Team Name:
Data Revolution
Team Members:
Data Revolution
The transport sector contributed 18.6% of Australia’s greenhouse gas emissions in 2021 and now in the post-pandemic era is once again beginning to trend upwards in terms of raw emissions. [1] As we did with the pandemic, we must flatten the curve to help Australia reach net zero emissions by 2050 and reduce the impacts of climate change. Now we have an opportunity as everyday Australians are feeling the pinch when filling up, to spur further conversion to alternative modes of transport.
Our objective is to reduce the current financial burden of petrol and diesel vehicles on cost of living, whilst simultaneously increasing uptake in alternative modes of transport such as electric vehicles (EVs), buses, bikes and electric scooters.
Cartesian Pin enables users to compare different modes of transport, while relating it to things that truly matter to them such as:
-how many dollars they could save at the pump with an EV
-how many burgers they can eat, highlighting the number of calories burned
-how many trees they can save, highlighting reductions in emissions
Users can also find links to government incentives for further greener alternatives.
Flattening the curve starts with you, together let’s bring the future here now, go out and reduce emissions now.
The transport sector contributed 18.6% of Australia’s greenhouse gas emissions in 2021 and now in the post-pandemic era is once again beginning to trend upwards in terms of raw emissions. [1] As we did with the pandemic, we must flatten the curve to help Australia reach net zero emissions by 2050 and reduce the impacts of climate change.
We looked at how we might solve this through Matthew, who uses a V8 as his daily car but is feeling the pinch when filling up, particularly with Annual Consumer Price Index (CPI) inflation at 6.1% and automotive fuel cost having risen 32.1% over the last 12 months. [2] We thought about how we could leverage circumstance to incentivise Matthew to adopt sustainable energy behaviours that simultaneously relieve the financial burden he is experiencing.
As part of creating and developing the solution, the following datasets were analysed:
We utilised Daily Queensland fuel prices from 123 unique service stations recorded between December 2018 and July 2022. Each month was a separate csv file and we needed to analyse the daily price data, separate it by year, and find the average for each available year.
A python script was developed to iterate over the separate files that together made up the dataset, and to extract the daily fuel prices column from each file. Statistical analysis was then performed on the extracted data to discover the mean fuel prices by year.
It was found that there was a clear rise in petrol prices each year, with the current 2022 average at $2.11
The Census dataset was analysed to evaluate and compare the number of SA citizens who have one main mode of transportation to work to the number of SA citizens with two or more main modes of transportation to work. The census data was aggregated to summarise different modes of transportation.
From the Census dataset we were also able to explore the number of cars per household. This data was aggregated by postcode. We found that the number of cars per household increased with distance from the CBD.
We analysed the Banded Validation Dataset provided by the Department of Infrastructure and Transport. This dataset was analysed to get the information for the use of Public Transport. The data pack contained quarterly trips of metro card validation for all the public transport in Adelaide. We filtered out information not related to buses, trains and trams. Datasets were analysed for the financial year 2021-2022 (July-21 to June 2022).
Each dataset contained more than 1 million observations per quarter and was analysed using R. We found that between July 2021 and June 2022, there were more than 1.5 million metrocard swipes on public transport. It was also found that there were, on average, 7000 distinct people using metrocards using public transport in each quarter.
To gain an insight into the cost involved in driving electric vehicles, government EV trial data was utilised. We aggregated and applied calculations to find an average battery percentage consumption per kilometre and average energy consumption in kWh per kilometre. These averages were then used to estimate an average cost per kilometre when driving an electric vehicle. Our calculations showed that, on average, it costs $0.11 per kilometre to run an EV.
The solution we came up with is this incredible idea where we took data from the Australian Bureau of Statistics, the Clean Energy Regulator, Queensland Government and Department of Industry, Science, Energy & Resources to create Cartesian Pin, a fantastic comparison platform showcasing alternative modes of transport to people like Matthew.
We used data to tell a story about how our methods of transit affect our lives as an individual. Large population level statistics lead people to feel out of touch with their actions and the impact. Cartesian Pin enables users to compare different modes of transport, while relating it to things that truly matter to them… Matthew can see how much an EV would save him at the pump, he can understand how many calories he would burn, in something he can relate to… burgers. Matthew doesn’t usually think about his emissions, but seeing it in the context of trees has made him consider his impact. He can also find links to government incentives for further greener alternatives.
By incentivising cleaner modes of transport, we can drive sustainable energy behaviours that improve the quality of life and fight climate change. Cities will have less noise pollution… we won't be hearing Matthew’s noisey V8, better air quality… no more obnoxious engine fumes. We can reduce traffic… no more of those annoying peak hour traffic jams.
Flattening the curve starts with you, together let’s bring the future here now, go out and reduce emissions now.
Description of Use This dataset was analysed to get the number of frequent travelers taking public transport as their regular mode of transport. (Buses, trains & trams)
Description of Use Number of Motor Vehicles by Dwellings: To evaluate the distribution of motor vehicles per dwelling. Total Personal income (Weekly) by age by sex: To evaluate the distribution of weekly income based on sex and age.
Description of Use Inflation and average fuel price data was evaluated
Description of Use Used to evaluate the impact of the transport sector in Australia’s production of greenhouse gas emissions
Description of Use Used data to calculate average fuel price per year
Description of Use Used trial data (battery charge difference before and after trips, efficiency kWh) to get an average battery and kWh consumption per Kilometre
Eligibility: Teams are encouraged to use datasets from a variety of sources, but at least one must be drawn from the ABS Census 2021.
Go to Challenge | 24 teams have entered this challenge.
Eligibility: This question is open to all participants, excluding those currently working at CER, AER, DISR or ACCC.
Go to Challenge | 18 teams have entered this challenge.
Go to Challenge | 10 teams have entered this challenge.
