Onboard Railway Inspection System
Responsive Design
Project Overview
The legacy Onboard Railway Inspection System was transformed into a smart, AI-powered platform to improve rail reliability and enhance the operations team’s user experience. The new system offers clear visuals and interactive dashboards, enabling users to efficiently identify and prioritize track issues with over 90% detection accuracy. Real-time cloud analytics and machine learning provide seamless access to updated track information from any device, streamlining workflow and reducing unnecessary inspections for better efficiency and safety.
This transformation relied on thorough user interviews, workflow analysis, and direct collaboration with rail operations staff. Key feedback shaped rapid alerts, adaptive reporting tools, and a UI tailored for day-to-day operational needs. Throughout, accessibility and ease-of-use were prioritized to ensure intuitive navigation and actionable data for all users.
Role
Design Lead
Type
Public Transport
Year
2024
Design Process
System Analysis > Design System > Wireframe > UX Flow > Mood Board > UI Design > UI Flow > Prototyping > Usability Testing > UI Development
Challenge
The legacy rail monitoring system produced overwhelming volumes of data, disrupting timely decision-making on critical issues.
Siloed systems caused fragmented workflows, while users struggled with interfaces that failed to reflect their operational reality.
Manual review of frequent fault alerts resulted in productivity losses and slow response to genuine railway issues.
Stakeholders lacked efficient tools for rapid notification and prioritization, resulting in delays and lost productivity.
Guiding Question
How might we reimagine rail operations dashboards so teams receive timely, relevant data and actionable alerts?
What design choices can empower users to efficiently navigate complex workflows, even under pressure?
How do we ensure the UI adapts to prolonged monitoring on large screens, while maintaining clarity and reducing fatigue?
Project Management
The project was adopted agile sprint cycles, integrating continuous feedback from frontline users and stakeholders. Each design iteration was validated against real-world usage scenarios, ensuring that improvements addressed authentic needs and operational goals.
UI Design
User involvement was woven into every stage. Through card sorting, workflow analysis, and onsite interviews, I ensured the UI matched users’ mental models and operational habits. Machine learning components were embedded for long-term scalability, delivering smart features and predictive alerts for inspection teams. Understanding that dashboards would be monitored for extended periods and alerts are continuously monitored on large displays, I championed dark mode and high-contrast designs to optimize readability and reduce eye strain.
The Solution
My design resulted in a suite of intuitive dashboards that support front-line inspection teams with fast, accurate alerts and streamlined reporting. Every component was shaped by user requirements, from notification logic and data visualization to adaptive layouts for large screens. Accessibility, reliability, and operational efficiency were designed into the core—from AI-driven insights to seamless cross-team communication. Powered by an advanced AI detection system boasting over 90% accuracy, the platform significantly reduces false alarms, allowing users to focus on critical issues. Additionally, built-in continuous learning capabilities allow the system to adapt and improve from ongoing real-time data, enhancing precision over time. Teams can now make better decisions in real time, reduce maintenance downtime, and proactively address safety concerns, all thanks to a solution built around their workflows and preferences.