Team Name

Best Käse Scenario

Team Members

Project Description

HAiLO is our solution to help keep Victoria's roads safe. It's an AI web tool with an interactive map that utilizes datasets across many sectors to find the main contributing factors of road crashes and provide suggestions. These suggestions can be anything from who to target in a marketing campaign to which intersections need changing to reduce road risks. This increases efficiency & transparency of data by providing easy access to all relevant information in a user-friendly form.

Data Story

The Issue

Reducing the road toll is not just about getting smaller numbers in some list, it's about saving lives. Organisations such as the DTP, VicRoads, VicPol and the Traffic Accident Commision (TAC) are doing a great job at preventing road fatalities, and we want to further support them with the best resources to continue with their work.
But there is so much data, and no practical way to use it, so how can we do this?

Our Solution

We've come up with a Ai bot and map that can help government officials visualise and utilize important data. Ask it anything and it will do it's best to help, providing fact-based suggestions (with the statistics to back it up!) as well as providing a pdf in executive brief format.

HAiLO is currently assisted to make more accurate recommendations by pulling in the excellent crash data set released by DTP, and a related data set about traffic signals.

HAiLO is built to be easily expanded upon, with the ability to integrate with separate AI agents, solely focused on areas such as:
* Parks and Land Use data, and Animal distribution data: Are wombats likely to wander across the road anywhere in gippsland, or just in a particular belt of farmland?
* BAC, fatigue: Would better bus travel in specific areas help reduce accidents from tired or intoxicated patrons returning home?
* Public Holiday dates: If many accidents happen on public holidays, how many of them are preventable by improving public holiday transport and would the cost be justified?
* sunrise/sunset: One of our Bendigo case study intersections involved right hand turn accidents, but interestingly they were mostly around 3pm-6pm (was the sun in their eyes?)
* Proximity to hospitals, ambulance response times: Does this change the odds of a major injury becoming a fatality? Is it statistically significant?
* Street trees: are large trees likely to be blocking the view of a driver approaching the intersection
* Speed Limit zones: the speed zone at the time of the accident is already covered by the crash data, however it would be good to ask a GPT to systematically review the areas with changed speed limits to identify if there have been less or no further accidents, which would be excellent. (OpenStreetMap commit data may help identify the time frame of speed limit changes if the data isn't available elsewhere)
* Population or car registration data: identify crash stats by LGA, combined with population to work out per-capita riskiest and where interventions may have best payoff
* Preventable Mortality: were there any risk factors related to cardiac, mental health or other conditions which became road incidents? Prevention for these measures will be different (e.g. heart screening for at-risk groups, mental health support).

Challenges Entered

• AI applications using Open Road Crash data - This is our main challenge. We are creating an AI agent. Due to technical difficulties with locally run models (we spent hours trying, I promise), we're currently using Google Gemini 1.5 flash as a stand-in. ChatGPT 4o was also been used to explore the data as it incorporates a number of sub-models and has done an excellent job crunching a reduced dataset for Bendigo, creating some example briefs. For the curious, the example generated briefs from the Bendigo data are included in the Github repository under 'AI-generated-briefs', but these weren't generated by HAiLO.

• AI in Governance - this project aims to build transparency and trust by interpreting data, and making recommendations (in the form of PDF briefs) which give their statistical reasoning behind recommending various interventions, based on historical data.

• Smart Mobility: Optimizing Urban Infrastructure for a Sustainable Future - One of the things we realised on reading this challenge is that identifying ways to support the adoption of newer, safer cars and more public transport (Bus home from the pub) is well within the scope of this AI.