Problem Statement: Bus transit is a critical part of urban infrastructure, but unpredictable delays can significantly impact rider satisfaction and operational efficiency. The current process for analyzing these delays is manual and fragmented, relying on static data logs and ad-hoc analysis. This often misses systemic issues and correlations with external factors like weather.

Objectives:

  • Primary: To design and build an interactive visualization that helps urban transit planners identify and analyze bus delay hotspots across a city.
  • Secondary: To support the exploration of delay patterns based on time of day, day of the week, and weather conditions.
  • Tertiary: To serve as a proof-of-concept for a repeatable design study process.

Target Audience:

  • Domain Experts: Transport planners, city officials, and public transport data analysts. Their primary tasks include route optimization, schedule adjustment, and resource allocation.
  • Computer Experts: Students learning D3.js or other visualization tools.

Assumptions & Constraints:

  • Assumptions: Data is generally reliable, and domain experts are available for regular feedback.
  • Constraints: Project timeline is 8 weeks; tools are restricted to open-source software (e.g., D3.js, Observable, Figma). Data size is limited to 3 months to manage scope.