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Team Name:

Way2School


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Evidence of Work

Way2School

Project Info

Team Name


Way2School


Team Members


Gaurab Shrestha , Nikesh and 2 other members with unpublished profiles.

Project Description


Project Introduction

School transport in the ACT is one of the most complex daily logistics challenges. Parents face congestion and uncertainty at school gates, while children face safety risks on their way to school and way back home. Population growth and expansion into new suburbs like Molonglo are making the system even more strained. Our project, Way2School, demonstrates how open government data and graph analytics can be combined into a simple app that improves school journeys for families and provides actionable insights for planners.
Way2School is an interactive planning and analysis tool designed to reduce school drop-off and pick up congestion, improve child safety, and make public transport more attractive for families. The app integrates multiple ACT government datasets with real pedestrian networks from OpenStreetMap to deliver live insights, safe routes, and recommendations for new Park & Ride locations. It supports both daily users (parents and students) and policy-makers (Transport ACT, schools, councils).

Problem Statement

Morning and afternoon peaks in the ACT create bottlenecks around school zones. Parents driving directly to schools cause congestion, unsafe road conditions, and delays for everyone. Current transport solutions often react to congestion but don’t anticipate future growth or provide families with easy-to-use alternatives. Without intervention, traffic around schools will worsen, and students’ safety will remain at risk.

Project Objective

The primary objectives of this project are:
1. Reduce school gate congestion by diverting cars to Park & Ride and alternative safe parking sites.
2. Improve safety for children by ensuring walking paths are based on real pedestrian networks.
3. Support working parents by offering clear, predictable choices that save time and stress.
4. Assist planners with data-driven recommendations for future Park & Ride investments and growth planning.

Key Features

• Morning Brief: Predicts Park & Ride availability and highlights risk of filling.
• Planner Tab: Suggests multimodal routes (drive, bus, light rail, walk, bike) with ETA, fare, and a safety score.
• Park & Ride Tab: Displays all lots with current vs +20 minute capacity and enables quick reservations.
• Safety Tab: Live incident feed with factor breakdown (night, rain, CCTV, school zone) to explain safety scores.
• Growth Tab (Molonglo): AM peak line chart highlights congestion times and suggests adjustments.
• Parent Tab: Live trip simulation with slider, map updates, ETA, and notifications (boarded, transfer, arriving soon).
• Recommendations Map: Identifies stations lacking Park & Ride, shows nearest alternatives, and draws OSM-based walking paths.

What our app does

Our app helps parents decide where and when to drop children using Park & Ride, while automatically generating safe, multimodal routes to school. It dynamically scores routes for safety and congestion, predicts parking space availability, and simulates real-time trip progress. For planners, it highlights candidate stations for new Park & Ride investments and visualises congestion peaks, turning raw datasets into practical insights.

Conclusion

Way2School demonstrates how data and graph analytics can directly address everyday family challenges and broader transport planning. For parents, it reduces stress, increases safety, and simplifies daily routines. For government, it offers data-driven insights into where new infrastructure can have the greatest impact. This project shows how open data can be transformed into a practical tool with real human benefits, ready to be piloted in the ACT and adapted for other jurisdictions across Australia.


#govhack2025 #way2school #smarttransport #schoolcommute #parkandride #publictransport #openstreetmap #studentsafety #graphanalytics

Data Story


We used a mix of ACT government datasets and open data:
• ACT School Bus Services – bus stop locations and timetables.
• Park & Ride Locations – facilities and capacity.
• Census Data for ACT Schools – enrolment numbers per school.
• Students by SA1 – student origin distances to schools.
• Bus Routes (MULTILINESTRING) – geometry for overlaying the network.
• OpenStreetMap (OSM) – pedestrian networks to model real walking paths.
By combining these, we scored each station’s demand, checked Park & Ride coverage within 600 m, and recommended new sites or alternatives within 2 km. We then overlaid congestion colour-coding for peak periods and generated safe walking paths from Park & Ride to school using OSM shortest-path routing.


Evidence of Work

Video

Homepage

Team DataSets

Bus Routes

Description of Use Used this dataset to get the bus routes along with Park and Ride Locations.

Data Set

ACT School Bus Services

Data Set

Open Street Map

Description of Use Used this to render map in our app to show Park and Ride locations, shortest distance to stations for schools, etc.

Data Set

Census Data for all ACT Schools

Description of Use Used this dataset to get school name, locations and number of students.

Data Set

Park and Ride Locations

Description of Use Used this dataset for finding nearest Park and Ride Locations near schools and stations.

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.