Smart Parking Management System using Deep Learning

Project Info

After Realism  thumbnail

Project Description

Have you ever found yourself driving around looking for a parking spot? And by the time you find one, it’s far away from where you need to be, and you’re already running late? This is a problem many of us face every day, and it costs individuals, businesses and the economy millions of dollars. According to academic literature, the average commuter spends at least 30 minutes per day trying to find a parking spot. Many have tried and failed to solve this problem before, however they are often restricted by high infrastructure costs. That is where our solution differs.

Data Story

- Using AI/ML and IoT (surveillance/traffic cameras) we will build an application which shows customers which parking spaces are free, and allows them to book and pay through the application for a specific parking spot. This will save the average ANU student 45 minutes per day in time, stress (very stressful experience trying to find a park) and money (reduced number of parking fines).

- Our trained neural net has a prediction accuracy of 95% on average across all weather conditions (rain, wind, overcast etc.) Based on a dataset of 695,000 images.

Evidence of Work



Team DataSets


Description of Use Help us identify the problem and short down solution

Data Set

Challenge Entries

Public Transport for the Future

How might we combine data with modern technologies - such as AI/ML, IoT, Analytics or Natural Language interfaces - to better our public transport services. Outcomes could take the form of new commuter experiences, reduced environmental impact, or helping plan for the future.

Go to Challenge | 45 teams have entered this challenge.

Reducing CBD Traffic Congestion

How to reduce traffic congestion or parking problems in CBD?

Go to Challenge | 39 teams have entered this challenge.

🌟 Canberra 2029 – Inclusive; Progressive; Connected

How do we use data from the past to predict a better future for Canberra? How do we best support the diversity of our community? Optimise the way we travel and transport goods throughout our city? Predict the jobs of the future – and the skills needed for them? Connect our citizens with their environment?

Go to Challenge | 21 teams have entered this challenge.