ParkFinder
A data-driven parking recommendation system that helps users to quickly find the optimal parking in areas with abundant parking and high crowd density.
A data-driven parking recommendation system that helps users to quickly find the optimal parking in areas with abundant parking and high crowd density.
Targeted primarily at a diverse demographic of drivers, each with unique parking needs, our solution offers tailored recommendations.
We utilise real-time, open-source data from URA parking and Data.gov APIs to power our model. This data-driven approach allows personalised suggestions, prioritising factors like cost based on user preferences. For example, cost-conscious users will receive predominantly affordable parking recommendations.
Team members: Faith Lim, Kelvin Thian, Ooi Jia Sheng, Jordan Lee