extension. To improve accessibility, we also recommended implementing frequent shuttle services connecting these peripheral lots to key locations on campus. The shuttle services would run at regular intervals to ensure convenience and reliability, thereby incentivizing students to choose peripheral parking options over congested central areas.

Recognizing the need for efficient parking management, we suggested implementing a reservation system for parking spots in high-demand areas around UT Austin. This system would allow students and staff to reserve parking spots in advance, ensuring that parking spaces are usedmore effectively and reducing instances of overcrowding.

To formalize these recommendations, we're preparing a detailed policy revision proposal outlining the financial incentives, shuttle service schedules, and reservation system specifications to reduce traffic congestion, improve parking accessibility, and increase campus mobility. By presenting this proposal to UT Austin higher-ups, we aim to garner support for policy changes that address parking challenges posed by the Orange Line.

To promote transport multimodality, we initiated discussions with leading micromobility shaaring company Lime — who we'd worked with for a similar project with UCLA — to introduce their e-bikes and scooters to Austin. We negotiated establishing 10 Lime scooter and bike stations placed across Austin and its residential areas, each equipped with secure docking and charging facilities to ensure availability throughout the day. The partnership included a commitment from Lime to maintain a fleet of 200 vehicles, split evenly between electric scooters and bikes, to meet student preferences.

Working closely with UT Parking and Transportation services, we successfully advocated for an increase in bike parking capacity by 45%. This expansion involved installing 40 additional bike racks at high-demand locations, including academic buildings and student housing complexes. To enhance security, UT Austin students established a dedicated bicycle patrol unit consisting of 4 security officers who conducted regular patrols to deter theft and ensure safety.

To improve wayfinding, we collaborated with high school computer science students, clubs, and classes across Texas to design, test, and launch a revised app in collaboration with UT Austin's shuttle provider CapMetro, this time with additional features:

2. Real-time shuttle tracking: GPS-enabled tracking for university shuttles, allowing students to monitor shuttle locations in real-time and plan their commute.
   

3. Parking reservation system: Reserving parking spots in peripheral lots at discounted rates during peak hours.
   

4. Wayfinding: Incorporated interactive campus maps with detailed navigation features, guiding users to bike parking areas, shuttle stops, and alternative transport nodes.
   

Prior to launch, we're currently conducting a testing phase involving 20 beta testers from the UT Austin student body. These testers are providing valuable feedback on usability, functionality, and user experience. We plan to he include promotional incentives like free Lime ride credits for downloads upon launch.