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Team Name:

Way2Learn


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Evidence of Work

Safe2School

Project Info

Team Name


Way2Learn


Team Members


Priyab

Project Description


Safe2School ACT

Safe2School ACT is a data-driven prototype to make student transport safer, faster, and more flexible.

It integrates open datasets such as ACT schools census, student distances, bus routes, Park and Ride locations, and population projections with geospatial analytics and visualization.

Safety Rings let you visualise safe walking zones around schools.

Park and Stride estimates how many cars can be diverted to nearby Park and Ride sites.

Safest Path traces student journeys from home to school while measuring exposure.

Future View shows how population growth could shape transport planning for safer, sustainable choices.


Data Story


Safe2School ACT – Data Story

Every school day, thousands of Canberra families face the same challenge: how to get children to school safely, on time, and without adding to congestion. Parents often rely on cars, leading to crowded drop-off zones, stressful commutes, and higher risks for kids walking or cycling.

Our project brings the data together to change this story. By combining school census data, student travel distances, bus routes, Park and Ride sites, and future population projections, we reveal hidden patterns and possibilities.

With safety rings, Park & Stride simulations, and safest path mapping, planners and parents can re-imagine student journeys: safer, quicker, and better for the community.


Evidence of Work

Team DataSets

Census data for schools

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 | 13 teams have entered this challenge.