Team Name

Milo 3 in 1

Group Member Name

Loh Kai Xuan (Leader), Yap Shuet Khey, Seong Ming Rou, Lew Jie Xi

What inspires us

The "traffic is toxic" problem and the shocking statistic comparing traffic exposure to smoking.

We were deeply inspired by the urgency of urban health challenges, specifically the fact that commuting exposes residents to toxic fumes daily, leading to high risks of respiratory failure and cardiovascular diseases. The stark reality that "1 Hour in Traffic ≈ 3 Cigarettes" drove our team to align directly with SDG 3: Good Health and Well-Being. Our motivation was to transform passive pollution exposure into active health protection with ZenWay—a navigation partner that promotes activity and minimizes exposure to PM2.5 and NO emissions.

How we built it

The AI Route Engine using pollution data, the lack of proprietary hardware , and the core features.

We developed ZenWay, a software-only solution that relies on existing IoT networks rather than proprietary sensors. Our build focused on the AI Route Engine , which performs multi-modal routing based on pollution data. We employed a hybrid AI Model using LSTM networks to predict crowd surges and pollution spikes 15-30 minutes in advance. This AI integrates real-time Air Quality Index (AQI) and crowd density APIs to construct the healthiest routes, featuring Health-Optimized Routing and contextual Wellness Nudges (posture, hydration, walking reminders).

Challenges we ran into

Data granularity , real-time latency , and shifting user habits.

We faced three primary challenges during development:

Data Granularity: Capturing hyper-local air quality variations (street-by-street) without a dense sensor network was technically demanding.
Real-Time Latency: Processing massive IoT data streams to provide immediate, reliable rerouting required optimizing our backend infrastructure.
User Habits: The design challenge was shifting user mindset from prioritizing the "fastest route" to accepting the "healthiest route". We addressed this via our "Health First" UI philosophy, highlighting safety metrics prominently over simple time-to-destination.

Accomplishments that we're proud of

The successful functional prototype demonstrating Health-Optimized Routing and the financial viability model.

We are most proud of developing a functional prototype UI that successfully demonstrates health-optimized routing. This prototype integrates real-time simulated AQI layers to visibly recommend routes for health. Our ability to construct a robust business model targeting the entire B2G, B2B, and B2C ecosystem—with SaaS subscriptions for operators, data partnerships for insurers, and a freemium model for users—proves ZenWay's immediate market value and feasibility.

What we learned

The feasibility of the AI models, the power of the API economy, and the necessity of a health-first design. We learned that the convergence of smart mobility and health tech presents a massive, surging market opportunity. Technically, we confirmed the feasibility of our approach, leveraging mature technologies like LSTM models for prediction and the robust foundation provided by the API economy (e.g., Google Maps, OpenWeather). Most importantly, we solidified that a "Health First" UI philosophy is essential for encouraging behavior change and delivering on our SDG 3 mission.

What's next for C3G13_Milo 3 in 1

Rewards system , pilot testing , and partnerships

Our roadmap for ZenWay includes several key phases: Active Nudges Enhancement We will enhance our "Active Nudges" by integrating a rewards system to better promote active mobility. Pilot Testing We plan for pilot testing in a high-density urban zone to validate real-world performance. Full Launch & Partnerships We will pursue a full public launch and strategic API integration partnerships with operators and insurers