RailWise: Empowering SBB with Staff-Driven AI Insights

AI Quick Summary

The project proposes an AI-driven feedback and recommendation system for SBB, utilizing real-time insights gathered consistently from frontline train operators. This system addresses current gaps in understanding passenger needs, such as peak-time crowd management, luggage and bike storage availability, and specialized support for elderly or mobility-reduced passengers. The AI engine converts operator observations into actionable recommendations to optimize staff scheduling, streamline ticket inspections, and proactively allocate resources for passenger assistance. Benefits include improved operational efficiency, enhanced passenger experience, optimized resource utilization, and strengthened safety and inclusivity, directly supporting SBB's goals for reliable, comfortable, and sustainable mobility services. OverviewWe are developing an AI-powered feedback and recommendation system designed to capture real-time insights from SBB’s frontline train operators. By systematically collecting feedback, this system will generate actionable recommendations to enhance staff deployment, improve passenger support, especially for those with special needs, and optimize ticket inspection procedures.This structured plan outlines a five-month timeline for building a Minimum Viable Product (MVP) in close collaboration with SBB. While collaboration is key, our approach is designed to minimize data-sharing requirements and resource demands from SBB while still delivering tangible value.Five-Month Action PlanMonth 1: Setting the Scope & Aligning GoalsProject KickoffMeet with a small, designated SBB project liaison group.Define key goals and success metrics for the MVP.Establish a manageable project scope (e.g., focusing on a single train line or select routes).System Design & Data RequirementsIdentify essential data points for staff feedback, such as: Crowd density levelsBike/luggage storage utilizationPassenger assistance needsTicket inspection frequencyDefine confidentiality and data-sharing protocols to ensure privacy without overcomplicating data transfers.Collaborative EngagementEstablish communication channels for updates (e.g., weekly check-ins, monthly workshops).Introduce a small pilot group of train operators who will provide early feedback on the concept.Month 2: Pilot Program & Initial FeedbackPilot Group Onboarding & TrainingTrain selected SBB staff on how to quickly and easily provide feedback (via a simple mobile interface or short forms).Provide clear guidance on what types of insights to report, including: Crowding observations (e.g., "moderate," "heavy")Space constraints (e.g., bike compartments, luggage areas)Passenger assistance needs (e.g., wheelchair users, elderly passengers)Ticket inspection trendsInitial Data Collection & Pattern RecognitionGather real-world feedback from pilot routes over a few weeks.Manually analyze trends to confirm feasibility and adjust data collection if needed.Review & RefinementHost a joint review session with SBB to analyze early data and refine processes.Make improvements where necessary (e.g., simplify the feedback process if it proves too complex for staff).Month 3: AI Model Development & RecommendationsBuilding the AI ModelDevelop an AI engine that processes staff feedback and identifies recurring patterns, such as: Peak congestion timesFrequent assistance requestsAreas needing better ticket inspection coverageImplement an initial rule-based recommendation engine, e.g.: “Increase staff presence in Carriage 2 between 5 PM–7 PM on Fridays.”Testing & IterationContinue collecting staff observations to refine AI model accuracy.Present early AI-generated recommendations to pilot users for feedback.Adjust the data collection process if necessary (e.g., introducing “priority” tags for urgent reports).SBB Collaboration & AlignmentMaintain regular check-ins to ensure alignment with operational realities.Share interim results to demonstrate how staff feedback directly informs AI recommendations.Month 4: MVP Integration & ValidationMVP System IntegrationBuild a user-friendly dashboard for SBB staff leads and managers.Incorporate key performance indicators, such as: Staff deployment efficiencyPassenger satisfaction trendsField Testing & Scalability AssessmentExpand the pilot program to a larger staff group and additional routes.Collect feedback on: Ease of useAccuracy of AI recommendationsClarity of insights providedFine-Tuning & EnhancementsConduct workshops to refine the recommendation system based on real-world staff input.Ensure that suggestions are practical and easy to act upon, e.g.: “Notify station staff 10 minutes before arrival of a wheelchair passenger in Carriage 3.”Month 5: Final Adjustments & DemonstrationFinal Optimization & ImprovementsIntegrate lessons learned from the expanded pilot into the final MVP version.Improve the AI model’s recommendation accuracy to reduce false positives and enhance relevance.Performance Review & ReportingEvaluate results based on key impact metrics, such as: Better staff allocationReduction in passenger complaintsImproved experience for passengers needing assistanceCompile a summary report showcasing the MVP’s benefits and potential ROI for SBB.Final Demonstration & Roadmap for ExpansionDeliver a working MVP to SBB stakeholders (e.g., a dashboard or lightweight mobile app showcasing real-time insights).Propose a roadmap for broader adoption, outlining next steps such as: Expanding to more routes and staffIntegrating AI recommendations into existing SBB systemsExamples of AI-Driven RecommendationsSpace Management:“Train IC8 consistently experiences high demand for bike storage between 4 PM–6 PM. Consider allocating an extra bike compartment or directing passengers to specific storage areas.”Passenger Assistance:“Staff reports indicate an average of 3 passengers requiring special assistance daily on Route AB. Recommend scheduling a support staff member at the busiest station during peak hours.”Ticket Inspections:“Data shows a reduced need for inspectors during off-peak hours, but a higher requirement on Fridays due to increased crowd density.”Anticipated Benefits for SBB✔ Optimized Staff Deployment: Ensures that personnel are assigned where they are most needed, improving efficiency.✔ Enhanced Passenger Support: By identifying crowding or assistance needs in advance, SBB can offer better service and accessibility to passengers.✔ More Inclusive Travel: Ensuring equitable service for passengers with diverse needs—such as those requiring wheelchair access or extra assistance—fosters a culture of inclusivity and bolsters SBB’s reputation as a forward-thinking mobility provider.✔ Improved Resource Utilization: Helps prevent underuse or overuse of staff, reducing inefficiencies.✔ Higher Service Quality & Public Perception: With real-time, data-driven recommendations, SBB can maintain a high standard of service, improving customer satisfaction and public trust.By following this structured approach, we will deliver a fully functional MVP within five months—ensuring minimal disruption to SBB’s daily operations while demonstrating the value of staff-driven AI insights in improving efficiency, safety, and passenger experience.

