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.
Team Name:
Top Gun
Team Members:
CarPark
Project Info
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5 members with unpublished profiles.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.
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.
On-street Car Parking Sensor Data - 2017
Description of Use We used this dataset to cross check and determine on-street parking locations.
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.
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.
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".
Off-street car parks with capacity and type
Description of Use We used this dataset to determine the location of disabled parking.
On-street Parking Bays
Description of Use We used this dataset to determine locations of on-street parks.
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.
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.