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Team Name:

Top Gun


Team Members:


Evidence of Work

CarPark

Project Info

Top Gun  thumbnail

Team Name


Top Gun


Team Members


5 members with unpublished profiles.

Project Description


CarPark is an app developed using open data sets supplied by the City of Melbourne to solved the CBD congestion problem by navigating drivers to unoccupied parks to reduce the number of cars on the road while also reducing emissions.


Data Story


Files containing status and location of car parks was manipulated to filter for unoccupied parks and overlay onto a mapping interface. Using the number of occupied parks, peak traffic times were identified for the suggestion of alternative transport methods.


Evidence of Work

Video

Homepage

Team DataSets

Off-street car parking 2017 map

Description of Use We used this dataset to determoine the locations of off-street car park locations.

Data Set

On-street Car Parking Sensor Data - 2017

Description of Use We used this dataset to cross check and determine on-street parking locations.

Data Set

Parking bay arrivals and departures 2014

Description of Use We used this data to determine times that these parks were considered busy so that our app could recommend public transport to users that were heading to those locations at peak times.

Data Set

On-street Car Park Bay Restrictions

Description of Use We hoped to use this dataset to determine the parking bay restrictions of the available parks.

Data Set

On-street Parking Bay Sensors

Description of Use We used this dataset to determine the latitude and longitude coordinates of on-street parking bays and determine whether it is "available" or "occupied".

Data Set

Off-street car parks with capacity and type

Description of Use We used this dataset to determine the location of disabled parking.

Data Set

On-street Parking Bays

Description of Use We used this dataset to determine locations of on-street parks.

Data Set

Challenge Entries

Reducing CBD Traffic Congestion

How to reduce traffic congestion or parking problems in CBD?

Eligibility: Use any open dataset to support your entry.

Go to Challenge | 39 teams have entered this challenge.

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.

Eligibility: Use any open dataset to support your entry.

Go to Challenge | 45 teams have entered this challenge.

🌟 Pedestrian and Air Quality Sensor Data

How might we improve users’ experience of their city by using data from pedestrian and vehicle counters and/or air quality sensors?

Eligibility: Use any open dataset to support your entry.

Go to Challenge | 15 teams have entered this challenge.