Project Idea Metadata

Project Idea Name: RailWise: Empowering SBB with Staff-Driven AI Insights
Date: 3/16/2025 5:59:59 PM
Administrators:
- M.Milad Ameri

Project Idea Description

Overview

We are developing an AI-powered feedback and recommendation system designed to capture real-time insights from SBB’s frontline train operators. By systematically collecting feedback, this system will generate actionable recommendations to enhance staff deployment, improve passenger support, especially for those with special needs, and optimize ticket inspection procedures.

This structured plan outlines a five-month timeline for building a Minimum Viable Product (MVP) in close collaboration with SBB. While collaboration is key, our approach is designed to minimize data-sharing requirements and resource demands from SBB while still delivering tangible value.

Five-Month Action Plan

Month 1: Setting the Scope & Aligning Goals

1. Project Kickoff
- Meet with a small, designated SBB project liaison group.
- Define key goals and success metrics for the MVP.
- Establish a manageable project scope (e.g., focusing on a single train line or select routes).
1. System Design & Data Requirements
- Identify essential data points for staff feedback, such as:
  - Crowd density levels
  - Bike/luggage storage utilization
  - Passenger assistance needs
  - Ticket inspection frequency
- Define confidentiality and data-sharing protocols to ensure privacy without overcomplicating data transfers.
1. Collaborative Engagement
- Establish communication channels for updates (e.g., weekly check-ins, monthly workshops).
- Introduce a small pilot group of train operators who will provide early feedback on the concept.

Month 2: Pilot Program & Initial Feedback

1. Pilot Group Onboarding & Training
- Train selected SBB staff on how to quickly and easily provide feedback (via a simple mobile interface or short forms).
- Provide clear guidance on what types of insights to report, including:
  - Crowding observations (e.g., "moderate," "heavy")
  - Space constraints (e.g., bike compartments, luggage areas)
  - Passenger assistance needs (e.g., wheelchair users, elderly passengers)
  - Ticket inspection trends
1. Initial Data Collection & Pattern Recognition
- Gather real-world feedback from pilot routes over a few weeks.
- Manually analyze trends to confirm feasibility and adjust data collection if needed.
1. Review & Refinement
- Host a joint review session with SBB to analyze early data and refine processes.
- Make improvements where necessary (e.g., simplify the feedback process if it proves too complex for staff).

