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Team Name:

GridReaper

Team Members:

Evidence of Work

GridReaper

Project Info

Team Name

GridReaper

Team Members

5 members with unpublished profiles.

Project Description

GridReaper is a congestion forecaster for commuters. It provides commuters an accurate forecast of congestion in various transportation methods in the following days, weeks, and months.

Data Story

Starting from available usage data on transportation methods and routes, we applied regression techniques to determine functional relationships with other datasets we considered unusual, like weather time-series.

The resulting regression models are then made available as services for GridReaper to use.

Team DataSets

Long-term temperature record

Description of Use We used this dataset to help us map out correlations between congestion and maximum temperature per given area such as New South Wales and Queensland.

Data Set

Public transport opal all trip all modes

Description of Use We used this dataset to help us map out correlations between the max temperature in a given area to all the trips that have been made on a monthly basis.

Data Set

Data and Networks

Description of Use We used this dataset to help us map out correlations between congestion and maximum temperature per given area such as New South Wales and Queensland.

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.

Eligibility: Use any open dataset to support your entry.

Go to Challenge | 45 teams have entered this challenge.

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.

What are the key levers that can be affected to ease congestion in NSW?

Using open data and other data sources, what can you infer that can be changed by Transport for NSW to help ease congestion? This can be congestion from people, cars, train passengers, on a platform or queuing for a bus or just generally on a road. What has happened in the past? What information can we provide customers, bus drivers or employers to assist in easing congestion? Note: this is not just road congestion. It can be viewed holistically or at a microlevel – for say an intersection.

Eligibility: Use of open data or other data. Please reference what datasets you have used (or would have to use) for your solution.

Go to Challenge | 7 teams have entered this challenge.

🌟 What's the coolest way to travel across the city?

Using datasets which map urban heat and green cover across Greater Sydney, we challenge you to develop a tool which visualises green routes through the city. Help people avoid urban heat and move across the city in comfort by mapping out green streets and pathways which connect shopping centres, public transport stops and public spaces.

Eligibility: Must use at least the Urban Heat & Green Cover dataset(s) from the SEED portal.

Go to Challenge | 18 teams have entered this challenge.

🌟 Climate Change Issues in Hobart

What local climate change issues can you help solve or identify by integrating data sources?

Eligibility: Use one or more datasets from CoH Open data portal and ensure the submission relates to climate change issues in Hobart

Go to Challenge | 12 teams have entered this challenge.

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