Many people have been frustrated with Detroit's ease of transportation. Our project is about finding a vacant parking lot when people need one. We tackled an issue that many people experience from all over the world. We hope that with this webapp we created, we can improve people's quality of life by spending less time angry behind the wheel, and more time doing the errands they needed.
What it does
Overlays a heatmap near areas with parking lots to help residents of the city be able to locate a vacant parking lot within their area of interest.
How I built it
We used the parking lot data given by Data Driven Detroit as a starting point, then we used Google's Geocoding API to convert the parking lot data given by Data Driven Detroit. We made the assumption that a popular point of interest will decrease the availability of parking spaces within the area, so we created an algorithm that uses Foursquare's API to find locations near the parking lots and determine the probability that a person will be able to find a parking space there.
Our algorithm will account for the time of day, the type of location, the popularity of the business, and the foot traffic of the location to determine the probability of finding a parking space within the parking lot.
Challenges I ran into
API issues with rate limits and UI design choices were the main issues we have ran into. Firstly, a data-intensive operation such as this project does not fare well with APIs relating to searching locations. To alleviate this issue, and increase the speed of our project, we have cached the necessary information received from the APIs to provide accurate data with a near-real-time implementation.
Secondly, creating an easy to use user interface is difficult task, especially when dealing with data-related projects when our audience is usually less computer-savvy. We have researched into what type of UI is best for abstracting away extraneous details, and we have decided on a right-sidebar implementation, animations, and a heatmap to allow people to easily understand our data. For our branding, we decided on a bold font and bright-yellow color to bring a more memorable and authoritative theme.
Accomplishments that I'm proud of
Being able to represent near-real-time data with the large amounts of information needed to make the heatmap more accurate. Creating UI that is simple to understand requires a lot of visual queues, and we think we made a great MVP given the time constraints.
What I learned
Some of our members did not have prior experience with the tech we worked with, like talking to APIs and clientside programming. If we were to pick one, learning to read and use an API is probably the most we have learned, given that we worked on a lot of data from other organizations.
What's next for ParkD
More coverage in the city, and eventually expand to more cities. Tweaking algorithm to better predict the availability of parking spaces by including more mediums of identifying the car traffic of a location. Mobile app to let users find a location on the fly. User accounts for better tracking and provide bookmarks for commonly accessed locations. Warn users to be aware of a surge of people at a specified location.