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Pretty Skool


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Evidence of Work

Smart Steps

Project Info

Team Name


Pretty Skool


Team Members


Sharmila Kumar , Pranav Karthik Bharath , Navitha Elanchelian and 5 other members with unpublished profiles.

Project Description


Smart Steps is a data-powered solution to optimise school transport networks so that every child enjoys a faster, safer and smarter journey to school.

By integrating open transport data, school catchment boundaries, census demographics and safety insights, Smart Steps identifies inefficiencies in current routes and proposes smarter alternatives.

Smart Steps enables:
* Safer journeys through heatmaps of accident-prone areas near schools.
* Smarter planning with AI-driven route optimisation.
* Equity in access by highlighting underserved regional and low-income areas.
* Sustainability gains by reducing congestion and emissions around schools.

The result? A transport ecosystem that works for students, parents, schools and governments ensuring that getting to school supports learning, wellbeing and community safety.


#smartsteps transportinnovation aiforgood

Data Story


The story of how ACT school kids travel is hidden in data. By connecting 10 key datasets, SmartSteps transforms raw numbers into insights that help governments, parents, and schools create safer, faster, and more equitable journeys.

  1. How kids travel today Dataset: ACT Daily Public Transport Passenger Journeys by Service Type Shows daily peaks in bus journeys during school start/end times. Insight: School-related trips create sharp demand spikes → buses overcrowded at 8–9 AM, near-empty mid-day. Story: Current scheduling is inefficient, with mismatch between capacity and demand.
  2. Which schools are served (and which are left behind)
    Datasets: ACT School Bus Services + ACT Bus Routes
    Maps all dedicated school services vs. public bus routes.
    Insight: Urban schools often enjoy multiple school-only services, while outer suburbs depend on long, indirect public bus routes.
    Story: This creates a postcode lottery: your address determines how long and safe your school commute is.

  3. How far students travel
    Dataset: Student Distance from Schools
    Measures actual distances between where students live and their schools.
    Insight: Some regional students travel 2–3x farther than urban peers, with commutes over 60 minutes.

Story: Distance creates hidden inequality → rural kids spend less time studying, playing, or resting.

  1. The first/last mile challenge Dataset: Park and Ride Locations Identifies where parents must drive children before they catch buses. Insight: Park & Ride reduces congestion at schools, but creates multi-stage, stressful journeys for families.

Story: Without integrated solutions, many trips remain fragmented (car + bus + walk).

  1. Who is most affected Dataset: Census Data for all ACT Schools Shows socio-economic and demographic diversity of student populations. Insight: Families in low-income areas rely more heavily on school buses and public transport.

Story: Transport equity is an education equity issue, poor access hits disadvantaged students hardest


Evidence of Work

Homepage

Team DataSets

Road Crash Data

Description of Use Helps us find dangerous routes near schools and compare ACT safety with the rest of Australia

Data Set

Road spending

Description of Use Helped us see if areas with lots of school kids are getting enough road investment for safety.

Data Set

National Roads by Geoscape

Description of Use We used it to check road safety and which roads are used for school trips

Data Set

Census Data for all ACT Schools

Description of Use Used to see which communities depend most on public transport and where equity is an issue.

Data Set

Daily Public Transport Passenger Journeys by Service Type

Description of Use We use this dataset to see when buses are crowded (like before and after school) and when they are empty, so routes can be planned better.

Data Set

Challenge Entries

Optimising Transport Networks for School Kids

How can we leverage graph analytics, generative AI and other data approaches to optimise public school transport networks to make it simple to get the next generation of students to school with less hassle?

#Reimagining-school-transport-networks

Eligibility: Open to all, although special consideration will be given to teams with a lead based in ACT. Contestants are strongly encouraged to use multiple sources of data including datasets outside of those listed below.

Go to Challenge | 10 teams have entered this challenge.