Month 3: AI Model Development & Recommendations

1. Building the AI Model
- Develop an AI engine that processes staff feedback and identifies recurring patterns, such as:
  - Peak congestion times
  - Frequent assistance requests
  - Areas needing better ticket inspection coverage
- Implement an initial rule-based recommendation engine, e.g.:
  - “Increase staff presence in Carriage 2 between 5 PM–7 PM on Fridays.”
1. Testing & Iteration
- Continue collecting staff observations to refine AI model accuracy.
- Present early AI-generated recommendations to pilot users for feedback.
- Adjust the data collection process if necessary (e.g., introducing “priority” tags for urgent reports).
1. SBB Collaboration & Alignment
- Maintain regular check-ins to ensure alignment with operational realities.
- Share interim results to demonstrate how staff feedback directly informs AI recommendations.

Month 4: MVP Integration & Validation

1. MVP System Integration
- Build a user-friendly dashboard for SBB staff leads and managers.
- Incorporate key performance indicators, such as:
  - Staff deployment efficiency
  - Passenger satisfaction trends
1. Field Testing & Scalability Assessment
- Expand the pilot program to a larger staff group and additional routes.
- Collect feedback on:
  - Ease of use
  - Accuracy of AI recommendations
  - Clarity of insights provided
1. Fine-Tuning & Enhancements
- Conduct workshops to refine the recommendation system based on real-world staff input.
- Ensure that suggestions are practical and easy to act upon, e.g.:
  - “Notify station staff 10 minutes before arrival of a wheelchair passenger in Carriage 3.”

Month 5: Final Adjustments & Demonstration

1. Final Optimization & Improvements
- Integrate lessons learned from the expanded pilot into the final MVP version.
- Improve the AI model’s recommendation accuracy to reduce false positives and enhance relevance.
1. Performance Review & Reporting
- Evaluate results based on key impact metrics, such as:
  - Better staff allocation
  - Reduction in passenger complaints
  - Improved experience for passengers needing assistance
- Compile a summary report showcasing the MVP’s benefits and potential ROI for SBB.
1. Final Demonstration & Roadmap for Expansion
- Deliver a working MVP to SBB stakeholders (e.g., a dashboard or lightweight mobile app showcasing real-time insights).
- Propose a roadmap for broader adoption, outlining next steps such as:
  - Expanding to more routes and staff
  - Integrating AI recommendations into existing SBB systems

Examples of AI-Driven Recommendations

- Space Management:
- “Train IC8 consistently experiences high demand for bike storage between 4 PM–6 PM. Consider allocating an extra bike compartment or directing passengers to specific storage areas.”
- Passenger Assistance:
- “Staff reports indicate an average of 3 passengers requiring special assistance daily on Route AB. Recommend scheduling a support staff member at the busiest station during peak hours.”
- Ticket Inspections:
- “Data shows a reduced need for inspectors during off-peak hours, but a higher requirement on Fridays due to increased crowd density.”

Anticipated Benefits for SBB

✔ Optimized Staff Deployment:

Ensures that personnel are assigned where they are most needed, improving efficiency.

✔ Enhanced Passenger Support:

By identifying crowding or assistance needs in advance, SBB can offer better service and accessibility to passengers.

✔ More Inclusive Travel:

Ensuring equitable service for passengers with diverse needs—such as those requiring wheelchair access or extra assistance—fosters a culture of inclusivity and bolsters SBB’s reputation as a forward-thinking mobility provider.

✔ Improved Resource Utilization:

Helps prevent underuse or overuse of staff, reducing inefficiencies.

✔ Higher Service Quality & Public Perception:

With real-time, data-driven recommendations, SBB can maintain a high standard of service, improving customer satisfaction and public trust.

By following this structured approach, we will deliver a fully functional MVP within five months—ensuring minimal disruption to SBB’s daily operations while demonstrating the value of staff-driven AI insights in improving efficiency, safety, and passenger experience.

The project proposes an AI-driven feedback and recommendation system for SBB, utilizing real-time insights gathered consistently from frontline train operators.

This system addresses current gaps in understanding passenger needs, such as peak-time crowd management, luggage and bike storage availability, and specialized support for elderly or mobility-reduced passengers.

The AI engine converts operator observations into actionable recommendations to optimize staff scheduling, streamline ticket inspections, and proactively allocate resources for passenger assistance.

Benefits include improved operational efficiency, enhanced passenger experience, optimized resource utilization, and strengthened safety and inclusivity, directly supporting SBB's goals for reliable, comfortable, and sustainable mobility services.