Inspiration
Transit systems shape how people access work, school, healthcare, and community, yet many planning decisions remain difficult to evaluate in a clear, interactive way. Our inspiration for TransitShark came from that gap. We wanted to create a tool that transforms transit planning from a static, opaque process into something visual, data-driven, and easy to explore.
Rather than treating route changes, stop placement, or service adjustments as isolated decisions, we wanted to make their broader impact immediately visible. Our team was motivated by the opportunity to build something that addresses a real-world challenge while pushing ourselves technically. We were especially inspired by the idea that better tools can lead to better decisions, and that even small improvements in transit planning can have meaningful effects on efficiency, accessibility, cost, and sustainability.
What it does
TransitShark is an interactive transit optimization platform designed to help users evaluate proposed changes to a transportation network. It allows users to model scenarios such as moving stops, adding stops, or changing service frequency, then analyze the results through meaningful performance metrics.
The platform provides insight into factors such as optimization score, projected savings, cost, profit potential, and carbon impact. By combining map-based interaction with scenario analysis, TransitShark helps users understand the tradeoffs behind transit decisions in a way that is both intuitive and actionable.
In practice, the platform empowers users to test ideas that would otherwise be difficult to evaluate manually. Instead of relying on guesswork, they can interact with the network directly and receive immediate feedback supported by data.
How we built it
We built TransitShark as a full-stack hackathon project centered on responsiveness, usability, and decision support. The backend was developed in Java with Spring Boot, which handled scenario management, optimization workflows, API design, and the core logic required to process proposed network changes. The project architecture also supports map and heatmap data, scenario editing, and optimization result retrieval through a structured API layer.
On the frontend, we focused on creating an interactive experience that would allow users to engage directly with the network visually rather than through raw data alone. This included map-based exploration, real-time scenario interaction, and a clear presentation of optimization outcomes. We also integrated external transit-related data to ground the project in a realistic use case.
From a collaboration standpoint, we used GitHub to coordinate development across the team, which allowed us to work in parallel, iterate quickly, and combine different technical strengths into one cohesive product.
Challenges we ran into
One of the most significant challenges was designing a system that felt truly interactive while still performing meaningful computation. Once a user modifies part of a network, the application must process that change, evaluate its consequences, and return results quickly enough to preserve a smooth user experience. Bridging live user interaction with backend-driven analysis required careful coordination across the stack.
Another major challenge was scope. Transit optimization is a complex problem space, and building a credible prototype within hackathon constraints forced us to prioritize wisely. We had to make tradeoffs between ambition and execution, choose where precision mattered most, and keep the product focused on delivering a compelling end-to-end experience.
Like many real software projects, we also faced the challenge of integrating multiple components under time pressure. This included synchronizing frontend behavior, backend APIs, data flow, and collaborative development across the team. Those challenges ultimately strengthened the project by pushing us to think more clearly, communicate more effectively, and adapt quickly.
Accomplishments that we're proud of
We are proud that we built a working platform around a genuinely difficult problem. Transit planning involves multiple competing priorities, and we were able to translate that complexity into a system that is interactive, understandable, and analytically useful.
We are also proud of the product’s balance between technical depth and user experience. Rather than building only a backend model or only a visual interface, we created a tool that connects both: users can interact with the network and receive metrics that help explain the impact of their choices.
Most importantly, we are proud of the way we worked as a team. This project reinforced that ambitious ideas become possible when people with different skills contribute toward a shared goal. Our final result reflects not just technical execution, but also collaboration, adaptability, and persistence under pressure.
What we learned
This project taught us a great deal about full-stack system design, especially in the context of real-time interaction. We learned more about connecting an interactive frontend with a backend capable of processing scenarios and returning useful optimization insights. We also gained experience thinking more deeply about product design: not just what a system does, but how to make its outputs understandable and valuable to users.
Equally important, we learned from one another. Each teammate brought a different perspective, and that diversity of thought improved both the technical outcome and the problem-solving process. TransitShark reminded us that strong engineering is rarely just about writing code; it is also about communication, iteration, and combining complementary strengths effectively.
What's next for TransitShark
Our next step is to continue evolving TransitShark from a strong prototype into a more robust decision-support platform. We want to improve the sophistication of the optimization logic, refine the quality and interpretability of the metrics, and further strengthen the real-time responsiveness of the user experience.
We also see clear potential to expand the project’s practical value. With continued development, TransitShark could serve as a useful tool for transit agencies, planners, researchers, or organizations exploring ways to improve mobility systems. What began as a hackathon project has the foundation to grow into something with real-world relevance: a platform that helps make transit planning more transparent, data-informed, and effective.
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