Team Name:
Unlucky Magician
Team Members:
Unlucky Magician
Car park unknown. aims to use parking sensor data as a springboard to determine in real-time where parking violations are occurring in the Melbourne CBD. It also assists the public by helping drivers to locate a free car park based on the historical data from the last 4 years. We have designed a web application where users can select the day, time of day, sensor and whether they want input or output for the park.
If we continued on this project we would want to overlay other maps showing pedestrian and sidewalk infrastructure to show improvements resulting from previous projects and where there is currently still congestion. It also aims to reimagine the interaction between vehicles, bicycles, sidewalks and pedestrian traffic to utilise the roads we have to move people around the city both quicker and safer.
We have used the datasets provided by the City of Melbourne that provides the location and vacancy of the car park bay in real time through the use of in-ground sensor systems. It also shows where parking violations are occurring in real time. Finally the historical data provides insight into how the parking bays are being used by the user through time and location. The Melbourne Council are able to use this data to determine where there is congestion, where is not enough car parks and where parking times are inadequate. Through the use of additional overlays on our map, the data would be able to show where there is mobility support for those needed and if these car parks are being used adequately or if improvements need to be made.
Description of Use Used in conjunction with parking data to determine which streets have less cars entering/leaving parking spots, making the street safer for cyclists
Description of Use Historical on-street car parking sensor data to predict trends
Description of Use On-Street Car Park Bay locations
Description of Use Real-time on-street car park status.
Description of Use Historical on-street car parking sensor data to predict trends
Description of Use Historical on-street car parking sensor data to predict trends
Description of Use Historical on-street car parking sensor data to predict trends
Eligibility: The winning entry will: * Be an interactive/gamified solution * Use time based data to show change across the city * Use at least one City of Melbourne Open Dataset
Go to Challenge | 10 teams have entered this challenge.
Eligibility: Use at least two data sets (at least one from data.gov.au) to help government make a decision that will improve services for people. Any code produced for your entry must be published on github under an open license. If your entry is not software, you will need to show the working behind your use of data along with any calculations and analysis you did. You must indicate which specific government agency (at any level of government) can take action based on your entry.
Go to Challenge | 58 teams have entered this challenge.
Eligibility: The winning entry will: * Include city activation information (such as events, busking, pedestrian activity). * Be viewable as a map of activated (and unactivated) areas. * Be fun to use. * Support new businesses and a start-up economy. * Use at least one City of Melbourne Open Dataset.
Go to Challenge | 10 teams have entered this challenge.
Go to Challenge | 61 teams have entered this challenge.
Eligibility: The winning entry will: * Allow users to plan to move effectively through the city (and to avoid congestion). * Use at least one City of Melbourne Open Dataset
Go to Challenge | 6 teams have entered this challenge.
Go to Challenge | 27 teams have entered this challenge.
Go to Challenge | 17 teams have entered this challenge.
Eligibility: The winning entry will: * Visualise a comparison of car parking with other potential uses of on-street car parks. * This will quantify the economic contribution of an on- and off- street space and what it could contribute were it put to alternate uses. * Use at least one City of Melbourne Open Dataset
Go to Challenge | 8 teams have entered this challenge.
